Patent Application
20230297914
The present disclosure generally relates to managing workflows, and more particularly to analyzing a workflow for optimization.
There is a need to establish methods that capture, frame, and manages the user design experience around intelligent workflows (IW). For example, the recruiting and human resources (HR) spaces are very crowded with platforms, and solutions that focus on job-skill-matching, but the majority of the focus is on hiring. This is done with little understanding of the job without any holistic business perspective of the roles that are the best fit for a candidate. In addition, role deprecation, outsourcing, new directives of strategic leadership can influence the allocation of resources, including changes in skill sets leaving gaps in the existing workflow.
In accordance with one aspect of the present disclosure, a computer-implemented method is described for using artificial intelligence to assess a user's workflow on a task. In one embodiment, the computer-implemented method includes receiving data regarding a workflow of a user completing a task; and assessing the data to identify attributes of the workflow that is expressed in a series of steps. The method may further include analyzing the steps of the workflow to identify areas of improvement; and generating augmentations from a plurality of technology fitments matched to the areas for improvement in the steps of the workflow. In one embodiment, the method may include generating a user experience template, wherein content of the user experience template is selected based upon at least an archetype for which the generating the augmentation from the plurality of technology fitments was performed. The method can further include sending the augmentations formatted in the user experience template to a user device for communicating to the user.
In another aspect, a system is described for using artificial intelligence to assess a user's workflow on a task. The system can include a hardware processor; and a memory that stores a computer program product. The computer program product when executed by the hardware processor, causes the hardware processor to receive data regarding a workflow of a user completing a task; and assess the data to identify attributes of the workflow that is expressed in a series of steps. The computer program product can also employ the hardware processor to generate augmentations from a plurality of technology fitments matched to the areas for improvement in the steps of the workflow; and generate a user experience template. The content of the user experience template is selected based upon at least an archetype for which the generating the augmentation from the plurality of technology fitments was performed. The computer program product can then send, using the processor, the augmentations formatted in the user experience template to a user device for communicating to the user.
In yet another aspect, a computer program product is described for using artificial intelligence to assess a user's workflow on a task. The computer program product can include a computer readable storage medium having computer readable program code embodied therewith. The program instructions executable by a processor to cause the processor to receive data regarding a workflow of a user completing a task; and assess the data to identify attributes of the workflow that is expressed in a series of step. The computer program product can also analyze, using the processor, the steps of the workflow to identify areas of improvement; and generate, using the processor, augmentations from a plurality of technology fitments matched to the areas for improvement in the steps of the workflow. The computer program product can also generate, using the processor, a user experience template. The content of the user experience template is selected based upon at least an archetype for which the generating the augmentation from the plurality of technology fitments was performed. The computer program product can also send, using the processor, the augmentations formatted in the user experience template to a user device for communicating to the user.
The following description will provide details of preferred embodiments with reference to the following figures wherein:
The methods, systems, and computer program products described herein relate to frameworks for providing an intelligent workflow (IW) user experience (UX) that includes a human centered element for providing an optimal and viable process. An intelligent workflow can be the orchestration of automation, AI, analytics, and skills to fundamentally change how work gets done. An intelligent workflow can minimize friction through automation.
Through the intelligent workflow framework, the methods, systems and computer program products of the present disclosure can facilitate a human centered design engagement model that can increase attentiveness and responsiveness that is directly correlated and linked to an increase in optimization, productivity and efficiency. The methods, systems and computer program products of the present disclosure can start with user behavior inputs, core technology and industry to shape the context of where we can apply an intelligent workflow adjustment, and how an intelligent workflow adjustment can apply to an existing process. Intelligent workflow adjustments are configured from a series of users to form “experience patterns”, and their associated activities. The experience patterns can drive the formation of an intelligent workflow to determine the best suited user experience for the user (employee and/or customer).
There is an emergent need to establish methods that capture, frame and manage the user design experience (UX) around intelligent workflows. This methods, systems and computer program products described herein establish a system and method that can lock down the frame and associated method to the workflow as a base invention. The three stages of insight and visibility include 1) the existing work process, 2) identifying intelligent workflows and assigning technologies, and 3) appropriating best design system and workflow optimization purposes. As a result of all this, increased transparency into the various workflows and constant monitoring of machine leaning of the new methods for user and workflow optimization purposes is provided.
