System and methods for resource analysis, optimization, or visualization

Abstract

A system and method herein may be for analyzing a process having a plurality of workstations performing subtasks to complete the process across the plurality of workstations. The system may be configured to perform the methods. The method may include receiving data from one or more data sources; analyzing the data from the one or more data sources to determine a workstation cycle time for each of the plurality of workstations; and providing an output to a user based on the analyzed data. The system may be configured to provide dynamic, contextual responses and data analysis to make efficient use of the information from the plurality of data sources. The system may therefore analyze the data, visualize the data, make action recommendations, identify events, create content or perform other functions. The system may be contextual by using a large language model to train the system based on the utilization of resources, including operators, workstations, and materials.

Description

Docket No. I001-0007PCT-PROU.S. PATENT AND TRADEMARK OFFICESYSTEMS AND METHODS FOR RESOURCE ANALYSIS, OPTIMIZATION, OR VISUALIZATIONPrashanth IyengarBACKGROUND

[0001] Many processes occur that require repetition or step-wise application of resources. For example, in conventional assembly line manufacturing, an object is created as a part, passes through different stations and additional components are built or received and then assembled to the part. Other processes that include repetitive actions may include quality inspections of finished products. Other processes may include inspections during field use, such as, for example, inspection of oil pipes for assessing defects or determining the need for repairs. Many inefficiencies arise in such systems as one part of the line may be backed up, while other parts are not utilized, during such back up or otherwise.

[0002] US Patent No. 11,443,513, registered September 13, 2022, incorporated herein in its entirety, addresses some issues created by inefficiencies in a processing line and automates the observation, detection, and response to identified inefficiencies using cameras and other sensors.SUMMARY

[0003] Exemplary embodiments of the systems and methods for resource analysis, optimization, and visualization described herein may be used for line automation, detection of inefficiencies, and recommendations for automation including quality, audits, training, etc.

[0004] Exemplary embodiments may include one or more modules, where no module is necessary for the performance of the invention. For example, users may select combinations of features for just training, monitoring, audit, etc. but do not necessarily have to implement all features or modules shown or described herein. Any combination of technology including cameras, sensors, body cameras, input devices, smart watches, etc. can be incorporated in anyDocket No. I001-0007PCT-PROcombination based on the objectives and functionality and the system and the associated budget for the line automation.

[0005] A method of analyzing a process having a plurality of workstations is provided herein. The method may include receiving data from one or more data sources; analyzing the data from the one or more data sources to determine a workstation cycle time for each of the plurality of workstations; and providing an output to a user based on the analyzed data.

[0006] The process of the method may include a plurality of subtasks to complete the process, and a plurality of operators to perform the subtasks to complete the process.

[0007] The method may further including assigning each subtask to one of the plurality of workstations; assigning each of the operators to perform the subtasks based on a cycle time each operator performs a subtasks and on the availability of operators to perform the subtasks; determining a maximum cycle time of each subtask for a workstation maintaining a required sequence of subtasks and accounting for unavailability of a workstation for a subtask based on a previous subtasks needed to maintain the required sequence of subtasks; determining a maximum process time by summing the maximum cycle times of each subtask to perform the process; reassigning operators to perform subtasks to reduce bottleneck times identified from the determined maximum cycle times of each subtasks identifying cycle times that are longer than required.

[0008] The method may also include determining an updated maximum cycle time of each subtask for the workstation maintaining the required sequence of subtasks and accounting for the unavailability of a workstation for a subtasks based on the previous subtasks needed to maintain the required sequence of subtasks and accounting for the reassigned operator to the subtask; determined an updated maximum process time by summing the updated maximum cycle times of each subtask to perform the process based on the redetermined; and determining operator assignments to subtasks and workstations based on a minimum of the maximum process time and the updated maximum process time.

[0009] The method may also include repeating the reassignment of operators, determination of an updated maximum cycle time, and determination of an updated maximumDocket No. I001-0007PCT-PRQprocess time through different combinations of allocations of operators to sub-tasks to determine operator assignments to subtasks to minimum the maximum process time.

[0010] The reassignment of operators may be based on operator training, available operators, and minimizing cycle times of subtasks that create a bottleneck in that the subtask creating a bottleneck has a longer than normal cycle time because the subtask cannot be performed because a resource is not available or the operator assigned to the subtasks takes longer than compared to another operator to complete the subtask.

[0011] The method may also include assigning each subtask to one of the plurality of workstations; assigning each of the resources to perform the subtask requiring the resource based on a cycle time to perform the subtask with the resource and on the availability of the resource to perform the subtask; determining a maximum cycle time of each subtask for a workstation maintaining a required sequence of subtasks and accounting for unavailability of a workstation for a subtask based on a previous subtasks needed to maintain the required sequence of subtasks; determining a maximum process time by summing the maximum cycle times of each subtask to perform the process; and reassigning resources to perform subtasks to reduce bottleneck times identified from the determined maximum cycle times of each subtasks identifying cycle times that are longer than required.

[0012] The method may also include determining an updated maximum cycle time of each subtask for the workstation maintaining the required sequence of subtasks and accounting for the unavailability of a workstation for a subtasks based on the previous subtasks needed to maintain the required sequence of subtasks and accounting for the reassigned operator to the subtask; determining an updated maximum process time by summing the updated maximum cycle times of each subtask to perform the process based on the redetermined; determining resource assignments to subtasks based on a minimum of the maximum process time and the updated maximum process time.

[0013] The method may also include tracking a training level for each of the plurality of operators for each of the workstations; analyzing the available plurality of operators trained for one or more of the workstations; and identifying a skill to train one or more other operators ofDocket No. I001-0007PCT-PROthe plurality of operators that are not the available plurality of operators to maintain a desired number of available plurality of operators for each of the one or more of the workstations.

[0014] The identification of a skill to train one or more other operators is determined based on a maximum impact of the skill to the operator.

[0015] The method may include tracking a skill set of each of the operators associated to perform the subtasks at each of the workstations; identifying a missing skill for an operator; and assigning training to the operator based on the missing skill.

[0016] The method may include a plurality of missing skills for the operator to perform untrained subtasks, where an untrained subtasks of the operator is a subtask in which the operator is not authorized to perform based on a lack of training and / or certification.

[0017] The method may include identifying the missing skill based on an assessment of all of the plurality of missing skills to determine the missing skill that maximizes the training of the operator by applying to the most workstations and / or subtasks.

[0018] The method may include identifying the missing skill based on an assessment of all of the plurality of missing skills of all of the operators to determine a missing skill that is missing from a maximum number of the plurality of operators.

[0019] The method may include tracking resource utilization based on the received data from the one or more data sources; and the output to a user comprises any combination of artificial intelligent assistance, dynamic recommendations for action items, data visualization, or content generation.

[0020] The method may include tracking resource utilization based on the received data from the one or more data sources, and the output comprises artificial intelligent assistance configured to provide contextual dynamic action items.

[0021] The system may be configured to associate various metrics with one or more resources based on historic data from the one or more data sources and the output is based on one or more of the associated various metrics related to the output and the contextual dynamic action items are related to one or more of the associated various metrics related to the output.Docket No. I001-0007PCT-PRO

[0022] The output may include specific action items related to the one or more of the associated various metrics.

[0023] The associated various metrics may be related to an assessment of a resource.

[0024] The assessment of a resource may include any combination of a training level of an operator, experience of an operator, throughput of a process, throughput of a workstation based on an operator, cycle time associated with an operator, utilization of a resource, availability of a resource, and the resource is equipment, workstations, materials, or operators.

[0025] Using artificial intelligence with the analyzed data from the one or more data sources to provide dynamic action recommendations by analyzing one or more metrics associated with one or more resources.

[0026] The output comprises a visual display of a skills matrix of a plurality of operators compared to the plurality of workstations to perform subtasks.

[0027] The visual display of the skills matrix includes a list of the plurality of operators, a list of the plurality of workstations, an indication of an amount of time each operator of the plurality of operators has with each workstation of the plurality of workstations, and a training level of each operator with each workstation.

[0028] The data from one or more data sources comprises a standard; and the method further comprises comparing the received data from the one or more data sources to determine if a resource is complying with the standard.

[0029] The standard may be a proper posture and at least one data from the one or more data sources comprises an image of an operator showing a posture of the operator and analyzing the data comprising analyzing the image of the operator for the posture of the operator and comparing the posture of the operator from the image with the posture of the operator from the standard.

[0030] The standard may be a user process having a plurality of steps for performing a subtask and the determination if a resource is complied with the standard comprises identifyingDocket No. I001-0007PCT-PROeach step of the plurality of steps for performing the subtask from the data from one or more data sources.

[0031] The method may include creating a training session based on a missing step when the comparison of the plurality of steps for performing the subtask to the data from one or more data sources identifies the missing step.

[0032] The output may include a leaderboard ranking operator(s) based on the analyzed data from the one or more data sources.

[0033] The method may include generating content based on the analyzed data from the one or more data sources.

[0034] The generated content may include training material including video received as data from the one or more data sources.

[0035] The method may include using analyzing the received data from one or more data sources to train a large language model to associate attributes to the operators, workstations, and subtasks and identifying events.

[0036] The method may include receiving a user inquiry about the event, parsing the natural language to define query actions, entities, and values, and using the parsed natural language to generate a database query to return results relevant to the user inquiry, and the output comprises an output based on the user inquiry.

[0037] The output may use the large language model to provide context based on the associated attributes associated with the event.

[0038] The associated context relates to a specific operator from the plurality of operators involved in the event, a specific workstation from the plurality of operators involved in the event, a timing of the event, a nature of the event.DRAWINGS

[0039] FIG. 1 illustrates an exemplary production area having a plurality of stations in which the system includes cameras, sensors, and other system inputs for resource analysis,Docket No. I001-0007PCT-PRGoptimization, and visualization and communication to a remote management location for observation of system dashboards to retrieve information from the system.

[0040] FIG. 2 illustrates an exemplary system for quick-install components for deployment of input and / or output devices into the system according to embodiments described herein.

[0041] FIGS. 3A-3B illustrates exemplary embodiments of processing data from a camera feed, such as cameras 102, 112, and / or cameras as part of other devices such as headset 114 and / or tablet 116.

[0042] FIG. 4A illustrates an exemplary user interface for displaying one or more inefficiency events. FIG. 4B illustrates an exemplary user interface for displaying options for analyzing the inefficiency events as shown and described herein.

[0043] FIG. 5 illustrates an exemplary user interface providing a visualization of resource allocation.

[0044] FIGS. 6A-6B illustrate an exemplary user interface in which the system for resource analysis, optimization, and visualization may be used to optimize a given production line if a given number of operators (and / or other resources) are provided. FIG. 6C illustrates an exemplary user interface in which the system displays a bar graph of the number of available operator resources and the optimized throughput based on the line balancing optimization shown and described herein.

[0045] FIG. 7 illustrates an exemplary user interface 700 for displaying skills matrix according to embodiments described herein.

[0046] FIG. 8 illustrates an exemplary block diagram of a method 800 for receiving a user query and providing an answer thereto based on the large data sets managed, stored, and / or generated by the system and methods described herein.

[0047] FIG. 9 illustrates an exemplary flow diagram for an audit module according to embodiments of the systems and methods for resource analysis, optimization, and visualization.Docket No. I001-0007PCT-PRO

[0048] FIG. 10 illustrates an exemplary flow diagram for an audit module according to embodiments of the systems and methods for resource analysis, optimization, and visualization.

[0049] FIG. 11 illustrates an example comparing an audit trail from paper or digital according to embodiments of the systems and methods for resource analysis, optimization, and visualization.

[0050] FIG. 12 illustrates an exemplary leaderboard 1200 identifying a plurality of users and ranking(s) the user according to one or more variables.

[0051] FIG. 13 illustrates an exemplary embodiment representing the user of virtual assistants that may be implemented using embodiments of the systems and methods shown and described herein.

[0052] FIG. 14 illustrates exemplary system level diagram for the systems and methods for resource analysis, optimization, and visualization according to embodiments described herein.DESCRIPTION

[0053] The following detailed description illustrates by way of example, not by way of limitation, the principles of the invention. This description will clearly enable one skilled in the art to make and use the invention, and describes several embodiments, adaptations, variations, alternatives and uses of the invention, including what is presently believed to be the best mode of carrying out the invention. The drawings are diagrammatic and schematic representations of exemplary embodiments of the invention and are not limiting of the present invention nor are they necessarily drawn to scale.

[0054] Exemplary embodiments of the systems and methods for resource analysis, optimization, and visualization as shown and described herein may receive data from any combination of data sets. The system may take the received data and analyze the data to generate cycle times, performance metrics, or other calculations as shown and described herein. The resulting datasets and database of information may become quite large. Processing and understanding all of the information received may be difficult and daunting. Therefore, theDocket No. I001-0007PCT-PROsystem may be configured to provide actionable items to improve efficiency. Exemplary embodiments of the systems and methods to optimize may therefore include recommendations based on analysis of the dataset to provide suggestions and actions to improve a process line. Improvements may be made across resources available to the process line, such as in how operators are place, utilization of workstations, utilization of equipment, or any other resource.

[0055] Exemplary embodiments may include a system and methods that receives data from various sensors to track resource utilization, inefficiencies, throughput, cycle times and provide a response thereto. The response may be any combination of artificial intelligent assistance, dynamic recommendations for action items, data assistance and visualization, or content generation.

[0056] For example, the system may provide artificial intelligence assistance that is configured to provide contextual customization and dynamic knowledge. The system may be configured to associate various metrics with one or more resources based on historic input of information through the system. The metrics may be any assessment of the resource, such as, for example, training levels, experience, throughput, cycle times, utilization, etc. The one or more resources may include any combination of equipment, workstations, materials, or operators, for example. The system may thereafter use these metrics to analyze incoming information from a plurality of sources to provide real time knowledge and / or analysis to a user about resources.

[0057] For example, the system may provide dynamic action recommendations generated by the artificial intelligence of the system analyzing the various metrics associated with one or more resources and / or the historical and / or current received data from various sensors. Dynamic action recommendations may include any combination of training recommendations for operators, suggested use of resource(s), line balance and / or alignment of resource(s), maintenance suggestions, process improvements and / or operator critiques of implementation, warnings for unsafe conditions, etc.

[0058] For example, the system may provide visualization of the received data. The system may provide dashboards of summaries for resources, matrixes of utilization and / or skillsets, leaderboards, inefficiencies, root causes, implementation, reports, etc. The system may permit users to query the information to obtain any combination of summary, analysis,Docket No. I001-0007PCT-PROrecommendations, etc. as shown and described herein to efficiently use the retrieval, retention, and analysis of the received data.

