Patent Publication Number: US-11651704-B2

Title: System and method for autoproducing a learning plan

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of prior-filed, U.S. Provisional Patent Application No. 62/754,965, filed on Nov. 2, 2018, the contents of which are incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     The present disclosure is directed to a method for computer analysis, specifically a method for analyzing data and creating a learning plan. 
     In modern business environments, learning plans are designed to address gaps in an employee&#39;s skillset and work towards specific goals. By way of non-limiting example, many high-volume customer engagement centers (CECs) require utilization of a wide variety of communication protocols and resources for various customer contacts and communications. Computerized scripts provide predetermined optimized customer service dialogue, desktop help screens provide information outside of normal channels, quality assurance systems provide real-time monitoring of the quality of an employee&#39;s work product, software routing engines direct communications to appropriate employees, and so forth. 
     A new employee begins without any familiarity with the systems used by their employers or the myriad resources available to them. As a result, they may use these systems and resources inefficiently or completely miss vital protocols and resources. Employees with low proficiencies in certain skills may require additional training and assistance. Furthermore, when a new system or other resource becomes available, an employee may initially be unfamiliar with how to best integrate it into their existing work routine. A new system or other resource may have a synergistic effect when used another system or other resource, but be useless or even detrimental without combination of the functions. Any of the above scenarios may lead to work errors, delay, missed or improper communications, decreased efficiency, or increased customer dissatisfaction. 
     For example, an employee assigned to communicate with a high-value customer may mistakenly continue to use an old communication protocol as opposed to a new protocol designed to take into account the customer&#39;s new preferences. By the time the employee is informed of their mistake, the customer relationship may be damaged. In another example, a new employee may receive an email because it was flagged for potentially fraudulent activity, but not know how to access a routing log listing why the email was flagged. This employee is required to undertake time-consuming research and review, potentially delaying a transaction that was not fraudulent, but had still triggered an automatic fraud review protocol. An employee may be the only member of an institution to have a high competency in a difficult subject which requires frequent retraining due to evolving regulations. Failure to properly continue training for the employee may result in a loss of their unique competency. In another example, a new resource may be vital to a first employee in one company division, while only occasionally useful to a second employee in a different company division. Prioritizing training in the resource for both employees would be a poor use of institutional resources. 
     Unfortunately, supervisory or human resources (HR) staff may suffer many of the same problems when designing learning plans for employees. If, for example, a member of HR staff is unaware of continuing education requirements, they are unable to include these requirements in a learning plan. If a member of supervisory staff is unaware of new resources for their division, they cannot instruct employees under them to acquire the skills needed to utilize these resources. Furthermore, members of HR or supervisory staff may not be objective in assigning or evaluating an employee&#39;s continuing education. Companies with large numbers of employees may be unable to timely and adequately monitor individual employee compliance with established learning plans, while large numbers of incoming employees may overwhelm HR staff and lead to delays in forming individualized learning plans. Rather than relying on goals for the employee set by supervisory or HR staff, use of an automated system could determine the goals that a learning plan needs to achieve for each specific employee. 
     There is an unmet need in the art for a system and method capable of automatically assessing an employee&#39;s skill and learning level. There is a further unmet need in the art for a system and method capable of integrating these assessments with a complimentary learning program to address deficiencies and maintain core competencies. 
     SUMMARY 
     An exemplary embodiment of the present application is a method for automatically producing a learning plan. The method receives at least one type of data input, analyzes the data input, and produces a learning plan based on the results of the analysis of the data input. 
     Another exemplary embodiment of the present application is a system for automatically producing a learning plan. The system includes a processor and a non-transitory computer readable medium programmed with computer readable code that upon execution by the processor causes the processor to execute the above-mentioned method for automatically producing a learning plan. 
     Another exemplary embodiment of the present application is a non-transitory computer readable medium programmed with computer readable code that upon execution by a processor causes the processor to execute the above-mentioned method for automatically producing a learning plan. 
     The objects and advantages will appear more fully from the following detailed description made in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING(S) 
         FIG.  1    depicts an exemplary embodiment of a planning system for creating a learning plan. 
         FIGS.  2   a  and  2   b    depict a flowchart of an exemplary embodiment of a method for creating the learning plan. 
         FIG.  3    depicts an exemplary embodiment of a computing system for performing the method for creating the learning plan. 
