Patent Application
20240070618
Various embodiments relate generally to predictive systems, methods, devices and computer programs and, more specifically, relate to using machine learning to predict the risk of a worker missing their clocking and delivering alerts to the worker or their manager.
This section is intended to provide a background or context. The description may include concepts that may be pursued but have not necessarily been previously conceived or pursued. Unless indicated otherwise, what is described in this section is not deemed prior art to the description and claims and is not admitted to be prior art by inclusion in this section.
Worker clockings (also known as “punches”) are a critical component of labor timekeeping systems. These clockings 112 are entered on timecards 110 by workers into a mechanical timekeeping system to indicate the exact day and time the worker arrived for work, left work, took a break, switched activities, or any other timekeeping event. However, workers often forget to enter these clockings, leading to a “missed clocking” 114 that needs to be fixed manually with edited clockings 124 by a manager in order to make the employee's timecard valid 120 (see
The below summary is merely representative and non-limiting.
The above problems are overcome, and other advantages may be realized, by the use of the embodiments.
Various embodiments provide a system that predicts the risk of potential missed worker clockings and delivers alerts to the workers and/or their managers when that risk is high. Missing clockings occur when a worker who is supposed to make a timecard entry to indicate they have arrived at or are leaving from work (finished or on a break), does not clock within a certain grace period around the scheduled time, even though they were present to do so. Such missed clockings can be costly to a manager, worker, or organization as the timecard entries need to be edited after the fact to reflect the worker's actual time worked.
Such a system uses a machine learning method to predict the likelihood of a missed clocking based on features of the worker, their organizational units, and the day/time. In particular, features from important categories are extracted from timekeeping and scheduling information available before the worker's scheduled clocking. These features include, but are not limited to, organizational features indicating what department, location, and other properties an employee has, features capturing the employee's scheduling and timekeeping history, features capturing the record of this employee conditional on date and time features, and the date and time features for the scheduled clocking. This information is extracted not just for upcoming clockings, but also from the historical record.
The historical data is used to calibrate the risk prediction system based on statistical correlations of the extracted features and previously recorded missing clockings. In one embodiment, a machine learning algorithm such as gradient boosted decision trees (GBDT) is trained using the features described above as inputs and the occurrence of a missed clocking or a non-missed clocking as the variable to be predicted. An important nuance of this system, illustrated in
As seen in
In either the batch or online modes, the result of the prediction phase is a risk score for every scheduled clocking for every worker in the organization. The system then uses these risk scores to determine if an alert should be sent to an employee, or their manager, or both. The alerting system is configurable with a set of parameters including but not limited to thresholds on the risk score set by the organization, parameters controlling how often or when an employee or manager should receive alerts, their preferred devices, and how often alerts should repeat without acknowledgement. These configuration parameters and the risk scores themselves allow the system to be tuned to only send alerts when there is a high risk of a missed clocking, thereby limiting false positives and unnecessary alerts. Alternative configurations may choose to send alerts at moderate risk scores to reduce false negatives at the cost of sending additional alerts.
The alerts are delivered to an employee or manager's device, such as a mobile smartphone or tablet. Some of the parameters of the alerting system may specify particular devices where an alert with certain properties should be delivered. In some embodiments, certain alerting parameters may be calculated on the device itself, as highlighted in
Aspects of the described embodiments are more evident in the following description, when read in conjunction with the attached Figures.
Various embodiments provide a system to alert employees or their managers when the employee is at a high risk of missing a clocking. Clockings in a workforce timekeeping system are timestamps indicating when an employee came to work, left or returned from a break, transferred to another part of their work, or left work. These clockings are used across industries to determine how much time an employee has worked, and subsequently how much they should be paid. However, because clockings are manually input by the employee to a time clock, terminal, or other device, employees often forget or fail to enter their clockings, creating a missed clocking.
In some embodiments, an employee cannot be paid for their labor if a missed clocking remains on their timesheet, and even if they can be paid, the payment will often be incorrect if a missed clocking is present. Therefore, there is substantial business and human value to preventing missed clockings from occurring in the first place. In addition to ensuring proper payment of employees, missing clockings also cause substantial work for managers and payroll administrators who must determine proper actions to take, including filling in the missing clockings with appropriate timestamps.
A system that alerts users to the risk of missing clocking improves over systems that create reports or flag missed clockings in historical data by potentially avoiding these misses altogether. Such an alerting system also extends the scope of prediction and alerting systems to the area of timekeeping clockings. A missing clocking alerting system benefits from using computerized algorithms because of the size of the data that needs to be processed, including potentially tens of millions of historical clockings and schedule entries. Using parallel computing as in
Importantly, missed clockings often follow patterns. Particular employees, departments, or clockings at specific times of the day or on particular days of the week can all be more or less at risk. Quantifying that risk and using it to alert workers or managers about expected missed clockings could decrease the number of actual missed clockings, thereby saving organizations time, money, and making pay processes based on the clockings more efficient and reliable.
