Computer-implemented methods and systems of managing engagement and wellbeing in an organization

Abstract

The present application is at least directed to a method for managing attributes associated with personnel in an organization using an electronic platform integrated with a trained ML model. The method involves receiving a request via a user interface to obtain a resource for managing an attribute related to one or more personnel. This request is transmitted to the ML model, which has been trained on various data sets, including organizational traits, personnel traits, behavioral toolkits, and external inputs. The ML model processes the request and determines the appropriate resource to manage the specified attribute. Finally, the determined resource is outputted as a graphical representation over the user interface. The graphical representation provides a visual and actionable way to manage the attribute effectively.

Description

COMPUTER-IMPLEMENTED METHODS AND SYSTEMS OF MANAGING ENGAGEMENT AND WELLBEING IN AN ORGANIZATIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The instant application claims the benefit of priority of U.S. Provisional Application No. 63 / 640,515 filed April 30, 2024 entitled, “Microlearning Card Game” the contents of which is incorporated by reference in its entirety herein.TECHNOLOGICAL FIELD

[0002] The subject technology generally relates to computer-implemented methods, systems, and computer program products for managing engagement and wellbeing in an organization. More particularly, the subject technology employs an electronic platform with a trained machine learning model configured to provide effective solutions to users for managing engagement and wellbeing in an organization.BACKGROUND

[0003] In today's dynamic and competitive business environment, organizations are recognizing the importance of effectively managing various attributes associated with their personnel. Attributes such as engagement and wellbeing are critical for maintaining a productive and motivated workforce. Unfortunately, traditional methods of managing these attributes often fall short due. This may be attributed to their inability to account for the diverse and complex nature of organizational and personnel traits.

[0004] Generally, engagement in an organization may refer to the level of commitment, involvement, and enthusiasm personnel exhibit towards theirorganization. High levels of engagement are associated with increased productivity, less stress and turnover, and improved overall performance. Conversely, low levels of engagement can lead to decreased morale, higher absenteeism, burnout and a negative impact on organizational success.

[0005] Generally, wellbeing may encompass the physical, mental, and emotional health of personnel. It is influenced by various factors, including work environment, stress levels, work-life balance, and access to resources that promote healthy behaviors. Organizations that prioritize wellbeing often experience lower healthcare costs, reduced absenteeism, and higher levels of satisfaction and retention.

[0006] To address these significant challenges in organizations, there is a clear need for advanced technical solutions for effectively managing engagement and wellbeing of personnel in an organization.BRIEF SUMMARY

[0007] One aspect of the subject technology is directed to a method for managing attributes associated with personnel in an organization using an electronic platform integrated with a trained ML model. The method involves receiving a request via a user interface to obtain a resource for managing an attribute related to one or more personnel. This request is transmitted to the ML model, which has been trained on various data sets, including organizational traits, personnel traits, behavioral toolkits, and external inputs. The ML model processes the request and determines the appropriate resource to manage the specified attribute. Finally, the determined resource is outputted as a graphical representation over the user interface. This provides a visual and actionable way to manage the attribute effectively.

[0008] Another aspect of the subject technology is directed to a system for managing attributes associated with personnel in an organization. The system includes a non-transitory memory including instructions stored thereon. The system also includes a processor configured to execute the stored instructions. One of the instructions involves receiving a request via a user interface to obtain a resource for managing an attribute related to one or more personnel. Another instruction involves transmitting the request to the ML model. The ML model has been trained on various data sets, including organizational traits, personnel traits, behavioral toolkits, and external inputs. Even another instruction includes the ML model processing the request and determining the appropriate resource to manage the specified attribute. Finally, the instructions include outputting the determined resource as a graphical representation over the user interface.

[0009] Yet a further aspect of the application is directed to a non-transitory computer readable medium for managing attributes associated with personnel in an organization. The non-transitory computer readable medium includes instructions that when executed by a processor effectuate receiving a request via a user interface to obtain a resource for managing an attribute related to one or more personnel. The instructions that when executed by the processor also effectuate transmitting the request to the ML model. The ML model has been trained on various data sets, including organizational traits, personnel traits, behavioral toolkits, and external inputs. The instructions that when executed by the processor further effectuate processing the request and determining the appropriate resource to manage the specified attribute. The instructions that when executed by the processor even further effectuate outputting the determined resource as a graphical representation over the user interface.

[0010] Additional advantages will be set forth in part in the description which follows or may be learned by practice. The advantages will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive, as claimed.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The summary, as well as the following detailed description, is further understood when read in conjunction with the appended drawings. For the purpose of illustrating the disclosed subject matter, there are shown in the drawings examples of the disclosed subject matter; however, the disclosed subject matter is not limited to the specific methods, compositions, and devices disclosed. In addition, the drawings are not necessarily drawn to scale. In the drawings:

[0012] FIG. 1 A illustrates a diagram of an exemplary network environment, in accordance with one or more example aspects of the subject technology.

[0013] FIG. 1 B illustrates a diagram of an exemplary communication device, in accordance with one or more example aspects of the subject technology.

[0014] FIG. 1C illustrates an exemplary computing system, in accordance with one or more example aspects of the subject technology.

[0015] FIG. 1 D illustrates a machine learning and training model framework, in accordance with one or more example aspects of the subject technology.

[0016] FIG. 1 E illustrates an electronic platform on a user interface in accordance with one or more example aspects of the subject technology.

[0017] FIG. 1 F illustrates a resource on a user interface of the platform in accordance with one or more example aspects of the subject technology.

[0018] FIG. 1 G illustrates a flow diagram of an example aspect of the subject technology.

[0019] FIG. 2A illustrates card tops in accordance with one or more example aspects of the subject technology.

[0020] FIG. 2B illustrates card bottoms in accordance with one or more example aspects of the subject technology.

[0021] FIG. 2C illustrates exemplary card bottoms in accordance with one or more example aspects of the subject technology.

[0022] FIG. 2D illustrates card bottoms in accordance with one or more example aspects of the subject technology.

[0023] FIG. 2E illustrates card bottoms in accordance with one or more example aspects of the subject technology.

[0024] FIG. 2F illustrates card bottoms in accordance with one or more example aspects of the subject technology.

[0025] FIG. 2G illustrates card bottoms in accordance with one or more example aspects of the subject technology.

[0026] FIG. 2H illustrates card bottoms in accordance with one or more example aspects of the subject technology.

[0027] FIG. 21 illustrates card bottoms in accordance with one or more example aspects of the subject technology.

[0028] FIG. 2J illustrates card bottoms in accordance with one or more example aspects of the subject technology.

[0029] FIG. 2K illustrates card bottoms in accordance with one or more example aspects of the subject technology.

[0030] FIG. 3A illustrates 5R model definition cards in accordance with one or more example aspects of the subject technology.

[0031] FIG. 3B illustrates instruction cards with instructions in accordance with one or more example aspects of the subject technology.

[0032] FIG. 3C illustrates instruction cards with instructions in accordance with one or more example aspects of the subject technology.

[0033] FIGs. 4A, 4B, 4G, 4D, and 4E illustrate 5R issue cards with issues for a use case in 5R in accordance with one or more example aspects of the subject technology.

[0034] FIGs. 5A, 5B, and 5C illustrate use cases in accordance with one or more example aspects of the subject technology.

[0035] FIG. 6 illustrates method flow for a card game in accordance with one or more example aspects of the subject technology.

[0036] The figures depict various examples for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative examples of the structures and methods illustrated herein may be employed without departing from the principles described herein.DETAILED DESCRIPTION

[0037] Some examples of the subject technology will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all examples of the subject technology are shown. Indeed, various examples of the subject technology may be embodied in many different forms and should not beconstrued as limited to the examples set forth herein. Like reference numerals refer to like elements throughout.