The methods, systems and computer program products described herein establish a framework for assessing an existing process. For example, based on technology fitment, the system defines a user experience that establishes a method for how an intelligent workflow engages with a user prompting interaction to continue to obtain higher levels of optimization.
In some embodiments, intelligent workflows are about the recognition of patterns dependencies and conditions that make up those patterns. Triggers are identified to alert the user to a new flow or the possibility of a new flow being formed.
A method of sequencing can be transparently visualized by the system for increased understanding and accountability of automation. Situation analysis of the user experience and recommendations that transparently demonstrate how the intelligent workflow augments and redistributes existing processes with new streamlined experience are provided. In some embodiments, through the user interacting with the system, patterns are identified to inform a recommendation technology fitment, such as blockchain, hybrid cloud, Internet of things (IoT), artificial intelligence (AI), and edge and 5G computing to communicate the experience of the workflows, before, during and after they occur. The user experience template output is flexible and adaptable according to the persona, area of focus and sector, plus other defining variables. The modality of the experience presentation layer is determined by the observation of context, environment, data display an interaction level requirements, e.g., mobile display or voice user interface (UI). The modality can be the interface. It can be classified as a desktop computer or a device as part of a mobile application.
In some embodiments, the methods, systems and computer program products of the present disclosure provide a dynamically generated interface driven by an intelligent workflow augmentation, identified archetype roles, and the archetypes primary factors of situation and context, data inputs and data outputs. In some embodiments, the methods, systems and computer program products can provide continuous monitoring and automatic adjustments to the user experience (UX) based on usability findings, adoption by the user, and workflow system change inputs.
In these embodiments, by making automatic adjustments to the user experience (UX) based on the aforementioned factors, methods, systems and computer program products can optimize the overall intelligent workflow. In some examples, by providing the above, the methods, systems and computer program products can provide a seamless integration of a human user into an intelligent workflow via dynamic UX creation and delivery.
In some embodiments, the methods, systems and computer program products can assess the existing experience to identify and rank intelligent workflows or the digitization scenario and journey The existing experience being assessed can include situational content analysis and/or the breakdown of applicable scenarios. Situational awareness can include elements of when a user is performing within an intellectual workflow, and where the user is performing within an intellectual workflow, e.g., remote work, in field work, and mobile work. This intelligent system will be able to identify matching intelligent work flows (WFs) and for each IW augment the steps with additional activities collected from the environment, personas, industry and business cases.
The systems and methods described herein can assess the user experience based on user archetypes (personas) and workflow framework. The archetypes are a description of a person from the perspective of a workflow, and the types of roles the person would perform. The framework could include technologies, such as artificial intelligence (AI), extended reality (XR) and edge computing. This can create a standardization of how the UX can be implemented based on an overarching framework. In some embodiments, this case include digital overlays to assist in work and could be applied to enable automation of activities.
In some embodiments, the methods, systems and computer program products that are described herein can predict/forecast and recommend UX changes in an intelligent workflow based on a plurality of changes to individual activities and sensor readings. In some embodiments, the system and method that take input from different tools and applications and draws a user experience intelligent workflow that is backed by a toolchain, known to the user. In some embodiments, the system and method can authenticate and monitor preferred behavioral actions from various channels, such as multifactor audio/video sensor's ability to meet primary IoT feeds. In some embodiments, the systems and methods can customize the environment based on type of activity with predetermined tools associated with that activity.
In some embodiments, the methods, systems and computer program products that are described herein can predict/forecast and recommend UX changes in an intelligent workflow based on a plurality of changes to individual activities and sensor readings. In some embodiments, the system and method that take input from different tools and applications and draws a user experience intelligent workflow that is backed by a toolchain, known to the user. In some embodiments, the system and method can authenticate and monitor preferred behavioral actions from various channels, such as multifactor audio/video sensor's ability to meet primary internet of things (IoT) feeds. In some embodiments, the system and method customize the environment based on type of activity with predetermined tools associated with that activity. In some embodiments, the system and method can refresh the environment to optimize the participants engagement and productivity level.