Machine learning algorithms can be used to calibrate models based on historical data. In the context of missed clockings, this means building a model that quantifies the risk of a particular clocking being missed. The model is calibrated based on historical data and then applied to features that characterize the scheduled clocking. Unlike hand-tuned, rule-based, or heuristic risk prediction systems, machine learning systems tailor the model to historical data, meaning the model will likely make different risk prediction scores under the same context at different organizations.
Alerting systems are traditionally used in timekeeping and scheduling systems to alert managers and employees of inconsistencies after an event has already occurred. For instance, prior works raise alerts when an employee works beyond a given time limit or other timekeeping violations. However, such alerts are rarely raised before an event takes place, except in the case of projected total hours calculations, which are merely deterministic sums performed based off of past events and scheduled time in the future. By contrast, various embodiments provide a system that raises alerts based on a risk score dynamically generated based on historical trends and assigned to a future event before it has happened.
A system 200, such as that seen in
Timekeeping and Scheduling Data
The system consumes timekeeping and scheduling data from a workforce management system. Specifically, timekeeping data in some embodiments include but are not limited to the elements listed in Table 1.
Scheduling data in some embodiments include but are not limited to the following
elements listed in Table 2.
Timekeeping and Scheduling Alignment
In order to extract the features of each employee's timekeeping record at the time of an expected clocking, the system performs an alignment of the timekeeping and scheduling data, indicating when clockings were expected, and what the state of the employee's timekeeping and scheduling records were at those times. In some embodiments, the procedure for this alignment (see Timekeeping/Scheduling Alignment in the top left and bottom left of
First, the historical clockings and scheduled clockings are loaded into tabular representations in computer memory. The schedule as it exists 3 hours before the shift to be worked is used because schedules can be changed both before and after a shift is worked (see Daily Schedule blocks in
Note, this alignment is performed in two different parts of the missing clocking prediction process (see Timekeeping/Scheduling Alignment blocks in
Feature Extraction
The next step in the process requires extracting features from the aligned timekeeping and scheduling data. These features capture the general context of the worker's scheduling and timekeeping data at each scheduled clocking time. In one embodiment, the features fall into categories (see below and Feature Extraction blocks in
Additional features related to specific worker actions, such as the use of a particular card, clocking device, or location information can also be utilized in some embodiments. Such features capture information about the likelihood of a missed clocking when a worker is in a particular location, especially an unfamiliar one, or using a device they have not typically used before.
The feature extraction procedure, which takes a set of aligned timekeeping clockings and scheduled clockings is used in two different parts of the missing clocking prediction process (see Feature Extraction blocks in
Missing Clocking Labeling
In order to calibrate the risk scoring model, historical data is not only annotated with features, but also with labels indicating, for each potential clocking, whether the clocking was missed or not. In some embodiments of the system, this procedure may be straightforward. For instance, if the timekeeping system logs scheduled clockings that do not have an associated clocking based on an existing pairing system, this information could be directly used as the labels for the historical data. However, general systems may not have such historical labels readily computed, and even if they do, the heuristics used to label these clockings as missing may not be appropriate for predicting the risk of future clockings. Therefore, in some embodiments of the system, the following heuristic matching procedure (labeled “matching” in
The procedure above essentially minimizes the total alignment difference between clockings that were entered on the day of the clocking and the final clockings. Any final clocking that is left unmatched is considered missing, while matched clockings are labeled as “aligned” (see Missing Clocking Labeling blocks in
Machine Learning and Construction of the Model
The final procedure in the training of a model for missing clocking risk based on historical data is to calibrate (or “fit”) the model itself to the historical data. As illustrated in
For further detail on the fitting of Gradient Boosted Decision Trees see: Ridgeway, Greg. “Generalized Boosted Models: A guide to the gbm package.” Update 1.1 (2007): 2007; and Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. Springer, Berlin: Springer series in statistics, 2001.
Irrespective of the specific type of model trained, it will be capable of turning a vector of features representative the context of a scheduled clocking into a risk score r(x) that is monotonically increasing in the probability that the upcoming scheduled clocking will be missed.