[0038] As used herein, the terms “data,” “content,” “information,” and similar terms may be used interchangeably to refer to data capable of being transmitted, received and / or stored in accordance with examples of the disclosure. Moreover, the term “exemplary,” as used herein, is not provided to convey any qualitative assessment, but instead merely to convey an illustration of an example. Thus, use of any such terms should not be taken to limit the spirit and scope of examples of the disclosure.

[0039] As defined herein, a “computer-readable storage medium,” which refers to a non-transitory, physical or tangible storage medium (e.g., volatile or non-volatile memory device), may be differentiated from a “computer-readable transmission medium,” which refers to an electromagnetic signal.

[0040] As referred to herein, an “application” may refer to a computer software package that may perform specific functions for users and / or, in some cases, for another application(s). An application(s) may utilize an operating system (OS) and other supporting programs to function. In some examples, an application(s) may request one or more services from, and communicate with, other entities via an application programming interface (API).

[0041] The present application describes technology that aims to fulfill the above- mentioned deficiencies in the art. Namely, the subject technology implements methods and systems employing an electronic platform including one more trained machine learning models. In an example embodiment, the electronic platform may be a human resources platform accessible by personnel of the organization to obtain solutions for managing engagement and / or wellbeing. The provided solutionsdiscussed herein enable organizations to receive personalized recommendations and resources, arranged and presented in a particular manner over a user interface, to radically improve usage and adoption by personnel. By obtained specific feedback of the personnel and deploying a trained machine learning model trained on a robust library of training data, it is envisaged that the overall organizational performance and employee satisfaction will significantly be elevated.

[0042] In one or more embodiments, the subject technology may refer to the use of a scientific engagement model to create a community work environment.Personnel engagement may be considered as setting the right conditions for members of the organization to give their best each day, committed to their organization’s goals and values, motivated to contribute to organizational success, with an enhanced sense of their own wellbeing and effective or efficient employee engagement.

[0043] The technical improvements defined by the subject technology foster realtime feedback to key individuals (e.g., managers) of an organization to improve engagement of personnel in the organization. The real-time feedback also helps personnel self-manage their wellbeing. More specifically, the system employs and electronic platform including a trained machine learning (e.g., ML) model trained on hundreds of thousands of data points to implement a structured and easy to follow protocol.

[0044] One or more technical solutions of the subject technology allows the system to provide highly personalized resource recommendations tailored to the specific needs of the organization and its personnel. In addition, by leveraging machine learning, the platform is configured to analyze complex and multifaceted data to determine customized and effective resources for managing engagementand wellbeing. Moreover, the subject technology allows the system to continuously learn and adapt based on new data and feedback. The subject technology also allows the system to learn from comprehensive internal and external data. In so doing, the outputted recommendations remain relevant and effective over time.

[0045] Further, the subject technology provides an improvement in the graphical presentation and arrangement of recommendations over a user interface. This in turn improves usability and ultimate adoption. Overall, this invention represents a significant technical improvement in the field of human resources management by providing a sophisticated, data-driven, and adaptable solution for managing engagement and wellbeing of personnel.Exemplary System Architecture

[0046] Reference is now made to FIG. 1A, which is a block diagram of a system according to exemplary embodiments. As shown in FIG. 1A, the system 100 may include one or more communication devices 105, 110, 115 and 120 and a network device 160. Additionally, the system 100 may include any suitable network such as, for example, network 140. In some examples, the network 140. In other examples, the network 140 may be any suitable network capable of provisioning content and / or facilitating communications among entities within, or associated with the network 140. As an example and not by way of limitation, one or more portions of network 140 may include an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN),a cellular telephone network, or a combination of two or more of these. Network 140 may include one or more networks 140.

[0047] Links 150 may connect the communication devices 105, 110, 115 and 120 to network 140, network device 160 and / or to each other. This disclosure contemplates any suitable links 150. In some exemplary embodiments, one or more links 150 may include one or more wired and / or wireless links, such as, for example, Digital Subscriber Line (DSL) or Data Over Cable Service Interface Specification (DOCSIS)), wireless (such as for example Wi-Fi or Worldwide Interoperability for Microwave Access (WiMAX)), or optical (such as for example Synchronous Optical Network (SONET) or Synchronous Digital Hierarchy (SDH). In some exemplary embodiments, one or more links 150 may each include an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portion of the PSTN, a cellular technology-based network, a satellite communications technology-based network, another link 150, or a combination of two or more such links 150. Links 150 need not necessarily be the same throughout system 100. One or more first links 150 may differ in one or more respects from one or more second links 150.

[0048] In some exemplary embodiments, communication devices 105, 110, 115, 120 may be electronic devices including hardware, software, or embedded logic components or a combination of two or more such components and capable of carrying out the appropriate functionalities implemented or supported by the communication devices 105, 110, 115, 120. As an example, and not by way of limitation, the communication devices 105, 110, 115, 120 may be a computer system such as, for example, a desktop computer, notebook or laptop computer, netbook, a tablet computer (e.g., a smart tablet), e-book reader, Global Positioning System(GPS) device, camera, personal digital assistant (PDA), handheld electronic device, cellular telephone, smartphone, smart glasses, augmented / virtual reality device, smart watches, charging case, or any other suitable electronic device, or any suitable combination thereof. The communication devices 105, 110, 115, 120 may enable one or more users to access network 140. The communication devices 105, 110, 115, 120 may enable a user(s) to communicate with other users at other communication devices 105, 110, 115, 120.

[0049] Network device 160 may be accessed by the other components of system 100 either directly or via network 140. As an example and not by way of limitation, communication devices 105, 110, 115, 120 may access network device 160 using a web browser or a native application associated with network device 160 (e.g., a mobile social-networking application, a messaging application, another suitable application, or any combination thereof) either directly or via network 140. In particular exemplary embodiments, network device 160 may include one or more servers 162. Each server 162 may be a unitary server or a distributed server spanning multiple computers or multiple datacenters. Servers 162 may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein, or any combination thereof. In particular exemplary embodiments, each server 162 may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented and / or supported by server 162. In particular exemplary embodiments, network device 160 may include one or more data stores 164. Data stores 164 may be used to store various types ofinformation. In particular exemplary embodiments, the information stored in data stores 164 may be organized according to specific data structures. In particular exemplary embodiments, each data store 164 may be a relational, columnar, correlation, or other suitable database. Although this disclosure describes or illustrates particular types of databases, this disclosure contemplates any suitable types of databases.

[0050] Network device 160 may provide users of the system 100 the ability to communicate and interact with other users. In particular exemplary embodiments, network device 160 may provide users with the ability to take actions on various types of items or objects, supported by network device 160. In particular exemplary embodiments, network device 160 may be capable of linking a variety of entities. As an example and not by way of limitation, network device 160 may enable users to interact with each other as well as receive content from other systems (e.g., third- party systems) or other entities, or allow users to interact with these entities through an application programming interfaces (API) or other communication channels.