The methods, systems and computer program products are now described in greater detail with reference to
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
Turning to the never frozen supply chain, the process flow may also include sourcing livestock 501 and butchering the livestock 502, but does not include freezing product for storage 503 prior to shipping 504a. In the fresh food process flow depicted in
In connection with providing an intelligent work flow for the fresh food supply chain, the methods, systems and computer program products including a user experience matching system 100 that can provide a user experience template 500b corresponding to the changes provided by the intelligent workflow. The changes may be illustrated in the user experience template by the data box “B”. The data box B may be an alert for when food products are outside the temperatures for suitable consumption during transport.
It is noted that the application depicted in
In some embodiments, the methods, systems and computer program products that are described herein provide for an intelligent workflow with a process flow that can begin with user needs being defined from research inputs.
Block 2 of the process flow illustrated in
It is noted that at block 3 continuous observations and adjustments are made to the archetypes based on the entire workflow. Through the intelligent workflow (IW) framework, the methods, systems and computer program products can facilitate a human-centered design engagement model that can increase attentiveness and responsiveness of the users. Intelligent workflows can be about the recognition of patterns, e.g., the dependencies and conditions that make up those patterns. The intelligent workflow framework can be directly correlated, and linked to an increase in optimization, productivity, and efficiency.
In some embodiments, the system assembles an interface with inputs and outputs. Branding elements are applied from a data source. Branding elements may be products having a brand name, e.g., third party products.
In some embodiments, the user experience (UX) is multi-modal and can manifest depending on the environment and user driven needs. The UX is delivered to the user via identified modality based on archetype, e.g., desktop, watch, mobile device (tablet, laptop, smartphone, and voice (user interface). The UX is tested ongoing with user. Adjustments are made to the UX design and delivery based on data changes, specific user preferences, and the level of actual engagement.
In some embodiments, the UX is tested throughout the process flow, e.g., being ongoing, for the users. Adjustments can be made to the UX design and delivery based on data changes, specific user preferences, and level of actual engagement.
Referring to block 3 of the method depicted in
In some examples, the archetypes are pre-determined based on task. Tasks can also be pre-determined. For example, a task may be book-keeping, while an archetype may be an accountant. The intelligent workflow user experience (UX) matching system for archetypes and user experience rendering 100 can provide a user experience (UX) method based on primary factors, such as situation and context, data inputs, and data outputs; and is based on secondary factors, such as archetype/persona preferences (explicitly determined or most favored learned over time). Another example of a secondary factor is UX efficiency adaption (efficiency is monitored and adjusted if determined as less effective).
In some embodiments, the intelligent workflow UX matching system for archetypes and user experience rendering 100 includes an archetype generator 101. The archetype generator 101 determines the archetype. The archetype generator 101 is in communication with a data input interface 102. In some embodiments, the data input interface 102 includes environmental factors reviewed 103, data inputs 104 and data outputs 105. The environmental factors reviewed 103 can include environmental conditions, such as weather variants, lighting conditions (e.g., dark or light), and ambient noise (e.g, loud or quite noise environments). Other environmental factors that can be reviewed can include the location where a user is working. For example, the location could be a factory, a field (e.g., field work application), a desk job, as well as a mobile application.
The data inputs 104 can include data elements, such as a data display, type of visuals required, content required, interactivity required, conditions/dependencies and combinations thereof. The data inputs 104 can be directed to what the user will see or the user will do for the user experience (UX). It is data directed to the functions being performed by the user, and hence used to define an archetype.
The data outputs 105 can include data elements, such as action/response required, type of interaction, timing (e.g., how often something occurs), variables, conditions and combinations therefore. Data outputs 105 are typically concerned with what output or action is needed from the user in the user experience.
Referring to
Still referring to
In some embodiments, following the user experience (UX) modality generator 107 generating the modality, a UX template generator 108 can generate UX templates. The UX template generator 108 can render UX templates based on the modality determined by the user experience (UX) modality generator 107. The type of templates that can be output by the UX template generator can include user interface (UI), voice user interface, haptic feedback type interfaces, and augmented reality (AR) and/or virtual reality (VR) user interfaces.
