Organizational activity support systems, methods, and programs

Abstract

We continuously provide individuals and organizations with frequent analysis and feedback based on records of their actual activities. [Solution] System 100 acquires voice / text input information related to the organization's actual activities (meetings, business negotiations, 1-on-1s, consultations, etc.) from the user device, and stores the input information, generated text, the organization's shared knowledge (reference information = organizational shared knowledge), and analysis results. The analysis engine acquires and references the reference information, performs multifaceted analysis, and generates analysis results. Furthermore, based on the analysis results, it generates feedback for individuals and / or the organization and outputs it to the user device. Reference information (organization-shared locations) is dynamically updated based on the analysis results, and the analysis engine continues the analysis by referring to the updated reference information.

Description

Technical Field

[0001] The present invention relates to an organizational activity support system, method and program. Background Art

[0002] Companies conduct sales training, management training, on-the-job training (OJT), classroom training and the like for human resource development and organizational development, aiming to promote improvement of employees' skills and behavioral change. However, in OJT, the content of guidance tends to depend on the experience and skills of the supervisor, become personalized to individuals and have reduced reproducibility. Further, when a supervisor is busy, opportunities for feedback are limited, and it takes time for employees to grow.

[0003] In addition, classroom training tends to deviate from actual business activities such as actual business negotiations and one-on-one meetings, and there are cases where the learned content cannot be applied to practical work. In addition, it is difficult to measure the effect of training, and it tends to take a long time for the effect to be reflected in business performance. In such conventional forms, training, practice and evaluation tend to be cycled every several months to annually, and a time lag tends to occur before the amount of actual business activities is reflected in growth and business performance.

[0004] Companies may introduce systems such as talent management, HR tech, and organizational surveys to visualize the state of human resources and the organization (for example, Patent Document 1). However, these existing solutions grasp the organizational state based on input results from questionnaires and surveys, so they tend to grasp the state in a snapshot manner with an update frequency of about once a year to once a quarter, and input also tends to depend on arbitrary timing.

[0005] As a result, it can be difficult to frequently track real-time changes based on continuous records of actual activities. For example, when grasping engagement, it is common practice to conduct questionnaires once or twice a year and score engagement based on self-reporting. As a result, dynamic grasping based on daily changes in dialogue and behavior is difficult.

[0006] Furthermore, companies operate various systems such as knowledge management, SFA / CRM, and risk management individually, accumulating data. However, when each system is statically isolated, it becomes difficult to integrate them across systems or to utilize them as unified feedback that directly leads to improved performance.

[0007] Furthermore, information such as meetings, business negotiations, and individual thoughts tend to remain as tacit knowledge and remain with individuals. Even when companies attempt to share this information company-wide, creating shared documents can be time-consuming, making it difficult to continuously implement the measures. As a result, many companies may be unable to break free from the problem of information being dependent on individuals.

[0008] In addition, it may not be possible to frequently allocate time for providing feedback to each member during performance reviews or one-on-one meetings. Due to these time constraints, support may not be provided when members need it most, potentially leading to their departure from the company.

[0009] Therefore, companies have a need to provide frequent feedback that directly addresses actual activities such as business negotiations and one-on-one meetings, presenting analysis results immediately after the activity and linking them to improvement actions. Compared to traditional feedback cycles of several months to a year, there is a strong demand for improvements to be implemented on a daily or weekly basis.

[0010] Furthermore, companies have a need to detect individual and organizational issues early and address them before problems become apparent. In particular, there is a demand for mechanisms that enable early detection of signs of employee turnover and risks, and connect them to appropriate responses.

[0011] Furthermore, companies have a need to quantitatively understand the effectiveness of their training and system investments. However, traditional methods make it difficult to measure the effectiveness of training, and ROI tends to be unclear. In this regard, companies have a demand to visualize indicators that directly impact performance, such as the relationship between activity level and growth rate, and to understand the return on investment of improvement measures.

[0012] Furthermore, companies sometimes envision a system where meetings and business negotiations are recorded by voice, and the accumulated data is used to provide optimized reports to each level (individuals, managers, department heads, HR, board members, etc.) on a daily basis. However, the value added through secondary and tertiary utilization of the accumulated data may not progress, and the large amount of data accumulated daily may not be fully utilized.

[0013] Therefore, companies need technology that can provide continuous, high-frequency analysis and feedback based on records of actual activities to both individuals and organizations, without relying on conventional static individual systems or infrequent evaluations. This technology can eliminate reliance on individual expertise, reduce employee turnover risk, and quantify the effectiveness of training. [Prior art documents] [Patent Documents]

[0014] [Patent Document 1] Japanese Patent Publication No. 2025-49255 [Overview of the project] [Problems that the invention aims to solve]

[0015] This invention has been made in view of these circumstances, and in particular aims to continuously provide individuals or organizations with high-frequency analysis and feedback based on records of actual activities. [Means for solving the problem]

[0016] To solve this problem, one aspect of the present invention has the following configuration: An organizational activity support system that is communicatively connected to multiple user devices via a computer network, acquires input information regarding actual activities within an organization, and provides analysis and feedback, comprising an information acquisition unit that acquires input information from user devices, including audio data and / or text data related to meetings, business negotiations, one-on-one meetings, or consultations. Further comprising a storage unit that stores at least input information acquired by an information acquisition unit, text generated based on said input information, organizational shared space as reference information that is reference information or knowledge serving as a shared asset of an organization, and analysis results. Further comprising: an analysis engine unit that acquires and refers to reference information based on the input information or text stored in the storage unit, executes multifaceted analysis, and generates an analysis result; and a feedback generation unit that generates personal feedback and / or organizational feedback based on the analysis result. In addition, comprising an information output unit that outputs feedback generated by the feedback generation unit to a user device. The reference information is dynamically updated based on the analysis result. The analysis engine unit executes multifaceted analysis while acquiring and referring to the updated reference information. [Effects of the Invention]

[0017] According to the present invention, it becomes possible to continuously provide high-frequency analysis and feedback based on records of actual activities to individuals or organizations. Problems, configurations, and effects other than those described above will be clarified by the following description of embodiments. [Brief Description of Drawings]

[0018] [Figure 1] It is a block diagram showing an example of the overall configuration including an organizational activity support system according to an embodiment of the present invention. [Figure 2] It is a block diagram showing a configuration example of the organizational activity support system shown in FIG. 1. [Figure 3] It is a block diagram showing a hardware configuration example of the organizational activity support system shown in FIG. 1. [Figure 4] It is a flowchart showing an example of basic operation of the organizational activity support system shown in FIG. 1. [Figure 5] It is a block diagram showing an example of the data structure of the organizational shared space storage unit 21 shown in FIG. 1. [Figure 6] It is a flowchart showing an example of philosophy gap analysis processing in the organizational activity support system of FIG. 1. [Figure 7] Figure 1 is a flowchart illustrating an example of risk scoring processing in the organizational activity support system. [Figure 8] Figure 1 is a flowchart showing an example of the feedback generation process in the organizational activity support system. [Figure 9] Figure 1 is a flowchart illustrating an example of spiral-up control processing in the organizational activity support system. [Figure 10] Figure 1 is an explanatory diagram illustrating the dual feedback loop structure in the organizational activity support system. [Figure 11] This flowchart shows an example of a sales improvement feedback process according to Embodiment 1 of the present invention. [Figure 12] This flowchart shows an example of a management capability improvement feedback process according to Embodiment 2 of the present invention. [Figure 13] This flowchart shows an example of the daily feedback report generation process according to Embodiment 3 of the present invention. [Figure 14] This is a comparative diagram showing the challenges of conventional training and the improvements made by the embodiments of the present invention. [Modes for carrying out the invention]

[0019] Hereinafter, embodiments for carrying out the present invention will be described with reference to the accompanying drawings. In this specification and drawings, components having substantially the same function or configuration are denoted by the same reference numerals, and redundant descriptions are omitted.

[0020] Figure 1 is a diagram showing the overall system configuration including an organizational activity support system 100, which is an embodiment of the present invention. The organizational activity support system 100 is configured as a server device and cloud infrastructure, and is communicated with multiple user devices 300 via a computer network 200.