[0051] It should be pointed out that although FIG. 1 A shows one network device 160 and four communication devices 105, 110, 115 and 120, any suitable number of network devices 160 and communication devices 105, 110, 115 and 120 may be part of the system of FIG. 1 A without departing from the spirit and scope of the present disclosure.Exemplary Communication Device

[0052] FIG. 1 B illustrates a block diagram of an exemplary hardware / software architecture of a communication device such as, for example, user equipment (UE) 30. In some exemplary respects, the UE 30 may be any of communication devices105, 110, 115, 120. In some exemplary aspects, the UE 30 may be a computer system such as, for example, a desktop computer, notebook or laptop computer, netbook, a tablet computer (e.g., a smart tablet), e-book reader, GPS device, camera, personal digital assistant, handheld electronic device, cellular telephone, smartphone, smart glasses, augmented / virtual reality device, smart watch, charging case, or any other suitable electronic device. As shown in FIG. 1 B, the UE 30 (also referred to herein as node 30) may include a processor 32, non-removable memory 44, removable memory 46, a speaker / microphone 38, a display, touchpad, and / or user interface(s) 42, a power source 48, a GPS chipset 50, and other peripherals 52. In some exemplary aspects, the display, touchpad, and / or user interface(s) 42 may be referred to herein as display / touchpad / user interface(s) 42. The display / touchpad / user interface(s) 42 may include a user interface capable of presenting one or more content items and / or capturing input of one or more user interactions / actions associated with the user interface. The power source 48 may be capable of receiving electric power for supplying electric power to the UE 30. For example, the power source 48 may include an alternating current to direct current (AC-to-DC) converter allowing the power source 48 to be connected / plugged to an AC electrical receptacle and / or Universal Serial Bus (USB) port for receiving electric power. The UE 30 may also include a camera 54. In an exemplary embodiment, the camera 54 may be a smart camera configured to sense images / video appearing within one or more bounding boxes. The UE 30 may also include communication circuitry, such as a transceiver 34 and a transmit / receive element 36. It will be appreciated the UE 30 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0053] The processor 32 may be a special purpose processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Array (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. In general, the processor 32 may execute computer-executable instructions stored in the memory (e.g., non-removable memory 44 and / or removable memory 46) of the node 30 in order to perform the various required functions of the node. For example, the processor 32 may perform signal coding, data processing, power control, input / output processing, and / or any other functionality that enables the node 30 to operate in a wireless or wired environment. The processor 32 may run applicationlayer programs (e.g., browsers) and / or radio access-layer (RAN) programs and / or other communications programs. The processor 32 may also perform security operations such as authentication, security key agreement, and / or cryptographic operations, such as at the access-layer and / or application layer for example. The non-removable memory 44 and / or the removable memory 46 may be computer- readable storage mediums. For example, the non-removable memory 44 may include a non-transitory computer-readable storage medium and a transitory computer-readable storage medium.

[0054] The processor 32 is coupled to its communication circuitry (e.g., transceiver 34 and transmit / receive element 36). The processor 32, through the execution of computer-executable instructions, may control the communication circuitry in order to cause the node 30 to communicate with other nodes via the network to which it is connected.

[0055] The transmit / receive element 36 may be configured to transmit signals to, or receive signals from, other nodes or networking equipment. For example, in an exemplary embodiment, the transmit / receive element 36 may be an antenna configured to transmit and / or receive radio frequency (RF) signals. The transmit / receive element 36 may support various networks and air interfaces, such as wireless local area network (WLAN), wireless personal area network (WPAN), cellular, and the like. In yet another exemplary embodiment, the transmit / receive element 36 may be configured to transmit and / or receive both RF and light signals. It will be appreciated that the transmit / receive element 36 may be configured to transmit and / or receive any combination of wireless or wired signals.

[0056] The transceiver 34 may be configured to modulate the signals that are to be transmitted by the transmit / receive element 36 and to demodulate the signals that are received by the transmit / receive element 36. As noted above, the node 30 may have multi-mode capabilities. Thus, the transceiver 34 may include multiple transceivers for enabling the node 30 to communicate via multiple radio access technologies (RATs), such as universal terrestrial radio access (LITRA) and Institute of Electrical and Electronics Engineers (IEEE 802.11), for example.

[0057] The processor 32 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 44 and / or the removable memory 46. For example, the processor 32 may store session context in its memory, (e.g., non-removable memory 44 and / or removable memory 46) as described above. The non-removable memory 44 may include RAM, ROM, a hard disk, or any other type of memory storage device. The removable memory 46 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other exemplary embodiments, the processor 32may access information from, and store data in, memory that is not physically located on the node 30, such as on a server or a home computer.

[0058] The processor 32 may receive power from the power source 48 and may be configured to distribute and / or control the power to the other components in the node 30. The power source 48 may be any suitable device for powering the node 30. For example, the power source 48 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, and the like. The processor 32 may also be coupled to the GPS chipset 50, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the node 30. It will be appreciated that the node 30 may acquire location information by way of any suitable location-determination method while remaining consistent with an exemplary embodiment.Exemplary Computing System

[0059] FIG. 1C is a block diagram of an exemplary computing system 175. In some exemplary embodiments, the network device 160 may be a computing system 175. The computing system 175 may comprise a computer or server and may be controlled primarily by computer-readable instructions, which may be in the form of software, wherever, or by whatever means such software is stored or accessed. Such computer-readable instructions may be executed within a processor, such as central processing unit (CPU) 91 , to cause computing system 175 to operate. In many workstations, servers, and personal computers, central processing unit 91 may be implemented by a single-chip CPU called a microprocessor. In other machines, the central processing unit 91 may comprise multiple processors. Coprocessor 81may be an optional processor, distinct from main CPU 91 , that performs additional functions or assists CPU 91.

[0060] In operation, CPU 91 fetches, decodes, and executes instructions, and transfers information to and from other resources via the computer’s main data- transfer path, system bus 80. Such a system bus connects the components in computing system 175 and defines the medium for data exchange. System bus 80 typically includes data lines for sending data, address lines for sending addresses, and control lines for sending interrupts and for operating the system bus. An example of such a system bus 80 is the Peripheral Component Interconnect (PCI) bus.

[0061] Memories coupled to system bus 80 include RAM 82 and ROM 93. Such memories may include circuitry that allows information to be stored and retrieved. ROMs 93 generally contain stored data that cannot easily be modified. Data stored in RAM 82 may be read or changed by CPU 91 or other hardware devices. Access to RAM 82 and / or ROM 93 may be controlled by memory controller 92. Memory controller 92 may provide an address translation function that translates virtual addresses into physical addresses as instructions are executed. Memory controller 92 may also provide a memory protection function that isolates processes within the system and isolates system processes from user processes. Thus, a program running in a first mode may access only memory mapped by its own process virtual address space; it cannot access memory within another process’s virtual address space unless memory sharing between the processes has been set up.

[0062] In addition, computing system 175 may contain peripherals controller 83 responsible for communicating instructions from CPU 91 to peripherals, such as printer 94, keyboard 84, mouse 95, and disk drive 85.

[0063] Display 86, which is controlled by display controller 96, may be used to display visual output generated by computing system 175. Such visual output may include text, graphics, animated graphics, and video. The display 86 may also include or be associated with a user interface. The user interface may be capable of presenting one or more content items and / or capturing input of one or more user interactions associated with the user interface. Display 86 may be implemented with a cathode-ray tube (CRT)-based video display, a liquid-crystal display (LCD)-based flat-panel display, gas plasma-based flat-panel display, or a touch-panel. Display controller 96 includes electronic components required to generate a video signal that is sent to display 86.

[0064] Further, computing system 175 may contain communication circuitry, such as for example a network adapter 97, that may be used to connect computing system 175 to an external communications network, such as network 12 of FIG. 1 B, to enable the computing system 175 to communicate with other nodes (e.g., UE 30) of the network.