The UX template generator 108 takes into account the modality and provides a template that is formatted to meet the display requirements for the modality that the user is employing. For example, a mobile device screen can have a different orientation than a desktop computer, and can have a different orientation than an interface of a motor vehicle. In addition to orientation, size, i.e., the physical dimensions, of the interfaces are also considered. The UX template generator 108 has access to a template repository 112. The template repository 112 includes templates, e.g., wireframes, for user experiences used in prior intelligent workflows. In some embodiments, the templates may be wireframes that have been tagged for data fields and input fields that correspond to elements of the intelligent workflows, such as archetypes for users in the workflow and modalities of the users. Other elements can be the tasks performed by the archetypes, as well as the environment in which the workflow is being performed.
The UX template generator 108 also includes an artificial intelligence (AI) model 113 to match the archetypes and modalities from new intelligent workflows (IW) to the fields in the wireframes of the repository 112. The artificial intelligence (AI) model 113 may include a neural network that is trained to match the archetypes and modalities from the new intelligent workflow inputs to the tagged elements in the repository 112.
One element of ANNs is the structure of the information processing system, which includes a large number of highly interconnected processing elements (called “neurons”) working in parallel to solve specific problems. ANNs are furthermore trained using a set of training data, with learning that involves adjustments to weights that exist between the neurons. An ANN is configured for a specific application, such as pattern recognition or data classification, through such a learning process.
Referring now to
ANNs demonstrate an ability to derive meaning from complicated or imprecise data and can be used to extract patterns and detect trends that are too complex to be detected by humans or other computer-based systems. The structure of a neural network is known generally to have input neurons 402 that provide information to one or more “hidden” neurons 404. Connections 408 between the input neurons 402 and hidden neurons f04 are weighted, and these weighted inputs are then processed by the hidden neurons f04 according to some function in the hidden neurons f04. There can be any number of layers of hidden neurons f04, and as well as neurons that perform different functions. There exist different neural network structures as well, such as a convolutional neural network, a maxout network, etc., which may vary according to the structure and function of the hidden layers, as well as the pattern of weights between the layers. The individual layers may perform particular functions, and may include convolutional layers, pooling layers, fully connected layers, softmax layers, or any other appropriate type of neural network layer. Finally, a set of output neurons 406 accepts and processes weighted input from the last set of hidden neurons 404.
This represents a “feed-forward” computation, where information propagates from input neurons 402 to the output neurons 406. Upon completion of a feed-forward computation, the output is compared to a desired output available from training data. The error relative to the training data is then processed in “backpropagation” computation, where the hidden neurons 404 and input neurons 402 receive information regarding the error propagating backward from the output neurons 406. Once the backward error propagation has been completed, weight updates are performed, with the weighted connections 408 being updated to account for the received error. It should be noted that the three modes of operation, feed forward, back propagation, and weight update, do not overlap with one another. This represents just one variety of ANN computation, and that any appropriate form of computation may be used instead.
To train an ANN, training data can be divided into a training set and a testing set. The training data includes pairs of an input and a known output. During training, the inputs of the training set are fed into the ANN using feed-forward propagation. After each input, the output of the ANN is compared to the respective known output. Discrepancies between the output of the ANN and the known output that is associated with that particular input are used to generate an error value, which may be backpropagated through the ANN, after which the weight values of the ANN may be updated. This process continues until the pairs in the training set are exhausted. In some embodiments, the AI model 113 trains to match archetypes and modalities of intelligent workflows to tagged data fields for UX renderings that is stored in repositories 112.
After the training has been completed, the ANN may be tested against the testing set, to ensure that the training has not resulted in overfitting. If the ANN can generalize to new inputs, beyond those which it was already trained on, then it is ready for use. If the ANN does not accurately reproduce the known outputs of the testing set, then additional training data may be needed, or hyperparameters of the ANN may need to be adjusted.
ANNs may be implemented in software, hardware, or a combination of the two. For example, each weight 408 may be characterized as a weight value that is stored in a computer memory, and the activation function of each neuron may be implemented by a computer processor. The weight value may store any appropriate data value, such as a real number, a binary value, or a value selected from a fixed number of possibilities, that is multiplied against the relevant neuron outputs. Alternatively, the weights 408 may be implemented as resistive processing units (RPUs), generating a predictable current output when an input voltage is applied in accordance with a settable resistance.
In one example, the trained artificial intelligence model can provide for matching archetypes and modalities of intelligent workflows to wireframes suitable to provide user experience (UX) renderings.