[0059] For example, the system may be used to generate content. The system may use the received data and / or any data source of the system to generate content. Content may include summaries for use in maintenance logs, audit trails, training, etc. The content may include training sessions such as written explanations, videos using samples of operators on the line, questionnaires / quizzes, reports, logs, etc. Content created may utilize the augmented reality system herein. The content created may be used for updating training based on identified knowledge gaps.

[0060] Embodiments described herein may be implemented to enhance decision-making through automated and / or artificial intelligence operations platform designed to guide operators, supervisors, continuous improvement teams, environmental health and safety managers, and others in taking the most efficient operational steps. Exemplary embodiments may include automated modules for non-value-added tasks to enable teams to work optimally and efficiently.

[0061] Exemplary embodiments of the platform features shown and described herein may be used in any combination for different aspects of operations, including, for example, quality, safety, training, maintenance, production, to achieve different benefits and improvements. With respect to production, embodiments of the platform described herein may be used to generate any combination of checklists, pass down sheets, audits, or line downtime tracking. With respect to maintenance, embodiments of the platform described herein may be used to generate any combination of troubleshoot guides, equipment checklists, documents and records, maintenance schedules and tracking, or maintenance training. With respect to training, exemplary embodiments of the platform described herein may be used to generate work instructions, skills matrix of a worker and / or workforce job tracking, language translations, training materials, test materials, and training tracking. With respect to quality, exemplary embodiments may be used to generate inspection checklists, quality audits, defect tracking, defect correction or minimization actions, compliance guides, or quality training. With respect to safety, exemplary embodiments described herein may be used to generate and track safety audits and checklists, incident reports, OSHA compliance documentation, chemical handlingDocket No. I001-0007PCT-PROinstructions and tracking, or safety training. With respect to floor management, exemplary embodiments of the platform may use a combination of connected cameras and / or other sensors and / or system inputs (including without limitation, body cameras, smart watches, cycle time monitoring) to generate real-time dashboards of the production floor, current production inefficiencies, current production floor personnel performance, and combinations thereof, video capture and playback of line performance, playbooks

[0062] Although embodiments of the invention may be described and illustrated herein in terms of a processing line including a production floor having discrete workstations, it should be understood that embodiments of this invention are not so limited but are additionally applicable to other repeated process actions or workflows.

[0063] FIG. 1 illustrates an exemplary production area 100 having a plurality of stations in which the system includes cameras 102, sensors, and other system inputs for resource analysis, optimization, and visualization.

[0064] As seen in FIG. 1, a manufacturing floor may include a plurality of workstations to perform sequential steps in a manufacturing line in which different operators work at different workstations of the production line. The system may include a plurality of cameras positioned about the production floor and focused on various areas of the production line.

[0065] The system may be configured to retrieve the images from the cameras and process the images to obtain information about the occurrence of events within the frames. For example, the system may be configured to determine up and down times of machines, locations of personnel and / or the absence or presence of personnel at a workstation, the throughput of objects through a station, the quality of products through a station, and any combination thereof.

[0066] The cameras may each be configured to observe specific locations, workstations, operators, actions, or objects within the line.

[0067] A camera 102 may monitor a workstation 104. The camera 102 may therefore be focused or configured to view a portion of the workstation such as the system display or user interface, machine operation, component entry, component exit, etc. The images from the cameras may be analyzed, such as through image recognition or other algorithms to detectDocket No. I001-0007PCT-PROconditions, actions, or other metrics observed by the camera. The system may be configured to retrieve and detect any combination of: the user inputs, including without limitation text, voice commands, gestures and / or visual inputs, buttons and / or sensor inputs, etc., into the system, the system outputs such as images of the object in the line, sensor readings, system displays, the condition of the workstation, hazards, the environment of the workstation, etc.

[0068] Although shown as viewing the workstation user interface, the entire workstation or portions thereof may be monitored. For example, the environment of the workstation may be observed for the correct or incorrect placement of materials, machines, objects, etc. The environment of the workstation may be observed for the identification of hazards such as wires or other tripping objects on the floor or in the pedestrian areas. The environment of the workstation may be observed for the identification of other hazards such as proper positioning, retention, or other requirements of hardware, such as machines and equipment at a workstation.

[0069] A camera 102 may track material 106. The camera 102 may therefore be focused or configured to view a portion of the production line to observe and detect the passage of objects on the line. The system may be configured to retrieve and detect any combination of: the presence of an object on the line, the throughput of objects on the line, the image of the object, defects or variations of objects on the line, quality of objects on the line, consistency of objects on the line relative to a standard and / or to each other, status of object on the line, etc.

[0070] A camera 102 may track one or more operators 108. The camera 102 may therefore be focused or configured to view a portion of the production line to observe and detect the location of one or more operators or locations in which operators should be positioned. The system may be configured to retrieve and detect any combination of: the presence of an operator, the absence of an operator, the length of time an operator is at the station, the posture of the operator, the identity of the operator, etc.

[0071] A camera 102 may track one or more processes 110 of the line. The camera 102 may therefore be focused or configured to view a portion of the production line to observe and detect the actions of a process on the line. The process may include any combination of movement of objects, assembly or disassembly of objects, actions on an object, etc. The system may be configured to retrieve and detect any combination of: the process, comparison of theDocket No. I001-0007PCT-PROperformed process to a standard, defects in the performed process, variations in the performed process, time of the performed process, etc.

[0072] US Patent No. 11,443,513, registered September 13, 2022, incorporated herein in its entirety, addresses exemplary system components and configurations in order to determine inefficiencies in a production line using cameras and other sensors.

[0073] FIG. 1 illustrates the possible inclusion of additional hardware to improve the analysis, optimization, and visualization system according to embodiments described herein. Additional devices may include any combination of cameras, analog signals coming from one or more sensors, digital signals coming from one or more sensors, communication and / or notification signals, user inputs, machine outputs, mobile cameras, microphones, buttons, sensors, scanners, devices and / or machines, etc.

[0074] The system may include additional or alternative cameras. For example, cameras may be included on body cams 112, virtual reality and / or augmented reality headsets 114, tablets 116, or any combination thereof.

[0075] The system may include additional or alternative displays. For example, displays may be provided through virtual reality and / or augmented reality headsets 114, tablets 116, watches 118, etc.

[0076] The system may include sensors 120. These may include any combination of analog and / or digital sensors. For example, the system may include temperature sensors, vibration sensors, counters, scanners, radio frequency identification (RFID) devices, barcode scanner, etc.

[0077] The system may include counters 122. The system may include sensors configured to count or determine throughput of objects through a workstation.

[0078] The system may include any combination of other user inputs and outputs. For example, the system may receive user inputs, such as start and / or stop times, object counting, or other indicator through a user input through a touch screen such as tablet 116, buttons, such as on watches 118, headsets 114, or other input device.Docket No. I001-0007PCT-PRO

[0079] As an example, the system may include sensors such as user inputs and / or counters, or other system detectors. These may be used in any combination to provide information to the system for analysis. For example, the sensors may be configured to detect, receive input related to, or determine material or object throughput, start and / or stop times of a process cycle, the absence and / or presence of objects and / or personnel at a station, the quantity of objects, the location of objects, the quality of objects, the presence or absence of defects on objects, or any combination thereof. System detectors may therefore include microphones, gesture recognition, proximity detector, buttons, scanner, code reader, switches, cameras, digital sensors, analog sensors, temperature senor, vibration sensor, volume sensor, etc.

[0080] As an example, the personnel may include markers or other identifier that may be observed and / or detected by the one or more cameras and / or one or more sensors. The markers on the operator may assist in the detection of the personnel in locations and track their movement for more efficient processing. As an optional example, the one or more cameras and / or one or more sensors may be configured to detect the presence or absence of a marker at a workstation or a product. The marker may be configured to identify the personnel associated with the marker so that the presence or absence of specific workers can be tracked in relation to specific workstations. As an optional example, the one or more cameras and / or one or more sensors may be able to track the presence and / or absence of a marker to identify the use and / or up / down time of stations and / or determine cycle times for a station.

[0081] As an example, the personnel may use virtual reality (VR) and / or augmented reality (AR) headsets 110 that may capture their environment such as through one or more cameras and / or provide information to the user such as through one or more virtual objects overlaid into the view of the operator wearing the VR / AR headset. Exemplary embodiments of the VR / AR headsets shown herein may be used in various applications, such as, for example, training, audit, quality assurance, maintenance, etc. As an optional embodiment, the VR / AR headset may provide information about detected defects such as how to correct, mitigate, and / or report the defect. As an optional embodiment, the VR / AR headset may be used in training and / or maintenance to illustrate how to operate machinery, provide instructions, identify component parts, etc. As an optional embodiment, the VR / AR headset may be used in audits to identify and / or capture information about process flow, quality, etc.Docket No. I001-0007PCT-PRO

[0082] As an example, the personnel may use smart watches 118. The smart watches may be configured as a user input, such as to indicate start and / or stop types of cycles through the press of a button or other user input on the watch. The system may optionally be configured to detect the orientation and / or position of the watch to determine actions of the personnel. The system may optionally be configured to use the watch as the marker described herein to detect one or more personnel in a location.

[0083] As another example, the system may include tablet(s) 116. Tablets may be used alone or in combination with one or more other input devise such as watches, sensors, etc. for receiving and / or managing data as a collection hub to independently track different combinations of measurements, inputs, and / or analysis

[0084] The exemplary production area implementing an exemplary embodiment of the systems and methods for resource analysis, optimization, and visualization according to embodiments described herein may include remote management location 124 separate from or as part of the production area 100.

[0085] The system may include additional computing devices 126 to receive additional inputs into the system and / or provide outputs from the system. As shown and described herein, the additional computing devices 126 are illustrated in relation to a supervisor or other separated or remove location to the processing floor of the operators. However, the invention is not so limited. Instead, additional and / or other computing device 126 may be integrated into machines on the process floor, may be added to the process floor, may be fully remove from the process floor, or communicate with the system from any desired location.

[0086] The system may also be configured to access, generate, retrieve, and / or otherwise incorporate documents, worksheets, spreadsheets, and / or other informational sources as described herein. The system may optionally be configured to scan and incorporate paper materials such as forms and written documents to be recognized by the system according to embodiments described herein.Docket No. I001-0007PCT-PRG

[0087] As illustrated, the system may be configured to receive inputs from other sources. For example, the system may receive work instructions that may include any combination of station set ups, steps for a process, machine configurations, machine manuals, etc.

[0088] Work instructions may be provided through electronic documents, such as documents 128, or spreadsheets 130, or paper manuals or documents 132. The system may be configured to receive these documents, such as uploaded through the system, document sharing through the system, image scanning and / or recognition from cameras, scanners, etc.

[0089] Exemplary embodiments may include one or more cameras for receiving inputs into the system. For example, the system may include a camera for capturing an image of a process, audit, maintenance, or other form and / or checklist. The system may be configured to perform image recognition on the form and / or extract information from the scanned image to populate the database of the system shown and described herein. The system may also or alternatively be configured to generate electronic forms and / or checklists based on the scanned for so that form may be reused in an electronic media such as through the tablets for direct inputs into the system.

[0090] Exemplary embodiments may be configured to compare system inputs for consistency, compliance, etc. For example, the system may use image recognition and / or a combination of input sources to determine actions taken at a workstation. The system may compare those actions to process steps, such as in an operations manual for a machine to compare the actual activities at the workstation with the stated process to perform the process at the workstation. The system may be configured to recognize deviations therebetween to identify issues.

[0091] The exemplary production area implementing an exemplary embodiment of the systems and methods for resource analysis, optimization, and visualization described herein may include communication to local outputs, such as workstation 104, headsets 114, tablets 116, watches 118, etc. and / or to additional electronic devices 126 to provide outputs from the system. The outputs may include any combination of warnings, alerts, instructions, dashboards, videos, display from system inputs, assessments, reports, etc. The system may be used for improving operations, safety, compliance, maintenance, training, etc.Docket No. I001-0007PCT-PRO

[0092] Conventional systems for monitoring process lines can take extensive time to set up. The individual component parts may need to be integrated into the system to provide one or more inputs to the system. The system set up may therefore become very expensive and intrusive as the system components are installed, calibrated, and configured to communicate to the rest of the system. The system may therefore not provide dynamic assessment of process line areas or workstations that can be easily changed, modified, or analyzed in a timely manner.

[0093] Exemplary embodiments shown and described herein may include efficient deployment components and / or methods. Exemplary embodiments of the systems and methods described herein may include a quick-install component.

[0094] FIG. 2 illustrates an exemplary system for quick-install components for deployment of input and / or output devices into the system according to embodiments described herein.

[0095] As illustrated, the system may include a quick install set up 200 including one or more components 202 for incorporating into the system. The one or more components 202 may be any input and / or output device. For example, the one or more components 202 may be a sensor, scanner, button, other input and / or output device.

[0096] The quick install set up 200 may permit the one or more components 202 to be assigned to a workstation, operator, etc.

[0097] The quick install set up 200 may permit the one or more components 202 to be assigned to one or more data types and / or actions. The system may be configured receive configuration instructions to associate the input of the one or more components 202 with an action within the system.

[0098] As illustrated, the input device is a push button. The system may therefore be configured to log times associate with the pushes of the button and associate an action. For example, for a push button, the system may be configured to detect the push of the button, such as by an operator, and may configure the system to start and / or stop timers associate with the system. The system may be configured to correlate the button push to start and / or stop instances of a process, a count of a component part through the system, an indication of an error or otherDocket No. I001-0007PCT-PRGabnormality of the system or quality issue, the call for equipment, material, or other resource, etc.

[0099] In an exemplary embodiment, the push button may be integrated into a wearable. In an exemplary embodiment, the wearable is a watch, but other configurations are also possible such as a necklace, pin, ring, etc. The wearable may be a component that can be configured and integrated into the system through the quick install set up 200 as shown and described herein.

[0100] The quick install set up 200 may permit a user to select the action and / or measurement received from the one or more components.

[0101] The input device may be a sensor. Exemplary sensor(s) may include, for example, temperature, vibration, pressure, photo-eye etc. The system may then be configured to receive data associated with the sensor, such as in receiving temperature measurements, etc., depending on the sensor.

[0102] In an optional embodiment, the system may be configured to correlate the sensor data to an action of the system, such as, for example, duration of events or cycle times, frequency of signal transitions, throughput, occurrences of readings outside and / or inside an identified range or above and / or below one or more thresholds.