Predictions (Batch or Event Driven)
After the training phase, the calibrated model is deployed to score the risk of future scheduled clockings being missed (see
Risk predictions for potential clockings can be made on many different timescales. Two non-limiting modes of prediction are an “online” mode that updates the features and risk of a missed clocking after every timekeeping and scheduling event or a “batch” mode where the next time period's (day's, week's, etc.) scheduled clockings are assessed once before the period starts. In either case, once the prediction process is invoked, the same steps are followed, as seen in
The online prediction procedure is invoked by an event processor that detects a relevant event in the timekeeping and scheduling system. This event could, for example, be a new scheduled shift, indicating a new set of potential clockings to be evaluated for risk. Another example of an invoking event is a new clocking or missed clocking by a worker, which changes the feature values calculated during prediction. In addition, worker actions, either within the timekeeping or scheduling systems, or in other connected systems, including computer log-ins, or the use of various hardware or other devices could trigger a risk assessment. Any such event that either requires a new set of potential clockings to be scored, or changes the features around an existing scheduled shift, requires the corresponding clockings to be re-scored under the online model. In some embodiments, this extensive processing could be accomplished in a distributed computing system utilizing multiple copies of the trained model, as seen in
The batch prediction procedure is, in some embodiments, invoked not by a specific timekeeping or scheduling event, but by a timed event, such as a daily invocation at midnight, a weekly invocation on a Sunday, or a periodic procedure tied to a business calendar, such as the typical day of schedule posting. Once the batch prediction process is invoked, all scheduled clockings within a given time period are scored at once using available information and the prediction pipeline described above. As with the online prediction mode, some embodiments may use multiple copies of the trained model and invoke prediction on multiple servers to parallelize and speed up the task.
Alert Configurations
Once each scheduled clocking is assigned a risk score in the prediction phase, the system produces alerts based on the scores, as well as the alert histories. Configuration settings are used to control how often such alerts are sent and are crucial to ensure the alerts do not overwhelm workers or managers (for instance reducing false positives). In some embodiments, these configurations can be set at various organizational levels such as for specific employees, employee groups, managers, manager groups, departments, locations, or regions. The configurations set at each level cascade down to lower levels unless they are superseded by more specific configurations. Note the higher levels can contain partial specifications as well. This cascading of configuration properties is illustrated in
The non-limiting alert configurations in at least one embodiment include the following elements in table 4, which are referred to as Alert Configurations in
Preferred methods and devices indicate, for each worker or manager, what combination of method and device, for instance SMS/Phone, App-Push/Tablet, or Email/All-Devices should be used to deliver standard messages. However, in some embodiments, the severity of the alert may over-ride these preferences (see
This procedure checks the severity of each alert, which may be, in some embodiments, determined by a combination of the risk score, time and day of the shift, and the current day and time. The procedure uses this severity to determine the recipients and methods/devices for the alerts, checks the other conditions for filtering alerts based on blocks, timing, and limits, taking into account the severity and devices, and then attempts to send repeated alerts until they are acknowledged.
The procedure above states the logic of processing alert configurations, but many of the steps can be performed either on the alerting server(s) or on mobile devices themselves. In particular, the processing of Do not Disturb rules, Limit rules, and Timing rules, could all be done on the device they are delivered to, assuming proper synchronization with the current alerting history.
Delivering Alerts to Mobile and Other Devices
Once the alerts are determined based on the algorithm above, they can be delivered to the selected devices based on the method selected. These methods include any of the following non-limiting approaches, also illustrated in
These alerts may be delivered to many types of devices, including but not limited to mobile phones, tablets, laptop computers or desktop terminals. These alerts may, in some embodiments, be sent to a device prior to the alert actually surfacing, or the alert may surface as soon as it is received. This functionality is of benefit when system access may be unavailable near the time that the reminder would be sent, for instance if the device communicates only through Wi-Fi and the employee commutes to work on a path without a Wi-Fi connection. In some embodiments, the alerts sent to individual workers may contain the information detailed in table 5.
In some embodiments, alerts sent to managers contain all of the information listed above as well as the name of the employee who has a high risk of missing a clocking. The information above can, in some embodiments, be processed into natural language sentences such as “This is a reminder to clock at 9:00 am on Monday” using organization-specific templates that combine multiple pieces of information. The response options included in the alert allow an employee or a manager to acknowledge the receipt of the alert and potentially take another action. The possible actions may be limited by timekeeping business rules. For instance, a “Clock Now” action may not be allowed until within a certain time before the shift starts. In some embodiments, the system will continue to send repeated alerts to devices until an acknowledgement is received or until the number of repeats stipulated in the individual's configuration is exceeded. As noted in the feedback loop in
These alerts represent the end point of the system, which uses historical data to calibrate a missing clocking risk predictor. The predictor is used in either an online or batch mode to predict the risk of workers missing upcoming scheduled clockings. Based on these risk scores and system configurations, alerts are generated for employees and managers reminding them about the upcoming clockings.
As described above, various embodiments provide a method, apparatus and computer program(s) to provide alerts prior to potential missed clockings.