Still referring to
Referring to
The computer implemented method depicted in
At step 2 (identified by reference number 305) of the artificial intelligence (AI) model, the method can continue with recommending a technology fitment. In some embodiments, recommending a technology fitment can include associating an industry and use case at block 306. At block 307, recommending a technology fitment may continue with augmenting the intelligent workflow with the identified attributes and augment steps and activity list.
At step 3 (identified by reference number 308) of the artificial intelligence (AI) model, an intelligent workflow is provided with steps, activities, personals and channel. Channel is the where the user experience is occurring. For example, whether the user experience is mobile or fixed, e.g., at a desktop.
In one example, the method provided by steps 1-3 of the model depicted in
Referring to
Referring to block 7, the method may continue with identifying the need for the intelligent workflow (IW). This step defines the reasons why the workflow is needed. For example, what is the objective of the workflow. In the example, in which the sector is auditing, the need for the intelligent workflow (IW) can be to visualize regulation, fast access to information, fast access to systems and information flow, visual signoff, onsite applications at the point of the audit, as well as timely ease of use. In the example, in which the sector is manufacturing or industry, the need for the intelligent workflow (IW) can be to visualize regulation, fast access to information, fast access to systems and information flow, visual signoff, onsite applications at the point of the audit, as well as timely ease of use. In the example, in which the sector is emergency services, the need for the intelligent workflow can be to provide for AI analysis based on multiple inputs in real time, sequencing events, and insights to view situations from varying perspectives. The system 200 has access to a corpus of data on workflows 207, which may also have similar needs.
Blocks 6 and 7 of
Referring to block 301 of
In one example of the embodiment depicted in
The feedback surveys 301b are another input of data for use in providing an optimized workflow. The feedback surveys 301b can be quizzes, blogs, recorded comments or interviews based upon an existing workflow. These elements can all be referred to data that is collected from capture feedback artificial intelligence. In some instance, in which the collected data is in text, natural language processing may be employed to extract relevant data from the inputs. Recorded content, such as interviews, can be an analyzed for relevant input data using voice detection artificial intelligence based technology.
Social media may be the source of input data that can be extracted using a web crawler and natural language processing from web based platforms. Select employer groups (SEG) and events with particular invite lists may also be used to provide inputs.
The input data at block 301 can also include activities 301d, which also includes personas, processes, planning and related timing for the processes and planning. These activities 301d may be entered as input for the different elements that can be performed as the workflow. Personas are inputs that provide information on the people in the organization that is using the workflow. Personas include information on the identity of the people, their personal demographics, their normal tasks within the workflow, etc. A persona can also include information on what motivates them and what also frustrates them. Persona information can be provided by forums, direct observation, and interviews.
The input data at block 301 can also include Enterprise Design Thinking (EDT) 301e. Enterprise design thinking includes sessions with the users to capture the objects of the existing workflow, the steps of the existing workflows, and if the personas for the organization/user are following the procedures of the workflows. The EDT can particularly be helpful in identifying pain points.
In some embodiments, Enterprise Design Thinking 301e starts by bringing together a series of design techniques, such as personas, empathy maps, as-is scenarios, design ideation, to-be scenarios, wireframe sketches, hypothesis-driven design, and minimum viable product (MVP) definition, and adds three principles titled hills, playbacks, and sponsor users. Hills are rooted in user needs and desires. Each hill is expressed as an aspirational end state for users that is motivated by market understanding. Hills define the mission and scope of a release and serve to focus the design and development work on desired, measurable outcomes. Playbacks provide input on the user value in the existing workflow of a project. Sponsor users is the input of people operating in the existing workflow.
The input at blocks 6 and 7 of
As noted, the inputs are employed by an artificial intelligence (AI) system.
Referring back to the inputs at blocks 6, 7 and 301, empathy maps can define how one or more personas feel regarding an existing workflow, and what they do with the existing workflow. Empathy map can identify the major pain points of the personas.
“Current state journeys” can identify existing activities and tasks per user, e.g., per a persona. A “current state journey” data entry for existing activities and tasks per user can be ingested by the system, e.g., ingested by the machine learning system 203, for providing a work experience (UX) framework 200, and tagged as activities, and can help to cluster the user as a persona with other personas based upon interactions.