[0103] The system for quick install set up 200 may include a mobile computing device 204. As illustrated, the mobile computing device 204 may include a tablet computer. The mobile computing device may be configured to communicate with component 202. A wired connection 206 is illustrated as an exemplary communication between component 202 and mobile electronic device 204. Other communication interfaces may also or alternatively be used including wireless connections.

[0104] Exemplary embodiments may couple a mobile computing device with one or more components. For example, a single mobile computing device may be used to deploy multiple components for receiving system inputs in parallel.

[0105] The mobile computing device 204 may be configured to communicate with component 202 and receive information from and / or about the component. For example, theDocket No. I001-0007PCT-PRGmobile computing device may comprise non-transitory computer readable instructions stored in memory of the mobile electronic device that when executed by a processor of the mobile electronic device is configured to receive data and / or signals from the component 202 and / or receive configuration instructions from a user through a user input / output interface of the mobile electronic device.

[0106] For example, in an optional configuration, the mobile electronic device 204 may include a display 208 for displaying a user interface to the user. The display 208 may be a touch screen configured to permit the user to make selections displayed on the screen and / or provide system inputs through the touch screen. The system may be configured to permit a user to indicate and / or permit the system to identify the component and / or one or more actions associated with the component.

[0107] The system for quick install set up 200 may be configured to permit a user to couple a component 202 to the mobile electronic device 204, configure the component 202, and / or associate the component to one or more actions within the system. The mobile electronic device and / or the component may thereafter communicate with the system to assist and / or perform the action.

[0108] The mobile electronic device 204 of the system for quick install set up 200 may include a camera 210. The camera 210 may be used as one or more of the cameras and / or inputs to the system.

[0109] For example, the mobile electronic device 204 may be positioned at a workstation. The component 202 may be used to generate an action relating to the workstation. For example, the component may be a button that is pressed to indicate a start cycle for a process at the station. The tracking of successive start indicators may be used to determine a cycle time for a process at that workstation. The mobile electronic device 204 may also be positioned at the workstation. The camera 210 may be used to observe the operator, workstation, etc. according to any embodiment herein. For example, the camera may be positioned to observe an operator and configured to identify the operator.Docket No. I001-0007PCT-PRO

[0110] The quick install set up 200 may be configured to automatically detect a type of component and / or associate one or more actions with the signals received from that component. For example, a proximity sensor may detect the presence / absence of an object in front of the sensor. The system may automatically assign a default function to the detected signal, such as cycle time based on the detected presence / absence of an object.

[0111] The quick install set up 200 may permit the user to change the automatically assigned component and / or associated action to customize the system or define desired uses of one or more components.

[0112] The quick install set up 200 may automatically configure one or more features of the mobile electronic device of the quick install set up. For example, if a tablet is used, the system may automatically turn on and / or off the camera and / or record a camera feed and associate an action to the signal. The camera feed may be associated with the identity of an operator and / or to detection of the presence and / or absence of the operator. The quick install set up 200 may therefore automatically control the camera of the mobile electronic device and process the image at the mobile electronic device to provide preprocessed information associated with a desired action.

[0113] The mobile electronic device 204 may be associated with one or more components.

[0114] Exemplary embodiments of the quick install set up 200 shown and described herein may include components and / or actions that can define cycle times. The system may be automatically configured to track times and provide time studies. For example, the system may determine cycle times based on the pushes of a button. After a duration of time, such as, for example, an operator shift or any portion thereof, the system may use the signals from the components to generate a time study. The time study may be over any preconfigured, selected, or inputted time frame. For example, a time study may be conducted from when the component is connected, associated with a user, the start of a session, or any combination thereof. The system may then track cycle times and provide time studies over a duration, such as providing average times, statistics related to the cycle times such as variances, minimum times, maximum times, trends, etc.Docket No. I001-0007PCT-PRO

[0115] Exemplary embodiments of the quick install set up 200 for incorporating components into a process herein may be used to provide dynamic operation observation. For example, the quick install set up 200 may be used to integrate one or more sensors into a portion of a process line to conduct a time study within a portion of the line dynamically with minimal set up time for efficient analysis and results.

[0116] Exemplary embodiments of the quick install set up 200 may permit plug and play type integration for dynamic set up and real time assessment of a line or a portion of a line that can be modified at any time.

[0117] Exemplary embodiments of the quick install may also be used to obtain information from components and / or sensors that may not easily integrate into the system. For example, if a system component includes a sensor that does not have a communication interface to the system, exemplary embodiments of the quick install system may be used to obtain and integrate the data from the component into the system.

[0118] In an exemplary embodiment, the quick install may include the electronic mobile device having a camera. The electronic mobile device may be configured to identify one or more components to integrate into the system. The electronic mobile device may be configured to obtain a picture of the component and / or a data display including a measurement or other data point from the component. The electronic mobile device may be configured to integrate the data feed into the system through image recognition and programming of the device to associate an action with the component.

[0119] For example, a thermostat within a production area may not be network capable or have a data communication interface to provide its temperature data to the system. A mobile electronic device, such as a tablet or smartphone, may be used to take a picture of the readout of the thermostat indicating the temperature of the room. The system may be configured to detect the readout from the image and associate the readout with a system output of sensor. For example, the system may use image processing and recognition to obtain the temperature readout from the thermostat and communicate the temperature to the system as a temperature sensor data point.Docket No. I001-0007PCT-PRG

[0120] Exemplary embodiments of the quick install system and method provided herein may therefore be used to integrate system components. The system may be used to reduce user error or data entry errors in that a user may not be required to manually read and / or manually enter information into the system. The captured images may also or alternatively be used as records retention as proof of input and / or audit trail tracking.

[0121] In an exemplary embodiment, the system may be configured to provide a periodic and / or continuous image monitoring of the component to permit continual and / or periodic automatic data input into the system. For example, a camera may be set up to observe an output interface of the component, process the image, and obtain a data point associated with the image continuously and / or at a predetermined data rate.

[0122] Exemplary embodiments of the system for resource analysis, optimization, and visualization may include substantial data handling within the system. As illustrated, the system may include a plurality of cameras, sensors, inputs, outputs, machines, etc. The system may be configured to communicate data from one or more components.

[0123] Exemplary embodiments of the resource analysis, optimization, and visualization system may be configured to process data at one or more of the components such as at the sensors, cameras, mobile electronic device(s), or combinations thereof. The system may therefore be configured to pre-process data at the components to reduce the data transmission and / or central data processing.

[0124] “At a component” may include processing at a specific component such as with the memory and / or processor of the component, but may also include processing with adjacent components. For example, a mobile electronic device may be configured to receive data from one or more components and perform processing of the component signals at the mobile electronic device before passing the preprocessed data to a central processing center.

[0125] Referring to FIG. 1, for example, a tablet 116 may be configured to communicate with a component, such as sensor 120. The tablet 116 may be configured to preprocess the sensor data before sending preprocessed data to a remote server or the remote management station 124.Docket No. I001-0007PCT-PRO

[0126] The system may store, communicate, and / or analyze any combination of data. For example, the system may store the raw signals received from one or more components, may store the preprocessed the raw signals, may store the resulting analysis of the raw signals and / or preprocessed signals. The system may perform the processing and / or storage at the components and / or at any combination of system components as shown as described herein. For example, a copy may be sent to the cloud for storage and / or stored at a remote server and / or stored at the components.

[0127] The system may therefore be configured for edge and / or distributive processing and / or storage.

[0128] In an exemplary embodiment, the system may be configured to perform edge processing and create pre-processed data that indicates any action or condition as shown and described herein. For example, the pre-processed data may be used to detect: motion, the presence and / or absence of a component, the presence and / or absence of an operator, the identity of an operator, an action, an event, cycle times, wait times, machine up time, machine down time, voice recognition, image recognition, voice command recognition, facial recognition, action recognition, posture recognition, start action, end action, or any combination thereof.

[0129] The system may then be configured to communicate the pre-processed information to a remote server for further resource analysis, optimization, and / or visualization. The pre-processed information may permit lower data transmission amounts and / or may speed up processing by distributing the processing requirements of the system.

[0130] The system may be configured to determine actions at the one or more components. For example, the system may be configured to detect a warning condition at the component. The system may thereafter signal a warning such as by providing an alarm, indicator, etc. once the event is detected at the component. The system may optionally be configured to log the event at the remote server or database for reporting, analysis, or other function as shown or described herein.

[0131] The system may be configured to perform different pre-processing functions as shown and described herein. Although illustrated in terms of examples for pre-processing at theDocket No. I001-0007PCT-PROedge and / or at a component, the system is not so limited. Alternative embodiments may include any combination of processing at the edge, at the component, at a central server, and / or at one or more remote processor(s). Exemplary data processing are provided herein that may be performed at the component, at the edge, distributed within the line, at one or more processors, or any combination thereof.

[0132] FIGS. 3A-3B illustrates exemplary embodiments of processing data from a camera feed, such as cameras 102, 112, and / or cameras as part of other devices such as headset 114 and / or tablet 116.

[0133] FIG. 3A illustrates an exemplary embodiment in which processing includes image recognition to identify one or more operators within a frame of a camera feed.

[0134] FIG. 3B illustrates an exemplary heatmap generated from the detection of an operator within specific locations over time at a workstation.

[0135] FIG. 3C illustrates an exemplary embodiment in which processing includes image recognition to identify a posture of one or more operators within a frame of a camera feed.

[0136] Processing of data may include image detection to identify any combination of operators, objects, resources, components, etc.

[0137] Processing of data may include identifying the presence and / or absence of any combination of operators, objects, resources, components, etc.

[0138] Processing of data may include any combination of determining cycle times of a process, detection of initiation and / or termination of a process, uptime and / or downtime of a machine, start and / or stop times of any combination of cycle, machine usage, operator presence, operator absence, resource use, etc.

[0139] Processing of data may include any combination of audio recognition such as voice identification, voice to text transcription, audio recording, etc.

[0140] Processing of data may include action detection and / or event detection.Docket No. I001-0007PCT-PRO

[0141] Processing of data may include automated measurements.

[0142] Processing of data may include job tracking by associating an operator with time performing one or more tasks and / or skills, working at one or more workstations, working with one or more machines, performing training, etc.

[0143] The system provided herein permits users to retrieve specific information from large data sets that are received by the system. The system therefore may permit users to interact with the system by asking questions, generating materials, visualizing data, providing actionable insights, or combinations thereof.

[0144] The systems and methods for resource analysis shown and described herein are configured to receive large amounts of information from various sources. To be useful, the data may be analyzed to provide actionable results. For example, if the system identifies an inefficiency, the system may provide a response to that inefficiency, including any combination of identifying the root cause, providing solutions, providing implementation plans, providing training related to solving the inefficiency, or any other response as shown and described herein.

[0145] The system maty be configured to visualize the analyzed data in a fashion that is efficient to digest and use to optimize the processes being monitored.

[0146] For example, the system may be used to generate summaries, maintenance forms, audit trails, charts, graphs, records, training materials, process manuals, etc. from the information saved and analyzed within the system.

[0147] As shown and described, exemplary embodiments of the system provided herein include analysis of large data sets. The system may be used in various methods to provide relevant information back to a user.

[0148] The systems and methods for resource analysis, optimization, and visualization may be configured to receive data from the one or more cameras, bodycams, headsets, watches, counters, scanners, and other sensors to analyze the process flow and generate one or more documents, and / or dashboards including information about the process workflow to be observed by a manager at a workstation.Docket No. I001-0007PCT-PRO

[0149] Exemplary embodiments of the systems and methods for resource analysis, optimization, and visualization may be used to improve floor management. For example, the system may be configured to receive data from various sources such as one or more cameras, one or more bodycams, one or more watches, one or more counters, and / or one or more other sensors as described herein. The system may be configured to receive this information and generate usable interfaces for the personnel and / or management to monitor and / or optimize the line.

[0150] Exemplary embodiments as shown and described herein may include a user interface for displaying and / or analyzing inefficiency events within a process.

[0151] For example, as shown and described herein, the system may be configured to analyze resource downtimes, uptimes, cycle times, start and / or stop times, etc. to identify inefficiency events. An inefficiency event may be any occurrence in the line that is not optimized and / or causes a delay.

[0152] An inefficiency event may be detected when a cycle time is longer than expected, when a resource is not identified where it should be, when a line is not operating optimally such as if an operator and / or resource is not optimally assigned, when a resource is not being used, when downtimes are longer than expected, when a process is not being performed according to a standard, when an event is detected, etc.

[0153] An inefficiency may be determined when an action is outside a preset parameter -downtime, temperature, cycle time, etc. This configuration may be used to detect extreme conditions, events, and / or provide warnings. For example, the system may include a temperature signal configured to generate temperature measurements. The system may be configured to process the generated temperature measurements and compare the temperature measurements to a threshold temperature to determine if the temperature rises above the threshold. If the threshold temperature is met, the system may be configured to perform an action such as provide a warning and / or shut down the machine, component, and / or process generating the temperature. Another example may include a camera monitoring an area configured to recognize objects on the floor of an area. If objects are detected, such as wires, or anything not an operator or intended equipment in the area, the system may determine that a tripping hazard is present and issue a warning.Docket No. I001-0007PCT-PRG

[0154] An inefficiency may be determined when a process is not being performed according to a standard. The standard may be any comparable process that may include, for example, proper posture control, procedure steps, etc. As an optional example described herein, the system may detect a posture of a user through image recognition of a camera. The system may then detect whether an operator is using proper posture during actions such as lifting and / or carrying to ensure safety. As another optional example described herein, the system may receive process steps such as through the inclusion of manuals, documents, etc. The instructions may be how to operate a machine and / or perform process steps at a given workstation. The system, through the various inputs such as cameras, counters, sensors, etc. may monitor the actions of an operator at a workstation to compare detected actions of the operator with the inputted standard and determine adherence to the inputted standard.

[0155] An inefficiency may be determined when an event is detected. The event may include any combination of hazards, spills, equipment failure, component breaks, anomalies, etc.

[0156] FIG. 4A illustrates an exemplary user interface for displaying one or more inefficiency events. As illustrated, the user interface 400 identifying one or more inefficiency events 402. The inefficiency events 402are identified on a timeline 404 to illustrate the occurrence of the event and / or duration of the event. The inefficiency events may be illustrated in other formats, such as in a list, chronological order, order of impact or delay on the overall process, grouped by cause, grouped by resource, etc. The illustration of inefficiency events is exemplary only.

[0157] In an exemplary embodiment, the system is configured to provide a visualization of the input data used to determine the inefficiency event. For example, the camera feed 406 may be provided that captured the occurrence of the inefficiency event. One or more inputs may be provided. For example, sensor data associated with identifying an event may be provided, one or more camera feeds may be provided, etc.