[0021] The organizational activity support system 100 receives voice data or text data (including consultation information) related to work from user devices 300 (employees, managers, executives) via a computer network 200 as input information, and performs analysis and generates feedback based on the received input information.

[0022] Furthermore, the organizational activity support system 100 optimizes and delivers feedback reports, dashboard display information, improvement suggestion information, and information used for task creation or progress management to each user device 300 according to the user's layer (individual, manager, department head, HR, director, etc.).

[0023] The computer network 200 is configured as the Internet or the like, and provides a communication path between the organizational activity support system 100 and multiple user devices 300 for transmitting input information to the organizational activity support system 100 and for transmitting output information (feedback reports, dashboard display information, etc.) generated by the organizational activity support system 100 to the user devices 300.

[0024] Each of the multiple user devices 300 is a PC, smartphone, or recording device, and is provided for employees, administrators, and managers.

[0025] The user device 300 (employee) acquires audio data related to work such as meetings, business negotiations, and one-on-one sessions, or text data and consultation information entered by the user, as input, and transmits this input information to the organizational activity support system 100 via the computer network 200. In addition, as output, it receives and displays or presents personal activity summaries, growth points, improvement suggestions, skill evaluations, etc., distributed from the organizational activity support system 100.

[0026] The user device 300 (administrator) accesses information about the team or members to be managed (including information based on employee input) as input, and inputs additional management information as needed, sending it to the organizational activity support system 100. It also receives, displays, or presents manager reports (team performance, member status, bottleneck analysis, communication quality, capacity management, etc.) distributed from the organizational activity support system 100 as output.

[0027] The user device 300 (manager) can send inputs such as requests for information necessary for management decisions, condition settings, or instructions for feedback to the organizational activity support system 100. It can also receive, display, or present reports for directors or management dashboards (sales, operating profit, engagement, strategic challenges, organizational health, return on investment, etc.) distributed from the organizational activity support system 100 as outputs.

[0028] Figure 2 is a block diagram showing an example configuration of the organizational activity support system 100. The information acquisition unit 1 acquires input information from the user device 300 via the computer network 200. The information acquisition unit 1 acquires voice data and text data (including consultation content, etc.) in situations such as business negotiations or 1-on-1 meetings as input information, and outputs the acquired input information to the subsequent processing within the organizational activity support system 100. For example, the information acquisition unit 1 outputs the acquired voice data to the voice recognition processing unit 3, or outputs the acquired input information to the storage unit 2 for storage. When the input information is provided as text data, the information acquisition unit 1 identifies the speaker based on the speaker identifier, user ID or role information specified at the time of input, etc., contained in the text data, and assigns speaker information. Speaker information is information that can uniquely identify the speaker (the same applies hereinafter).

[0029] In addition to acquiring input information from the user device 300, the information acquisition unit 1 may also acquire input information from existing business information systems that the user uses on a daily basis without requiring any additional input operations. Existing business information systems include, for example, business communication systems (including conferences, chats, and calls), business email and schedule management systems, cloud document management systems, business log recording systems, and voice memo recording means. The information acquisition unit 1 acquires conversation history, sent and received messages, meeting records, documents, task records, editing history, etc., stored in these systems and outputs them as input information to the voice recognition processing unit 3, storage unit 2, or analysis engine unit 4. As a result, the user does not have to perform any new input work, and input information is automatically accumulated as they perform their normal work.

[0030] The memory unit 2 receives various types of information acquired or generated by the organizational activity support system 100 as input, stores such information, and outputs it as reference information to each part of the organizational activity support system 100. The memory unit 2 includes an organizational shared information memory unit 21, a user attribute information memory unit 22, an audio / text memory unit 23, an analysis result memory unit 24, a feedback history memory unit 25, and a training information memory unit 26.

[0031] Memory unit 2 may hold a first description file and a second description file associated with each user. The first description file is a file that describes the user's repeatedly used work procedures, judgment criteria, rules to be followed, work regulations, values, etc. The second description file is a file that describes the user's acquired ingenuity, new procedures, key points for improvement, key points for skill transfer, etc. Memory unit 2 may generate or update the contents of the first description file and the second description file and hold them based on input information, speech recognition result text, or analysis results. This separates information corresponding to work patterns from information corresponding to work techniques, improving the efficiency of referencing and updating.

[0032] The Organization Commons Storage Unit 21 accepts standard information or knowledge that constitutes the organization's shared assets as input and stores such standard information or knowledge as the Organization Commons. The Organization Commons Storage Unit 21 outputs the stored Organization Commons to the Philosophy Gap Analysis Processing Unit (Philosophy Gap Analysis Unit 41) for reference, and uses it, for example, to extract relevant philosophies or behavioral guidelines and departmental skill requirements. The Organization Commons Storage Unit 21 also accepts update proposals generated by the Spiral Up Control Unit 10 as input and functions as an update destination for the Organization Commons.

[0033] The user attribute information storage unit 22 receives attribute information associated with each user as input and stores said attribute information. User attribute information includes, for example, job title level, department, length of service, and area of ​​expertise. The user attribute information storage unit 22 outputs the stored user attribute information to the philosophy gap analysis unit 41 for reference, or to the risk analysis unit 45 for reference. The philosophy gap analysis unit 41 and the risk analysis unit 45 refer to the user attribute information corresponding to the user to be treated as the speaker and use said user attribute information to calculate a weighting coefficient based on the speaker's attributes.

[0034] The voice / text storage unit 23 receives voice data and text data acquired by the information acquisition unit 1, as well as speech recognition result text generated by the speech recognition processing unit 3, as input and stores the data. The voice / text storage unit 23 outputs the stored speech recognition result text, etc., to the analysis engine unit 4 (for example, the ideology gap analysis unit 41) for reference and uses it as input data for multifaceted analysis processing.

[0035] The analysis result storage unit 24 receives the results of the multifaceted analysis processing performed by the analysis engine unit 4 as input and stores the analysis results. The analysis result storage unit 24 outputs the stored analysis results to the feedback generation unit 7 for reference and uses them as input data for feedback generation.

[0036] The feedback history storage unit 25 receives the feedback history generated by the feedback generation unit 7 and presented or distributed via the information output unit 9 as input and stores the history. The feedback history storage unit 25 outputs the stored history to the spiral-up control unit 10 for reference and uses it as input data for measuring the feedback effect.

[0037] The training information storage unit 26 receives information related to training as input and stores that information. The training information storage unit 26 outputs the stored training information to the training recommendation unit 8 for reference and uses it as reference information for training recommendations.

[0038] The speech recognition processing unit 3 receives the audio data acquired by the information acquisition unit 1 as input. The speech recognition processing unit 3 performs speaker separation processing according to the number of speakers included in the audio data to distinguish the speech segments for each speaker. The speech recognition processing unit 3 performs speaker identification processing to associate the distinguished speakers or speech segments with the speakers and generates speaker information. The speech recognition processing unit 3 attaches the speaker information to the audio data, the speech recognition result text, or both. The speech recognition processing unit 3 converts the audio data into text data as needed to generate the speech recognition result text. The speech recognition processing unit 3 outputs the audio data, the speech recognition result text, and the speaker information to the audio / text storage unit 23 and supplies them as input data for the analysis engine unit 4 to refer to. The same speaker information may be associated with various data or information subsequently generated based on the audio data or text data to which speaker information has been attached (the same applies hereinafter).

[0039] The speaker identification process may be as follows: The speech recognition processing unit 3 may associate a speaker or utterance with a user based on metadata associated with the audio data (such as a user ID specified at the start of recording, terminal login information, meeting ID or participant list, or information on the combination of superior and subordinate in a 1-on-1 meeting). The speech recognition processing unit 3 may associate a speaker or utterance with a user based on the speaker or role (superior / subordinate, sales / customer, etc.) entered by the operator of the recording terminal at the start of recording. The speech recognition processing unit 3 may perform a comparison or similarity determination with registered voice features for speaker clusters or single speakers obtained through speaker separation processing, and associate a speaker or utterance with a user. The speech recognition processing unit 3 may associate speaker information with user attribute information held by the user attribute information storage unit 22 so that it can refer to the corresponding user attribute information.