The “to be journeys” define the experiences the personas would like to have with key insights on removing bottlenecks, e.g., removing pain points, in workflows. From this input with the perception provided by the empathy maps, the AI intelligent system 203 for the system for providing a work experience (UX) framework 200 can create a problem-need-intelligent outcome” resolution pattern.
In some embodiments, the quantitative types of inputs can be “current state journey” and “pain points”. These types of inputs can be extracted from user interviews. User interviews can be provide by the feedback surveys 301b of the feedback input 301 for
The artificial intelligence algorithm may be provided by the rules engine 204 depicted in
Pain points can also be extracted from user interviews. Pain points can include patterns where fragmentation in a work flow occur. Pain points can show a failure in collaboration. Pain points can also show a redundancy in activities. Data on pain points can be ingested by the AI algorithm, e.g., provided by the rules engine 204, of the system for providing a work experience (UX) framework 200 so that potential areas can be identified to streamline the workflow in a manner acceptable to the personas (users).
In some embodiments, the qualitative research data for the inputs can results from observational research for over the shoulder progress. This can include visual tagging of activities.
Quantitative research types for inputs can include surveys, documentation, and performance reports, which also provides insights/features for the artificial intelligence algorithm of the system for providing a work experience (UX) framework 200. Documents 305a and procedures 305b are identified as inputs for the method flow described with reference to
Performance reports can also provide data inputs. For example, systems in use in the workflow may have associated performance reports indicating service issues, incidents that result in service disruptions, and reports on overall system performance. This data can be tagged to be associated with the activities in the work flow that they impact.
The type of research data that can serve as input to the system 200 can also include planning and execution data. This data can be directed to time sheets recording and project management reports. The time sheet recordings can be associated with task and personas in the work flow. The management reports can provide data for recurring and in development activities that can impact the work flow.
Additional research data may include collaboration type data. Collaboration type data can include data from a text based messaging system or video conferencing recordings. The text based messaging system can provide common team threads including conversations to detect potential bottlenecks, risk, sentiments or pain points. Similar data can be provided from transcripts of video conferencing recordings.
Analysis of the data can begin with establishing an intelligent work flow taxonomy for training the artificial intelligence model.
For example, an activity list is developed. Each activity in a work flow may include a sequence of steps. Each step in a work flow can be associated with a person. The person can be identified by their function, e.g., a claims analyst or an accountant. The person can also be identified as a function, such as being a beneficiary. In some examples, each step in the workflow has a Boolean attribute: sequential/parallel. A Boolean attribute is an attribute that can only be true or false. Each step as a Boolean attribute marking one path of decision. The timeline, duration of the activity in the activity list is also recorded for consideration.
The taxonomy for the intelligent workflow training also includes personas. Each persona has a set of steps.
The taxonomy for training the artificial intelligence model (e.g., rules engine 204) for intelligent workflow also includes pathways. For example, each intelligent workflow has a list of pathways marked as succeeded or blocked depending upon if the intelligent workflow is fully completed.
The intelligent workflow taxonomy for training the artificial intelligence model can include industries.
The intelligent workflow taxonomy can also include use cases. The use case being why the workflow is being performed. One intelligent workflow can fit multiple use cases.
In some embodiments, the intelligent workflow taxonomy may also include a Boolean attribute for marking a whether the workflow is a stand alone process, or whether the workflow is a component of a larger sequence.
In some embodiments, the intelligent workflow taxonomy can also include environmental attributes, such as weather being hazardous.
In some embodiments, Named Entity Recognition (NER), which can employ neural networks (NN), automatically extract features from text that is configured using the above described taxonomy for the purposes of training the artificial intelligence model.
In some embodiments, following the establishment of the taxonomy for training, the method may continue with employing the taxonomy to train models for the intelligent workflow. More particularly, a number of classifiers (provided in the rules engine 204) may be trained for the artificial intelligence model. In some embodiments, a corpus of data on historical workflows, may be employed to train the artificial intelligence model using the taxonomy of terms. For example, training may include the system ingesting the existing intelligent workflow with segmented features by taxonomy for identifying classes. For example, for each intelligent workflow the segmented features of the existing workflow are used to train individual or weighted classifiers selected from an input list activities classifier, a input list of persona and activities, an input business cases/industry classifier, and an input environment data classifier. Training the classifier (rules engine 204) of the artificial intelligence model 203 also includes entity recognition for technology.
