DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a Final Action in response to the claims filed on 10/28/2025.
Claims 1, 3 – 9, and 11 – 16 are currently pending in this application.
Response to Remarks
Examiner’s Response to Remarks
I. Claim Rejections – 35 U.S.C. § 101
II. Claim Rejections – 35 U.S.C. § 102
III. Claim Rejections – 35 U.S.C. §103.
Examiner’s Response to I. Claim Rejections – 35 U.S.C. § 101.
Applicant argues the claims are patent eligible because they are not directed to judicial exceptions.
Examiner respectfully disagrees. Applicant’s claim 1 is directed to a method, which is a statutory category. However, claim 1 recites the abstract idea of certain methods of organizing human activity. Particularly claim 1, recites business relations where the claim manages interactions between a person and a computer as we have certain methods of organizing human activity between a requestor for an unallocated shift and a computer where rules or instructions are recited in the claim; for example receiving allocation requests for a plurality of previous unallocated shifts; determining for each previous unallocated shift, a label based on a number of the allocation requests that correspond to the previous unallocated shift; evaluating a classification model based on (i) the labels and (ii) the previous unallocated shifts; detecting an unallocated shift record, the unallocated shift record defining a time period and a task identifier; evaluate an index corresponding to the unallocated shift record, the index being a value among a range of values indicative of a forecasted interest in the unallocated shift record; evaluating an impact indicator for the unallocated shift record based on the time period, the task identifier, and the index; and these all recite commercial interactions, business relations, between a human and a computer such as the requestor and computing device. Claim 9 is substantially similar and recites the same subject matter as claim 1 and recites the same abstract idea.
The additional elements recited are a memory, a processor, a computing device, training a classification model, corresponding to a facility, and executing, at the computing device, the trained classification model. However these additional elements are considered generic computer components; and the additional elements do not integrate the judicial exception into a practical application. Training a classification model and executing the trained classification model is not significantly more than the recited judicial exception. The claim does not recite additional elements individually nor in combination that amount to significantly more than the judicial exception, as the claims merely add the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The dependent claims are rejected by virtue of depending on the independent claims. Accordingly, all pending claims are rejected under 35 U.S.C. § 101.
Examiner’s Response to II. Claim Rejections – 35 U.S.C. § 102.
Examiner has removed rejection under 35 U.S.C. § 102 due to the amendments to the independent claims.
Examiner’s Response to III. Claim Rejections – 35 U.S.C. § 103.
Applicant argues that Aslam and Westland do not disclose, teach or suggest amended independent claims 1 and 9.
Examiner respectfully disagrees. A new search was necessitated due to the amendments to the independent claims. Examiner has applied new art to the independent claims. All pending claims are rejected under 35 U.S.C. § 103.
Claim Rejections – 35 U.S.C. §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3 – 9, and 11 – 16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1 and 9 recites:
receiving allocation requests for a plurality of previous unallocated shifts;
determining, for each previous unallocated shift, a label based on a number of the allocation requests that correspond to the previous unallocated shift;
generating based on (i) the labels and (ii) the previous unallocated shifts;
detecting an unallocated shift record the unallocated shift record defining a time period and a task identifier;
generate an index corresponding to the unallocated shift record, the index being a value among a range of values indicative of a forecasted interest in the unallocated shift record;
generating an impact indicator for the unallocated shift record based on the time period, the task identifier, and the index;
selecting a control action for the unallocated shift record according to the impact indicator;
and executing the selected control action.
Claim 1 recites certain methods of organizing human activity, and particularly business relations where the claim involves commercial interactions between a human and a computer. For example, claim 1 recites observing allocation requests for a plurality of previous unallocated shifts; evaluating, for each previous unallocated shift, a label based on a number of the allocation requests that correspond to the previous unallocated shift; evaluating a classification model based on (i) the labels and (ii) the previous unallocated shifts; detecting an unallocated shift record the unallocated shift record defining a time period and a task identifier; evaluate an index corresponding to the unallocated shift record, the index being a value among a range of values indicative of a forecasted interest in the unallocated shift record; evaluating an impact indicator for the unallocated shift record based on the time period, the task identifier, and the index; and observing a control action for the unallocated shift record according to the impact indicator; and these all recite commercial interactions, business relations, between a human and a computer such as the requestor and computing device. Claim 9 is substantially similar and recites the same subject matter as claim 1. Accordingly, claims 1 and 9 recite certain methods of organizing human activity.