In accordance with an embodiment a method performs, at Block 1110, a step of retrieving historical employee clockings. Missed clockings are identified/labeled based on an alignment of the historical employee clockings at Block 1120. At Block 1130, a step of extracting features representative of the missed clockings is performed. The method performs, at Block 1140, a step of generating a predictive model based on statistical correlations of the extracted features. At Block 1150, for each upcoming scheduled clocking in a prediction window, a risk factor of the upcoming scheduled clocking being missed is determined based on the predictive model. The method also performs a step of presenting an alert prior to the upcoming scheduled clocking based at least in part on the risk factor at Block 1160.
The various blocks shown in the Figures may be viewed as method steps, as operations that result from use of computer program code, and/or as one or more logic circuit elements constructed to carry out the associated function(s).
An embodiment provides a method for signaling alerts prior to potential missed clockings. The method includes retrieving historical employee clockings and identifying missed clockings based on an alignment of the historical employee clockings. Features representative of the missed clockings are extracted and a predictive model is generated based on statistical correlations of the extracted features. For each upcoming scheduled clocking in a prediction window, a risk factor of the upcoming scheduled clocking being missed is determined based on the predictive model. The method also includes presenting an alert prior to the upcoming scheduled clocking based at least in part on the risk factor.
A further embodiment provides an apparatus for signaling alerts prior to potential missed clockings. The apparatus includes at least one processor and at least one memory storing computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus to perform actions. The actions include to retrieve historical employee clockings and to identify missed clockings based on an alignment of the historical employee clockings. Features representative of the missed clockings are extracted and a predictive model based on statistical correlations of the extracted features is generated. For each upcoming scheduled clocking in a prediction window, a risk factor of the upcoming scheduled clocking being missed is determined based on the predictive model. The actions also include to present an alert prior to the upcoming scheduled clocking based at least in part on the risk factor.
In another embodiment of the apparatus above, the apparatus is embodied in a web-based server.
In a further embodiment of any one of the apparatus above, the apparatus is embodied in an integrated circuit.
A further embodiment provides a computer readable medium tangibly encoded with a computer program for signaling alerts prior to potential missed clockings. The computer program is executable by a processor to perform actions. The actions include retrieving historical employee clockings and identifying missed clockings based on an alignment of the historical employee clockings. Features representative of the missed clockings are extracted and a predictive model based on statistical correlations of the extracted features is generated. For each upcoming scheduled clocking in a prediction window, a risk factor of the upcoming scheduled clocking being missed is determined based on the predictive model. The actions also include presenting an alert prior to the upcoming scheduled clocking based at least in part on the risk factor.
In another embodiment of the computer readable medium above, the computer readable medium is a non-transitory computer readable medium (e.g., CD-ROM, RAM, flash memory, etc.).
In a further embodiment of any one of the computer readable media above, the computer readable medium is a storage medium.
Various operations described are purely exemplary and imply no particular order. Further, the operations can be used in any sequence when appropriate and can be partially used. With the above embodiments in mind, it should be understood that additional embodiments can employ various computer-implemented operations involving data transferred or stored in computer systems. These operations are those requiring physical manipulation of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic, or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated.
Any of the operations described that form part of the presently disclosed embodiments may be useful machine operations. Various embodiments also relate to a device or an apparatus for performing these operations. The apparatus can be specially constructed for the required purpose, or the apparatus can be a general-purpose computer selectively activated or configured by a computer program stored in the computer. In particular, various general-purpose machines employing one or more processors coupled to one or more computer readable medium, described below, can be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations.
The procedures, processes, and/or modules described herein may be implemented in hardware, software, embodied as a computer-readable medium having program instructions, firmware, or a combination thereof. For example, the functions described herein may be performed by a processor executing program instructions out of a memory or other storage device.
The foregoing description has been directed to particular embodiments. However, other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. Modifications to the above-described systems and methods may be made without departing from the concepts disclosed herein. Accordingly, the invention should not be viewed as limited by the disclosed embodiments. Furthermore, various features of the described embodiments may be used without the corresponding use of other features. Thus, this description should be read as merely illustrative of various principles, and not in limitation of the invention.
This application claims priority to and is a continuation of U.S. patent application Ser. No. 16/381,472, filed Apr. 11, 2019, published as U.S. Patent Publication No. US 20190318320 A1, and entitled “PREDICTING UPCOMING MISSED CLOCKINGS AND ALERTING WORKERS OR MANAGERS” (UKGP-0002-U01). U.S. patent application Ser. No. 16/381,472 claims the benefit of priority to U.S. Provisional Patent Application Ser. No. 62/656,589, filed Apr. 12, 2018, and entitled “PREDICTING UPCOMING MISSED CLOCKINGS AND ALERTING WORKERS OR MANAGERS” (UKGP-0002-P01). All of the foregoing listed patent documents are incorporated herein by reference in their entirety for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 62656589 | Apr 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16381472 | Apr 2019 | US |
| Child | 18338899 | US |
