US-20260127021-A1 - ADAPTIVE AI COWORKER FOR ORGANIZATIONAL OPERATIONS

US20260127021A1US 20260127021 A1US20260127021 A1US 20260127021A1US-20260127021-A1

Abstract

Examples relate to an adaptive AI coworker system for enhancing organizational operations. The system generates personalized AI coworkers based on role requirements, employing adaptive learning to understand unique organizational practices. It utilizes multi-agent coordination for complex task execution, automatically generating, prioritizing, and allocating tasks based on organizational context. The system integrates data from various sources, implementing data governance measures. Customized large language model instances are created for specific roles and organizations, incorporating organization-specific data and operational practices. The system provides explainable AI features for operational decision-making, ensuring transparency in critical tasks. Security measures and access controls are implemented to maintain data integrity and compliance with relevant regulations and standards.

Inventors

Deepti Chafekar
Afrozy Ara

Assignees

LuminaData, Inc.

Dates

Publication Date: 20260507
Application Date: 20251104

Claims (20)

1 . A computer-implemented method for automated task management in a digital co-worker system, the computer-implemented method comprising: receiving, by at least one processor, a role description for an artificial intelligence (AI) co-worker of an organization, the role description including role requirements of a particular role that is associated with the AI co-worker; identifying, by the at least one processor, required skills and responsibilities corresponding to the AI co-worker from the role description by analyzing the role description using natural language processing techniques; selecting, by the at least one processor, a combination of AI agents from a pool of specialized agents based on the identified skills and responsibilities; creating, by the at least one processor, a customized AI co-worker by integrating the selected AI agents; automatically generating, by the at least one processor, a plurality of tasks to be performed by the customized AI co-worker based on the role requirements included in the role description and a company context that describes characteristics of the organization; prioritizing, by the at least one processor, the plurality of tasks, each of the plurality of tasks prioritized based on a time sensitivity and a task importance of the task; allocating, by the at least one processor, the prioritized plurality of tasks to the AI co-worker; and executing, by the at least one processor, the allocated plurality of tasks using the AI agents of the AI co-worker.
2 . The computer-implemented method of claim 1 , wherein the pool of specialized agents comprises: one or more data access agents, each data access agent to retrieve data from a plurality of different sources; one or more data processing agents, each data processing agent to standardize the retrieved data into a standardized format; one or more analytics agents, each analytics agent to analyze the standardized data and generate insights based on the analysis; and one or more application agents, each application agent to perform a domain-specific task.
3 . The computer-implemented method of claim 1 , further comprising: onboarding the customized AI co-worker by providing the customized AI co-worker access to company-specific data sources and documents.
4 . The computer-implemented method of claim 3 , wherein onboarding the customized AI co-worker further comprises: fine-tuning a machine-learning model of the AI co-worker using company-specific organizational data and practices.
5 . The computer-implemented method of claim 1 , wherein automatically generating the plurality of tasks comprises: identifying recurring tasks based on an analysis of role-specific responsibilities associated with the particular role for the customized AI co-worker and historical task data; and creating periodic tasks based on the identified recurring tasks.
6 . The computer-implemented method of claim 1 , wherein automatically generating the plurality of tasks further comprises: monitoring user inputs and system events; identifying immediate needs within the organization based on the monitored user inputs and system events; and generating one or more ad-hoc tasks based on the identified immediate needs.
7 . The computer-implemented method of claim 1 , wherein prioritizing the plurality of tasks comprises: generating a priority score for each of the plurality of tasks based on predefined criteria including at least one of a deadline, impact on operations, and manager input, wherein the plurality of tasks are prioritized based on the priority score for each of the plurality of tasks.
8 . The computer-implemented method of claim 1 , wherein allocating the prioritized plurality of tasks comprises: determining an execution sequence for the prioritized plurality of tasks; and scheduling the prioritized plurality of tasks according to the determined execution sequence.
9 . The computer-implemented method of claim 1 , further comprising: monitoring task execution progress of the plurality of tasks; determining workload balance between the AI agents of the personalized AI coworker; and dynamically adjusting task priorities and allocation of the adjusted prioritized plurality of tasks to the AI agents based on the monitored progress and workload balance.
10 . The computer-implemented method of claim 1 , wherein executing the allocated plurality of tasks comprises: retrieving data relevant to the allocated plurality of tasks from a plurality of integrated data sources; processing the retrieved data using the AI agents; and generating output based on the processed data, the output including a visualization of the processed data.