The dependent claims encompass the same abstract ideas as well. For instance, claims 3 and 11 are directed evaluating a random forest classification model configured to output a label for an unallocated shift record; claims 4 and 12 are directed towards observing a task priority corresponding to the task identifier; and evaluating a component of the impact indicator based on the task priority; claims 5 and 13 are directed towards observing unallocated shift record includes a portion of the time period corresponding to the task identifier; and evaluating the component of the impact indicator; claims 6 and 14 are directed towards observing a reference time period, evaluating a component of the impact indicator based on an overlap between the time period and the reference time period; claims 7 and 15 are directed towards observing a plurality of visual indicators associated with respective thresholds; evaluating one of the visual indicators by comparing the impact indicator to the thresholds; and evaluating the selected control action; and claims 8 and 16 are directed towards evaluating an incentive value based on the impact indicator. Thus the dependent claims further limit the abstract ideas.
These judicial exceptions are not integrated into a practical application. Claim 1 recites a computing device, training a classification model, corresponding to a facility, executing, at the computing device, the trained classification model, in the various steps. Claim 9 recites the additional elements of a memory, a processor, a computing device, training a classification model, corresponding to a facility, and executing, at the computing device, the trained classification model. The additional elements of a memory, a processor, a computing device, training a classification model, corresponding to a facility, and executing, at the computing device, the trained classification model are considered generic computer components (see at least Specifications ¶ 0017), as in “operate a respective client computing device” performing generic computer functions as per Applicant’s Specifications shown below:
“[0017] A schedule generated by such an automated generation system can be deployed to the employees 104 by, for example, transmitting shift definitions to the employees 104 to whom those shifts are assigned. For example, each employee 104 can operate a respective client computing device 108-1, 108-2, 108-3, such as a smartphone, a tablet computer, or the like. Shifts can be transmitted to the client devices 108 for viewing by the relevant employees via a web browser, a dedicated scheduling application, or the like. The automatically generated schedule can also be provided to an administrator 112, such as a manager of the facility (the facility may have more than one manager in some examples), via a client computing device 116 operated by the administrator 112. The administrator 112 and the employees 104 may, for example, have account credentials or the like permitting access to a web-based application where schedule data can be viewed and/or edited.”
and thus are not practically integrated nor significantly more.
Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea. Thus, the claims are directed to an abstract idea. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 3 – 8, and 11 – 16, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they merely provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1, 3 – 9, and 11 – 16, are not patent eligible.
Claim Rejections – 35 U.S.C. §103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. Claims 1, 3 – 9, and 11 – 16, are rejected under 35 U.S.C. § 103 as being unpatentable over Guiffre, Christopher et al. (U.S. Publication No. 2022/0180266) hereinafter “Guiffre” in view of Wayne, David Henry et al. (U.S. Publication No. 2024/0020756) hereinafter “Wayne”.
Claims 1 and 9:
receiving, at a computing device, allocation requests for a plurality of previous unallocated shifts; Guiffre teaches in ¶ 0102, shift owners responsible for managing and filling shifts may interact with to perform various tasks. Fig. 5A shows a shift owner's dashboard. Dashboard may include a schedule view having a plurality of features. One example feature enables the shift owner to select one or more types of shifts 504, including scheduled shifts, published shifts, unpublished shifts, training, accepted shifts, and the like, and a defined period of time 506 (e.g., a day, a week, or a month), to generate a schedule 508 that provides an overview of shifts of the selected types spanning the defined period of time. Additionally, the shift owner is able to view the shifters who have accepted shifts within the defined period of time (active shifters 510). Further, the shift owner is enabled to print or export the schedule 508 by selecting a print control element 512 or an export control element 514, respectively. Guiffre teaches in ¶ 0147, an upcoming shift may be a shift that has yet to be opened. In other examples, an upcoming shift may be a shift for which opening has been initiated but the shift is not yet published, and/or filled. The computer-implemented method of claim 1, wherein receiving the indication to open the shift associated with the shift owner comprises at least one of: detecting an actuation by the shift owner to open the shift; receiving an indication to open the shift based on a predicted demand associated with the shift; detecting that a previous shifter to which the shift was allocated subsequently declined the shift; and detecting a number of requests for the shift from the plurality of shifter profiles is above a predefined threshold.
