A computer-implemented method for generating and deploying a reinforced learning model to train a chatbot. The method includes selecting a plurality of conversations, wherein each conversation includes an agent and a user. The method includes identifying, in each of the conversations, a set of turns and on or more topics. The method further includes associating one or more topics to each turn of the set of turns. The method includes, generating a conversation flow for each conversation, wherein the conversation flow identifies a sequence of the topics. The method includes applying an outcome score to each conversation. The method includes creating a reinforced learning (RL) model, wherein the RL model includes a Markov is based on the conversation flow of each conversation and the outcome score of each conversation. The method includes deploying the RL model, wherein the deploying includes sending the RL model to a chatbot.

A method, computer system, and a computer program product for dynamic role assignment is provided. The present invention may include identifying a crisis event based on data collected by one or more internet-connected sensors. The present invention may then include selecting an incident plan based on the identified crisis event. The present invention may then include communicating an incident role to a registered device of a crowd member.

Systems and methods for generating prioritization models and predicting workflow prioritizations are disclosed. Exemplary implementations may: manage environment state information maintaining a collaboration environment; effectuate presentation of a series of questions via a first client computing platform associated with the first user; receive user input from the first client computing platform; generate a first prioritization model based on the response information; and determine one or more priorities for the first user based on the first prioritization model such that a primary first unit of work within the first set of units of work is determined to be a higher priority than a secondary first unit of work within the first set of units of work.

The present disclosure describes techniques for dynamically monitoring and collating data associated with an agricultural operation for the purpose of demonstrating compliance with an agricultural compliance plan (ACP). More specifically, a decentralized governance compliance (D-GRC) controller is described that is configured to generate a distributed ledger that dynamically processes compliance of individual actions associated with an ACP. The distributed ledger may be configured to track regulatory compliance associated with a cycle of agricultural activities associated with an agricultural product. Agricultural activities may include an inventory inspection of agricultural products, a facility inspection of a facility used for an agricultural operation, or vehicle inspection of vehicles used to transport agricultural products. Further, the distributed ledger may be configured to automate the governance of an ACP across various entities such that are each compliant or non-compliant response from an entity, automatically triggers an update to the ACP.

A computerized task automation system operates to permit automation of processes comprising one or more computer implemented tasks. Software robots may be created and retrieved to automate human user interaction with computer systems. The software robots each take one of three forms: (i) an attended software robot that is encoded to interact with one or more application programs and to accept one or more inputs from the user as required by the one or more application programs, (ii) an unattended software robot that is encoded to automatically interact with one or more application programs without any user input; and (iii) a cognitive software robot that is encoded to automatically interact with one or more application programs without any user input and is further encoded to automatically alter its interactions with the one or more application programs by way of a machine learning engine.

Systems and methods for tailoring a single prescription for different users for completing tasks on equipment are provided. A prescription representative of a flowchart or decision tree for performing a main set of the tasks is obtained. Characteristics of the equipment and/or a user of the prescription are obtained. Content of the prescription that is displayed to the user during performance of at least some of the tasks on the equipment is tailored based on the characteristics. The content of the prescription is tailored by hiding one or more of the tasks that are not applicable to the equipment from display to the user. This can occur without any data or information being removed from or added to the prescription. Instead, the prescription can be tailored by changing what information is automatically and/or initially displayed to the user.

System and method for performing experiments. For example, the method includes receiving indications of experimental workflows, generating workflow configuration requirements for each experimental workflow, configuring each experimental workflow based upon parameters associated with the workflow configuration requirements, receiving experimental requests for the experimental workflows, determining a schedule for executing the experimental requests, assigning each experimental request one or more remote laboratories for execution based upon the schedule, generating a set of instructions for performing experiments related to each experimental request, determining a plurality of sequence schedules for completing the set of instructions, receiving an indication of a sequence schedule selected from the plurality of sequence schedules, and transmitting commands to execute the set of instructions according to the selected sequence schedule.

System and method for processing an experimental request. For example, the method includes generating a set of instructions for performing experiments related to the experimental request, determining a plurality of sequence schedules for completing the set of instructions, receiving an indication of a sequence schedule selected from the plurality of sequence schedules, and transmitting commands to execute the set of instructions according to the selected sequence schedule.

Methods, apparatus, and computer program product, the method for determining an executable solution for a problem of scheduling work orders within an organization, comprising: obtaining a sample collection of solutions from a solution space of the problem, wherein the sample collection comprising a plurality of solutions to the problem based on a collection of goals, and wherein the sample collection includes one or more solutions optimizing a subset of the goals, the subset of the goals different from the collection of goals; and in an interactive stage: receiving from a user a collection of actual work orders to be executed; and providing to the user a suggested solution for scheduling the actual work orders, the suggested solution based on one or more solutions from the sample collection, wherein the suggested solution is a practical solution.

A computer system and process is described to alleviate the complexity of visualizing projects that are comprised of myriad inter-dependent tasks by using a 3D visualization technique that utilizes the hierarchical nature of the task data that defines a project in order determine the visualized objects and to permit a user to interact with the visual objects.