[0040] The analysis engine unit 4 receives data held by the voice / text storage unit 23 as input and performs multifaceted analysis processing. The analysis engine unit 4 performs analysis from the perspectives of gap, engagement, business negotiations, skills, and risk, and outputs the obtained analysis results to the analysis result storage unit 24.

[0041] The analysis engine unit 4 may perform analysis from the perspective of relationships or sociality. Analysis from the perspective of relationships or sociality may include, for example, extracting the triggers for contact with others, the interactions that initiated collaboration, the occurrence and resolution of consultations or requests, etc., and calculating features related to connection formation. Features related to connection formation may include, for example, the number of types of people with whom contact occurred, the number of types of affiliations or roles, the distribution of contact destinations, the amount of change in contact destinations, the increase or decrease in contact frequency, and the number of new contacts. The analysis engine unit 4 may use these features to calculate evaluation values ​​that indicate changes in the breadth or diversity of relationships. Furthermore, the analysis from the perspective of relationships or sociality may include extracting the sending and receiving of expressions of gratitude or praise, the sharing or forwarding of expressions of gratitude, the introduction or citation to third parties, etc., and calculating feature quantities that indicate the frequency of gratitude occurrence, the number of times gratitude is propagated, the length of the gratitude chain, and the diffusion path of expressions of gratitude. The analysis engine unit 4 outputs the calculated feature quantities or evaluation values ​​to the analysis result storage unit 24.

[0042] The Philosophy Gap Analysis Unit 41 receives input information held by the voice / text storage unit 23 as input. The input information may include speech recognition result text, voice data, and speaker information. Based on the speaker information included in the input information, the Philosophy Gap Analysis Unit 41 obtains the corresponding user attribute information held by the user attribute information storage unit 22. The Philosophy Gap Analysis Unit 41 obtains the organizational shared information held by the organizational shared information storage unit 21 as reference information. Based on the correspondence between the input utterance content, etc., and the philosophy or behavioral guidelines, etc., obtained as reference information, the Philosophy Gap Analysis Unit 41 extracts features, performs gap score calculation using the generated AI 6, and identifies the gap area. The Philosophy Gap Analysis Unit 41 outputs the identified gap area to the analysis result storage unit 24.

[0043] The engagement analysis unit 42 receives input information held by the voice / text storage unit 23 as input and performs an analysis on engagement based on the input information. The input information may include speech recognition result text, voice data, and speaker information. The engagement analysis unit 42 may analyze engagement by identifying the content of each speaker's utterance or the content of each role's dialogue based on the speaker information included in the input information. The engagement analysis unit 42 outputs the obtained analysis results to the analysis result storage unit 24.

[0044] The business opportunity analysis unit 43 receives input information held by the voice / text storage unit 23 as input and performs analysis on the business opportunity based on the input information. The input information may include speech recognition result text, voice data, speaker information, and business opportunity identification information indicating that it is a business opportunity. The business opportunity analysis unit 43 identifies the input information related to the business opportunity using the business opportunity identification information and performs analysis on the business opportunity, including business opportunity stage analysis. The business opportunity analysis unit 43 outputs the business opportunity stage analysis results, etc., to the analysis result storage unit 24. The business opportunity identification information may be, for example, type information specified at the time of input, meeting ID or participant information, case ID of an external system, or type estimation result based on text content.

[0045] The skill analysis unit 44 receives input information held by the voice / text memory unit 23 as input and performs skill analysis based on said input information. The input information may include speech recognition result text, voice data, and speaker information. The skill analysis unit 44 obtains skill requirements, evaluation axes, or success patterns as reference information from the organizational shared information held by the organizational shared information memory unit 21. The skill analysis unit 44 extracts features based on the correspondence between the input utterances, etc. and the reference information, calculates a skill score, and identifies skill gaps. The skill analysis unit 44 outputs the skill gap analysis results, etc. to the analysis result memory unit 24. The skills may be, for example, skills related to listening ability, proposal ability, closing ability, active listening, questioning, acknowledgment, or feedback.

[0046] The risk analysis unit 45 receives as input issues or risk candidates extracted based on input information held by the voice / text storage unit 23, and user attribute information held by the user attribute information storage unit 22. The input information may include speech recognition result text, voice data, and speaker information. The risk analysis unit 45 may detect issues or risk candidates from the speech content etc. included in the input information, or accept them as detection results extracted by other analysis processes. The risk analysis unit 45 calculates attribute weight coefficients based on the user attribute information, calculates a risk score using the attribute weight coefficients, and outputs the final risk score to the analysis result storage unit 24. Note that the issues or risk candidates may be, for example, signs of employee turnover, signs of compliance violations, risk of online crises, or a decline in psychological safety.

[0047] The AI ​​collaboration unit 5 receives information generated or held by each unit within the organizational activity support system 100 as input and transmits and receives it with the external AI system 400. The AI ​​collaboration unit 5 outputs input data (e.g., analysis results, reference information, etc.) to the external AI system 400 for use in processing executed by the generating AI 6, and outputs the response received from the external AI system 400 to the generating AI 6 or the feedback generation unit 7.

[0048] The generation AI 6 receives data and reference information from various parts of the organizational activity support system 100 as input, and performs generation processes such as gap score calculation, feedback design based on coaching theory, and feedback loop quality evaluation. The generation AI 6 outputs the processing results to the philosophy gap analysis unit 41, the feedback generation unit 7, or the spiral-up control unit 10, which are then used for subsequent analysis result confirmation, feedback generation, and continuous improvement control.

[0049] The feedback generation unit 7 receives the analysis results held by the analysis result storage unit 24 and the feedback design results output by the generation AI 6 as input, and generates feedback based on this input. The feedback generation unit 7 outputs the feedback generated by the individual feedback generation unit 71 and the organizational feedback generation unit 72 to the information output unit 9, and presents it to the user device 300 in the form of immediate distribution to employees and supervisors or presentation of a dashboard to management. The feedback generation unit 7 outputs the history of the generated feedback to the feedback history storage unit 25 for storage.

[0050] The personal feedback generation unit 71 generates personalized feedback based on the analysis results and feedback design results received as input by the feedback generation unit 7. The personal feedback generation unit 71 outputs the generated personalized feedback to the information output unit 9, which then displays it to the user devices 300 of the employee and their supervisor.

[0051] The personal feedback generation unit 71 may include feedback based on the analysis results from the perspective of relationships or sociality in the personal feedback. Feedback from the perspective of relationships or sociality may include, for example, evaluation values ​​regarding connection formation, increases or decreases in contact frequency, occurrence of new contacts, changes in the number of types of contacts or types of affiliations or roles, and changes in the distribution of contacts. Feedback from the perspective of relationships or sociality may also include the frequency of expressions of gratitude or praise, the number of times they are propagated, the chain length, and the characteristics of the diffusion path. The personal feedback generation unit 71 formats this content into a description, summary, or indicator that the user can understand and presents it to the user device 300 via the information output unit 9.

[0052] The organizational feedback generation unit 72 generates organizational feedback based on the analysis results and feedback design results received as input by the feedback generation unit 7. The organizational feedback generation unit 72 outputs the generated organizational feedback to the information output unit 9, which then presents it to user devices 300, such as managers, as a dashboard.

[0053] The organizational feedback generation unit 72 may generate organizational feedback based on the results of an analysis from the perspective of relationships or sociality. Organizational feedback may include, for example, the occurrence of contacts across departments or roles, the trend in the diversity of contacts, the trend in interactions that initiated collaboration, the frequency and propagation trend of expressions of gratitude or praise, and the amount of change in these. The organizational feedback generation unit 72 may set the aggregation unit to individuals, teams, or organizational units, visualize the trends for each aggregation period, and present it to the user device 300 as a dashboard or the like via the information output unit 9.

[0054] The training recommendation unit 8 receives analysis results stored in the analysis results storage unit 24 and training or training information stored in the training / training information storage unit 26 as input. The training recommendation unit 8 identifies training needs based on gap areas, skill gaps, issues, or risks included in the analysis results, and extracts recommendation candidates based on their correspondence with the training content, target skills, difficulty level, or eligibility requirements included in the training or training information. The training recommendation unit 8 calculates the degree of suitability of the recommendation candidates, ranks them, and performs training recommendations. The training recommendation unit 8 outputs the results of the training recommendations to the information output unit 9 and presents them to the user device 300.