determining, for each previous unallocated shift, a label based on a number of the allocation requests that correspond to the previous unallocated shift; Guiffre teaches in ¶ 0061, an indication to initiate an opening of a shift associated with at least one of the one or more shift owner profiles generated at 202 may be received. In some examples, the indication may be received based on a shift owner actuating the generation of a new shift, updating a previous shift, copying a previous shift to generate a new shift (e.g., using a template), or the like. The indication may be any applicable manner in which a shift owner may initiate a shift opening such as via an application, application portal, a calendar, a scheduling GUI, or the like. Guiffre teaches in ¶ 0062, in other examples, the indication may be received based on a detected call-out by a shifter who had previously accepted a shift that now must be re-opened and re-filled. In further examples, the indication may be received based on a detected number of requests for a shift being above a predefine threshold. In yet further examples, the indication may be received based on data received from a demand forecasting model that indicates that there is a high demand (e.g., a demand exceeding a threshold) for shifts of certain types and/or at certain times within a next predefined period of time (e.g., within the next week, month, etc.). The demand forecasting model may be a machine learning model that may receive, as inputs, one or more of shift histories, shift owner attributes, past call-outs, shifter history, shift demand, shift likelihood of being filled, or the like. The forecasting model may output an indication to generate the shift at 206.
detecting, at the computing device, an unallocated shift record corresponding to a facility, the unallocated shift record defining a time period and a task identifier; Guiffre ¶ 0070, based on an understanding that some shifts (e.g., day, time, length, etc.) jobs, and locations may be more difficult to fill than others, the adaptive pay machine learning model may be implemented to increase desirability/uptake of shifts and may normalize the amount made for more desirable shifts compared to less desirable shifts. For example, at 252, current shift data may be provided as inputs to the adaptive pay machine learning model. At 254, a predicted length of time between publishing and filling of the shift based on the current shift data may be received as output of the adaptive pay machine learning model. Based on the predicted length of time (e.g., if the length of time exceeds a predefined threshold), adjustments to at least a portion of the current shift data associated with a pay rate may be determined to decrease the length of time at 256. In some examples, the predefined threshold may be dependent on temporal aspects (e.g., if the shift is to start within a short time window of generation/publishing) or need (e.g., in an all hands on deck scenario) associated with the shift. At 258, the adjustments may be implemented automatically to modify the portion of the current shift data based on the adjustments or the adjustments may be provided as recommendations or suggestions to the shift owner to prompt the shift owner to increase shift uptake (e.g. by adjusting the wage rate or pay, real-time, for the shift). In some examples, an interface may be used to show shift owner's the probability a shift will be picked up based on the recommended adjustments. Guiffre teaches in ¶ 0072, As one example, location may be one factor included in the minimum matching criteria. That is, the initial subset of shifter profiles may include only shifters that are qualified to or able to work within a location perimeter associated with the shift. For example, a shifter in the state of Pennsylvania would not meet a minimum matching criteria for a shift designated for the state of Georgia. However, a shifter that is based in Georgia but currently located in the state of Nevada (e.g., on a project or on vacation) may receive the Georgia based shift based on the shifter being based in Georgia. According to an implementation, a calculation may be made that a given shifter in a first location may be able to travel to a second location based on one or more factors. For example, a shifter in North Carolina may receive the shift published in Georgia based on distance between the shifter's location in the state of North Carolina being within a threshold distance from the location associated with the shift in Georgia. Additionally, a shifter's profile may include scheduled travel such that a shifter based in Arizona that calendars a trip to Georgia may see the published shift in Georgia if the shift date and the travel date overlap;
executing, at the computing device, the trained classification model to generate an index corresponding to the unallocated shift record, the index being a value among a range of values indicative of a forecasted interest in the unallocated shift record; Guiffre teaches in ¶ 0149, As one example, a demand forecasting machine learning model may be trained and deployed to predict demand for upcoming shift, as discussed above with reference to Fig. 2B. In one non-limiting example, the demand forecasting machine learning model may be a supervised deep learning regression model with given input features and optimized hyperparameters, as disclosed herein. A related network may include residual connections to avoid vanishing gradients. For training the demand forecasting machine learning model, the specific types of historical shift data 1016 that are provided as inputs 1014 may include, but are not limited to: a shift type, a shift start time, a shift stop time, a location identifier, a number of rooms occupied, a year, a day of year, a day of week, a holiday, weather, a temperature, a hotel type, a hotel class, a region, a previous day's actual demand, a day of year (DOY) demand for a number of previous years, identifiers of a number of most likely shifters; regional weather data, regional transportation data, regional event data, crowdsourced data (e.g., related to labor and scheduling), and scheduling data associated with the given historical shift. The types of labels 1018 used to train the demand forecasting machine learning model may include a label for each of the plurality of historical shifts, the label indicating the actual demand associated with the respective historical shift.