[0158] FIG. 4B illustrates an exemplary user interface for displaying options for analyzing the inefficiency events as shown and described herein.Docket No. I001-0007PCT-PRO

[0159] Once an inefficiency event is detected, the system may permit the user to analyze the event, identify one or more root causes of the event, provide solutions for the identified root causes, provide further explanation of the root cause, provide training materials related to the process, machine, or root cause.

[0160] As illustrated in FIG. 4B, the user interface may be configured to permit a user to select one or more inefficiency events from the user interface. A user interface 408 may be provided with additional information about the inefficiency event such as, for example, the date, time, resources involved, tags, identification of the inefficiency, etc.

[0161] The user interface may be configured to permit a user to make selections 410 and / or provide further input into the system to perform one or more functions such as, for example, identify the root cause, obtain more information about the root cause, receive suggested solutions based on a root cause, obtain additional explanation about a root cause, ask further questions and / or interact with the system.

[0162] As illustrated, the exemplary user interface 400 permits the user to select an inefficiency event 402. Once selected, the user may obtain additional information 408 from the system about the inefficiency event. The user may then select to identify root causes associated with the event 410, provide solutions associated with a root cause, provide an implementation plan associated with a solution, provide training materials related to a root cause, provide a sustainability plan associated with an implementation plan, provide an audit process for the implementation plan, provide a monitoring method for the implementation plan, provide regression and / or escalation procedures for the inefficiency event and / or the implementation plan, create a virtual model of the implementation plan, or other functions as shown and described herein.

[0163] The system may be organized to facilitate the identification of an inefficiency event and provide information about that inefficiency event and action items in response thereto in an efficient and time effective manner.

[0164] As shown and described herein, the system may be customizable and / or combined to provide individual assistance and data analysis and / or visualization. For example, the systemDocket No. I001-0007PCT-PROmay be customized by individual user, role of user, root cause, implementation, line, hierarchy of actions, display, options and / or user selections, etc. For example, a supervisor may be provided with information about an inefficiency and displayed with a user interface for providing suggestions to remediate the inefficiency, explanation of root causes, and / or implementation instructions on how to incorporate the suggested remediation. An operator, however, may be a different user that may be provided with information about an inefficiency and displayed with a user interface for providing more information about the inefficiency, the cause of the inefficiency, training related to countering and / or solving the inefficiency. The system may therefore consider user roles, operator experience, the root cause, or any combination of system inputs and / or analysis to provide specific and / or customizable outputs to the user.

[0165] In an exemplary embodiment, the system may be configured to display drill down options to provide desired information related to an inefficiency event. For example, the user may select different options such as an explanation of the inefficiency event, one or more identifications of potential root causes, explanations about how to implement a given solution to a root cause, etc. The system may alternatively and / or in addition thereto permit a user to input queries as shown and described herein

[0166] The system for analysis shown and described herein is also configured to provide feedback to optimize the process line and provide actionable insights and suggestions to users. The system therefore is configured to digest the large amounts of data and formulate improvements in the line.

[0167] Artificial intelligent systems using large language models receive a prompt from a user and provide an answer based on the receipt of large amounts of data. However, conventional systems using large language models require large amounts of data processing, power, and memory, and may not generate reliable, reproduceable results. Exemplary embodiments shown and described herein specifically analyze the large amounts of data to provide efficient analysis with reproduceable and reliable results that is not present in conventional large language models.

[0168] As shown and described herein, the system is configured to determine cycle times for different sub-tasks performed at different workstations. The cycle times may be determinedDocket No. I001-0007PCT-PRObased on a standard time, theoretical time to operate a workstation, measured times across all operators, measured times across a subset of operators, measured times across a particular operator, or other analysis as shown and described herein.

[0169] Exemplary embodiments of the system and methods shown and described herein may be used to optimize an observed production line. The system may be configured to minimize the maximum cycle time of a workstation. The process may be defined as a series of sequential and / or concurrent subtasks. Each subtask may be assigned to a workstation. The resources necessary to perform the subtask may be assigned to reduce the cycle time associated with the subtasks. For example, operators that perform a task can be assigned to the subtask that are trained on the subtask and complete the subtask with lower cycle times compared to other operators. Similarly, resources may be assigned that can be used in a faster period as they are not needed to transport across large distances and / or where a resource was previously unused so that it can be appropriate positioned and prepared for use without delay from use by another subtasks. The system is configured to maintain proper sequencing of subtasks and ensure every subtasks is assigned and performed. The system may thereafter compare various combinations of assignments to determine a minimum overall cycle time for the process and / or individual subtasks and / or over downtime and / or delay in the process line to assign resources and / or recommend actions and / or improvements.

[0170] For operator assignments, the system may be configured to allocate the right workstation for every operator to minimize the bottleneck cycle time while ensuring operator rotations for trained operators, considering factors like product type, available operators, and output demand.

[0171] For operator training, the system may recognize the need for more efficient operators at one or more workstations. For example, if there is an operator lag at one or more workstations or difficultly in assigning operators because of limited trained operators, the system may recommend an optimization to train one or more additional operators to provide more flexibility on the line and further improve throughput.

[0172] The system minimizes the maximum cycle time of a workstation / process in a manufacturing line by allocating the appropriate subtask to a workstation such that the sum ofDocket No. I001-0007PCT-PROthe subtask cycle times for that workstation is level balanced across all the workstations, while maintaining the allowed sequence of subtask execution order and ensuring that every subtask is assigned to one and only one workstation, see FIGs. 6A, 6B and 6C.

[0173] The system may also or alternatively measure the amount of time an operator spends working at a particular workstation using facial recognition, user inputs, user recognition, user detection. For example, the user may be identified by a target tracker at a given station. The user may be identified by image recognition of a user’s face and / or features. The user may be identified by an associated input device, such as a watch associated with the operator for entering start / stop times. The user may be identified by user log in and / or user of a machine and / or workstation. The user may be identified by an combination herein of detection methods.

[0174] The system may also or alternatively measure the cycle times of an operator working at a specific workstation. The optimization mechanism allocates the right workstation for every operator such that the bottleneck workstation cycle time for the line is minimized while ensuring appropriate sufficient operator rotations across workstations for proper minimum training. A bottleneck workstation cycle time is a cycle time for a given subtask at an associated workstation the creates a delay in the line because another subtask is delay such as because a resource is not available for use and / or because the cycle time is longer than necessary (such as from an inexperienced operator). The measurement and comparison of cycle times may be performed for different product types, different combinations of operators, the availability of operators, shift output demands, etc. Optimization may be extended across various lines and / or products within a facilitate to optimize an entire or sub-portions of a plant.

[0175] The system may thereafter determine the total process time by summing the total cycle times for each sub-task required to complete a process for each workstation if / when a workstation is available, ensuring proper sequential ordering of subtasks that rely on previous subtasks, and comparing the total process times for different combinations of resources.

[0176] The resources may be assigned to workstations and / or ordered processes that may be used to determine the cycle times and / or availability of a workstation. For example, if a time study is being conducted for the use of particular operators at the various workstations, each operator will have their own cycle times for different workstations. The system may thereforeDocket No. I001-0007PCT-PRGsum the individual cycle times as determined by the individual operator (i.e. resource associated with that workstation) to determine a total process time. Similarly, the system may determine if a workstation and / or resource is available by the presence or absence of a resource at a workstation and / or in the relationship to other cycle times and / or processes being performed at other workstations. For example, if a forklift is required to transport material from workstation 1 to 2 and 3 to 4, the forklift cannot be at both places at once and therefore will perform one task while the other is not being performed until the completion of the one task.

[0177] The resources may be any available resource that may be assigned as part of the process line. For example, resources may be operators, materials, equipment, machines, etc.

[0178] The system is therefore configured to create a specific optimization standard that provides repeatable results and achieves the desired optimization objectives. Exemplary embodiments of the systems and methods shown and described herein for optimization are therefore configured to provide specific, expected, and repeatable results that are not left to the arbitrary learning like a general large language model could create.

[0179] The system for resource analysis may be used to visualize and optimize resource handling and allocation.

[0180] FIG. 5 illustrates an exemplary user interface providing a visualization of resource allocation. The exemplary material handling provides information about a type of resource. For the illustrated example, the resource is a forklift, however, any resource may be identified.

[0181] As illustrated, the user interface 500 may identify resources used in a line 502. The identified resources may be any combination of resources used in a production line. For example, as illustrated, the resources may include all of the resources at a location, all of the resources at a specific location, all of the resources available for a given portion of the production line, etc.

[0182] The user interface 500 may provide information about the use of the one or more resources 504. For example, the user interface may provide a total time a specific resource is in use and / or not in use.Docket No. I001-0007PCT-PRO

[0183] The user interface 500 may provide all of the entities 506 in which the resource is used. For example, the entities using a workstation may be operators, the entities using equipment may be workstations and / or operators, etc. The user interface may provide information about the use of the resource for a given entity 508, 510. For example, the total time an entity uses a resource may be provided, such as information 508, or the total number of entities that use a given resource, such as information 510.

[0184] The user interface 500 may also provide an indicator 512 of the distribution of time any given resource is used by a specific entity. For example, portions of a pie chart are illustrated to represent the percentage of time a resource is used by a given entity. If the entire time of a resource is used is by a single entity, then the pie chart will be a full circle such as represented by indicator 512.

[0185] Exemplary embodiments of system inputs shown and described herein may include push buttons or other user input. A push button and / or user input may be configured as a call for material or resource. For example, if a forklift were needed at a workstation, a user may push a button configured with the action to call the forklift. The system may then queue a forklift to that workstation. The time and use of the resource may be tracked according to the exemplary embodiments of material handling provided herein.

[0186] In an exemplary embodiment, the system may be configured to provide a user output, such as the user interface of FIG. 5 to a user when the user input is received to call for material. The user may therefore identify the call of the resource to its location such as by providing an indicator 514. The user may see information about the use of the resource, such as, for example, its current location, estimated time to reach the workstation, effect on downtime of the current workstation(s), possible other resources to call, etc.

[0187] Exemplary embodiments of the system for resource analysis shown and described herein may be configured to suggest one or more alignments of resources to one or more entities. For example, the system may observe use patterns, timing alignment, resource utilization, resource underutilization, downtimes, or any combination thereof and align resources to entities to optimize the use of one or more resource.Docket No. I001-0007PCT-PRO

[0188] Exemplary embodiments of the system for resource analysis shown and described herein may be configured to identify inefficient uses of one or more resources. For example, the system may be configured to compare uses by different entities, analyze the training level of one or more entities relative to a resource, analyze the use times and / or cycle times for a resource by one or more entities, etc. The system may be configured to recommend action items by identifying inefficiencies. For example, if an operator is faster at using a machine than another station, the system may look at training records or experience of the operator between the stations. The system may then recommend training if the cycle times correlates with the training time. The system may also look at the heat maps to determine travel times and / or patterns to align resources to entities.

[0189] The system may be configured to display a timeline of use for a given resource through the entities. For example, if a first resource, such as forklift 1 of FIG. 5 were selected, the user interface may provide a list of events in which the resource is in use. The list of events may include start and / or stop times of use, duration of use, sensor data related to use, one or more camera feeds associated with the use, or any combination thereof.

[0190] Exemplary embodiments shown and described herein may, for example, use the quick set up of FIG. 2 with one or more resources. The system may therefore include a camera associate with a resource. A video may be associated with the resource and may permit object analytics related to the resource.

[0191] The system may be able to provide time studies and / or real time data regarding the efficiency of a line. For example, the analysis of images from the plurality of cameras and / or with the input of smart watches, operators may be subject to time studies on the shop floor that may be used for operator flexing and / or line balancing.

[0192] FIGS. 6A-6B illustrate an exemplary user interface in which the system for resource analysis, optimization, and visualization may be used to optimize a given production line if a given number of operators (and / or other resources) are provided. For example, FIG. 6A illustrates a suggested line optimization for sixteen resource operators while FIG. 6B illustrates a suggested line optimization for fifteen resource operators.Docket No. I001-0007PCT-PRO

[0193] As illustrated, the system may be configured to identify and / or receive a plurality of sub-tasks to be performed for a production line. Each sub-task may be assigned to a workstation, and each sub-task may be assigned to only one workstation. Each sub-task may also be assigned a sub-task cycle time.

[0194] The sub-task cycle time may be determined by the system, such as by taking an average, minimum, maximum, or other assigned time from the system for the sub-task as determined by the analysis of the use of the resources to perform the sub-task as monitored by the system. The determined sub-task cycle time may be in relation to an optimized or desired cycle time, may be based on one or more specific resource operators, may be based on an average across a plurality of operators, any measurement or analysis as performed herein, or any combination thereof. The sub-task cycle time may also be determined automatically by the system and / or may be entered by a user as an input into the system.

[0195] The system may then be configured to balance the line by assigning resources to given workstations to perform specific sub-tasks to optimize throughput based on the number of identified resources. For example, the system may determine a maximum time for a product to be completed by summing across the cycle times for each sub-task required to complete a process considering whether the workstation is available at a given time to permit a given subtask to be performed and in an appropriate order. The system may then determine the minimum time associated with a combination of the cycle time and availability of a given workstation to find the best-case scenario or distribution of resources to workstations.

[0196] The system may be configured to display through a user interface 600A, 600B the optimized result for a given resource allocation. For example, FIGS. 6A and 6B illustrate the optimized distribution for a given number of operators across sub-tasks 602 performed at different workstations 604. The resource is identified by different textured fills for each subtask (which may be represented by other indicators on the user interface, such as colors, labels, etc.). For example, the operator performing one of the sawing sub-tasks at station 1, the saw, is also used to perform the welding sub-tasks at the fifth workstation, welder. The same operator therefore performs work at the first workstation, the saw, the fifth workstation, the welder, and the tenth workstation, the sill.Docket No. I001-0007PCT-PRO

[0197] The system may also be configured to determine a throughput of products 606 that may be produced in a given amount of time associated with the best-case scenario for each number of available resources.

[0198] The system may also be configured to determine a total delay or inefficiency time 608associated with the best-case scenario for each number of available resources.

[0199] FIG. 6C illustrates an exemplary user interface in which the system displays a bar graph of the number of available resources (in this example the operators) and the optimized throughput based on the line balancing optimization shown and described herein. The system may therefore be used to identify a target number of resources corresponding to the maximum throughput of a product.