[0065] FIG. 1 D illustrates a machine learning predication database 180, in accordance with an example of the subject technology. The machine learning predication database 180 includes a machine learning model 181 operably coupled to training database 182 including training data 183 residing therein. The machine learning model 180 may be hosted remotely on a server in communication with network device 160 shown in FIG. 1A. Alternatively, the machine learning framework 180 may reside within a server 162 of network device 160 shown in FIG. 1A. The machine learning model 181 may be communicatively coupled to the stored training data 183 in a memory or database (e.g., ROM, RAM) such as training database 182. In some other examples, the machine learning model 181 may be associated withother operations. The machine learning model 181 may be implemented by one or more machine learning models(s) and / or another device (e.g., a server and / or a computing system).

[0066] As envisaged in the application, the terms artificial neural network (ANN) and neural network (NN) may be used interchangeably. An ANN may be configured to determine a recommended course of action based on identified information. An ANN is a network or circuit of artificial neurons or nodes, and it may be used for predictive modeling. The prediction models may be and / or include one or more neural networks (e.g., deep neural networks, artificial neural networks, or other neural networks), other ML models, or other prediction models.

[0067] Disclosed implementations of ANNs may apply a weight and transform the input data by applying a function, where this transformation is a neural layer. The function may be linear or, more preferably, a nonlinear activation function, such as a logistic sigmoid, Tanh, or ReLU function. Intermediate outputs of one layer may be used as the input into a next layer. The neural network through repeated transformations learns multiple layers that may be combined into a final layer that makes predictions. This training (i.e. , learning) may be performed by varying weights or parameters to minimize the difference between predictions and expected values. In some embodiments, information may be fed forward from one layer to the next. In these or other embodiments, the neural network may have memory or feedback loops that form, e.g., a neural network. Some embodiments may cause parameters to be adjusted, e.g., via back-propagation.

[0068] An ANN is characterized by features of its model, the features including an activation function, a loss or cost function, a learning algorithm, an optimization algorithm, and so forth. The structure of an ANN may be determined by a number offactors, including the number of hidden layers, the number of hidden nodes included in each hidden layer, input feature vectors, target feature vectors, and so forth. Hyperparameters may include various parameters which need to be initially set for learning, much like the initial values of model parameters. The model parameters may include various parameters sought to be determined through learning. In an exemplary embodiment, hyperparameters are set before learning and model parameters can be set through learning to specify the architecture of the ANN.

[0069] Learning rate and accuracy of an ANN rely not only on the structure and learning optimization algorithms of the ANN but also on the hyperparameters thereof. Therefore, in order to obtain a good learning model, it is important to choose a proper structure and learning algorithms for the ANN, but also to choose proper hyperparameters.

[0070] The hyperparameters may include initial values of weights and biases between nodes, mini-batch size, iteration number, learning rate, and so forth. Furthermore, the model parameters may include a weight between nodes, a bias between nodes, and so forth.

[0071] In general, the ANN is first trained by experimentally setting hyperparameters to various values. Based on the results of training, the hyperparameters can be set to optimal values that provide a stable learning rate and accuracy.

[0072] A convolutional neural network (CNN) may comprise an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically comprise a series of convolutional layers that convolve with a multiplication or other dot product. The activation function is commonly a ReLLI layer and is subsequently followed by additional convolutions such as pooling layers, fully connected layersand normalization layers, referred to as hidden layers because their inputs and outputs are masked by the activation function and final convolution.

[0073] The CNN computes an output value by applying a specific function to the input values coming from the receptive field in the previous layer. The function that is applied to the input values is determined by a vector of weights and a bias (typically real numbers). Learning, in a neural network, progresses by making iterative adjustments to these biases and weights. The vector of weights and the bias are called filters and represent particular features of the input (e.g., a particular shape).

[0074] In some embodiments, the learning of models 181 may be of reinforcement, supervised, semi-supervised, and / or unsupervised type. For example, there may be a model for certain predictions that is learned with one of these types but another model for other predictions may be learned with another of these types.

[0075] Supervised learning is the ML task of learning a function that maps an input to an output based on example input-output pairs. It may infer a function from labeled training data comprising a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. And the algorithm may correctly determine the class labels for unseen instances.

[0076] Unsupervised learning is a type of ML that looks for previously undetected patterns in a dataset with no pre-existing labels. In contrast to supervised learning that usually makes use of human-labeled data, unsupervised learning does not via principal component (e.g., to preprocess and reduce the dimensionality of highdimensional datasets while preserving the original structure and relationshipsinherent to the original dataset) and cluster analysis (e.g., which identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data).

[0077] Semi-supervised learning makes use of supervised and unsupervised techniques described above. The supervised and unsupervised techniques may be split evenly for semi-supervised learning. Alternatively, semi-supervised learning may involve a certain percentage of supervised techniques and a remaining percentage involving unsupervised techniques.

[0078] Model 181 may analyze made predictions against a reference set of data called the validation set. In some use cases, the reference outputs resulting from the assessment of made predictions against a validation set may be provided as an input to the prediction models, which the prediction model may utilize to determine whether its predictions are accurate, to determine the level of accuracy or completeness with respect to the validation set, or to make other determinations. Such determinations may be utilized by the prediction models to improve the accuracy or completeness of their predictions. In another use case, accuracy or completeness indications with respect to the prediction models’ predictions may be provided to the prediction model, which, in turn, may utilize the accuracy or completeness indications to improve the accuracy or completeness of its predictions with respect to input data. For example, a labeled training dataset may enable model improvement. That is, the training model may use a validation set of data to iterate over model parameters until the point where it arrives at a final set of parameters / weights to use in the model.

[0079] In some embodiments, a training component 184 operably in communication with the machine learning model database 180 may implement analgorithm for building and training one or more deep neural networks. In some embodiments, the training component 184 may reside within the machine learning model database 180. A used model may follow this algorithm and already be trained on data. In some embodiments, a training component may train a deep learning model on training data 183 providing even more accuracy after successful tests with these or other algorithms are performed and after the model is provided a large enough dataset. For example, the training data obtained from training database 182 may comprise hundreds, thousands, or even many millions of pieces of information. The training data may also include personnel or company data, or internal or external inputs as disclosed herein. Weights for each of the model parameters may be adjusted through training. In some embodiments, training component 184 may be configured to obtain training data from any one or more suitable sources, e.g., via training database 182, data storage 164, external resources or network 140.

[0080] The training dataset may be split between training, validation, and test sets in any suitable fashion. For example, some exemplary embodiments may use about 60% or 80% of the training data 183 for training or validation, and the other about 40% or 20% may be used for validation or testing. In another example, training component 184 may randomly split the data, the exact ratio of training versus test data varies throughout. When a satisfactory model is found, training component 132 may train it on 95% of the training data and validate it further on the remaining 5%.

[0081] The validation set may be a subset of the training data, which is kept hidden from the model to test accuracy of the model. The test set may be a dataset, which is new to the model to test accuracy of the model.

[0082] In some embodiments, training component 184 may enable one or more prediction models to be trained. The training of the neural networks may beperformed via several iterations. For each training iteration, a classification prediction (e.g., output of a layer) of the neural network(s) may be determined and compared to the corresponding, known classification. As such, the neural network is configured to receive at least a portion of the training data as an input feature space. Once trained, the machine learning model(s) stored in database / storage 164 may be employed to classify received requests from personnel of an organization and provide a recommended course to improve wellbeing and engagement.

[0083] In some embodiments, K-means clustering may be employed to define the characteristics of the most engaged personnel based on company issues. For random Forest processing with weight ranking may be processed including one or more of the following categories: individual and company Profiling, Issues, 5R Behavior Model (discussed in detail below) and Breaks / Burnout / Stress.Electronic Platform with Trained Machine Learning Model

[0084] According to another aspect of the subject technology, one or more embodiments may depict an electronic platform for helping an organization manage engagement and wellbeing of its personnel. An example embodiment of the platform 190 (e.g., manager assistant) is illustrated in FIG. 1 E. The platform 190 may receive queries from personnel including for example managers in the organization for purposes of improving engagement of their team members. Alternatively the platform 190 may receive queries from personnel, such as for example any employee of the organization for purposes of improving their wellbeing. Based upon one more determinations by the platform 190 and its trained machine learning model, a recommendation is output.