[0055] The information output unit 9 receives feedback generated by the feedback generation unit 7 and recommendation results generated by the training recommendation unit 8 as input, and outputs the input to the user device 300. The information output unit 9 outputs in a manner appropriate to the user's level, such as immediate distribution to employees and supervisors, or presentation of a dashboard to management.

[0056] The spiral-up control unit 10 receives the feedback history held by the feedback history storage unit 25 as input. The spiral-up control unit 10 measures the feedback effect based on the feedback history. The spiral-up control unit 10 performs a feedback loop quality evaluation using the generated AI 6. The feedback loop quality evaluation may include, for example, correlation analysis, extraction of effective feedback patterns, or identification of areas requiring improvement. Based on the results of the feedback loop quality evaluation, the spiral-up control unit 10 outputs updated learning data information to the external AI system 400, or outputs update suggestions to the organizational shared storage unit 21. The spiral-up control unit 10 generates a spiral-up effect report and outputs it to the information output unit 9 for presentation to the user device 300.

[0057] The spiral-up control unit 10 may quantitatively measure the status of sharing and utilization of the contents described in the second description file within the organization. The spiral-up control unit 10 may record usage events such as referencing, search hits, citations, applications, and derivative creation for the second description file or a part thereof registered in the shared area, and generate usage performance information including the creator, user, number of uses, time of use, and usage context, and store it in the storage unit 2. Based on the usage performance information, the spiral-up control unit 10 may calculate indicators showing the amount created, the amount distributed, and the amount used, and output them to the information output unit 9 as part of the spiral-up effect report. This enables not only the accumulation of improvement knowledge but also improvements based on the utilization status of that improvement knowledge.

[0058] The spiral-up control unit 10 may perform control to gradually expand the scope of support for the information processing system. This gradual expansion may be carried out, for example, by first focusing on supporting the efficiency of routine tasks, second adding support for autonomous execution of a part of the business process, and third adding co-creation support including hypothesis generation or alternative solution exploration for unknown issues. The spiral-up control unit 10 may use indicators showing the amount of accumulated input information, the amount of accumulated feedback history, the amount of second description files created, distributed, or used, or the results of feedback effect measurement, as conditions for switching between stages. The spiral-up control unit 10 may expand the scope of referenced data according to the stage, mainly referencing publicly available information and individual work data in the first stage, adding team data and tacit knowledge data results to the referenced data in the second stage, and adding a group of second description files shared across the organization to the referenced data in the third stage. This allows for expanding the scope of support as operations mature while suppressing the burden and risks during the initial stages of implementation.

[0059] Figure 3 shows which hardware resources each functional block shown in Figure 2 operates on. In Figure 3, the processor 11 (CPU / GPU) executes at least the following processes as a program: the information acquisition unit 1, the speech recognition processing unit 3, the analysis engine unit 4, the AI ​​collaboration unit 5, the generated AI 6, the feedback generation unit 7, the training recommendation unit 8, the information output unit 9, and the spiral-up control unit 10.

[0060] The temporary memory device 12 (RAM) functions as a work area used by the processor 11 when executing each process. The processor 11 proceeds with processing while temporarily storing the program state, data to be processed, and intermediate results in the temporary memory device 12.

[0061] The auxiliary storage device (storage unit) 2 implements the "storage unit 2" in Figure 2 as a non-volatile storage resource. The auxiliary storage device 2 holds the organization shared storage unit 21, the user attribute information storage unit 22, the voice / text storage unit 23, the analysis result storage unit 24, the feedback history storage unit 25, and the training information storage unit 26. The processor 11 reads the necessary information from the auxiliary storage device 2 and writes the processing results to the auxiliary storage device 2, for example, in the processing of the analysis engine unit 4 and the feedback generation unit 7.

[0062] The communication device 13 functions as a network interface responsible for sending and receiving data with the outside. The communication device 13 provides a communication path for the AI ​​collaboration unit 5 to send and receive data with the external AI system 400 (cloud LLM, etc.), and the processor 11, as part of the processing of the AI ​​collaboration unit 5, sends input data to the external AI system 400 and receives response data from the external AI system 400.

[0063] Furthermore, as shown in Figure 1, the organizational activity support system 100 connects to multiple user devices 300 via a computer network 200. The communication device 13 also handles communication via the computer network 200, with the information acquisition unit 1 receiving input information from the user devices 300 and the information output unit 9 transmitting result information to the user devices 300. Therefore, each functional block in Figure 2 is realized on the organizational activity support system 100, which is configured as a server device or cloud infrastructure, through the cooperation of the processor 11, temporary storage device 12, auxiliary storage device 2, and communication device 13.

[0064] Figure 4 shows the basic processing flow executed by the organizational activity support system 100, illustrating the flow from receiving input information (S1) to spiral-up processing (S6). The organizational activity support system 100 handles voice and / or text as input information. The organizational activity support system 100 may acquire and process the input information in real time, or it may acquire and process it in fixed units.

[0065] First, in step S1 (receiving input information), the information acquisition unit 1 acquires input information (voice data and / or text data) from the user device 300 and supplies the acquired input information to the subsequent processing. The input information may include content related to business negotiations, 1-on-1 meetings, or consultations. The information acquisition unit 1 supplies the acquired input information to the subsequent processing. If the input information includes voice data, the information acquisition unit 1 outputs the voice data to the voice recognition processing unit 3. The voice recognition processing unit 3 processes the voice data and, if necessary, generates voice recognition result text and speaker information and outputs them to the voice / text storage unit 23. If the input information is provided as text data, the information acquisition unit 1 outputs the text data to the voice / text storage unit 23 for storage. If the input information is provided as text data, the information acquisition unit 1 may perform speaker identification processing on the text data and add speaker information.

[0066] Next, in step S2 (matching with the organizational shared information), the analysis engine unit 4 acquires the input information held by the voice / text storage unit 23. The analysis engine unit 4 also acquires the organizational shared information held by the organizational shared information storage unit 21 as reference information. The analysis engine unit 4 performs a match to associate the utterances, etc., contained in the input information with the philosophy, behavioral guidelines, or evaluation criteria, etc., acquired as reference information, and generates a correspondence relationship or feature quantity as a result of the match. The organizational shared information held by the organizational shared information storage unit 21 can be dynamically updated. The analysis engine unit 4 acquires the organizational shared information including the updated content as reference information and uses it for the match.

[0067] Next, in step S3 (multifaceted analysis processing), the analysis engine unit 4 performs a multifaceted analysis from the perspectives of gap, engagement, business opportunities, skills, and risk, based on the input information and the matching results obtained in step S2. The analysis engine unit 4 may proceed with the analysis sequentially as input information arrives, or it may aggregate the input information in predetermined units and perform the analysis. The analysis engine unit 4 outputs the obtained analysis results to the analysis result storage unit 24 for storage.

[0068] Next, in step S4 (feedback generation process), the feedback generation unit 7 acquires the analysis results held by the analysis result storage unit 24 and generates individual-level and organizational-level feedback. The feedback generation unit 7 may generate the feedback immediately or in predetermined units. The feedback generation unit 7 outputs the generated feedback to the information output unit 9. The feedback generation unit 7 outputs the generated feedback as feedback history to the feedback history storage unit 25, and the feedback history storage unit 25 stores the feedback history.

[0069] Next, in step S5 (Result Output / Training Recommendation), the information output unit 9 transmits the feedback input from the feedback generation unit 7 to the user device 300. The training recommendation unit 8 acquires the analysis results held by the analysis result storage unit 24 and the training information storage unit 26, and executes the training recommendation process. The training recommendation unit 8 outputs the results of the training recommendation process to the information output unit 9. The information output unit 9 transmits the results of the training recommendation process to the user device 300.