[0200] For example, as seen in FIG 6C, if fifteen operators are used for the production line, approximately 2.45 products may be created. If sixteen resources are used, then 3.08 products may be created in the same amount of time. The added resource therefore increases throughput as the additional operator may perform sub-tasks at unused workstations to permit the process to be completed faster. However, if seventeen resources are used, the productivity actually goes down to 3.03 products per the same amount of time as the additional resource does not add to the productivity since the additional available resources may not be able to perform a sub-task if a workstation is not available.

[0201] The system can therefore be used to provide an optimized recommendation of the number of resources to use as well as or alternatively as to how to distribute them across the subtasks and associated workstations.

[0202] Referring to FIG. 6A, in which the line distribution is illustrated for the optimized sixteen resources, the user interface can identify where the respective resources should be positioned and / or used. For example, as illustrated, the x-axis illustrates the workstations as which sub-tasks are performed. The bubbles or sections of the bar graph for each workstation represents a sub-task being performed. The y-axis illustrates the cycle time to perform the given sub-task. The different shading illustrates the resource that should be used to perform the task, such as the various operators in this example.Docket No. I001-0007PCT-PRG

[0203] In an exemplary embodiment, the system may be configured to generate a skills matrix for one or more operators of the production line. The system may be configured to track the use of operators at various production locations and / or actions within the production line. The system may be configured to track the amount of time associated with activities for a given operator. The system may be configured to administer training and / or track training time at an activity or machine.

[0204] The system may be able to take all of the tracked data and determine the skill level of one or more operators. For example, the system may determine how much time is spent at an activity to determine if sufficient practice or time has occurred at a sub-task, an elapse time from the last time to perform the action to determine if their skills are up to date, the rotation through activities to reduce physical fatigue, an efficiency and / or proficiency of one operator compared to other operators (such as, for example, comparing throughput of each operator at the station compared to other operators using the station).

[0205] The system may be able to take all of the tracked data and provide suggested assignments of operators at one or more workstations and / or one or more sub-tasks of the process. For example, the system may consider the training and / or qualification of operators at one or more of the workstations and suggest locations to train personnel and maintain a desired number of personnel trained at a station, relieve or change stations for ergonomic considerations or focus improvement, efficiency of line throughput, comparison of line throughput to accommodate inefficiencies for training and / or quality level of personnel, and any combination thereof.

[0206] The system may be able to display a skills matrix. For example, the system may include a user interface identifying one or more operators, the workstations, the amount of time spent at a workstation, an elapse time since performing at the workstation, a level of training at the workstation as determined by tracking the amount of training sessions conducted and / or in quizzes or other tests taken with respect to the workstation or activity, or a combination thereof.

[0207] FIG. 7 illustrates an exemplary user interface 700 for displaying skills matrix according to embodiments described herein. As seen, the system may be configured to display a plurality of operators 702 or resources along with a plurality of workstations, sub-tasks, and / orDocket No. I001-0007PCT-PROskills 704. The system may then be configured to represent with a visual icon 706 the amount of time an operator spends at a given workstation. As illustrated, the visual icon is a pie chart in which a shaded area of the pie corresponds to a percentage of time from a whole for the time spent at a given workstation, sub-task, and / or skill compared to an entire amount of time over a given duration. The system may also provide a second visual icon 708 representing a completion of training materials and / or compliance with training, audit checks, or other requirements. The system may also be configured to display the number of operators trained and / or working at a particular workstation, sub-task, and / or skill 710 or the number of workstations, sub-tasks, and / or skill as given operator is experienced in 712.

[0208] The system may be configured to display a matrix of operators and skills and / or stations. The system may be able to display any combination of the amount of time each operator has performed on the skill and / or station, a skill level for the skill and / or station, amount of training for the skill and / or station, a proportion of time at the skill and / or station as compared to total work on the process line, efficiency and / or ranking compared to other operators at the skill and / or station (this may be based on amount of time of training and / or experience and / or lapse since last time used and / or throughput rate during operations and / or any combination thereof).

[0209] The system may be able to provide real-time job assignments based on the skills matrix and / or through put of the line and / or observed and / or detected inefficiencies in the line. For example, the system may be configured to move operators around on the line based on operator experience, operator preferences, operator training levels, operator throughput and / or efficiency, and / or other recent operator activities or lack thereof.

[0210] In an exemplary embodiment, the system may be configured to provide operator assignments to optimize throughput based on average cycle times for operators to perform subtasks. For example, the system may be configured to determine a maximum, minimum, average, or other determined cycle time for each operator over a period of time. The system may then use the determined cycle time per each operator to assess which sub-tasks to assign to each operator. The system may be configured to optimize the distribution of operators by minimizing the maximum production time each arrangement of operators to tasks provides.Docket No. I001-0007PCT-PRO

[0211] More specifically, the system may determine a production time by summing the cycle times for a given assigned operator to a sub-task for the use of a workstation, based on the availability of the operator to perform the sub-task. The system may then determine the minimum production time for a given combination of operators based on the assigned sub-tasks. The system may therefore minimize production time based on the actual production times of given operators to optimize the production line.

[0212] Exemplary embodiments of the resource analysis and optimization system and methods shown and described herein may be used to optimize routes for resources.

[0213] For example, a given resource may be required for use across multiple workstations. When a call for the resource is made or as the resource is being allocated during the process, the system may identify delays at workstations caused by the resource being used elsewhere. The system may propagate a delay generated at a given workstation to an overlay delay in the process to identify the impact of a given delay per workstation. The system may then identify and / or prioritize the use of the resource by reducing the overall impact of any given workstation requiring the resource.

[0214] For example, a forklift will be provided as illustrative of a shared resource that may be used and optimized according to embodiments described herein. A forklift may therefore be used to transport a part from one workstation to another. However, the forklift may be needed by two different workstations to transport a part. When a call for the forklift is made, the system may associate a delay for each of the workstations if the forklift is not provided at that time and / or associated by the wait time created by prioritizing one workstation over the other. The system may therefore calculate the overall delay and the weighted cost of the delay (and optimize according to that) in the process created by the forklift going to the first workstation and delaying the use of the second workstation and visa versa.

[0215] The system may account for the time required to perform each task. For example, the use of the forklift at the first workstation may be faster than the use of the workstation at the second workstation, but the overall impact to the process delay may be greater from a delay at the second workstation than the first workstation. The system may therefore be configured toDocket No. I001-0007PCT-PROcalculate a priority for the use of the forklift by minimizing the overall delay impact on the entire process by prioritizing one workstation over another.

[0216] Similarly, the system may be configured to optimize the use of a resource by considering the use of the resource by each workstation that requires the resource and iteratively making the decision between workstations and assessing the impact of the resource allocation being present or not for the given workstation. The system may therefore assign an overall route optimization and / or assigned use of the resource by minimizing the overall production time and / or maximizing the overall throughput of the entire process.

[0217] The system may then track in real time the use of the resource and be able to adaptively reallocate the resource as necessary during production to maintain the optimal use of the resource. For example, the optimal use of the resource can be determined based on theoretical cycle times of a workstation. However, if a particular operator is slow for a given day or shift, the optimization may be recalculated to account for the added cycle time and associated delays in the entire process to reprioritize the use of the resource.

[0218] As shown and described herein, the operators may be considered a resource and may be tracked and optimized. For example, the system may identify operators and correlate the one or more workstations and / or tasks that the operator has experienced. The system may indicate the percentage of time the operator spends at any one or more workstations and / or in performing any one or more tasks.

[0219] The system may be configured to determine an operator for a given workstation and / or task.

[0220] The system may be configured to track the experience of an operator with one or more workstations and / or sub-tasks. For example, the system may use facial recognition to identify an operator working at a given workstation. As another example, an operator may be detected such as from an identifiable marker on the worker that is detected by a sensor. The identifiable marker may be a tag that is identified through object recognition of an image, an RFID marker, another type of identifier such as a proximity card, by logging into the system, orDocket No. I001-0007PCT-PROany combination thereof. The workstation and / or skill associated with a given operator may be tracked.

[0221] The training module may be configured to permit users to search the system including data, analysis, documents, or other compilations of the data generated and / or analyzed by the system. For example, the system may be able to search documents, audits, recordings, etc. and provide answers to questions posed by users to the system. Exemplary embodiments may also be configured to generate information such as summaries, graphics, visuals, graphs, etc. to answer and / or present the requested information in a usable and / or desirable format.

[0222] The system may be configured to optimize a training program or suggest training that is optimized for a given operator. For example, the system as shown and described herein may track the skill levels associated with individual operators. The system may use gaps in skill sets across all of the operators, the overlap of known skills to those required for other operations and / or tasks that are not yet learned, the overlap of skills between different operations and / or tasks, the remaining skills required by an operator to be trained on a workstation compared to the skills already known by an operator and / or the remaining skills required by other operators, to identify operators and associate training sessions to optimize training to ensure appropriate operators for each workstation.

[0223] The system may be configured to optimize particular training for individual operators to determine the skills that will have the most impact on an operator, such as skills that are common to the most tasks, the skills that require the least amount of time to train as they are related to known skills, skills that will result in the operator being able to work a new workstation such as by completing an entire skill set to permit additional operations at a workstation, assessing the skills of an operator against skills for a workstation or sub-tasks for a workstation or sub-task needed because fewer than desired operators are already trained, or a combination thereof.

[0224] The system may be configured to automatically assign lessons based on the optimization training algorithm shown and described herein.Docket No. I001-0007PCT-PRO

[0225] The system may also be configured to track the completion of training and / or generate quizzes based on trainings achieved. The system may also be configured to rank the results of a training session and permit the operator to go onto additional training and / or skillsets if a desired minimum competency is shown and / or may repeat training if a desired minimum competency is not shown.

[0226] The system may also be configured to track and / or monitor the performance of an operator at a workstation, such as through the image detection and recognition as shown and described herein. When anomalies or deviations from a standard are detected and / or if adverse events are detected, the system may be configured to assign training related to the deviation, event, anomaly, workstation, and / or sub-task to address the issue.

[0227] Any of the embodiments and configurations of the system and methods for resource analysis shown and described herein may take advantage of artificial intelligence in the analysis of the received information in order to perform the functions as shown and described herein including any combination of the optimizations, material handling, line balancing, operator assignments, route optimization, training, reporting, content generation, visualization data including dashboards, etc.

[0228] The systems and methods for resource analysis shown and described herein may use large language models trained to associate historical metrics with resources to analyze the received data and / or provide outputs with respect to the received and analyzed inputs. The large language model may be trained to contextualize events and / or received information such as the associated metrics to resources to dynamically change knowledge base and provide customized outputs.

[0229] Exemplary embodiments of the large language model considers attributes contributing to an event, such as who is involved, such as the operator, the resources involved, such as the workstations, machines, and / or materials, when the event happens, such as the time of day, the time within a shift, etc. and what actually happened, such as an equipment failure, prolonged downtime of a machine, the absence of an operator, etc. The large language model may thereafter use the contextual associations to generate customized responses, such as,Docket No. I001-0007PCT-PROrecommendations, optimizations, action items, reports, summaries, etc. as shown and described herein specific to one or more attributes contributing to the event.

[0230] Exemplary embodiments of the large language model may also consider long term characterizations of the attributes that may contribute to an event. For example, the system may be aware over many resources and processes the implications and / or effects of attributes on events.

[0231] Exemplary embodiments of the artificial intelligence systems shown and described herein may be customizable and may be combined in any way into the systems and methods shown and described herein. For example, the Al assistants may be used to automatically identify events and recommend actions in response thereto. The Al assistants may be combinable into workflows such as in providing real time line balancing or suggestions for use of operators and / or materials as a shift progresses in real time throughout the day. The Al assistants may be responsive and adapt dynamically to what, where, when, and who is involved in observed issues and / or events.

[0232] The system may be configured to permit users to search the system including data, analysis, documents, or other compilations of the data generated and / or analyzed by the system. For example, the system may be able to search documents, audits, recordings, etc. and provide answers to questions posed by users to the system. Exemplary embodiments may also be configured to generate information such as summaries, graphics, visuals, graphs, etc. to answer and / or present the requested information in a usable and / or desirable format.

[0233] Exemplary embodiments of the systems and methods for resource analysis shown and described herein are unlike general large language models. The system is configured to use and benefit from a large amount of data similar to a large language model but does not rely on the unstructured associations that a large language model may make to generate system outputs and / or correlate input queries to generate outputs.

[0234] Exemplary embodiments of the systems and methods for resource analysis may include a user interface for receiving a user inquiry. The system may, by parsing natural language into metrics such as entity and action, convert the user query into an applicationDocket No. I001-0007PCT-PRGprogramming interface (API) call with a structured format associating known parameters from the user inquiry.

[0235] Conventional large language models require substantially more processing power and time to identify and create associations to make conclusions. Conventional large language models also do not provide verifiable, reliable, or repeatable results. A general large language model may therefore not actually provide the optimizations and / or answers to questions from the large amounts of data it is provided.

[0236] Exemplary embodiments of the systems and methods shown and described herein try to overcome the present deficiencies in general large language models. Specifically, general large language models, when applied to databases and structured data sets do not provide repeatable results. When a single textual query requires multiple join statements to generate a result, such a text to sequel (SQL) queries, the result is not repeatable, and the result is inaccurate and the query is generally oversimplified. Large language models also do not properly combine data from multiple databases, which again may lead to non-repeatable, non-verifiable, and / or inaccurate results.

[0237] Exemplary embodiments of the systems and methods for resource analysis provided herein therefore may permit users to query the datasets stored and / or generated by the system shown and described herein. Even though the system may use augmented or artificial intelligence, the system may not rely solely on unstructured large language model results.Instead, exemplary embodiments may employ a deterministic algorithm to query the datasets and provide an answer.

[0238] FIG. 8 illustrates an exemplary block diagram of a method 800 for receiving a user query and providing an answer thereto based on the large data sets managed, stored, and / or generated by the system and methods described herein.

[0239] First, a user can provide an inquiry to the system. The inquiry may be in the selection of available options and / or through a text input and / or spoken input into the system. The system may receive the input and convert the input to a text string. For example, if the inputDocket No. I001-0007PCT-PRGis spoken, then speech to text recognition may be used to generate a text string. If the input is typed, the input may already be in a text string. The resulting string defines a user query.

[0240] At step 802, the system is configured to extract meta query components from the user query. The meta query components may be extracted based on the values provided. For example, date inputs may correspond to date fields, while numbers may correspond to measurement fields. Query components may therefore be selected based on the words used, the value provided, or other correlation.