[0085] It can be appreciated that when an organization provides a nurturing, supportive environment for its personnel, such as for example employees, independent contractors or members, they feel valued, motivated, and engaged. Engaged employees are committed to their organization’s success. The subject technology may help an organization encourage and support its personnel’s wellbeing, further enhancing the overall health of the company. The customizable platform with a trained machine learning model may be employed to increase personnel engagement by creating a meaningful and collaborative work environment. That is, one where personnel are motivated to work together toward a common goal.

[0086] The platform 190 may obtain one more types of data to help train a machine learning model (e.g., training data). In an example, this may be machine learning model 181. The machine learning model may be deployed in order to provide a recommendation meeting the particular needs of the personnel with a high degree of confidence. In an embodiment, the machine learning model may ingest any one or more of training data, benchmarks, issues of the organization, decision trees, recommendations, playbooks or behavioral models.

[0087] As shown in FIG. 1 E, the platform may at least ingest data associated with the organization. This may include any one or more of size, public / private status, headquarter and satellite locations, collaborative work arrangements or organizational issues. In a further example, organizational issues may include any one or more of management and culture, complementarity, organization, transformation or harmony.

[0088] The platform 190 may also ingest data associated with personnel. This may include any one or more of gender, age, job position, remote, onsite or hybridwork, team size, stress exposure, burnout exposure, breaks, Al transformation. For example, the ingested data may be in the form of a questionnaire. The questionnaire may be customized for a particular company or organization. It will be understood fully from the scope of this application that 5R may be prioritized in view of received answers and perceived main issues in the questionnaire. The system is configured to output a playbook for personnel in the company or organization to deploy based on the received answers and perceived main issues in the questionnaire.

[0089] The platform may also ingest information from external inputs. These may include any one or more of external surveys, scientific research, articles, videos, books, authors or quotes.

[0090] According to even a further embodiment of this aspect, methods and systems are described for managing attributes associated with personnel in an organization through an electronic platform integrated with a trained machine learning model. The method involves receiving a request via a user interface to obtain a resource for managing an attribute related to one or more personnel. This request is transmitted to the ML model, which has been trained on diverse data sets, including organizational traits, personnel traits, behavioral toolkits, and external inputs. The ML model processes the request and determines the appropriate resource, which is then outputted as a graphical representation over the user interface. This graphical representation may include, for example cards in a card game, indicating actions or energizing activities tailored to enhance engagement and wellbeing among personnel.

[0091] As to the particular training data employed by the ML model for effective management of engagement and wellbeing, the organizational traits may vary widely in terms of size, public or private status, headquarters or satellite locations,collaborative work options, and specific organizational issues such as management and culture, complementarity, organization, transformation, and harmony. These traits influence the effectiveness of engagement and wellbeing initiatives.

[0092] Moreover, regarding the training data employed by the ML model for effective management of engagement and wellbeing, personnel traits within an organization may differ in terms of gender, age, job position, remote work status, team size, stress exposure, burnout exposure, and the need for breaks. These individual differences necessitate personalized approaches to managing engagement and wellbeing.

[0093] Further regarding the training data employed by the ML model for effective management of engagement and wellbeing, behavioral toolkits may include roles, routines, rules, recognition, and respect. Implementing these toolkits in a way that resonates with diverse personnel may be challenging and will be discussed in more detail below.

[0094] By leveraging machine learning in the electronic platform, the subject technology is effective at analyzing complex and multifaceted data to determine the most effective resource(s) for managing engagement and wellbeing. In so doing, the subject technology improves decision-making processes compared to traditional methods relying primarily on generic or one-size-fits-all approaches. Here, the subject technology is primed to continuously learn and adapt based on newly obtained data and feedback thereby ensuring the recommended resource remains relevant and effective over time. This dynamic adaptability is a significant technical improvement over static systems that may become outdated.

[0095] T urning to a further exemplary embodiment of this aspect, an example of appropriate resources is depicted in FIG. 1 F. Here, one of the appropriate resourcesmay be displayed on a user interface 194 of the electronic platform 190. The resource(s) may include an energizer activity to be performed by the personnel. For instance, these may include any one or more of wellbeing (e.g., meditation and mindfulness), sport, recreational, relationship or learning (e.g., cooking). Personnel may receive real-time feedback and updates via the user interface to track and improve their wellbeing.

[0096] In some embodiments, the user interface of the electronic platform 190 shown in FIG. 1 E may be configured to allow managers overseeing teams to monitor updates regarding wellbeing and engagement. Monitoring is performed in adherence with relative privacy protocols. This may help promote resources that are relevant and actionable for both different types of personnel including employees, independent contractors and members of the organization.

[0097] According to yet even a further exemplary embodiment of this aspect, an exemplary technique 195 is depicted in FIG. 1G. The technique 195 is at least directed to a method for managing attributes associated with personnel in an organization. As shown in FIG. 1G, the technique includes a step of receiving a request via a user interface of an electronic platform affiliated with an organization (step 196) to obtain a resource for managing an attribute related to one or more personnel. For example, the request may include data obtained in the form of a questionnaire. The questionnaire may be customized for a particular company or organization. It will be understood fully from the scope of this application that 5R may be prioritized in view of received answers and perceived main issues in the questionnaire. The system is configured to output a playbook for personnel in the company or organization to deploy based on the received answers and perceived main issues in the questionnaire. The technique 195 also includes a step oftransmitting the request to the ML model on the electronic platform (step 197) The ML model has been trained on various data sets, including organizational traits, personnel traits, behavioral toolkits, and external inputs. The technique 195 further includes a step of determining, via the ML model, the resource to manage the specified attribute (step 198). Even further, the technique 195 includes a step of outputting, via the ML model over the user interface, a graphical representation of the resource to manage the attribute. The graphical representation of the resource provides a visual and actionable way to manage the attribute effectively.5R Behavioral Model

[0098] As briefly introduced above, the platform 190 may ingest a behavioral model as training data. In an example embodiment, this may include a 5R Behavioral Model according to Metamorphose des managers a fere du numerique et de / 'intelligence artificielle (published 2018), and incorporated by reference in its entirety herein. The 5R Behavioral Model may refer to the roles, rules, respect, recognition, and routines of an organization or group. 5R may define what each of these behavioral categories means for its personnel. Based upon numerous surveys and methodologies, in exemplary embodiments where no specific issue is identified, routines and respect may be employed as the most impactful and sensitive factors of 5R Engagement. In this instance, start by having alignment on Routines and Respect selecting the right ones with PFT Cards game. If needed, create specific ones for the Team until full alignment and engagement. In other exemplary embodiments of this application, where specific issues exist and / or issues are understood from a completed questionnaire, the priorities and ranking may bedifferent. In one or more of the discussed exemplary embodiments, the outputs may include a playbook.

[0099] Managers are facing unprecedented challenges incurring a direct impact on personnel motivation. Below are three possible reasons for this according to the CNAM's Observatory of Managerial and HR Transformations (Learning Lab Human Change), with Julhiet Sterwen and Cornerstone. The first explanation is that, by putting the manager at the service of employees in a position of coach, self-service and empathy, a facilitator of collective intelligence, the latter no longer dares to lead and decide. To manage is to explain and enforce a framework. That is why managers need to have the courage to make choices. Our research shows that more and more companies are incorporating training on the courage to make choices and decision-making. In times of uncertainty, employees are looking for a tenured figure who knows how to make decisions as well as to reassure. Discipline is needed in a crisis, when things are difficult.