In some embodiments, the input list activities can included activities such as send or posting an explanation of benefits (EOB), a receive payment notification, a second payment confirmation, a step of creating an explanation in response to an inquiry. It is noted that the aforementioned list is provided for illustrative purposes only, and is not intended to be limiting. Activities can be used to train an activity identifier classifier, in which the input can be a list of steps in a day in a life or client process; and the output can be a set of activities and their associate workflow.
In some embodiments, the input list of persona and activity can train a classifier that takes as input steps specific actions performed by persons and personas, and learns an association between the activities and the personas. The persona and activity classifier can take as input the raw data from the mining steps, e.g., blocks 6 and 7 of
In some embodiments, the input business cases/industry classifier can be trained based on the intelligent workflow features of an intelligent workflow tagged with for an industry. The classifier can be used to ingest raw bundled client data and associate business cases and industry cases. The algorithm of the artificial intelligence model can ingest input features and output associated technology, e.g., 5G mobile, internet of things (JOT), blockchain memory, etc., for a given process discovery. The classifier for environmental data is trained similar to the input business case/industry classifier.
In some embodiments, named entity recognition (NER) is used to train and extract entities associated to specific technology patterns. In a following step, activities and steps are classified to the technology patterns. The classifier (e.g., provided by the rules engine 204) ingests client steps, processes business and performs named entity recognition (NER) to extract the technologies being used, and maps the technologies extracted to the activities, etc.
The intelligent workflow artificial intelligence models 203 can employ neural networks (NN) that employs name entity recognition (NER) to train the classifier 204 to be able to propose for a given input a setoff associated intelligent workflow for a given process discovery. The inputs from blocks 6 and 7 of
At this point of the present disclosure, the classifier of the artificial intelligence systems has been trained. Artificial Machine learning systems 203 can be used to predict outcomes based on input data. For example, given a set of input data, a machine learning system can predict an outcome. In some embodiments, the artificial machine learning system includes an artificial neural network (ANN). One element of ANNs is the structure of the information processing system, which includes a large number of highly interconnected processing elements (called “neurons”) working in parallel to solve specific problems. ANNs are furthermore trained using a set of training data, with learning that involves adjustments to weights that exist between the neurons. An ANN is configured for a specific application, such as pattern recognition or data classification, through such a learning process. The trained artificial intelligence model can provide intelligent workflow discovery. For example, when the input is empathy mapping, this information can be used for risk association and pain point identification.
For example, when the client input is a “current state journey”, the activity list classifier can help to decompose and find gaps in a current flow of an intelligent workflow being analyzed. Additionally, when the input is the current state journey, the persona classifier match existing client personas with proposed intelligent workflow steps.
When the input is a “to be journey”, the activity list classifier can provide outputs for a future state, which basically is a transition from the current state to a desired intelligent workflow state, e.g., by combining a series of intelligent workflows. Persona classifiers and industry/business case/environments classifier may also be applied to the input for the “to be journey”.
The intelligent workflow (IW) process discovery can also include analysis of performance and user feedback on the artificial intelligence algorithms and suggested improvements.
The intelligent workflow (IW) process discovery can also include analyzing the input from documentation, i.e., qualitative inputs, for the activity list classifier, persona classifier, technology classifier, as well as industry/business case/environmental classifiers.
Referring to
The system initiates attribute collection, which includes attribute collection, associate and alignment. More specifically, in some embodiments, artificial intelligence confidence levels can be used to assign confidence levels to technologies of the current process. Additionally, a user interface is provided that allows the user to override the confidence levels being set for the existing workflow.
The current state of assessment at block 8 may employ the trained artificial intelligence model that was described above with reference to blocks 6 and 7 of
Referring to
Referring to block 302, the output of the artificial intelligence model for intelligent workflows (IW) may include proposed ranked intelligent workflows (IW) with augmented steps and activity list.