     
    
    
     DETAILED DESCRIPTION 
     In the present description, certain terms have been used for brevity, clearness and understanding. No unnecessary limitations are to be applied therefrom beyond the requirement of the prior art because such terms are used for descriptive purposes only and are intended to be broadly construed. The different systems and methods described herein may be used alone or in combination with other systems and methods. Various equivalents, alternatives and modifications are possible within the scope of the appended claims. Each limitation in the appended claims is intended to invoke interpretation under 35 U.S.C. § 112, sixth paragraph, only if the terms “means for” or “step for” are explicitly recited in the respective limitation. 
     In CEC and other business environments, learning plans are designed to address gaps in an employee&#39;s skillset and work towards specific goals. Rather than goals for the employee being set by a supervisor or HR, the planning system instead determines the goals that a learning plan needs to achieve for each specific employee. These goals are based on the overall goals of the company, the specific requirements of the employee&#39;s position, and the employee&#39;s current skill level. For a customer-service company such as a CEC, typical goals are to decrease Average Handling Time (AHT), and increase Customer Satisfaction scores (CSAT). 
     The planning system receives and performs analysis of at least one of the following base data types. Such base data may be received as input from an automated system (including the planning system itself or an external system), manually input, or a combination of the two. Base data may be weighted according to predetermined criteria. The base data types may include, in both the short and the long trend, any and/or all of the following:
         Types of incoming work   Over what channels the work arrives   How work is distributed among employees   What tasks similar employees are able to perform   The skills gap between an employee&#39;s current capabilities and a required, desired, or projected skill level   Existing or projected demands for skills, both in terms of skills and skill levels required, along with any certification and/or continuing education requirements   Quality Assurance (QA) scores of the employee&#39;s previous work across the work type(s) of interest and/or all work types   Any other data of use or interest       

     This analysis leads to generation of the optimal, individual learning plan for each employee. Changes in performance and adherence to the learning plan may be monitored, with progress (or lack thereof) reported to a third party or used to adjust the learning plan. Feedback metrics may include improvement or decline in any skill or work quality data originally included in the base data, or to any other metric. In a CEC, feedback metrics could include changes to the AHT and CSAT. The learning plan may also be automatically modified when any of the base data changes, e.g. changes to work channels or education requirements. 
       FIG.  1    depicts an exemplary embodiment of a planning system  100  for creating a learning plan  90 . Planning system  100  includes a planning analytics engine  110  which receives input from at least one base data source  120 , feedback data source  130 , and/or external system  140  to produce at least one learning plan  90 . Production of the learning plan  90  may entail generating a new learning plan  90  and/or updating an existing learning plan  90 . An optional learning plan database  150  allows storage of learning plans  90 , while a desktop  160  allows additional input to planning analytics engine  110  as well as display of learning plans  90 . 
     The planning analytics engine  110  is configured to receive and analyze data within planning system  100 . Analysis may be a real-time analysis of streaming data or batch analysis of data. The planning analytics engine  110  includes at least one set of analytics rules  111  used to analyze data. The analytics rules  111  determine responses to information extracted from base data and/or feedback data, creating learning plan  90 . The analytics rules  111  may be static or may be dynamically updated by planning analytics engine  110 , external system  140 , and/or an employee or third party utilizing desktop  160 . Updates may be manual or automatic. Automatic updates to analytics rules  111  may be triggered by meeting certain criteria within analytics rules  111  of planning analytics engine  110  or within external system  140 , or may occur at predetermined intervals. The analytics rules  111  may be software programs or separate files executed by a software program. 
     In certain embodiments, the planning analytics engine  110  generates at least one compliance metric  95  utilizing the results of the analysis and comparison with the learning plan  90 . The compliance metric  95  measures a user&#39;s compliance with the learning plan  90 . In certain embodiments, the planning analytics engine  110  also includes planning analytics memory  112 . The planning analytics memory  112  can store learning plans  90 , data used for analysis, the results of that analysis, analytics rules  111 , or any other information or data used by planning system  100 . 
     While the exemplary embodiment includes one planning analytics engine  110 , planning system  100  may include more planning analytics engines  110 . In embodiments with multiple planning analytics engines  110 , CAEs  110  may be constantly connected, periodically connected, interconnected through at least one external system  140 , learning plan database  150 , and/or desktop  160 , or separate. In embodiments with a single planning analytics engine  110 , planning analytics engine  110  is connected, directly or indirectly, to all external systems  140 , learning plan databases  150 , and/or desktops  160 . 
