- Number
- 12165107
- Published
- 2024-12-10
- Filed
- 2022-01-06
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Bever; Rebecca et al.
- CPC
- G06F21/6218; G06Q10/101; G06Q10/063114
- Verdict
- Set aside software collaboration tooling, business
- Source
- Google Patents · FreePatentsOnline
Abstract
A system includes processing hardware, a user interface (UI), and a system memory storing a software code. The processing hardware executes the software code to receive from a first user, via the UI, project data identifying a project including multiple tasks, determine, using the project data, a node-based graph of the project including the tasks, receive from a first user or a second user via the UI, task action data producing a modification to the node-based graph, and identify, based on the modification to the node-based graph, one of a conflict or a potential future conflict among at least two nodes of the node-based graph. The software code further identifies a conflict avoidance strategy for resolving the conflict or preventing the potential future conflict, and displays, via the UI, the conflict avoidance strategy to the first user or the second user, or performs the conflict avoidance strategy.
Background
BACKGROUND (1) Large or complex projects increasingly require the collaboration of experts from a variety of fields who may be independent contractors, or experts affiliated with businesses or other entities independent of the initiator of the project. This need for collaboration between project initiators or “clients” and the third-parties with which they contract for collaborative services or “vendors” has led to the design of workflows to manage and synchronize the distribution and modification of data relevant to the collaborative project. Existing collaboration solutions typically result in both clients and their vendors being in possession of copies of substantially the same data. That duplication and proliferation of data is undesirable because it increases the cost and time required to ensure data consistency and security. In addition, the relative success or failure of collaborative projects may rely on marketplace or business changes that occur dynamically during work on the project and which may not be known to all vendors, resulting in conflicts or inconsistencies among the work product of different vendors working independently on different tasks of a common project. Consequently, there remains a need in the art for a solution enabling multidisciplinary collaboration and conflict avoidance for the successful management of complex projects in a dynamically changing project environment.
Claims
1. A system comprising: a processing hardware, a user interface (UI), and a system memory storing a software code and a conflict avoidance machine learning (ML) model trained using a deep reinforcement learning; the processing hardware configured to execute the software code to: receive from a first user, via the UI, project data describing a project including a plurality of tasks; determine, using the project data, a node-based graph of the project including the plurality of tasks; receive from one of the first user or a second user, via the UI, task action data producing a modification to the node-based graph; identify, based on the modification to the node-based graph, a conflict among at least two nodes of the node-based graph; predict, using the conflict avoidance ML model and the conflict, one or more candidate strategies for resolving the conflict; identify, using the conflict avoidance ML model and based on the one or more candidate strategies in an automated process independent of the first user and the second user, a conflict avoidance strategy for resolving the conflict, wherein the conflict avoidance strategy comprises decoupling a node of the node-based graph; display, via the UI, the node-based graph, a conflict alert indicating the node in the node-based graph and the conflict avoidance strategy to the one of the first user or the second user; receive, via the UI, an input from the one of the first user or the second user to decouple the node indicated by the conflict alert; in response to receiving the input, decouple the node indicated by the conflict alert; display, via the UI, the node-based graph with the node decoupled from the node-based graph; and further train the conflict avoidance ML model, using the deep reinforcement learning and the input from the one of the first user or the second user, to improve the conflict avoidance ML model in a feedback loop. ||
3. A system comprising: a processing hardware, a user interface (UI), and a system memory storing a software code and a conflict avoidance machine learning (ML) model trained using deep reinforcement learning; the processing hardware configured to execute the software code to: receive from a first user, via the UI, project data describing a project including a plurality of tasks; determine, using the project data, a node-based graph of the project including the plurality of tasks; receive from one of the first user or a second user, via the UI, task action data producing a modification to the node-based graph; identify, based on the modification to the node-based graph, a potential future conflict among at least two nodes of the node-based graph; predict, using the conflict avoidance ML model and the conflict, one or more candidate strategies for preventing the potential future conflict; identify, using the conflict avoidance ML model and based on the one or more candidate strategies in an automated process independent of the first user and the second user, a conflict avoidance strategy for preventing the potential future conflict, wherein the conflict avoidance strategy comprises decoupling a node of the node-based graph; display, via the UI, the node-based graph, a conflict alert indicating the node in the node-based graph and the conflict avoidance strategy to the one of the first user or the second user; receive, via the UI, an input from the one of the first user or the second user to decouple the node indicated by the conflict alert; in response to receiving the input, decouple the node indicated by the conflict alert; display, via the UI, the node-based graph with the node decoupled from the node-based graph; and further train the conflict avoidance ML model, using the deep reinforcement learning and the input from the one of the first user or the second user, to improve the conflict avoidance ML model in a feedback loop. ||
4. A method for use by a system including a processing hardware, a user interface (UI), and a system memory storing a software code and a conflict avoidance machine learning (ML) model trained using deep reinforcement learning, the method comprising: receiving from a first user, by the software code executed by the processing hardware and via the UI, project data identifying a project including a plurality of tasks; determining, by the software code executed by the processing hardware and using the project data, a node-based graph of the project including the plurality of tasks; receiving from one of the first user or a second user, by the software code executed by the processing hardware and via the UI, task action data producing a modification to the node-based graph; identifying, by the software code executed by the processing hardware and using the conflict avoidance ML model and based on the modification to the node-based graph, one of a conflict or a potential future conflict among at least two nodes of the node-based graph; predicting, by the software code executed by the processing hardware and using the conflict avoidance ML model and the identified one of the conflict or the potential future conflict, one or more candidate strategies for one of resolving the conflict or preventing the potential future conflict; identifying, by the software code executed by the processing hardware based on the one or more candidate strategies, in an automated process independent of the first user and the second user, a conflict avoidance strategy for the one of resolving the conflict or preventing the potential future conflict, wherein the conflict avoidance strategy comprises decoupling a node of the node-based graph; displaying, by the software code executed by the processing hardware and via the UI, the node-based graph, a conflict alert indicating the node in the node-based graph and the conflict avoidance strategy to the one of the first user or the second user; receiving, by the software code executed by the processing hardware and via the UI, an input from the one of the first user or the second user to decouple the node indicated by the conflict alert; in response to receiving the input, decouple, by the software code executed by the processing hardware, the node indicated by the conflict alert; in response to receiving the input, displaying, by the software code executed by the processing hardware and via the UI, the node-based graph with the node decoupled from the node-based graph; and further training, by the software code executed by the processing hardware, the conflict avoidance ML model, using the deep reinforcement learning and the input from the one of the first user or the second user, to improve the conflict avoidance ML model in a feedback loop.