11 . The computer-implemented method of claim 1 , further comprising: receiving feedback on task execution of the plurality of tasks; and updating task execution parameters of the AI co-worker based on the received feedback.
12 . The computer-implemented method of claim 1 , wherein automatically generating the plurality of tasks comprises: tuning one or more large language models (LLMs) by inputting the role requirements and company-specific data of the organization into the one or more LLMs, the tuned one or more LLMs understanding the role requirements and the company-specific data of the organization; inputting multiple data sources including emails, calendar events, and organizational system alerts into the one or more tuned LLMs, the one or more tuned LLMs outputting identified potential tasks; and generating relevant tasks based on the potential tasks identified by the one or more tuned LLMs.
13 . The computer-implemented method of claim 1 , wherein each of the executed plurality of tasks is associated with at least one task execution decision and the method further comprises: generating an explanation of the at least one task execution decisions for each of the executed plurality of tasks; and receiving feedback on the explanation; and retraining the AI coworker based on the feedback.
14 . The computer-implemented method of claim 1 , wherein allocating the prioritized plurality of tasks to the AI co-worker comprises: identifying required capabilities for each of the prioritized plurality of tasks; matching the required capabilities for each of the prioritized plurality of tasks to agent profiles of the AI agents selected for the AI co-worker; and assigning an agent from the AI agents selected for the AI co-worker to at least one of the prioritized plurality of tasks based on the assigned agent having a profile that matches the required capability for the task.
15 . The computer-implemented method of claim 14 , further comprising: managing resource allocation to assigned AI agents across distributed computing resources to optimize performance and efficiency of the execution of the allocated plurality of tasks.
16 . A computing system comprising: at least one processor; and at least one memory storing instructions that, when executed in cooperation with controlling the at least one processor, operate the computing system to perform operations comprising: receiving a role description for an artificial intelligence (AI) co-worker of an organization, the role description including role requirements of a particular role that is associated with the AI co-worker; identifying required skills and responsibilities corresponding to the AI co-worker from the role description by analyzing the role description using natural language processing techniques; selecting a combination of AI agents from a pool of specialized agents based on the identified skills and responsibilities; creating a customized AI co-worker by integrating the selected AI agents; automatically generating a plurality of tasks to be performed by the customized AI co-worker based on the role requirements included in the role description and a company context that describes characteristics of the organization; prioritizing the plurality of tasks, each of the plurality of tasks prioritized based on a time sensitivity and a task importance of the task; allocating the prioritized plurality of tasks to the AI co-worker; and executing the allocated plurality of tasks using the AI agents of the AI co-worker.
17 . The computer system of claim 16 , wherein the operations further comprise: fine-tuning a machine-learning model of the AI co-worker using company-specific organizational data and practices to onboard the customized AI co-worker.
18 . The computer system of claim 16 , wherein the operations further comprise: monitoring task execution progress of the plurality of tasks; determining workload balance between the AI agents of the AI coworker; and dynamically adjusting task priorities and allocation of the adjusted prioritized plurality of tasks to the AI agents based on the monitored progress and workload balance.
19 . A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: receiving, by the at least one processor, a role description for an artificial intelligence (AI) co-worker of an organization, the role description including role requirements of a particular role that is associated with the AI co-worker; identifying, by the at least one processor, required skills and responsibilities corresponding to the AI co-worker from the role description by analyzing the role description using natural language processing techniques; selecting, by the at least one processor, a combination of AI agents from a pool of specialized agents based on the identified skills and responsibilities; creating, by the at least one processor, a customized AI co-worker by integrating the selected AI agents; automatically generating, by the at least one processor, a plurality of tasks to be performed by the customized AI co-worker based on the role requirements included in the role description and a company context that describes characteristics of the organization; prioritizing, by the at least one processor, the plurality of tasks, each of the plurality of tasks prioritized based on a time sensitivity and a task importance of the task; allocating, by the at least one processor, the prioritized plurality of tasks to the AI co-worker; and executing, by the at least one processor, the allocated plurality of tasks using the AI agents of the AI co-worker.
20 . The non-transitory computer-readable medium of claim 19 , wherein the operations further comprise: fine-tuning a machine-learning model of the AI co-worker using company-specific organizational data and practices to onboard the customized AI co-worker.