     The external systems  140  may be connected to planning analytics engine  110 , other external systems  140 , learning plan databases  150 , and/or desktops  160 . In certain embodiments, some external systems  140  connect planning analytics engine  110  to certain other external systems  140  and/or learning plan databases  150 . The external systems  140  provide different data and functionalities to planning system  100 . By way of non-limiting example, external system  140  may be an email system, texting system, telephone system, internet access system, and/or another system for customer interaction. By way of another non-limiting example, external system  140  may be a customer service protocol database, work resource system, human resources system, internal work system, scheduling and/or calendaring system, and/or another system internal to a business. 
     A single external system  140  may perform multiple processes, a single process, and/or part of a larger process. In embodiments with multiple planning analytics engines  110 , each planning analytics engine  110  may be connected to a unique set of external systems  140  or may share all or some specific external systems  140 . External systems  140  can be updated by adding, updating, or removing specific external systems  140 . The connections between planning analytics engines  110 , external systems  140 , learning plan databases  150 , and desktops  160  may also be updated. 
     The external system  140  may also provide input from base data source  120  and feedback data source  130  to planning analytics engine  110  for analysis, or may analyze such data and apply the results directly. By way of non-limiting example, external systems  140  may perform voice and text analytics, quality analytics, analytics relating to usage of desktop  160  or other available resources, and any other analysis related to an employee&#39;s activities during customer service interactions. The external systems  140  may provide the results of such analyses to planning analytics engine  110  or to other external systems  140 . By way of further non-limiting example, external systems  140  may also interact with the functions of desktop  160 . Such interactions may allow another desktop  160  to observe and/or share another desktop  160 , and utilize, update, or transmit or retrieve information to or from certain functions of the desktop  160 . 
     The learning plan databases  150  are connected to planning analytics engine  110  and may be connected to other learning plan databases  150  and/or external systems  140 . The learning plan databases  150  store learning plans  90  for use by planning system  100  and system users. System users can include employees to whom learning plans  90  are directed, and HR and/or supervisory staff. By way of non-limiting example, learning plan databases  150  may store standardized and/or pregenerated learning plans  90  for basic onboarding of new employees, various iterations of specific learning plans  90 , learning plans  90  for a specific employee, and/or any combination thereof. The learning plans  90  stored in learning plan databases  150  may be categorized by employee, date created and/or updated, business division, purpose, or any other possible classification schema. The learning plan databases  150  can be updated by adding, changing, or removing learning plans  90 . The connections between planning analytics engine  110 , external systems  140 , and learning plan databases  150  may also be updated. 
     The desktop  160  receives and displays learning plans  90  from learning plan databases  150  and/or planning analytics engines  110 . Processes from external systems  140  may also interact with the processes of desktop  160 . By way of non-limiting example, an external system  140  may receive an email sent by an employee from desktop  160 , analyze its content, and determine if the employee&#39;s writing skills have improved. Optionally, desktop  160  may also be used to provide input from base data source  120  or feedback data source  130 , modify analytics rules  111 , or submit a request to planning analytics engine  110 . 
       FIGS.  2   a  and  2   b    depict a flowchart of an exemplary embodiment of a method  200  for creating and modifying learning plan  90 . It should be understood that the arrangement of the steps of method  200  may be reordered and/or certain steps performed simultaneously as long as the fundamental function of method  200  remains unaltered. 
     In step  202 , the planning analytics engine  110  of the planning system  100  receives data input from at least one base data source  120 , feedback data source  130 , and/or input from at least one external system  140 . 
     In step  204 , the planning analytics engine  110  utilizes at least one set of analytics rules  111  to analyze received data. This analysis may be a real-time analysis of streaming data or batch analysis of data. 
     In optional step  206 , the planning analytics engine  110  generates at least one new learning plan  90  utilizing the results of the analysis of step  204 . 
     In optional step  208 , the planning analytics engine  110  updates at least one existing learning plan  90  utilizing the results of the analysis of step  204 . 
     In optional step  210 , the planning analytics engine  110  generates at least one compliance metric  95  utilizing the results of the analysis of step  204 . 
     In optional step  212 , planning system  100  stores at least one of the learning plan  90 , the compliance metric  95 , or received data in the planning analytics memory  112 . 
     In optional step  214 , planning system  100  stores at least one learning plan  90  in the learning plan database  150 . 
     In optional step  216 , planning system  100  displays at least one learning plan  90  on at least one desktop  160 . 
     In optional step  218 , the planning system  100  transmits at least one of the learning plan  90 , the compliance metric  95 , or received data to at least one external system  140  or at least one desktop  160 . 