[0070] Furthermore, in step S6 (spiral-up processing), the spiral-up control unit 10 acquires the feedback history held by the feedback history storage unit 25 and performs measurement of the feedback effect and quality evaluation. Based on the evaluation results, the spiral-up control unit 10 outputs update information of the learning data to the external AI system 400, or outputs it to the organization shared area storage unit 21 to be stored as an update proposal. The spiral-up control unit 10 may, if necessary, output the updated organization shared area based on the update proposal to the organization shared area storage unit 21 to be stored. The updated organization shared area held by the organization shared area storage unit 21 is used as reference information in step S2 (matching with the organization shared area) in Figure 4 and in subsequent analysis processing. Through the above output, the spiral-up control unit 10 realizes a processing cycle that continuously improves the feedback loop.

[0071] Figure 5 is a diagram of the organizational commons referenced in S2 of Figure 4, showing the breakdown of reference information and knowledge held by the organizational commons storage unit 21 included in the storage unit 2. The organizational commons storage unit 21 holds dynamically accumulated and updated reference information as the organization's commons, and outputs this reference information to each processing unit such as the analysis engine unit 4 for reference.

[0072] The reference information held by the organizational shared storage unit 21 includes at least the management philosophy and vision, behavioral guidelines and values, management policies, departmental skill requirements, and the organizational knowledge base. The analysis engine unit 4 receives input information, retrieves this reference information from the organizational shared storage unit 21, and uses it for matching and analysis.

[0073] The management philosophy and vision are standard information that includes the mission statement, medium- to long-term vision, and core values. The analysis engine unit 4 acquires the management philosophy and vision as reference information in the philosophy gap analysis processing (philosophy gap analysis unit 41) based on the input information and uses it to evaluate the consistency with spoken content or behavioral content.

[0074] The code of conduct and values ​​are standard information that includes the Code of Conduct, compliance policy, and customer service standards. The analysis engine unit 4 receives input information, retrieves the code of conduct and values ​​as reference information, and uses it to evaluate the degree of compliance with the code of conduct or the appropriateness of customer service.

[0075] The management policy is standard information that includes evaluation criteria, grade definitions, 1-on-1 guidelines, and feedback principles. The analysis engine unit 4 accepts input information, retrieves the management policy as reference information, and uses it as the design basis for management analysis or feedback generation.

[0076] Departmental skill requirements are standard information indicating the skill requirements required for each department or job type, and include departmental items such as proposals for the sales department, technology and problem-solving for the engineering department, and legal and contract work for the legal department. The analysis engine unit 4 receives input information and user attribute information as input, retrieves departmental skill requirements as reference information, and uses them to evaluate the degree to which required skills are demonstrated or to assess skill gaps.

[0077] The organizational knowledge base is knowledge information including success stories, best practices, past problem-solving patterns, and FAQs (frequently asked questions and answers). The analysis engine unit 4 and the feedback generation unit 7 receive input information or analysis results as input, retrieve the organizational knowledge base as reference information, and use it as the basis for generating advice, suggesting solutions, or designing feedback.

[0078] Figure 6 is a flowchart of the philosophy gap analysis process performed by the analysis engine unit 4, showing the flow from the acquisition of input data (immediately) to the identification of the gap area as a real-time analysis (S11-S15). In Figure 6, the philosophy gap analysis unit 41 acquires speech recognition result text from the speech / text storage unit 23 as input. Based on the speaker information contained in the speech recognition result text, the philosophy gap analysis unit 41 acquires the corresponding speaker attribute information from the user attribute information storage unit 22 (S11).

[0079] The Philosophy Gap Analysis Unit 41 refers to the organizational shared information held by the organizational shared information memory unit 21 as reference information, extracts management philosophies and behavioral guidelines related to the acquired speech recognition result text, and obtains departmental skill requirements corresponding to the department to which the speaker belongs (S12). In addition, the Philosophy Gap Analysis Unit 41 performs feature extraction using natural language processing on the acquired speech recognition result text, including classification of the intent of the speech content, analysis of emotions and attitudes, and estimation of values ​​(S13).

[0080] The ideology gap analysis unit 41 uses the generation AI 6 to perform gap score calculation, and the gap score is calculated as: Gap score = Σ(d i ×w i ) is calculated as follows: Here, i is an index that identifies the concept element, and d i represents the degree of deviation from the i-th conceptual element, and w i This represents the importance of the i-th ideological element. The ideological gap analysis unit 41 calculates the contribution value (d) for each ideological element. i ×w i The gap score is used to identify gap areas and determine improvement priorities. The Philosophy Gap Analysis Department 41 includes consistency with the management philosophy, degree of adherence to the behavioral guidelines, and degree of performance of required skills as analytical perspectives in calculating the gap score (S14).

[0081] The philosophy gap analysis unit 41 identifies the gap area by determining the contribution value (d) for each philosophy element. i ×w i Based on this, the philosophy gap analysis unit 41 identifies philosophy elements with large discrepancies. The philosophy gap analysis unit 41 determines the priority for improvement based on whether the gap score exceeds a predetermined threshold, or based on the ranking of the gap score or contribution value. The philosophy gap analysis unit 41 determines the identified philosophy elements and their priority for improvement as gap areas and outputs the identification results immediately (S15). The philosophy gap analysis unit 41 outputs the above identification results as analysis results to the analysis result storage unit 24 and stores them in the analysis result storage unit 24.

[0082] Figure 7 is a flowchart of the risk scoring process executed by the analysis engine unit 4, showing the process of detecting issues or risk candidates from the input information and calculating the final risk score by weighting based on the speaker's attributes (S21-S24).

[0083] In Figure 7, the risk analysis unit 45, as a means of detecting issues or potential risks, performs a comparison with a risk pattern dictionary and anomaly detection using an anomaly detection model based on the speech recognition result text held by the speech / text storage unit 23, and calculates a basic score (S21). Next, as a means of acquiring user attribute information, the risk analysis unit 45 acquires the job title level, department, years of service, and area of ​​expertise corresponding to the speaker from the user attribute information storage unit 22 (S22).

[0084] The risk analysis unit 45 calculates the weight coefficient W as an attribute weight coefficient using the formula W = α × position coefficient + β × department relevance + γ × years of experience coefficient. The risk analysis unit 45 may also set the weight coefficient W to change depending on the speaker's attributes. For example, if a legal manager with 10 years of experience says "suspicion of illegality," the weight may be set to W=1.8, and if a general employee with 1 year of experience in general affairs says the same thing, the weight may be set to W=0.4 (S23).

[0085] The risk analysis unit 45 calculates the final risk score as follows: final risk score = base score × W. For example, if the base score is 70 points and W = 1.8, the final risk score will be 70 points × 1.8 = 126 points, and this final risk score may be judged as HIGH. On the other hand, if the base score is 70 points and W = 0.4, the final risk score will be 70 points × 0.4 = 28 points, and this final risk score may be judged as LOW (S24). The risk analysis unit 45 outputs the calculated final risk score as an analysis result to the analysis result storage unit 24 and stores it in the analysis result storage unit 24.

[0086] Here, the determination of HIGH and LOW may be made, for example, by comparing the final risk score with a predetermined threshold. The threshold may be a fixed value, dynamically set based on the distribution of past analysis results, or set according to the speaker's department or risk type.

[0087] Figure 8 is a flowchart of the feedback generation process performed by the feedback generation unit 7, showing the process from the integration of analysis results held by the analysis result storage unit 24, to feedback design based on coaching theory using the generation AI 6, generation of individual feedback and organizational feedback, immediate distribution to employees and supervisors and presentation of a dashboard to management, and accumulation of feedback history in the feedback history storage unit 25 (S31-S37).

[0088] In Figure 8, the feedback generation unit 7 acquires the analysis results held by the analysis result storage unit 24 as input and performs integration of the analysis results. The analysis results to be integrated include the results of the philosophy gap analysis, the engagement analysis, the skills gap analysis, and the negotiation stage analysis (S31).

[0089] Next, the feedback generation unit 7 uses the generation AI 6 to perform feedback design based on coaching theory, based on the integration results, and generates design information that constitutes the core of the feedback. The feedback generation unit 7 may also reflect in the design information the correspondence to the GROW model (Goal, Reality, Options, Will), a strengths-based perspective, the breakdown into specific actions, and the promotion of self-awareness as an application of coaching principles (S32).