Referring back to
Referring to
Still referring to
Referring back to
In the process flow depicted in
Turning to step 4 of the method depicted in
Referring to block 313 of
Referring to
When an intelligent workflow has been approved, the workflow may be added to the learning corpus 207. In this manner, the approved intelligent workflow can be added to the learning corpus 207 of the system for providing intelligent workflows depicted in
Referring to block 3 of the process flow depicted in
The system 200 applies artificial intelligence to assess a user's workflow on a task. The system can include a hardware processor 212; and a memory that stores a computer program product. The computer program product when executed by the hardware processor, causes the hardware processor to receive data through the input interface 201 regarding a workflow of a user completing a task, and assessing the data to identify attributes of the workflow that is expressed in a series of steps. The computer program product can also employ the hardware processor to analyze the steps of the workflow to identify areas of improvement. This can be done using the learning corpus 207 and the machine learning model 203. In some embodiments, the computer program product using the processor can generate augmentations from a plurality of technology fitments matched to the areas for improvement in the steps of the workflow. The computer program product can also generate, using the processor, a user experience template, wherein content of the user experience template is selected based upon at least an archetype for which the generating the augmentation from the plurality of technology fitments was performed. The user experience template can be provided by the user experience matching system for archetypes and UX rendering 100. The computer program product can also send, using the processor, the augmentations formatted in the user experience template to a user device for communicating to the user.
The processing system 400 includes at least one processor (CPU) 204 operatively coupled to other components via a system bus 102. A cache 106, a Read Only Memory (ROM) 208, a Random Access Memory (RAM) 110, an input/output (I/O) adapter 120, a sound adapter 130, a network adapter 140, a user interface adapter 150, and a display adapter 160, are operatively coupled to the system bus 102. The bus 102 interconnects a plurality of components has will be described herein.
The processing system 400 depicted in
A speaker 132 is operatively coupled to system bus 111 by the sound adapter 130. A transceiver 142 is operatively coupled to system bus 111 by network adapter 140. A display device 162 is operatively coupled to system bus 111 by display adapter 160.
A first user input device 152, a second user input device 154, and a third user input device 156 are operatively coupled to system bus 111 by user interface adapter 150. The user input devices 152, 154, and 156 can be any of a keyboard, a mouse, a keypad, an image capture device, a motion sensing device, a microphone, a device incorporating the functionality of at least two of the preceding devices, and so forth. Of course, other types of input devices can also be used, while maintaining the spirit of the present invention. The user input devices 152, 154, and 156 can be the same type of user input device or different types of user input devices. The user input devices 152, 154, and 156 are used to input and output information to and from system 400, which can include the system 200.
Of course, the processing system 400 may also include other elements (not shown), as readily contemplated by one of skill in the art, as well as omit certain elements. For example, various other input devices and/or output devices can be included in processing system 400, depending upon the particular implementation of the same, as readily understood by one of ordinary skill in the art. For example, various types of wireless and/or wired input and/or output devices can be used. Moreover, additional processors, controllers, memories, and so forth, in various configurations can also be utilized as readily appreciated by one of ordinary skill in the art. These and other variations of the processing system 400 are readily contemplated by one of ordinary skill in the art given the teachings of the present invention provided herein.
The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing apparatus receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, spark, R language, or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
In one embodiment, the present disclosure provides a non-transitory computer readable storage medium that includes a computer readable program for using artificial intelligence to assess a user's workflow on a task. The computer program product can include a computer readable storage medium having computer readable program code embodied therewith. The program instructions executable by a processor to cause the processor to receive data regarding a workflow of a user completing a task; assess the data to identify attributes of the workflow that is expressed in a series of steps; and analyze the steps of the workflow to identify areas of improvement. The program instructions can also generate, using the processor, augmentations from a plurality of technology fitments matched to the areas for improvement in the steps of the workflow; and generate, using the processor, a user experience template. The content of the user experience template is selected based upon at least an archetype for which the generating the augmentation from the plurality of technology fitments was performed. The program instructions can also send, using the processor, the augmentations formatted in the user experience template to a user device for communicating to the user.
It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment (e.g., Internet of thing (IOT)) now known or later developed. Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing
capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.
Referring now to
Referring now to
Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.
Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators.
Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 89 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and for an intelligent workflow system 96 in hardware devices in accordance with
While embodiments of the present invention have been described herein for purposes of illustration, many modifications and changes will become apparent to those skilled in the art. Accordingly, the appended claims are intended to encompass all such modifications and changes as fall within the true spirit and scope of this invention.