     In optional step  220 , the planning system  100  updates the analytics rules  111  of the planning analytics engine  110 . 
     In optional step  222 , the planning system  100  updates the connections between planning analytics engine  110  and at least one external system  140 . External systems  140  can be updated by adding, updating, or removing specific external systems  140 . 
     It should be understood that method  200  may be divided into separate parts and practiced separately. By way of non-limiting example, steps  202 ,  204 , and  208  may be repeated multiple times to update an existing learning plan multiple times. 
       FIG.  3    depicts an exemplary embodiment of computing system  300  for performing method  200  for creating learning plan  90 . 
     The system  300  is generally a computing system that includes a processing system  306 , a storage system  304 , software  302 , a communication interface  308 , and a user interface  310 . The processing system  306  loads and executes software  302  from the storage system  304 , including a software module  320 . When executed by computing system  300 , software module  320  directs the processing system  306  to operate as described in herein in further detail in accordance with the method  200 . 
     The computing system  300  includes a software module  320  for generating learning plan  90 . Although computing system  300  as depicted in  FIG.  3    includes one software module  320  in the present example, it should be understood that more modules could provide the same operation. Similarly, while the description as provided herein refers to a computing system  300  and a processing system  306 , it is to be recognized that implementations of such systems can be performed using one or more processors, which may be communicatively connected, and such implementations are considered to be within the scope of the description. It is also contemplated that these components of computing system  300  may be operating in a number of physical locations. 
     The processing system  306  can comprise a microprocessor and other circuitry that retrieves and executes software  302  from storage system  304 . The processing system  306  can be implemented within a single processing device but can also be distributed across multiple processing devices or sub-systems that cooperate in existing program instructions. Non-limiting examples of processing systems  306  include general purpose central processing units, application specific processors, and logic devices, as well as any other type of processing device, combinations of processing devices, or variations thereof. 
     The storage system  304  can comprise any storage media readable by processing system  306 , and capable of storing software  302 . The storage system  304  can include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as, but not limited to, computer readable instructions, data structures, program modules, or other information. The storage system  304  can be implemented as a single storage device but may also be implemented across multiple storage devices or sub-systems. The storage system  304  can further include additional elements, such a controller capable of communicating with the processing system  306 . 
     Non-limiting examples of storage media include random access memory, read only memory, magnetic discs, optical discs, flash memory, virtual memory, and non-virtual memory, magnetic sets, magnetic tape, magnetic disc storage or other magnetic storage devices, or any other medium which can be used to store the desired information and that may be accessed by an instruction execution system, as well as any combination or variation thereof, or any other type of storage medium. In some implementations, the storage media can be a non-transitory storage media. In some implementations, at least a portion of the storage media may be transitory. Storage media may be internal or external to system  300 . 
     As described in further detail herein, computing system  300  receives and transmits data through communication interface  308 . The data can include any of the above base data types, updates to learning plan  90 , feedback related to learning plan  90 , and/or any other data that may pertain to the creation, modification, or execution of learning plan  90 . In embodiments, the communication interface  308  also operates to send and/or receive information, such as, but not limited to, additional information to/from external systems  140  to which computing system  300  is communicatively connected, input related to initial and updated base data, updates to learning plan  90 , feedback related to learning plan  90  and/or any other information that may pertain to the creation or execution of learning plan  90 . 
     The user interface  310  can include one or more of a mouse, a keyboard, a voice input device, a touch input device for receiving a gesture from a user, a motion input device for detecting non-touch gestures and other motions by a user, and/or other comparable input devices and associated processing elements capable of receiving user input from a user. Output devices such as, but not limited to, a video display or graphical display can display textual or video portions of learning plan  90 , related documents, or another interface further associated with embodiments of the system and method as disclosed herein. Speakers, printers, haptic devices and other types of output devices may also be included in the user interface  310 . Employees, supervisory or HR staff, or other staff can communicate with computing system  300  through the user interface  310  in order to view learning plan  90 , base data, feedback, and documents, enter or receive base data or other information, enter feedback, manage an interaction or back-office task, or any number of other tasks the employee or other staff may want to complete with computing system  300 . 
     In the foregoing description, certain terms have been used for brevity, clearness, and understanding. No unnecessary limitations are to be inferred therefrom beyond the requirement of the prior art because such terms are used for descriptive purposes and are intended to be broadly construed. The different configurations, systems, and method steps described herein may be used alone or in combination with other configurations, systems and method steps. It is to be expected that various equivalents, alternatives and modifications are possible within the scope of the appended claims.