Description

CROSS-REFERENCE TO RELATED APPLICATION This non-provisional application claims the benefit of U.S. Provisional Ser. No. 63/716,444 , filed on Nov. 5, 2024, which is hereby incorporated by reference in its entirety. TECHNICAL FIELD The present disclosures relate to artificial intelligence systems and, in some examples, to algorithms and systems to generate and deploy customized AI agents for task automation and workflow management in organizational operations. BACKGROUND Operational teams within organizations face numerous technical challenges in managing complex workflows and tasks. The increasing volume and variety of data generated by enterprise systems, including customer relationship management (CRM) platforms, enterprise resource planning (ERP) software, and various internal databases, create significant hurdles in data integration and analysis. Processing and extracting meaningful insights from this diverse data landscape, which includes structured and unstructured information, requires sophisticated technological solutions. Natural language processing and machine learning techniques are often employed to interpret and categorize vast amounts of textual data from sources such as emails, documents, and internal communications. Task management and workflow optimization present another set of technical challenges. As organizational processes become more complex, there is a growing need for systems that can automate routine tasks, prioritize activities based on multiple factors, and adapt to changing operational requirements. This complexity is further compounded by the need to coordinate activities across different roles and departments within an organization. The integration of various enterprise systems and the need for seamless data flow between them pose significant technical hurdles. Ensuring data consistency, maintaining security protocols, and managing access controls across multiple platforms require robust architectural solutions. Additionally, the dynamic nature of modern business environments necessitates systems that can learn and adapt to new scenarios, understand context-specific requirements, and provide decision support based on historical data and current trends. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. FIG. 1 is a block diagram illustrating a networked computing environment for implementing a digital co-worker system, according to some examples. FIG. 2 is a block diagram illustrating a system architecture for a role-based AI coworker system, according to some examples. FIG. 3 is a block diagram illustrating a software architecture for generating and managing AI coworkers, according to some examples. FIG. 4 is a block diagram illustrating a database architecture for storing and processing data in the AI coworker system, according to some examples. FIG. 5 is a block diagram illustrating a task agent architecture for breaking down and executing tasks, according to some examples FIG. 6 is a block diagram illustrating a role agent architecture for understanding and managing role-specific tasks, according to some examples. FIG. 7 is a block diagram illustrating components of a single AI agent, according to some examples. FIG. 8 is a block diagram illustrating various components and processes of an AI coworker system, according to some examples. FIG. 9 is a flowchart illustrating a method for generating and managing an AI coworker, according to some examples. FIG. 10 is a flowchart illustrating a method for role-based AI coworker customization with adaptive learning, according to some examples. FIG. 11 is a flowchart illustrating a method for multi-agent coordination and communication, according to some examples. FIG. 12 is a flowchart illustrating a method for automated task generation, prioritization, and allocation, according to some examples. FIG. 13 is a flowchart illustrating a method for generating customized large language model instances, according to some examples. FIG. 14 is a block diagram illustrating an algorithm architecture for executing tasks within a digital co-worker system, according to some examples. FIG. 15 is a user interface diagram illustrating a login screen for a finance productivity application, according to some examples. FIG. 16 is a user interface diagram illustrating an account settings screen within a finance productivity application, according to some examples. FIG. 17 is a user interface diagram illustrating a home screen with task board and key performance indicators within a finance productivity application, according to some examples. FIG. 18 is a user interface diagram illustrating a task board interface within a finance productivity application, according to some examples. FIG. 19 is a user interface diagram illustrating a new task creation interf