[0241] At step 804 text is converted to searchable meta fields. The system may extract verbs, nouns, and / or adjectives that are related to known values within a database. For example, if the inquiry is “what is the average height of girls?” then the system can extract “average” as a calculation function as it is a recognized mathematical verb, while “height” and “girl” may be recognized as data categories and / or data values within a category of a data set. Therefore, “height” may be seen as a data category or metric while “girl” may be seen as a data point within a data category and therefore may act as a filter parameter.

[0242] At step 806, dates may be identified in the query and an associated date range may be generated. At this step, the system may convert casual language date phrases into specific date ranges. For example, references to the last week may be converted to an end date of today and a start date of today minus seven days.

[0243] At step 808, the system may be configured to clean up values and make corrections based on typographical inputs. Other hierarchical or relational corrections and / or constraints may also be created. Typographical errors may be corrected such as “gilr” for “girl”. Hierarchical relationships may be generated based on the known data sets. For example, girl may be correlated to under the age of 18 or as human, if such data is retained in the system and / or as determined by the hierarchical relationships of the data sets. Such hierarchical relationships may be determined from an entity relationship diagram (ERD) of the database.

[0244] At step 810, the category filters and / or data filter parameters may be converted based on information retained in the system. For example, if the filter is identified as “girl”, the system may relate “girl” to the category of “gender” to identify the category in which the filterDocket No. I001-0007PCT-PROfor “girl” may be searched. Tn addition or alternatively thereto, the data inputs may be corrected based on information in the system. For example, an inquiry search for “girl” is entered but the system actually includes a data set tracking male / female, the system may substitute “girl” with “female” as the search parameter. The system may also convert fdter values to filter IDs or numbers so that the system may accommodate changes in the values without affecting the results since the system may internally map names to identifiers.

[0245] At step 812, a query may be constructed from the meta fields and entityrelationship (ER) diagram. The ER diagram is a representation used to model the logic structure of a database and may associate entities categories, attributes, and relationships between them.

[0246] At step 814, for text based filter values, the system may perform weighted combination of subset string searches and / or semantic equivalent search to account for variations, synonyms, typos, etc. Additionally, visual tools like word clouds may be used to drill down and select filters.

[0247] At step 816, based on the dimensions of the returned data output, the system may select and appropriate output representation of the data set. For example, if the result from the query is not grouped, but is instead a single scalar output, the single stat may be provided to the user. If the result is grouped by a single dimension, the resulting dataset may be displayed to the user as a graph or table chart. If the result is grouped by two dimensions, then a stacked bar chart or table may be used to display the result. If the result is greater than two dimensions, then a dimensions table or other presentation of the information may be provided.

[0248] At step 818, the system is configured to receive the output of the query from the database sets and provide an answer to the user.

[0249] Exemplary embodiments of the methods for searching data provided herein permit a user to search within text through a combination of semantic and subset string searches for columns with text values. Additionally or alternatively, visual tools like word clouds may be used to drill down, provide content, and select filters.

[0250] Exemplary actions shown and described herein are associated with lines, workstations, operators, users, etc. For example, data is generated from the one or more dataDocket No. I001-0007PCT-PROsources (counters, sensors, cameras, machine manuals, process manuals, etc.) and may include and / or be associated with one or more operators. One or more operators may be associated with training sessions and / or skills tracking. Users may be associated with system inquiries and / or reports. The system may be configured to retain and associate use of the system with different entities, like lines, workstations, users, etc.

[0251] The system may use the personalization to therefore provide specific analysis include root causes identification, provide solutions or responses thereto, prioritize solutions, provide reports and / or dashboards, or any combination thereof that are personalized to the organization, user, operator, workstation, etc.

[0252] The system may, for example, log signals to actions and / or associate such actions with individual users, operators, workstations, lines, department, supervisors, plants, or other hierarchy of an organization.

[0253] The system may be configured to provide customized reports to an individual user. For example, daily reports, newsletters, or event notices and / or summaries may be provided. The system may be configured to share and / or collaborate on the generated reports. Exemplary embodiments also permit users to obtain information from a report and the through one or more connected data source and / or database, including, without limitation, any of the data sources and / or resource analysis shown and described herein to obtain information related to the generated report as it relates to any of these other received and / or generated data sources.

[0254] As shown and described herein, the system receives large amounts of data and analyzes the information for optimization and visualization to improve processes. The system may use this information to generate electronic actions that are Al assisted, assisted by artificial intelligence based on the analysis of the data sets provided herein. These Al assisted actions may be used to perform various functions.

[0255] Exemplary embodiments of the system may be used to perform actions through the system. For example, a user input may be received such as a verbal command and / or user input through an input device. The user may provide system commands to be performed within the digital environment, such as, for example, changing data names or labels. For example, aDocket No. I001-0007PCT-PROuser may provide a name to a specific time study by providing a voice command to change the time study name to a given name. The system may be configured to interpret the verbal command to edit as an action, the entity to edit would be the time study, the field to be changed would be file name, and the value would be the given name. Other actions such as controls to the system, machines, etc. may also similarly be controlled.

[0256] An exemplary embodiment of the systems and methods herein may be used for quality assurance of parts and / or processes. For example, a computer vision capable mobile electronic device may be used to detect defects in components. Defects may alternatively or additionally be identified by a user through a user input device that may be performed during the process and / or conducted by period audits by supervisor personnel. The indication or identity of a defect may be sent to a database, and the data analysis agent shown and described herein may be used to identity component areas of focus, provide contextual Al assistant workflow, using information from the workstation and product monitored, to provide corrective and / or preventative actions at the specified frequency to the floor to counter anticipated defects. Further the system may continue to detect and monitor the effect of those corrective actions to confirm progress toward a more efficient process line.

[0257] The system is configured to analyze resource utilizations. The system may therefore be used to suggest configurations to optimize production. For example, as shown and described herein, the system may be used to suggest line optimizations, plan routes, utilize resources, identify the quantity of resources to use, etc.

[0258] Exemplary embodiments of the systems and methods herein may be used to set and / or update performance goals, identify and suggest improvements for inefficiency events, provide root cause identifications for inefficiency events, explain solutions and / or causes of inefficiency events, and / or provide training related to inefficiency events, their root cause, and / or a solution to the root cause.

[0259] Exemplary embodiments of the systems and methods for resource analysis shown and described herein may be used for safety assessments. Exemplary embodiments may manage safety protocols, incidents, detection and / or avoidance, and / or compliance related to safety.Docket No. I001-0007PCT-PRO

[0260] As shown and described herein, the system may be used to identify safety incidences, such as identifying tripping hazard, improper posture, and / or accidents. The system may be used for incidence report such as for tracking incidence, making reports and / or explanations thereto, root causes, solutions, implementation, sustainability plans, etc. The system may be used for generating checklists, audit trails, or other reports, summaries, etc. Exemplary embodiments may be used to track machine and / or workstation health, maintenance, or monitoring.

[0261] Exemplary embodiments of the systems and methods for resource analysis shown and described herein may be used for training. The system may be used to create, assign, and / or manage training programs, identify skill development, or combinations thereof.

[0262] Exemplary embodiments of the systems and methods for resource analysis, optimization, and visualization may be used for training of operators.

[0263] Exemplary embodiments of the training module may be used to generate training materials automatically. For example, a user may search for material within the system. The system may be configured to create training programs such as, for example, schedules, training materials, quiz or test material, assignments, etc. For example, the system may be configured to create summary or teaching materials after a search or from one or more identified sources within the system. The system may be configured to attach videos, data, process analysis or other information from the system to support the training. The system may be configured to assign operators to take and track the completion and / or progress of training materials. The system may be configured to administer and / or track trainings. The system may also be used to translate materials for training. The system may also provide reminders or notifications of training events and / or actions to different personnel including operators, managers, etc. The system may be configured to grade or assess the user after training such as through quizzes or recognition of actions performed by the operator and captured by the one or more sensors of the system. The system may be configured to provide and / or suggest follow up and / or specific areas of additional and / or alternative training based on the performance of the operator.

[0264] Exemplary embodiments of the training module may be used to generate virtual labs or training scenarios. For example, the VR headset may be used to provide concurrentDocket No. I001-0007PCT-PROviews of digital information including instructions and / or examples of performing actions that the operator may see in real time or near real time to performing the action at their station.

[0265] Exemplary embodiments of the systems and methods for resource analysis, optimization, and visualization may include safety and audit modules. For example, the system may be configured to generate audit procedures, track defects, provide information for responding to an identified defect, capture images of actions for audit tracking, confirm audit actions, fill out and / or create audit reports with associated documentation of actions and / or results, or any combination thereof.

[0266] Exemplary embodiments of the systems and methods for resource analysis shown and described herein may be used for maintenance. The system may be configured to plan, track, execute preventative and / or corrective maintenance tasks.

[0267] In an exemplary embodiment, the system may be configured to automate maintenance functions. For example, the system may be able to generate maintenance schedules and / or alert operators to necessary maintenance actions. The system may provide training and / or information about the maintenance, and / or may provide documentation and / or confirmation tracking of the proper and / or completion of maintenance actions.

[0268] FIG. 9 illustrates an exemplary flow diagram for an audit module according to embodiments of the systems and methods for resource analysis, optimization, and visualization.

[0269] FIG. 10 illustrates an exemplary flow diagram for an audit module according to embodiments of the systems and methods for resource analysis, optimization, and visualization.

[0270] FIG. 11 illustrates an example comparing an audit trail from paper or digital according to embodiments of the systems and methods for resource analysis, optimization, and visualization.

[0271] As shown and described herein, the system may generate or use digital forms. However, as illustrated, digital forms requires hardware including the mobile electronic device, such as a tablet, laptop, and / or smartphone. Because of budgets and costs, there is likely only aDocket No. I001-0007PCT-PROlimited number of mobile electronic devices that may be used to have forms filled out. The acquisition of information by a plurality of users is therefore limited and / or inefficient.

[0272] As shown, the system described herein may permit the printing of physical forms that may be filled in by users. The system may therefore reduce costs by obtaining many copies and reduce time for acquisition by permitting a plurality of users to simultaneously fill out a form.

[0273] As shown and described herein, the system may be configured to use image detection and / or other input forms to extract information from forms, documents, spreadsheets, and other input formats. The system may therefore be used to scan and extract the relevant information from the physical form and populate a database. The system may therefore use a combination of the conventional paper forms to obtain information and current embodiments to digitize and analyze the information to transform the dataset into something that can be analyzed to create actionable optimizations.

[0274] Exemplary embodiments of the systems and methods for resource analysis shown and described herein may be used for quality assurance. For example, the system may be used to monitor product quality, identify defects, track requirements, etc.

[0275] In an exemplary embodiment, the system may be configured to generate audits and / or surveys. In an optional embodiment, the system may generate audits and / or surveys and / or checklists from one or more documents in the system. For example, the system may include procedures on how to operate a station and / or may observe the proper use at a station. The system may be configured to generate procedural steps, audit checks, and / or checklists in managing the function of that station and / or equipment. The system may thereafter monitor work at the station and may document the events associated with the audit such as the performance of maintenance, performance of certain safety actions, proper use of the machinery and / or process of the station, or any combination thereof. The system may then be configured to generate an audit report with the desired information and / or supporting documentation including images and / or results from one or more sensors. The material can be in textual / video or image form.Docket No. I001-0007PCT-PRO

[0276] Exemplary embodiments of the system and methods shown and described herein may be used to create content to fill content gaps associated with knowledge gaps identified by the system. For example, any one or group of operators are consistently performing an action incorrectly, the system may, by using data sources of process or machine manuals and / or video clips from operators properly executing an action, generate a training session to demonstrate proper operation and address the specific knowledge gap of the one or group of operators.Similarly, if the operators across a line are missing key skills, the system may identify the skill and generate a training manual and / or lesson based on the skill gap in the production line.

[0277] Similarly, the system may create content for logging, tracking, audit, maintenance, or other purpose. For example, the system may recognize a machine is undergoing maintenance and may track the actions taken, record one or more images or clips associated with the maintenance, and / or generate a report or other audit trail of the maintenance, including operator, time, operation, etc. to track the next maintenance or compliance for that workstation and / or machine.

[0278] As shown and described herein, exemplary embodiments of the system and methods for resource analysis may provide action items for users. For example, the system may recommend solutions for inefficiency events, may require training of operators, etc. The system may be configured to assign points to assigned tasks associated to users. The system may thereafter track the accumulation of points for a given user when a user performs the task.

[0279] The system may be configured to add points for a user for given positive action(s). For example, the system may add point for operators that have higher than average cycle times, higher than average throughput, are in the proper workstation location for a higher than average amount of time, perform functions according to process specifications, user proper posture during an action, complete or stay up to date on training, complete suggested action items within a predetermined amount of time, or any combination thereof.

[0280] The system may be configured to deduct points for a user for a given negative action(s). For example, the system may deduct points for operators that have lower than average cycle times, lower than average throughput, are not in a proper workstation location for a higher than average amount of time, do not perform functions according to process specifications, userDocket No. I001-0007PCT-PROimproper posture during an action, do not complete training, fail compliance or audit reviews, do not perform suggested actions within a predetermined amount of time, etc. or a combination thereof.

[0281] The system may permit supervisors, other users, coworkers, etc. to provide positive and / or negative feedback to a user that may positively and / or negatively affect their position as well. For example, the system may be configured to give another user a thumbs up or a thumbs down for a user based on interactions the other user had with the user.

[0282] The system may be configured to rank users based on any combination of inputs, such as points, actions completed, positive feedback, etc.

[0283] FIG. 12 illustrates an exemplary leaderboard 1200 identifying a plurality of users 1202 and ranking(s) 1204 the user according to one or more variables. As illustrated, the one or more users may be ranked by actions taken, overall points, or positive feedback obtained.

[0284] The system may be configured to average, add, and / or provided a weighted result of the one or more variables to create an overall rank 1206 of the one or more users and provide placed order on a leaderboard.

[0285] The system may be used to rank users based across different criteria such as, for example, recognition by supervisors, recognition by other users and / or co-operators, operator contributions, operator performance in safety, quality maintenance, or other functions.

[0286] The system may also permit the user to obtain additional information about a user. For example, the system may provide the name of the userjob title, amount of time at a current function, worksite, process, location, etc. their associated points with respect to one or more variables, training metrics, skills matrix, historical contributions across various functions, achievements leading to assignment or detriment of points, etc.

[0287] The system may automatically, with the help of data assistants, recognize ‘Employee of the month,’ ‘Employee of the Line,’ or other reward designation, etc. and automatically assign rewards that could be personalized based on the long term understanding ofDocket No. I001-0007PCT-PRGthe operator(s) for whom the reward is to be provided. Rewards may be, for example, time off, gift cards, bonus payments, other benefits, etc.