[0100] Second, large organizations have evolved towards matrix models and have pushed managers to take on several roles depending on the work groups to which they belong (contributors, coaches, trainers, etc.). This situation weakens their position as managers and helped smooth it out towards a consensual posture. This posture "horizontalizes" their profession.

[0101] Third, over the past ten years, managers have seen an increase in the number of tools and methodologies to be passed on to their teams (agile method, design thinking, collective intelligence, digital transformation, etc.). They are certainly the best relays for introducing employees to innovation, in all its forms, but faced with the volume of requests (currently, they are particularly solicited on generative artificial intelligence), they are exhausted, with a risk of burnout.

[0102] Managers are indispensable and deserve to be pampered by companies. However, companies are mainly focusing on training them in the new tools. This is not enough, managers should be introduced to a positive management system, adapted to our times. Research shows that, in order to reduce this risk of burnout and increase the commitment of managers and their teams, it is recommended to apply the scientifically validated 5R model, which is based on the following idea: team members may be engaged if together, they agree to: 1) build team routines, 2) assign a role to each team member, 3) setup an individual recognition for each project; 4) strengthen a culture of respect; and 5) recall the essential rules of the organization.

[0103] Building Team Routines. Routines, when they are well chosen, are unifying. Organizing relevant and regular meetings, sharing information and communicating via specific platforms, defining clear and common objectives, using digital organizational tools: these are all actions that make it possible to work together and structure the collective. Others, such as daily expressions of respect or words of encouragement, can improve employee well-being and day-to-day performance. However, as some studies show, too much collaboration can also be detrimental to the proper functioning of teams. It is therefore necessary to identify the usefulness of team routines, as well as individual routines, to send the right signals to employees.

[0104] Assign a Role to each team member. Not having a specific role can hurt the quality of work and engagement. Engagement at work tends to decrease if the individual's resources are exhausted, and roles are ill-defined. In many studies, role tensions (conflicts, ambiguities, communication problems) at the negative effects at the organizational and individual level have been shown to include job dissatisfactionand stress (Surana and Singh, 20131 , Bacharach et al. 19912). It is important that everyone has a role that contributes to the group's project, which is different from that of their job description. Thus, if everyone has a role in a group, they are less likely to seek to take on another member of the group. Each team member should have a role that contributes to the project and is different from their job description.

[0105] Set up an individual Recognition for each project. According to a study, more than half of employees want more recognition from their line manager, and 4 out of 10 people want more recognition from their immediate colleagues. But making sure employees feel recognized is not always easy. In addition to the successes and performances that can be celebrated, the informal efforts and roles played by employees should also be recognized. Recognition for the 5R include each team member being recognized for an individual contribution to a project.

[0106] Strengthening a Culture of Respect. Employees who do not feel respected are more likely to feel left out or even inferior. This often stems from a mismatch between employee expectations and leadership teams' incomplete understanding of what constitutes respect in the workplace. An analysis of 4,500 employees calls for a rethink of the strategies put in place to build a culture of respect within organizations. There are seven ways to develop a sense of respect within a team: valuing diversity, taking into account people's issues and concerns, building trust, resolving conflicts by avoiding getting bogged down, finding a balance between results and addressing each other's concerns, encouraging open discussions, and ensuring that employees receive honest feedback. The most effective managers build relationships based on trust and loyalty, rather than fear or the power of their position. In addition, the more diverse the team, the more it is recommended to agree on a charter that recalls the rules of respect at work.

[0107] Recall the Essential Rules of the Organization. Researchers wanted to know what the secret was to a company's top performers. Codenamed "Aristotle Project" - in homage to Aristotle's quote "The whole is greater than the sum of its parts" - the aim was to answer the following question: "What makes a team effective at the company?" They highlighted five key recipes, one of which is the importance of establishing rules: the presence of a minimum of structure, as well as the clarity of objectives and guidelines, are essential. Rules for the 5R include rules that are not open to question (e.g., security-related, confidentiality related, etc.). In the end, even if excessive rules can be akin to bureaucracy and micromanagement, the presence of a structure remains reassuring for the individual and makes the work more understandable.Use Cases

[0108] According to another aspect of the application, electronic platform 190 may be deployed in the context of a workshop associated with the behavioral model. In an embodiment, the materials may include an example representation of sail cards of a game or theme on a user interface 200 as shown in FIGs. 2A, 2B, 2C, 2D, 2E, 2F, 2G, 2H, 2I, 2J and 2K. FIG. 2A illustrates exemplary cards such as for example card top 201 , card top 202, card top 203, card top 204, or card top 205 for a card game, as disclosed herein. Card top 201 , card top 202, card top 203, card top 204, or card top 205 may have different symbols, letters, colors, or the like that may identify a category of the cards (e.g., each category corresponding to one of the 5Rs). FIG. 2B and FIG. 2C illustrate exemplary cards, such as routine card 210, respect card 211 , roles card 212, respect card 213, roles card 214 and roles card 215, and which may include card bottoms for a card game as disclosed in moredetail herein. The card bottoms may include an indication of the category of the cards as well a detailed description associated with playing the card game disclosed herein. FIG. 2B and FIG. 2C may correspond to the navy game. FIGs. 2D, 2E, 2F, 2G, 2H, 21, 2J and 2K illustrate exemplary cards, including card bottoms 216, 217, 218, 219, 220, 221 , 222, 223, 224, 225, 226, 227 and 228 for the card game that may indicate a category of the cards and a detailed description associated with playing the card game. FIGs. 2D, 2E, 2F, 2G, 2H, 2I, 2J and 2K may correspond to the regular game.

[0109] According to a further embodiment of this aspect, FIG. 3A illustrates exemplary 5R model definition card bottoms associated with the sail card game, which may include card side 311 or card side 312. Card side 311 or card side 312 may describe categories included in 5R for the disclosed card game.

[0110] According to yet a further embodiment of this aspect, FIG. 3B and FIG. 3C illustrate exemplary instruction cards, which may include card side 321 or card side 322. Card side 321 or card side 322 may describe a first phase instruction or a second phase instruction, as further described herein.

[0111] According to even another embodiment of this aspect, FIGs. 4A, 4B, 4C, 4D and 4E illustrate exemplary 5R issue card bottoms that may describe example issues 400 for a use case in 5R, as further described herein. For example, 5R issue card bottom 401 may be related to the issue of harmony, which may be associated with quality of work life (QWL) issues. 5R issue card bottom 402 may be related to the issue of transformation, which may be associated with work habits and / or organizations. 5R issue card bottom 403 may be related to the issue of complementarity, which may be associated with respecting differences between team members and turn them into a collective strength. 5R issue card bottom 404may be related to the issue of management and culture, which may be associated with the evolution of the managerial approach. 5R issue card bottom 405 may be related to the issue of organization, which may be associated with working differently, more efficiently and collaboratively, in a constrained organization.

[0112] According to yet even another embodiment of this aspect, FIGs. 5A, 5B and 5C depict exemplary use cases for deploying the platform in order to provide a resource. For instance, FIG. 5A depicts a use case for GAN Al call centers. The challenge to overcome in this use case involves how to use the tool as genuine support by customer support representatives instead of as a means for job replacement. Here, the two selected issues include transformation and complementarity. Based upon the techniques disclosed above and the 5R behavioral model, specific recommendations are provided as to roles, routines, respect, rules and recognition.

[0113] FIG. 5B depicts another use case regarding organization of an operation. The challenge to overcome in this use case involves improving customer service with more rigorous commercial and financial processes. Here, the three selected issues include transformation, organization and management. Based upon the techniques disclosed above and the 5R behavioral model, specific recommendations are provided as to roles, routines, respect, rules and recognition.