[0090] Based on the above integration results and design information, the feedback generation unit 7 causes the individual feedback generation unit 71 and the organizational feedback generation unit 72 to generate feedback. The individual feedback generation unit 71 generates individual feedback including feedback on consistency with principles, skill growth feedback, engagement feedback, and negotiation improvement feedback (S33). The organizational feedback generation unit 72 generates organizational feedback including organizational trends, degree of cultural penetration, identification of common issues, and proposals for organizational measures (S34).

[0091] The information output unit 9 presents or delivers the individual feedback generated by the individual feedback generation unit 71 to employees and their supervisors as immediate distribution (S35). The information output unit 9 also presents the organizational feedback generated by the organizational feedback generation unit 72 to management as a dashboard presentation (S36).

[0092] Furthermore, the feedback generation unit 7 outputs the generated feedback as feedback history to the feedback history storage unit 25 and stores the feedback history (S37).

[0093] Figure 9 is a flowchart of the spiral-up control process executed by the spiral-up control unit 10, showing the process from measuring the feedback effect based on the feedback history stored in the feedback history storage unit 25, to evaluating the feedback loop quality using the generated AI 6, outputting learning data update information to the external AI system 400, outputting organizational shared area update proposals to the organizational shared area storage unit 21, and generating a spiral-up effect report (S41-S45).

[0094] In Figure 9, the spiral-up control unit 10 acquires the feedback history stored in the feedback history storage unit 25 as input and performs a measurement of the feedback effect. The measurement of the feedback effect may include, for example, behavioral changes, skill growth, and changes in engagement (S41).

[0095] Next, the spiral-up control unit 10 performs a feedback loop quality evaluation using the generated AI 6. The feedback loop quality evaluation may include, for example, correlation analysis between feedback and behavioral change, extraction of effective feedback patterns, or identification of feedback areas that require improvement (S42).

[0096] Based on the results of the feedback loop quality evaluation described above, the spiral-up control unit 10 outputs updated learning data information to the external AI system 400. Based on the updated learning data information, the external AI system 400 may perform tasks such as creating effective patterns for learning data, accumulating organization-specific contextual information, and fine-tuning the AI ​​model (S43).

[0097] Furthermore, the spiral-up control unit 10 generates update proposals regarding the addition or updating of reference information for the organizational shared space based on the results of the feedback loop quality evaluation, and outputs these update proposals to the organizational shared space storage unit 21 as organizational shared space update proposals for storage. Organizational shared space update proposals may include, for example, the detection of outdated ideological elements, the extraction of new values ​​that should be codified, or proposals for reviewing skill requirements (S44).

[0098] Furthermore, the spiral-up control unit 10 generates a spiral-up effect report and outputs the spiral-up effect report to the information output unit 9. The information output unit 9 outputs the spiral-up effect report to the user device 300 and presents or distributes it. The spiral-up effect report may include, for example, the visualization of the knowledge creation cycle, the amount of conversion from tacit knowledge to explicit knowledge, the amount of accumulated organizational knowledge, the degree of improvement in the quality of the feedback loop, and the degree of improvement in the accuracy of AI analysis (S45).

[0099] Figure 10 is a conceptual diagram of a dual feedback loop, showing how the processes shown in Figures 4 to 9 cycle in two ways: as an [individual-level loop] (daily to weekly) and as an [organizational-level loop] (weekly to monthly), leading to the continuous improvement and spiral-up (knowledge creation) of the organizational commons.

[0100] In the [individual-level loop] of Figure 10, the organizational activity support system 100 acquires employee activity data (audio data and / or text data from business negotiations or 1-on-1 meetings, etc.) and records actions and statements. Based on the acquired activity data, the organizational activity support system 100 immediately performs matching and multifaceted analysis corresponding to steps S2 and S3 of Figure 4 (immediate analysis). As a process corresponding to Figure 8, the organizational activity support system 100 generates feedback based on the above analysis results and presents or distributes it to the employee and their supervisor (feedback presentation). Through the above feedback presentation, the organizational activity support system 100 promotes behavioral change or growth in employees and leads to behavioral change and growth. This forms the high-speed PDCA cycle shown in Figure 10. Since the employee's actions or statements may change as a result of the above feedback, the changed actions or statements are acquired as the next activity data, and the individual-level loop shown in Figure 10 cycles around.

[0101] Furthermore, in the [organizational-level loop] of Figure 10, the organizational activity support system 100 aggregates the analysis results and feedback history obtained from the individual-level loop for a predetermined period or unit (department, team, etc.) (organizational data aggregation). Based on the aggregated data, the organizational activity support system 100 performs trend analysis and generates organizational trends, cultural penetration levels, and common issues (trend analysis). Based on the above generated results, the organizational activity support system 100 generates proposals for organizational measures and presents them to management or HR as a dashboard, etc. (feedback to the organization).

[0102] Furthermore, corresponding to the "Ideology / Policy Improvement" and "Update" shown in Figure 10, the organizational activity support system 100, as a spiral-up control corresponding to Figure 9, receives the feedback history and quality evaluation results stored in the feedback history storage unit 25 as input, and performs measurement of the feedback effect and quality evaluation of the feedback loop. Based on the results of the quality evaluation, the organizational activity support system 100 generates an update proposal regarding the addition or update of reference information related to the organizational shared space, and outputs the update proposal to the organizational shared space storage unit 21 for storage. The organizational activity support system 100 uses the updated organizational shared space held by the organizational shared space storage unit 21 as reference information in step S2 of Figure 4 and subsequent matching and analysis.

[0103] Furthermore, the updated organizational shared data held by the organizational shared data storage unit 21 is used as reference information (aggregation axis or evaluation criteria) in the "aggregation" and "trend analysis" processes in the organizational-level loop shown in Figure 10. The organizational activity support system 100 performs "aggregation" and "trend analysis" based on the updated reference information, thereby circulating the organizational-level loop shown in Figure 10. In addition, the organizational activity support system 100 may output updated learning data information based on the quality evaluation results to the external AI system 400, and the accuracy of subsequent analysis and feedback generation may be improved by updating the external AI system 400.

[0104] Therefore, the dual feedback loop shown in Figure 10 visualizes the knowledge creation cycle that circulates from individual tacit knowledge to formalized knowledge, organizational knowledge, and new tacit knowledge by viewing "recording of actions and statements" and "behavioral change and growth" in the individual-level loop as the formation and updating of individual tacit knowledge, immediate analysis and feedback presentation as formalization of knowledge, and trend analysis, feedback to the organization, and continuous improvement of shared organizational knowledge in the organizational-level loop as organizational knowledge. [Examples]

[0105] Next, we will describe some examples of the embodiments described above. Figure 11 is a flowchart of a feedback process aimed at improving sales performance, showing a high-speed cycle of "sales negotiation activity → immediate feedback → sales performance improvement".

[0106] In Figure 11, the information acquisition unit 1 acquires the sales negotiation audio as input data (S51). Specifically, the information acquisition unit 1 receives the recorded data of the sales negotiation audio and outputs the received recorded data to the speech recognition processing unit 3. If the recorded data contains multiple speakers, the speech recognition processing unit 3 distinguishes the speech segments for each speaker through speaker separation processing. The speech recognition processing unit 3 outputs the sales negotiation audio or analysis data to be processed to the speech / text storage unit 23, and the speech / text storage unit 23 stores the data.

[0107] Next, the analysis engine unit 4 receives the sales negotiation audio or speech recognition result text held by the voice / text storage unit 23 as input and performs sales skill analysis using the generating AI 6. The sales skill analysis includes analysis of listening ability, proposal ability, closing ability, and the degree of use of sales techniques (response techniques, SPIN, etc.) (S52).

[0108] Next, the analysis engine unit 4 performs negotiation quality scoring using the generated AI 6 based on the results of the sales skill analysis. Negotiation quality scoring includes quantifying each skill item and identifying areas for improvement. The analysis engine unit 4 outputs the obtained scoring results and areas for improvement as analysis results to the analysis result storage unit 24 for storage (S53).

[0109] Next, the feedback generation unit 7 retrieves the analysis results held by the analysis result storage unit 24 and generates immediate feedback based on those analysis results. The immediate feedback includes highlighting positive aspects and providing specific suggestions for improvement. The information output unit 9 immediately delivers the generated feedback to the user device 300 immediately after the business negotiation (S54).