[0288] Exemplary embodiments of the rewards and recognition system may provide dynamic and / or real time updates to ranking for resources and / or operators. The system may provide live updates of critical events. The system may provide good and / or bad event tracking. The system may provide action response summaries, lessons, or other information in response to user actions and experiences.

[0289] Exemplary embodiments shown and described herein may be configured to augment manufacturing teams through artificial intelligent (Al) assistants. As shown and described herein, the system may be configured to analyze large amounts of data and provide insights and / or actions in response to the data analysis. The system may therefore be used to augment and / or assist line operators and / or supervisors.

[0290] FIG. 13 illustrates an exemplary embodiment representing the user of virtual assistants that may be implemented using embodiments of the systems and methods shown and described herein.

[0291] As illustrated, an operation 1300 may include a lead 1304. The lead may be any person in charge of a plant, line, operation, etc. This lead 1304 may supervise one or more supervisors 1306. The supervisors may be over one or more operators within a portion of the plant, line, operation, etc. The lead 1304 may not have real time or direct access to all actions under the supervisors.

[0292] A virtual assistant 1308 may be used between the lead and the supervisors (or any one in need of a service) to provide real time or aggregated information to the lead to provide insights into the operations, status, and performance of work performed under the supervisor.

[0293] Each virtual assistant 1308 may receive information from one or more input sources 1302. The input sources 1302 may be any combination of the raw data, pre-processed data, analyzed data from input / output devices such as cameras, sensors, counters, work instructions, user inputs, user content, etc.Docket No. I001-0007PCT-PRO

[0294] Each virtual assistant 1308 may be configured to analyze the action of the work performed under each supervisor and provide resources as shown and described herein. For example, the virtual assistant 1308 may identify inefficiency events within a process or line, identify root causes of inefficiency events, provide solutions in response to inefficiency events, prioritization of implementation strategies, provide virtual experimentation or hypothetical modeling of potential solutions, etc.

[0295] Each virtual assistant 708 may be configured to provide reports or status updates on demand or at preset time intervals. For example, reports may be generated weekly identifying statistics related to a given line, supervisor, operator, machine, process, resource, etc. The reports may include any combination of analysis, information, suggestions, etc. as shown and described herein.

[0296] Exemplary embodiments of the provided reports may include information identifying the top threshold of one or more categories. For example, for each desired hierarchy level (such as, for example, supervisor, line, operator, machine, process), the virtual assistant 708 may be configured to generate the top three, five, or other predetermined amount of root causes of inefficiency events, recommended follow up actions, training gaps, critical events, etc.

[0297] Exemplary embodiments of the provided reports may include a summary of when performance is not met.

[0298] As shown and described herein, exemplary embodiments of the systems and methods may apply to safety, maintenance, training, and / or quality control. Exemplary embodiments of the systems and methods described herein may therefore permit organizations to create virtual teams or assistants related to these areas and / or other defined areas as needed by the user.

[0299] Exemplary embodiments of the resource analysis system and method may be used for supervisors across functions like line performance, training, job rotation, audits, recommended actions, etc.

[0300] Assistants may be completely configurable by the user and multiple assistants can be combined into a multi assistant workflow.Docket No. I001-0007PCT-PRO

[0301] Assistants may be dynamic in their responses based on the context of where an issue is observed, who is interacting and when and how the interaction occurred. For example: if an inefficiency is observed at a specific workstation, the work instructions may be used from the skills matrix requirements for the workstation and / or the machine manuals may be used from the maintenance database to provide a customized pertinent and specific response to the ask.

[0302] Further assistants may also use long term characterization and understanding of all entities like users, lines, workstations based on several past interactions with the assistants, related to the entity.

[0303] Analysis assistants may analyze interactions between supervisors and other Al assistants. Gaps in training information (documents, videos, images etc.) or follow up action items (e An improvement experiment, time study, action item) may be identified and automatically generated with or without a human in the loop. These may then be automatically assigned to the right users based on the needs / past interactions and the training matrix or action items may be updated appropriately. These may also be achieved by the different assistants interacting with each other through a data base or direct requests and with or without a master Al assistant orchestrating these interactions.

[0304] Analysis assistants may also provide reports of performance across various functions like Quality, Performance, Safety etc. at the end of every shift similar to a newsletter. Users may ask further questions based on the report and further customize those report based on their needs

[0305] Further Al assistants may generate dynamic action items for all humans to follow up and complete. For example, a measurement Computer Vision assistant may identify and recommend critical improvement opportunities with / without video clips to supervisors to review and act on. Additional recommendations may include training operators on the line, rotating operator across workstations, tracking missed goals, implementing experiments, action items etc.

[0306] FIG. 14 illustrates exemplary system level diagram for the systems and methods for resource analysis, optimization, and visualization according to embodiments described herein. Exemplary embodiments of the systems and methods for resource analysis, optimization,Docket No. I001-0007PCT-PROand visualization described herein may include a computer, computers, electronic device, or electronic devices. As used herein, the term computer(s) and / or electronic device(s) are intended to be broadly interpreted to include a variety of systems and devices including personal computers 1002, laptop computers 1002, mainframe computers, servers 1003, set top boxes, digital versatile disc (DVD) players, mobile phone 1004, tablet, smart watch, smart displays, televisions, and the like. A computer can include, for example, processors, memory components for storing data (e.g., read only memory (ROM) and / or random access memory (RAM), other storage devices, various input / output communication devices and / or modules for network interface capabilities, etc. For example, the system may include a processing unit including a memory, a processor, an analog-to-digital converter (A / D), a plurality of software routines that may be stored as non-transitory, machine readable instruction on the memory and executed by the processor to perform the processes described herein. The processing unit may be based on a variety of commercially available platforms such as a personal computer, a workstation a laptop, a tablet, a mobile electronic device, or may be based on a custom platform that uses applicationspecific integrated circuits (ASICs) and other custom circuitry to carry out the processes described herein. Additionally, the processing unit may be coupled to one or more input / output (I / O) devices that enable a user to interface to the system. By way of example only, the processing unit may receive user inputs via a keyboard, touchscreen, mouse, scanner, button, or any other data input device and may provide graphical displays to the user via a display unit, which may be, for example, a conventional video monitor. The system may also include one or more large area networks, and / or local networks for communicating data from one or more different components of the system. The one or more electronic devices may therefore input a user interface for displaying information to a user and / or one or more input devices for receiving information from a user. The system may receive and / or display the information after communication to or from a remote server 1003 or database 1005.

[0307] A method of analyzing a process having a plurality of workstations is provided herein. The method may include receiving data from one or more data sources; analyzing the data from the one or more data sources; and providing an output to a user based on the analyzed data.Docket No. I001-0007PCT-PRO

[0308] As shown and described herein, the systems and methods have expansive applications including providing artificial intelligence trained on resource metrics of prior performance as determined from the received data; generating outputs, optimizing resources, line balancing (a type of resource optimizations), training optimizations, visualizing data, providing action items and suggestions to respond to identified events, providing outputs in response to user inquiries, and combinations thereof.

[0309] The method may include analyzing the data from the one or more data sources to determine a workstation cycle time for each of the plurality of workstations. The workstation cycle time may be used to identify an event. The event may be an inefficiency event, comparison to a standard, delay in cycle time from a preset cycle time and / or average cycle time, downtime of a resource, or a combination thereof.

[0310] The process of the method may include a plurality of subtasks to complete the process, and a plurality of operators may be assigned to perform the subtasks to complete the process at the workstation.

[0311] The method may be configured to provide an optimization.

[0312] The method may include assigning each subtask to one of the plurality of workstations; assigning each of the operators to perform the subtasks based on a cycle time each operator performs a subtasks and on the availability of operators to perform the subtasks; determining a maximum cycle time of each subtask for a workstation maintaining a required sequence of subtasks and accounting for unavailability of a workstation for a subtask based on a previous subtasks needed to maintain the required sequence of subtasks; determining a maximum process time by summing the maximum cycle times of each subtask to perform the process; reassigning operators to perform subtasks to reduce bottleneck times identified from the determined maximum cycle times of each subtasks identifying cycle times that are longer than required.

[0313] The method may also include determining an updated maximum cycle time of each subtask for the workstation maintaining the required sequence of subtasks and accounting for the unavailability of a workstation for a subtasks based on the previous subtasks needed toDocket No. I001-0007PCT-PROmaintain the required sequence of subtasks and accounting for the reassigned operator to the subtask; determined an updated maximum process time by summing the updated maximum cycle times of each subtask to perform the process based on the redetermined; and determining operator assignments to subtasks and workstations based on a minimum of the maximum process time and the updated maximum process time.

[0314] The method may also include repeating the reassignment of operators, determination of an updated maximum cycle time, and determination of an updated maximum process time through different combinations of allocations of operators to sub-tasks to determine operator assignments to subtasks to minimum the maximum process time.

[0315] The method may therefore be configured to assign resources to minimize the overall process time by analyzing the cycle times associated with subtasks when a resource is assigned to the subtask. The assignment of the resource to the subtask may impact the cycle time for the subtask. For example, an operator may take longer or shorter amount of time to perform a task based on their experience, skills, capabilities, etc. The assignment of an operator therefore impacts the cycle time for a subtask and the combination may impact the overall process time.

[0316] The reassignment of operators may be based on operator training, available operators, and minimizing cycle times of subtasks that create a bottleneck in that the subtask creating a bottleneck has a longer than normal cycle time because the subtask cannot be performed because a resource is not available or the operator assigned to the subtasks takes longer than compared to another operator to complete the subtask.

[0317] An operator may be considered a resource. The method may be used to optimize any resource similar to the operator. For example, the use of resources may be in equipment, operators, materials, etc. Resources may also include training.

[0318] The method may also include assigning each subtask to one of the plurality of workstations; assigning each of the resources to perform the subtask requiring the resource based on a cycle time to perform the subtask with the resource and on the availability of the resource to perform the subtask; determining a maximum cycle time of each subtask for a workstation maintaining a required sequence of subtasks and accounting for unavailability of a workstationDocket No. I001-0007PCT-PROfor a subtask based on a previous subtasks needed to maintain the required sequence of subtasks; determining a maximum process time by summing the maximum cycle times of each subtask to perform the process; and reassigning resources to perform subtasks to reduce bottleneck times identified from the determined maximum cycle times of each subtasks identifying cycle times that are longer than required.

[0319] The method may also include determining an updated maximum cycle time of each subtask for the workstation maintaining the required sequence of subtasks and accounting for the unavailability of a workstation for a subtasks based on the previous subtasks needed to maintain the required sequence of subtasks and accounting for the reassigned operator to the subtask; determining an updated maximum process time by summing the updated maximum cycle times of each subtask to perform the process based on the redetermined; determining resource assignments to subtasks based on a minimum of the maximum process time and the updated maximum process time.

[0320] The method may also include tracking a training level for each of the plurality of operators for each of the workstations; analyzing the available plurality of operators trained for one or more of the workstations; and identifying a skill to train one or more other operators of the plurality of operators that are not the available plurality of operators to maintain a desired number of available plurality of operators for each of the one or more of the workstations.

[0321] The identification of a skill to train one or more other operators is determined based on a maximum impact of the skill to the operator.

[0322] The method may include tracking a skill set of each of the operators associated to perform the subtasks at each of the workstations; identifying a missing skill for an operator; and assigning training to the operator based on the missing skill.

[0323] The method may include a plurality of missing skills for the operator to perform untrained subtasks, where an untrained subtasks of the operator is a subtask in which the operator is not authorized to perform based on a lack of training and / or certification.Docket No. I001-0007PCT-PRO

[0324] The method may include identifying the missing skill based on an assessment of all of the plurality of missing skills to determine the missing skill that maximizes the training of the operator by applying to the most workstations and / or subtasks.

[0325] The method may include identifying the missing skill based on an assessment of all of the plurality of missing skills of all of the operators to determine a missing skill that is missing from a maximum number of the plurality of operators.

[0326] As shown and described herein the system is shown and configured to provide dynamic, contextual responses and data analysis to make efficient use of the information from the plurality of data sources. The system may therefore analyze the data, visualize the data, make action recommendations, identify events, create content, etc. as shown and described herein. The system may be contextual by using a large language model to train the system based on the utilization of resources, including operators, workstations, etc.

[0327] The utilization of resources and / or metrics as shown and described herein may be any measured and / or analyzed data, such as cycle times, up times, down times, experience levels, or other metric as described herein.

[0328] The method may include tracking resource utilization based on the received data from the one or more data sources; and the output to a user comprises any combination of artificial intelligent assistance, dynamic recommendations for action items, data visualization, or content generation.

[0329] The method may include tracking resource utilization based on the received data from the one or more data sources, and the output comprises artificial intelligent assistance configured to provide contextual dynamic action items.

[0330] The system may be configured to associate various metrics with one or more resources based on historic data from the one or more data sources and the output is based on one or more of the associated various metrics related to the output and the contextual dynamic action items are related to one or more of the associated various metrics related to the output.Docket No. I001-0007PCT-PRO

[0331] The output may include specific action items related to the one or more of the associated various metrics.

[0332] The associated various metrics may be related to an assessment of a resource.

[0333] The assessment of a resource may include any combination of a training level of an operator, experience of an operator, throughput of a process, throughput of a workstation based on an operator, cycle time associated with an operator, utilization of a resource, availability of a resource, and the resource is equipment, workstations, materials, or operators.

[0334] Using artificial intelligence with the analyzed data from the one or more data sources to provide dynamic action recommendations by analyzing one or more metrics associated with one or more resources.

[0335] The output comprises a visual display of a skills matrix of a plurality of operators compared to the plurality of workstations to perform subtasks.

[0336] The visual display of the skills matrix includes a list of the plurality of operators, a list of the plurality of workstations, an indication of an amount of time each operator of the plurality of operators has with each workstation of the plurality of workstations, and a training level of each operator with each workstation.

[0337] The data from one or more data sources comprises a standard; and the method further comprises comparing the received data from the one or more data sources to determine if a resource is complying with the standard.

[0338] The standard may be a proper posture and at least one data from the one or more data sources comprises an image of an operator showing a posture of the operator and analyzing the data comprising analyzing the image of the operator for the posture of the operator and comparing the posture of the operator from the image with the posture of the operator from the standard.

[0339] The standard may be a user process having a plurality of steps for performing a subtask and the determination if a resource is complied with the standard comprises identifyingDocket No. I001-0007PCT-PROeach step of the plurality of steps for performing the subtask from the data from one or more data sources.

[0340] The method may include creating a training session based on a missing step when the comparison of the plurality of steps for performing the subtask to the data from one or more data sources identifies the missing step.