[0114] FIG. 5C depicts yet another use case regarding GenAI and HR recruitment. The challenge to overcome in this use case involves finding the right approach between Al recommendations on profiles and the human approach based on relationships and intuition. Here, the two selected issues include transformation and organization. Based upon the techniques disclosed above and the 5R behavioralmodel, specific recommendations are provided as to roles, routines, respect, rules and recognition.

[0115] According to even further aspect of the subject technology, an electronic card game representation, or even physical cards, may be employed using the 5R cards detailed above. In preparation for the game, the 5R cards may be separated into different stacks (e.g., in envelopes or otherwise separated). There may be a stack of routines cards in a first envelop, a second envelope for rules cards, a third envelope for recognition cards, a fourth envelope for respect cards, or a fifth envelope for roles cards. The team may be divided into 5 sub-groups, each group represents a different R. The sub-group of Routines may discuss which Routine is / are the best for them (Same for Roles, Respect, Recognition and Rules). A representative of each sub-group may report in front of the team which routine they have chosen and why at the end the team validates the selection of the Routine(s) as they may have to implement it in real life for a certain period of time (e.g., 3 months as a standard). In an example, the beginning of the game there may be 5 prepared envelopes, 5 instructions cards, and a chosen scenario card. It is contemplated that the number of items for certain features of the game may vary, for example the use of more or less than 5 instruction cards may not significantly hinder the implementation of the card game.

[0116] Logistics, Objectives, Time. A typical card game may have 5 to 25 card participants and may be 15 to 35 minutes. There may be multiple flow blocks for the card game which may be approximately 10 minutes per flow block. In a first flow block (e.g., Flow Block 1) may include a) team formation (e.g., 5 minutes) and b) scenario and rules briefing (e.g., 5 minutes). In a second flow block (e.g., Flow Block 2) may include a) first phase (e.g., 5 minutes) and b) second phase (e.g., 5 minutes).In a third flow block (e.g., Flow Block 3) may include a) group sharing time (e.g., 5 minutes) and b) share your R time (e.g., 5 minutes).

[0117] Flow Block 1 - Setting Up The Game. An objective of this flow block 1 may include dividing the players into teams and explain the game. The players may be placed into five teams of approximately equal players. Members of the same team should not be together (whenever possible). Adapt constraints to the type of group. Whimsical constraints for a group of players that may promote humor and a good atmosphere may be implemented, even if it means spending a few extra minutes on them, such as a) a group must represent a different continent and call itself such, b) each player in the group must have a first name whose first letter is no more than 5 letters apart in the alphabet. This is a time to get the group into a positive, playful frame of mind.

[0118] When the teams are created, begin briefing, such as the following. An example brief may be "Together, we're going to imagine an imaginary situation in which you experience one of the 5Rs." Another example brief, "You're all on board a sailboat, either as crew or passengers. The voyage will take several weeks." Another example brief may include "You'll all experience the same situation, but each group will respond in its own way." Read the scenario you've chosen for the workshop (The Bounty, The Esperanto, La Nina or The Nautilus), see Table 1 below. Give each group: a) one of the 5 envelopes, and b) an instruction card.Table 1

[0119] The players receive an explanation regarding how the next phase will unfold. For example, the explanation may include that the card game may take placein two phases. A first phase may be a free speech round in which each member of the group gives his or her point of view on the situation with the following constraints: a) do not open envelope until otherwise advised, b) each opinion should be unique (you can't say "all the same"!), c) remain in a position to analyze the situation. Remaining in a position to analyze the situation may include seeking to contribute your point of view, even if it differs from that of others. A solution should not immediately be proposed. Participants should be encouraged to be imaginative in sharing their view of the situation. Groups should be invited to make their own space in the room, or to isolate themselves for greater privacy. When everyone is ready, the go-ahead may be given for the first phase by reading the instructions (and without opening the envelope).

[0120] Flow Block 2 - Active Phase. An objective of this flow block 2 may include an objective of organizing experience sharing. In an example, for approximately 10 minutes, each group independently organizes two speaking rounds. They are guided by instructions, which may be verbal instructions given by the facilitator of the workshop when splitting in groups, to have alignment on the projects and also the issues which they want to address. If necessary, recall the questions for the first phase: a) “Why do you think this happened?”; “How do you see things?”; or “What troubles or disturbs you about the situation?” Be careful never to impose anything.

[0121] After 5 minutes, the groups should start opening the envelope. If this is not the case, go to the late groups and invite them to close the first phase. Participants should be invited to spread out the cards in the envelope in front of them (e.g., on a table or on the floor) for easier viewing.

[0122] In this second phase, participants should: a) Reassess the situation through the prism of the R in your envelope; and 2) Keep only one R-card to present to the rest of the crew.

[0123] Before the time runs out, participants should be reminded to keep just one card. If they feel that the cards do not fit the situation, the players should be invited to imagine variations on one of the cards by changing one of the statements. Make sure the groups finish more or less at the same time. When each group has chosen its card, invite everyone to return to the center.

[0124] Flow Block 3 - Restitution. An objective of this flow block 3 may include an objective of organizing experience sharing, which may include group sharing phase, card sharing R, and visual and photo restitution.

[0125] In the group sharing phase, participants to share their feelings: a) how did the experience go; and b) how did you get the word out. Distribute the floor and control the tempo so that the energy does not flag.

[0126] With reference to card sharing R, once the feedback has been collected, the groups should be invited to share their chosen card with everyone. For each group, before the revelation, recall the outline of the scenario and ask the presenting group for a few details of the imaginary world they have developed, such as what were their views on the situation. The group's feedback should be bounced backed and each proposal valued. In one or more embodiments, when each group has handed in its map, participants should be invited to place their map around the feedback map. Ultimately, a souvenir photo of the moment may be taken to foster bonding and congratulate the participants on their commitment.

[0127] According to even a further embodiment of this aspect, FIG. 6 illustrates an example method for a card activity (e.g., card game). The method may occur inthe context of the following scenario. A team of technical mechanics may have to use a new App to report on callback work of a customer job site on their mobile phones. There are young mechanics who love technology and are excited about this new App which provides recommendations on the route cause. There are also old mechanics who are reluctant as they believe it steals their know-how based on experience. A major car company may have announced to customers it will be done at the end of the quarter.

[0128] At step 601 , a script may be selected. The script may the Nautilus script described above. The leader of the Team to select Nautilus for scenario as the main issue of adoption of the Apps is generation gap between a) the young generation who appreciate and are comfortable using application (e.g., Apps); and b) the older generation who is more reluctant to use it at their know-how is experience based and they believe could “stolen” by the application.

[0129] At step 602, a scenario issue may be determined. The team may be split into 5 sub-Groups (e.g., 1 by each R). The team may discuss the scenario and agree on the issue which is generation gap for adoption but highlight some nuances as it is some cases, it is more a question of mindset than age.

[0130] At step 603, a Navy R card is selected for each sub-group. For example, the following cards may be selected for each sub-group.• Routines: “We tell each other everything” (card 201 ), as communication as to improve between 2 generations.• Roles: “Mediator” (card 202) as there are many different points of views to harmonize.• Respect: they are hesitating between “Do your Best” (card 204) and “Take Care of other people’s work” (card 203) and decide to speak about their hesitation to the entire team during the report out.• Rules: they hesitate between “The right to do mistakes” (card 206) and “Blame the process not the crew” (card 207) and decide to propose “the right to do mistake” (card 206) as part of the digital transformation journey.• Recognition: a “festive meal” (card 205) for Team effort.