[0110] Next, the organizational activity support system 100 stores the scoring results for each business negotiation and the corresponding time-series records in the analysis results storage unit 24 as growth tracking, and maintains the growth trend for each business negotiation (S55).

[0111] While traditional on-the-job training (OJT) often involves feedback over several months to a year, this embodiment enables immediate feedback (daily to weekly) immediately after a business negotiation. Other features of this embodiment include the fact that skills improve in proportion to activity level, that improvements can be immediately reflected in sales performance, and that ROI can be visualized. [Examples]

[0112] Next, Figure 12 is a flowchart of the feedback process aimed at improving management skills, illustrating a rapid cycle of "1-on-1 activities → immediate feedback → improved coaching skills."

[0113] In Figure 12, the information acquisition unit 1 acquires 1-on-1 audio as input data. Specifically, the information acquisition unit 1 receives recorded data from the user device 300 and outputs the received recorded data to the speech recognition processing unit 3. The speech recognition processing unit 3 performs speaker separation processing on the received recorded data to separate the statements of the superior and the subordinate. The speech recognition processing unit 3 outputs the result of the 1-on-1 audio or speaker separation processing to the speech / text storage unit 23 for storage (S61).

[0114] Next, the analysis engine unit 4 receives the 1-on-1 audio or speech recognition result text held by the voice / text storage unit 23 as input and performs coaching skills analysis using the generating AI 6. The coaching skills analysis includes analysis of listening ability (proportion of the supervisor's speaking), questioning ability, acknowledgment, and feedback quality. The analysis engine unit 4 outputs the obtained analysis results as analysis results to the analysis result storage unit 24 for storage (S62).

[0115] Furthermore, the analysis engine unit 4 uses the generated AI 6 to perform subordinate engagement analysis and detect the degree of positiveness in their statements and the severity of the issues. The analysis engine unit 4 outputs the obtained analysis results as analysis results to the analysis result storage unit 24 for storage (S63).

[0116] Next, the feedback generation unit 7 generates immediate feedback based on the results of the coaching skills analysis and the subordinate engagement analysis, and generates feedback that includes suggestions for improvement in coaching and follow-up suggestions for subordinates. The information output unit 9 immediately presents or delivers the generated feedback to the user device 300 (S64).

[0117] Next, the organizational activity support system 100 stores time-series records related to coaching quality and data related to changes in team engagement in the analysis result storage unit 24 as a management capability tracking function, and maintains the growth trend for each 1on1 (S65).

[0118] Coaching quality indicators used in management ability tracking include, for example, the supervisor's speaking ratio, which represents the degree of listening; the ratio of open-ended to closed-ended questions, which represents the quality of questions; the number of acknowledgments; the presence or absence of specific feedback; and the presence or absence of agreement on next actions. The analysis engine unit 4 calculates indicator values ​​corresponding to the coaching quality indicators based on 1on1 audio or analysis data, and uses the calculated indicator values ​​in the analysis results of coaching skill analysis and the time-series records of management ability tracking. For the supervisor speaking ratio indicator, for example, a standard value of 30% or less may be used, which is considered good.

[0119] The expected effects of this embodiment include improved employee engagement, reduced employee turnover, and enhanced team performance. [Examples]

[0120] Figure 13 illustrates the concept of a process that forms a daily feedback loop by generating a daily feedback report (seeds of growth report) based on daily activity data and distributing it daily.

[0121] In Figure 13, the information acquisition unit 1 acquires activity data related to business negotiations, 1-on-1 meetings, conferences, and chats as daily activity data collection. The daily activity data may be input information including voice or text. The information acquisition unit 1 outputs the acquired daily activity data to the voice / text storage unit 23 for storage, making it available for reference in subsequent analysis processing.

[0122] Next, the analysis engine unit 4 acquires the daily activity data held by the voice / text storage unit 23 and performs executive coaching analysis using the generated AI 6. The executive coaching analysis applies GROW, strengths-based, and SMART goals to extract insights from the day's actions and statements that will be useful for reflection and future actions. The analysis engine unit 4 outputs the obtained analysis results as analysis results to the analysis result storage unit 24 for storage.

[0123] Next, the feedback generation unit 7 uses the generation AI 6 to generate a growth seed report based on the results of the executive coaching analysis, and generates a daily feedback report that includes positive points, growth opportunities, and suggestions for tomorrow. The feedback generation unit 7 outputs the generated daily feedback report to the information output unit 9.

[0124] The information output unit 9 outputs a daily feedback report to the user device 300 and presents or delivers it as a daily delivery (S74). The timing of the daily delivery is arbitrary, but for example, it may include delivery at a time that is useful for morning reflection and delivery at a time that is useful for immediate feedback in the evening. According to this embodiment, the delivery of the daily growth seed report activates a daily feedback loop, and through the accumulation of daily behavioral changes, sales ability, management ability, communication ability, and team building ability improve each time there is dialogue or collaboration, providing a behavioral change mechanism that leads to improved performance and organizational growth.

[0125] [Embodiments and Effects of Examples] Figure 14 is a comparative diagram illustrating the effects and benefits realized by the embodiments or examples shown in Figures 1 to 13, in contrast to conventional training. Figure 14 shows that while conventional training tends to have a long cycle of "training → practice → evaluation (several months to a year)," this embodiment can form a short cycle of "activity → immediate feedback → improvement (daily to weekly)," and that this shortening of the cycle makes it easier to grasp the causal relationship with performance and ROI.

[0126] In Figure 14, under [Traditional Training], on-the-job training (OJT) tends to be highly dependent on the supervisor's experience, leading to personalization and low reproducibility. Furthermore, feedback frequency tends to be low. Classroom-based training is prone to disconnection from actual work, making it difficult to apply learned content to practical situations, and resulting in only temporary knowledge. As a result, traditional training often involves a period of several months to a year between training, implementation, and subsequent evaluation, making effectiveness measurement difficult. Consequently, the causal relationship between ROI and performance tends to be unclear.

[0127] In contrast, in the [Embodiment: High-Speed ​​Feedback Type] shown in Figure 14, the organizational activity support system 100 can form a feedback loop each time an activity is performed by performing objective and continuous evaluations based on analysis of input information that is in line with actual activities, and by generating and outputting feedback that includes improvement suggestions. Specifically, the organizational activity support system 100 may perform feedback on business negotiations as shown in Figure 11, and feedback on one-on-one meetings as shown in Figure 12. Feedback on business negotiations may include analysis in line with business negotiation activities, quantification of skill items, and presentation of improvement points. Feedback on one-on-one meetings may include visualization of coaching quality, tracking of subordinate engagement, and presentation of improvement points that contribute to improving management skills.

[0128] The effects and benefits obtained by this embodiment include the ability to carry out activity → immediate feedback → improvement on a daily to weekly basis, the ability to track behavioral changes in short cycles, and the ease of grasping ROI directly linked to performance. Furthermore, it is possible to explain performance improvement based on the relationship between activity volume and growth rate, and the continuation of activities can promote improvements in sales and management capabilities. Therefore, this embodiment realizes a system that supports human resource development and organizational development directly linked to performance through a high-speed feedback loop.

[0129] Furthermore, according to this embodiment, the organizational shared memory unit 21 dynamically stores the organization's philosophy, behavioral guidelines, management policies, etc., as "organizational commons," and updated reference information can be reintroduced for subsequent matching and analysis. As a result, knowledge such as the speaking styles and negotiation patterns of top performers, the structure of successful proposals, and effective coaching and 1-on-1 patterns can be utilized as shared organizational assets instead of being lost due to being individualized.

[0130] Furthermore, according to this embodiment, the risk analysis unit 45 compares the input information with a risk pattern dictionary and performs detection using an anomaly detection model to extract issues or risk candidates, which can then be visualized as a final risk score. This allows for the presentation of issues being discussed within the organization or individual problems to management as a risk map, contributing to the early detection of strategic issues and impact analysis.