[0341] The output may include a leaderboard ranking operator(s) based on the analyzed data from the one or more data sources.

[0342] The method may include generating content based on the analyzed data from the one or more data sources.

[0343] The generated content may include training material including video received as data from the one or more data sources.

[0344] The method may include using analyzing the received data from one or more data sources to train a large language model to associate attributes to the operators, workstations, and subtasks and identifying events.

[0345] The method may include receiving a user inquiry about the event, parsing the natural language to define query actions, entities, and values, and using the parsed natural language to generate a database query to return results relevant to the user inquiry, and the output comprises an output based on the user inquiry.

[0346] The output may use the large language model to provide context based on the associated attributes associated with the event.

[0347] The associated context relates to a specific operator from the plurality of operators involved in the event, a specific workstation from the plurality of operators involved in the event, a timing of the event, a nature of the event.

[0348] Exemplary embodiments of the system described herein can be based in software and / or hardware. While some specific embodiments of the invention have been shown the invention is not to be limited to these embodiments. For example, most functions performed byDocket No. I001-0007PCT-PROelectronic hardware components may be duplicated by software emulation. Thus, a software program written to accomplish those same functions may emulate the functionality of the hardware components in input-output circuitry. The invention is to be understood as not limited by the specific embodiments described herein, but only by scope of the appended claims.

[0349] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. These terms are merely intended to distinguish one component from another component, and the terms do not limit the nature, sequence or order of the constituent components.

[0350] It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items. Throughout the specification, unless explicitly described to the contrary, the word “comprise” and variations such as “comprises” or “comprising” will be understood to imply the inclusion of stated elements but not the exclusion of any other elements.

[0351] In addition, the terms “unit”, “-er”, “-or”, and “module” described in the specification mean units for processing at least one function and operation, and can be implemented by hardware components or software components and combinations thereof.

[0352] In this document, when terms such as “first” and “second” are used to modify a noun, such use is simply intended to distinguish one item from another, and is not intended to require a sequential order unless specifically stated. In addition, terms of relative position such as “vertical” and “horizontal”, or “front” and “rear”, when used, are intended to be relative to each other and need not be absolute, and only refer to one possible position of the device associated with those terms depending on the device’s orientation.Docket No. I001-0007PCT-PRO

[0353] An “electronic device” or a “computing device” refers to a device that includes a processor and memory. Each device may have its own processor and / or memory, or the processor and / or memory may be shared with other devices as in a virtual machine or container arrangement. The memory may contain or receive programming instructions that, when executed by the processor, cause the electronic device to perform one or more operations according to the programming instructions.

[0354] The terms “memory,” “memory device,” “computer-readable storage medium,” “data store,” “data storage facility” and the like each refer to a non-transitory device on which computer-readable data, programming instructions or both are stored. Except where specifically stated otherwise, the terms “memory,” “memory device,” “computer-readable storage medium,” “data store,” “data storage facility” and the like are intended to include single device embodiments, embodiments in which multiple memory devices together or collectively store a set of data or instructions, as well as individual sectors within such devices.

[0355] The terms “processor” and “processing device” refer to a hardware component of an electronic device that is configured to execute programming instructions. Except where specifically stated otherwise, the singular term “processor” or “processing device” is intended to include both single-processing device embodiments and embodiments in which multiple processing devices together or collectively perform a process.

[0356] The terms “instructions” and “programs” may be used interchangeably herein. The instructions may be stored in object code format for direct processing by the processor, or in any other computing device language, including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance. Functions, methods, and routines of the instructions are explained in more detail below. The instructions may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. For example, the instructions may be stored as computing device code on the computing device-readable medium.

[0357] The term “data” may be retrieved, stored or modified by processors in accordance with a set of instructions. For instance, although the claimed subject matter is not limited by any particular data structure, the data may be stored in computing device registers, in a relationalDocket No. I001-0007PCT-PRGdatabase as a table having a plurality of different fields and records, XML documents or flat files. The data may also be formatted in any computing device-readable format.

[0358] The term “module” refers to a set of computer-readable programming instructions, as executed by a processor, that cause the processor to perform one or more specified function(s).

[0359] Although exemplary embodiments are described as using a plurality of units to perform the exemplary process, it is understood that the exemplary processes may also be performed by one or plurality of modules. Additionally, it is understood that the term controller / control unit refers to a hardware device that includes a memory and a processor and is specifically programmed to execute the processes described herein. The memory is configured to store the modules and the processor is specifically configured to execute these modules to perform one or more processes that are described further below.

[0360] Further, the control logic of the present disclosure may be embodied as non-transitory computer readable media on a computer readable medium containing executable programming instructions executed by a processor, controller, or the like. Examples of computer readable media include, but are not limited to, ROM, RAM, compact disc (CD)-ROMs, magnetic tapes, floppy disks, flash drives, smart cards and optical data storage devices. The computer readable medium can also be distributed in network-coupled computer systems so that the computer readable media may be stored and executed in a distributed fashion such as, e.g., by a telematics server or a Controller Area Network (CAN).

[0361] As used herein, the terms "about," "substantially," or "approximately" for any numerical values, ranges, shapes, distances, relative relationships, etc. indicate a suitable dimensional tolerance that allows the part or collection of components to function for its intended purpose as described herein. Numerical ranges may also be provided herein. Unless otherwise indicated, each range is intended to include the endpoints, and any quantity within the provided range. Therefore, a range of 2-4, includes 2, 3, 4, and any subdivision between 2 and 4, such as 2.1, 2.01, and 2.001. The range also encompasses any combination of ranges, such that 2-4 includes 2-3 and 3-4.Docket No. I001-0007PCT-PRO

[0362] Although embodiments of this invention have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of embodiments of this invention as defined by the appended claims. Specifically, exemplary components are described herein. Any combination of these components may be used in any combination. For example, any component, feature, step or part may be integrated, separated, sub-divided, removed, duplicated, added, or used in any combination and remain within the scope of the present disclosure. Embodiments are exemplary only, and provide an illustrative combination of features, but are not limited thereto.

[0363] The features disclosed in the foregoing description, or the following claims, or the accompanying drawings, expressed in their specific forms or in terms of a means for performing the disclosed function, or a method or process for attaining the disclosed result, as appropriate, may, separately, or in any combination of such features, be utilised for realising the invention in diverse forms thereof.

Claims

Docket No. I001-0007PCT-PROClaims1. A method of analyzing a process having a plurality of workstations, comprising:receiving data from one or more data sources;analyzing the data from the one or more data sources to determine a workstation cycle time for each of the plurality of workstations; andproviding an output to a user based on the analyzed data.

2. The method of claim 1, wherein the process comprises a plurality of subtasks to complete the process, and a plurality of operators to perform the subtasks to complete the process, and the method further comprises:assigning each subtask to one of the plurality of workstations;assigning each of the operators to perform the subtasks based on a cycle time each operator performs a subtasks and on the availability of operators to perform the subtasks;determine a maximum cycle time of each subtask for a workstation maintaining a required sequence of subtasks and accounting for unavailability of a workstation for a subtask based on a previous subtasks needed to maintain the required sequence of subtasks;determine a maximum process time by summing the maximum cycle times of each subtask to perform the process;reassign operators to perform subtasks to reduce bottleneck times identified from the determined maximum cycle times of each subtasks identifying cycle times that are longer than required;determine an updated maximum cycle time of each subtask for the workstation maintaining the required sequence of subtasks and accounting for the unavailability of a workstation for a subtasks based on the previous subtasks needed to maintain the required sequence of subtasks and accounting for the reassigned operator to the subtask;determined an updated maximum process time by summing the updated maximum cycle times of each subtask to perform the process based on the redetermined;Docket No. I001-0007PCT-PROdetermine operator assignments to subtasks and workstations based on a minimum of the maximum process time and the updated maximum process time.

3. The method of claim 2, further comprising repeating the reassignment of operators, determination of an updated maximum cycle time, and determination of an updated maximum process time through different combinations of allocations of operators to sub-tasks to determine operator assignments to subtasks to minimum the maximum process time.

4. The method of claims 2 or 3, wherein the reassignment of operators is based on operator training, available operators, and minimizing cycle times of subtasks that create a bottleneck in that the subtask creating a bottleneck has a longer than normal cycle time because the subtask cannot be performed because a resource is not available or the operator assigned to the subtasks takes longer than compared to another operator to complete the subtask.

5. The method of claim 1, wherein the process comprises a plurality of subtasks to complete the process, and a plurality of resources to perform one or more subtasks to complete the process, and the method further comprises:assigning each subtask to one of the plurality of workstations;assigning each of the resources to perform the subtask requiring the resource based on a cycle time to perform the subtask with the resouce and on the availability of the resource to perform the subtask;determine a maximum cycle time of each subtask for a workstation maintaining a required sequence of subtasks and accounting for unavailability of a workstation for a subtask based on a previous subtasks needed to maintain the required sequence of subtasks;determine a maximum process time by summing the maximum cycle times of each subtask to perform the process;reassign resources to perform subtasks to reduce bottleneck times identified from the determined maximum cycle times of each subtasks identifying cycle times that are longer than required;Docket No. I001-0007PCT-PROdetermine an updated maximum cycle time of each subtask for the workstation maintaining the required sequence of subtasks and accounting for the unavailability of a workstation for a subtasks based on the previous subtasks needed to maintain the required sequence of subtasks and accounting for the reassigned operator to the subtask;determined an updated maximum process time by summing the updated maximum cycle times of each subtask to perform the process based on the redetermined;determine resource assignments to subtasks based on a minimum of the maximum process time and the updated maximum process time.

6. The method of any of the preceding claims, wherein the process performed by a plurality of operators, the method further comprising:tracking a training level for each of the plurality of operators for each of the workstations;analyzing the available plurality of operators trained for one or more of the workstations;identifying a skill to train one or more other operators of the plurality of operators that are not the available plurality of operators to maintain a desired number of available plurality of operators for each of the one or more of the workstations.

7. The method of claim 6, wherein the identification of a skill to train one or more other operators is determined based on a maximum impact of the skill to the operator.

8. The method of any of the preceding claims, wherein the process includes a plurality of operators to perform subtasks at the plurality of workstations to complete the process, the method further comprising:tracking a skill set of each of the operators associated to perform the subtasks at each of the workstations;identifying a missing skill for an operator; andassigning training to the operator based on the missing skill.Docket No. I001-0007PCT-PRO9. The method of claim 8, further comprising a plurality of missing skills for the operator to perform untrained subtasks, where an untrained subtasks of the operator is a subtask in which the operator is not authorized to perform based on a lack of training and / or certification.

10. The method of claim 9, further comprising identifying the missing skill based on an assessment of all of the plurality of missing skills to determine the missing skill that maximizes the training of the operator by applying to the most workstations and / or subtasks.

11. The method of claim 9, further comprising identifying the missing skill based on an assessment of all of the plurality of missing skills of all of the operators to determine a missing skill that is missing from a maximum number of the plurality of operators.

12. The method of claim 1, further comprising tracking resource utilization based on the received data from the one or more data sources; and the output to a user comprises any combination of artificial intelligent assistance, dynamic recommendations for action items, data visualization, or content generation.

13. The method of claim 1, further comprising tracking resource utilization based on the received data from the one or more data sources, and the output comprises artificial intelligent assistance configured to provide contextual dynamic action items.

14. The method of claim 13, wherein the system is configured to associate various metrics with one or more resources based on historic data from the one or more data sources and the output is based on one or more of the associated various metrics related to the output and the contextual dynamic action items are related to one or more of the associated various metrics related to the output.

15. The method of any of claims 13 or 14, and the output comprises specific action items related to the one or more of the associated various metrics.

16. The method of any of claims 13-15, wherein the associated various metrics is related to an assessment of a resource.

17. The method of claim 16, wherein the assessment of a resource is any combination of a training level of an operator, experience of an operator, throughput of a process, throughput of aDocket No. I001-0007PCT-PROworkstation based on an operator, cycle time associated with an operator, utilization of a resource, availability of a resource, and the resource is equipment, workstations, materials, or operators.

18. The method of any of the preceding claims, further comprising using artificial intelligence with the analyzed data from the one or more data sources to provide dynamic action recommendations by analyzing one or more metrics associated with one or more resources.

19. The method of any of the preceding claims, wherein the output comprises a visual display of a skills matrix of a plurality of operators compared to the plurality of workstations to perform sub tasks.

20. The method of claim 19, wherein the visual display of the skills matrix includes a list of the plurality of operators, a list of the plurality of workstations, an indication of an amount of time each operator of the plurality of operators has with each workstation of the plurality of workstations, and a training level of each operator with each workstation.

21. The method of any preceding claim, wherein the data from one or more data sources comprises a standard; and the method further comprises comparing the received data from the one or more data sources to determine if a resource is complying with the standard.

22. The method of claim 21, wherein the standard is a proper posture and at least one data from the one or more data sources comprises an image of an operator showing a posture of the operator and analyzing the data comprising analyzing the image of the operator for the posture of the operator and comparing the posture of the operator from the image with the posture of the operator from the standard.

23. The method of claim 21, wherein the standard is a user process having a plurality of steps for performing a subtask and the determination if a resource is complied with the standard comprises identifying each step of the plurality of steps for performing the subtask from the data from one or more data sources.Docket No. I001-0007PCT-PRG24. The method of claim 23, further creating a training session based on a missing step when the comparison of the plurality of steps for performing the subtask to the data from one or more data sources identifies the missing step.

25. The method of any of the preceding claims, wherein the output comprises a leaderboard ranking operators based on the analyzed data from the one or more data sources.

26. The method of any of the preceding claims, further comprising generating content based on the analyzed data from the one or more data sources.

27. The method of claim 26, wherein the generated content comprises training material including video received as data from the one or more data sources.

28. The method of any of the preceding claims, wherein the process is performed as a plurality of subtasks across a plurality of workstations by a plurality of operators, and the method further comprises using analyzing the received data from one or more data sources to train a large language model to associate attributes to the operators, workstations, and subtasks and identifying events.

29. The method of claim 28, further comprising receiving a user inquiry about the event, parsing the natural language to define query actions, entities, and values, and using the parsed natural language to generate a database query to return results relevant to the user inquiry, and the output comprises an output based on the user inquiry.

30. The method of claim 29, wherein the output uses the large language model to provide context based on the associated attributes associated with the event.

31. The method of claim 30, wherein the associated context relates to a specific operator from the plurality of operators involved in the event, a specific workstation from the plurality of operators involved in the event, a timing of the event, a nature of the event.

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