[0131] At step 604, the selection of each Navy R card for each sub-group may be communicated and adjusted. A representative of each sub-group presents to the Team (e.g., all sub-groups) their recommendation. They have been approved but for Rules, the Team prefers to select “Blame the process not the crew” (card 207) and for Respect “Do your Best” (card 204).

[0132] At step 605, issue card topics are selected from issue cards as shown in FIGs. 4A, 4B, 4C, 4D, and 4E, for example. In an example, the team of mechanics look at the cards and select transformation card (card 402 of FIG. 4): #2 Digital Transformation, #4 New process and #5 skills and also complementary card (card 403) with #3 generational and also card organization (card 405 of FIG. 4): #2 with short lead times. Discussions may include one sub-group highlights that for complementary (card 403) it touches also #1 cultural, #4 different approaches, and #5 different sensibilities, different point of view.

[0133] At step 606, 5R cards (e.g., FIGs. 2A, 2B, 2C, 2D, 2E, 2F and FIG. 2G) may be selected. The 5 Sub-groups are ready to discuss in depth about different 5R and decide to select for:Routines: “Shaker team” (Card 221) to have binomial work with 2 generations,“Tips meeting” (Card 222) and “Small Victory Storytelling” (Card 223)• Roles: “Customer representative” (Card 224), “Mediator” (Card 225), “Guardian of deadlines” (card 226)• Respect: “Kindness” (card 227), “Value ideas” (card 228), “Active presence” (card 229 of FIG. 1 F)• Rules: “Right to error” (card 232), “question the process not the People” (card 233)• Recognition: “Celebrate milestones” (card 231), “Praise in front of the team” (card 230 of FIG. 1 F)

[0134] At step 607, the selection of each 5R card for each sub-group may be communicated and adjusted. A representative of each sub-group presents to the Team (e.g., all sub-groups) their recommendation. In an example, they may finally select “Shaker team” (Card 221), “Tips meeting” (Card 222), “Customer representative” (Card 224), “Value ideas” (card 228), “Right to error” (card 232), or “Celebrate milestones” (card 231).

[0135] At step 608, These 5R cards may then be implemented over a period of time, such as 3 months, with intermittent follow-ups. At step 609, at the end of one or more periods, lessons learned may be communicated. The sub-group or entire team may follow-up with each other which through a central communication channel.

[0136] As disclosed herein, the card game may be a partially or substantially electronic card game with similar implementation as disclosed herein. For example, in an example of a partial implementation, machine learning algorithms may be used to process answers to one or more issues and guide one or more players to different steps in the card game.Alternative Embodiments

[0137] The foregoing description of the embodiments has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the patent rights to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure.

[0138] Some portions of this description describe the embodiments in terms of applications and symbolic representations of operations on information. These application descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as components, without loss of generality. The described operations and their associated components may be embodied in software, firmware, hardware, or any combinations thereof.

[0139] Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software components, alone or in combination with other devices. In one embodiment, a software component is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.

[0140] Embodiments also may relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and / or it may comprise a computing device selectively activated orreconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer-readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.

[0141] Embodiments also may relate to a product that is produced by a computing process described herein. Such a product may comprise information resulting from a computing process, where the information is stored on a non- transitory, tangible computer-readable storage medium and may include any embodiment of a computer program product or other data combination described herein.

[0142] Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the patent rights be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments is intended to be illustrative, but not limiting, of the scope of the patent rights, which is set forth in the following claims.

Claims

AMENDED CLAIMS received by the International Bureau on 12 November 2025 (12.11 .2025)1. A method comprising: receiving, via an electronic platform affiliated with an organization over a user interface, a request to obtain a resource to manage an attribute associated with one or more personnel in the organization; transmitting the request to a trained machine learning (ML) model on the electronic platform, wherein the ML model is trained on training data including any one or more of a trait of the organization, a trait of the one or more personnel in the organization, a behavioral toolkit, or an external input associated with managing the attribute; determining, via the trained ML model of the electronic platform, the resource to manage the attribute; and outputting, via the trained ML model over the user interface, a graphical representation of the resource to manage the attribute.

2. The method of claim 1 , wherein the attribute includes any one or more of engagement or wellbeing associated with the one or more personnel in the organization.

3. The method of claim 2, wherein the graphical representation of the resource includes one or more cards each including an indication associated with the behavioral toolkit for the one or more personnel to perform an action to manage the attribute.

534. The method of claim 3, wherein the behavioral toolkit includes any one or more of a role, routine, rule, recognition or respect.

5. The method of claim 2, wherein the graphical representation of the resource includes an indication of an energizing activity to be performed by the one or more personnel to manage the attribute.

6. The method of claim 1 , wherein the trait of the organization includes any one or more of a size, a public or private status, a headquarter or satellite location, collaborative work option, work breaks or an organizational issue.

7. The method of claim 6, wherein the organizational issues include any one or more of management and culture, complementarity, organization, transformation or harmony.

8. The method of claim 1 , wherein the trait of the one or more personnel includes any one or more of a gender, age, job position, remote, hybrid or onsite work status, or team size.

9. The method of claim 1 , wherein the external input includes any one or more of articles, videos, books, surveys, articles, authors, quotes or scientific research.5410. The method of claim 1 , wherein the request is received from a manager of personnel of the organization.11 . The method of claim 1 , wherein the organization includes a company with employees and / or independent contractors.

12. A system comprising: a non-transitory memory including instructions stored thereon; and a processor, operably coupled to the non-transitory memory, configured to execute the instructions comprising: receiving, via an electronic platform affiliated with an organization over a user interface, a request to obtain a resource to manage an attribute associated with one or more personnel in the organization; transmitting the request to a trained machine learning (ML) model on the electronic platform, wherein the ML model is trained on training data including any one or more of a trait of the organization, a trait of the one or more personnel in the organization, a behavioral toolkit, or an external input associated with managing the attribute; and determining, via the trained ML model of the electronic platform, the resource to manage the attribute; and outputting, via the trained ML model over the user interface, a graphical representation of the resource to manage the attribute.5513. The system of claim 12, wherein the attribute includes any one or more of engagement or wellbeing associated with the one or more personnel in the organization.

14. The system of claim 12, wherein the graphical representation of the resource includes one or more cards each including an indication associated with the behavioral toolkit for the one or more personnel to perform an action to manage the attribute.

15. The system of claim 14, wherein the behavioral toolkit includes any one or more of a role, routine, rule, recognition or respect.

16. The system of claim 12, wherein the graphical representation of the resource includes an indication of an energizing activity to be performed by the one or more personnel to manage the attribute.

17. The system of claim 12, wherein the trait of the organization includes any one or more of a size, a public or private status, a headquarter or satellite location, collaborative work option, work breaks or an organizational issue.

18. The system of claim 17, wherein the organizational issues include any one or more of management and culture, complementarity, organization, transformation or harmony.5619. The system of claim 12, wherein the trait of the one or more personnel includes any one or more of a gender, age, job position, remote, hybrid or onsite work status, or team size.

20. A non-transitory computer readable medium including stored instructions that when executed by a processor effectuate: receiving, via an electronic platform affiliated with an organization over a user interface, a request to obtain a resource to manage an attribute associated with one or more personnel in the organization; transmitting the request to a trained machine learning (ML) model on the electronic platform, wherein the ML model is trained on training data including any one or more of a trait of the organization, a trait of the one or more personnel in the organization, a behavioral toolkit, or an external input associated with managing the attribute; determining, via the trained ML model of the electronic platform, the resource to manage the attribute; and outputting, via the trained ML model over the user interface, a graphical representation of the resource to manage the attribute.

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