[0131] Furthermore, according to this embodiment, the speaker attribute information (job title level, department, years of service, area of ​​expertise, etc.) held by the user attribute information storage unit 22 is used to calculate attribute weight coefficients, allowing for weighting of the risk score according to the speaker's attributes, even for identical statements. For example, a statement from a general affairs employee with one year of experience and a statement from a legal employee with ten years of experience can be treated differently in terms of the magnitude of risk from a realistic perspective, thereby improving the validity of the prioritization.

[0132] Furthermore, according to this embodiment, based on the analysis results of the sales negotiation analysis unit 43, etc., recommended training or sales techniques can be immediately recommended, clarifying which training should be taken or which sales techniques should be learned. Specifically, as shown in the result output / training recommendation (S5) in Figure 4, recommendations based on the analysis results can be output.

[0133] Furthermore, according to this embodiment, the determination of the negotiation stage can be performed as an objective progress evaluation that does not rely on subjective opinions. This makes it easier to handle each phase and stage from negotiation preparation to closing using common criteria, and improves the consistency of sharing analysis results and improvement suggestions.

[0134] In addition, according to this embodiment, the spiral-up control unit 10 performs effectiveness measurement and feedback loop quality evaluation based on feedback history, and the results can be reflected in the updating of AI learning data and proposals for updating the organizational shared area. This allows effective speaking and coaching patterns and organization-specific contextual information to be converted into learning data, and feedback accuracy and recommendation accuracy can be continuously improved even after implementation through fine-tuning of the external AI system 400.

[0135] Furthermore, this embodiment enables a "no waiting / no accumulating" operation by acquiring input information in real time, conducting multifaceted analysis through stream processing (sequential analysis), and connecting to immediately generated feedback and training recommendations. This creates a high-speed cycle that advances processing in response to incoming input, thereby minimizing delays in analysis and output.

[0136] It should be noted that the present invention is not limited to the embodiments described above, and various other applications and modifications can be taken as long as they do not deviate from the gist of the present invention as described in the claims. For example, the embodiments described above are detailed and specific explanations of the system configuration in order to clearly illustrate the present invention, and are not necessarily limited to having all the configurations described. Furthermore, it is possible to add, delete, or replace some of the configurations in these embodiments with other configurations. Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and not all control lines and information lines are necessarily shown in the actual product. In reality, it is safe to assume that almost all components are interconnected. [Explanation of Symbols]

[0137] 1…Information Acquisition Unit, 2…Storage Unit, 3…Speech Recognition Processing Unit, 4…Analysis Engine Unit, 5…AI Collaboration Unit, 6…Generating AI, 7…Feedback Generation Unit, 8…Training Recommendation Unit, 9…Information Output Unit, 10…Spiral Up Control Unit, 11…Processor, 12…Temporary Storage Unit, 13…Communication Device, 21…Organizational Shared Storage Unit, 22…User Attribute Information Storage Unit, 23…Speech / Text Storage Unit, 24…Analysis Result Storage Unit, 25…Feedback History Storage Unit, 26…Training / Training Information Storage Unit, 41…Ideal Gap Analysis Unit, 42…Engagement Analysis Unit, 43…Sales Negotiation Analysis Unit, 44…Skill Analysis Unit, 45…Risk Analysis Unit, 71…Individual Feedback Generation Unit, 72…Organizational Feedback Generation Unit, 100…Organizational Activity Support System, 200…Computer Network, 300…User Device, 400…External AI System

Claims

1. An organizational activity support system that is connected to multiple user devices via a computer network, acquires input information regarding actual activities within an organization, and provides analysis and feedback, An information acquisition unit that acquires the input information, including audio data and / or text data related to meetings, business negotiations, one-on-one meetings, or consultations, from the user device, A storage unit that stores at least the input information acquired by the information acquisition unit, text generated based on the input information, organizational shared information as reference information which is standard information or knowledge that constitutes the shared property of the organization, and analysis results, wherein the organizational shared information includes at least normative information including the organization's management philosophy or code of conduct. An analysis engine unit that generates analysis results by performing a multifaceted analysis while acquiring and referencing the reference information based on the input information or text stored in the memory unit, wherein the multifaceted analysis includes an ideological gap analysis that extracts features based on the correspondence between the utterance content included in the input information and the normative information acquired as the reference information, and identifies gap areas using a generating AI. A feedback generation unit that generates individual and / or organizational feedback based on the analysis results, the feedback generation unit integrates the analysis results, uses a generation AI to design feedback based on coaching theory, and generates the individual and / or organizational feedback based on the design, An information output unit that outputs the feedback generated by the feedback generation unit to the user device, The system includes a spiral-up control unit that measures the feedback effect based on the feedback history, performs a quality evaluation of the feedback loop, and generates an update proposal for the organizational shared area based on the results of the quality evaluation and outputs it to the storage unit, The aforementioned reference information is dynamically updated based on the update proposal. The analysis engine unit performs the multifaceted analysis while acquiring and referring to the updated reference information. Organizational activity support system.

2. In the organizational activity support system according to claim 1, The analysis engine unit sequentially performs the multifaceted analysis in response to the arrival of the input information. Organizational activity support system.

3. In the organizational activity support system according to Claim 1, The aforementioned philosophy gap analysis calculates a gap score based on the sum of the products of the degree of deviation from each element constituting the normative information and the importance of each element, and determines the priority for improvement based on whether the gap score exceeds a predetermined threshold or the ranking of the products. Organizational activity support system.

4. In the organizational activity support system according to Claim 1, The feedback design based on the aforementioned coaching theory incorporates at least one of the following into its design information: mapping to the GROW model, a strengths-based perspective, translating into specific actions, and promoting self-awareness. Organizational activity support system.

5. In the organizational activity support system according to claim 1, The analysis engine unit aggregates the analysis results or the feedback history in predetermined period units and / or organizational units, and performs trend analysis based on the aggregated results. The feedback generation unit generates feedback for the organization based on the results of the trend analysis. The information output unit outputs the personal feedback at a frequency ranging from daily to weekly, and outputs the generated organizational feedback to the user device corresponding to the administrator or manager at a frequency ranging from weekly to monthly. Organizational activity support system.

6. In the organizational activity support system according to claim 1, The information acquisition unit acquires the audio data, including recorded data relating to the meeting, the business negotiation, the one-on-one meeting, or the consultation. The storage unit stores text generated by performing speech recognition processing, including speaker separation, on the recorded data. The analysis engine unit performs the multifaceted analysis based on the text. Organizational activity support system.

7. In the organizational activity support system according to claim 1, The information output unit immediately outputs the feedback generated by the feedback generation unit to the user device after acquiring the input information. Organizational activity support system.

8. In the organizational activity support system according to Claim 1, The analysis engine unit acquires candidate issues or risks extracted based on the input information and user attribute information corresponding to the speaker, and calculates a risk score using attribute weight coefficients based on the user attribute information. Organizational activity support system.

9. A method for supporting organizational activities performed by a computer that is communicatively connected to multiple user devices via a computer network, acquires input information regarding actual activities within an organization, and provides analysis and feedback, An information acquisition step of acquiring the input information, including audio data and / or text data related to a meeting, business negotiation, one-on-one, or consultation, from the user device, An analysis step to generate analysis results by performing a multifaceted analysis while referencing organizational shared information, which includes at least normative information including the organization's management philosophy or code of conduct, as reference information, based on the stored input information or text generated based on said input information, wherein the multifaceted analysis includes a philosophy gap analysis that extracts features based on the correspondence between the utterance content contained in the input information and the normative information obtained as reference information, and identifies gap areas using generating AI. A feedback generation step involves integrating the analysis results, using a generative AI to design feedback based on coaching theory, and generating individual and / or organizational feedback based on that design. An information output step which outputs the feedback generated by the feedback generation step to the user device, A spiral-up control step that measures the feedback effect based on the feedback history, performs a quality evaluation of the feedback loop, and generates update proposals for the organizational shared space based on the results of the quality evaluation, Includes, The aforementioned reference information is dynamically updated based on the update proposal. The method for supporting organizational activities involves performing the multifaceted analysis in the aforementioned analysis step, while obtaining and referring to the updated reference information.

10. An organizational activity support program that causes a computer to execute the organizational activity support method described in claim 9.

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