generating, at the computing device, an impact indicator for the unallocated shift record based on the time period, the task identifier, and the index; Guiffre teaches in ¶ 0151, a shift attractiveness machine learning model may be trained and deployed to predict a probability of an upcoming shift being accepted. The shift attractiveness machine learning model may be a supervised deep learning classifier model with given input features and optimized hyperparameters. The error or loss may be a probability such as a categorical cross entropy. For training the shift attractiveness machine learning model, the specific types of historical shift data provided as inputs may include, but are not limited to: a shift type, a shift rate adder, a shift start time, a shift stop time, a type of job function or specialty associated with the shift, a location identifier, a date and time the shift was posted, a length of time between publishing and filling the shift, a year, a day of year, a day of week, a holiday, weather, a temperature, a hotel type, a hotel class, a region, a previous day's actual demand, a day of year (DOY) demand for a number of previous years, and identifiers of a number of most likely shifters. The types of labels used to train the shift attractiveness machine learning model may include a label for each of the plurality of historical shifts, the label being a binary response (e.g., yes or no) as to whether the respective historical shift was filled.
selecting a control action for the unallocated shift record according to the impact indicator; Guiffre teaches in Fig. 2C, depicts a flowchart of an exemplary process 240 for using the shift attractiveness machine learning model to perform at least a portion of the shift opening process at 208 of Fig. 2A. For example, at 242, current shift data may be provided as inputs to the shift attractiveness machine learning model. At 244, a predicted probability that the shift will be accepted based on the current shift data may be received as output from the shift attractiveness machine learning model. In some examples, the probability (e.g., a percentage) may be visually displayed as a shift attractiveness score to the shift owner during the shift generation process, as shown in Fig. 5B. At 246, based on the predicted probability, adjustments to at least a portion of the current shift data that will increase the probability may be determined. At 248, the adjustments may be implemented automatically to modify the portion of the current shift data based on the adjustments or the adjustments may be provided as recommendations or suggestions to the shift owner. The recommendations may include an updated shift attractiveness score reflecting an updated probability output by the model given the adjusted shift data. Guiffre teaches in ¶ 0068, Therefore, utilizing the shift attractiveness machine learning model, the system and techniques disclosed herein may assist shift owners that have trouble filling shifts because the day, time and length, among other example data, of one or more given shifts are not appealing. As one specific but non-limiting example, shifters working another full-time job for another institution (e.g., Monday-Friday (M-F) 8:00 AM-5:00 PM) may not want to or may not be able to pick up a shift (e.g., from 5:30 PM-12:00 AM) but may eagerly pick up a different shift (e.g., from 6:00 PM-11:00 PM). The system may show shift owners a probability that a given shift initiated by the shift owner will be picked up for a given day, time and shift length. For example, a percentage of the probability of the given shift being picked up based on the given day, time and shift length may be visually displayed to the respective shift owner, along with suggestions on how to adjust the shift to make it more attractive (e.g., modified timings for the shift, modified qualifications, alternative shift days, length of shift, etc.) and the predicted adjusted probability (e.g., percentage) that that the shift would be picked up if one or more of the suggestions were to be implemented. Guiffre teaches in ¶ 0076, According to an implementation, shift prioritization may be used to present shifts in a streamlined manner to prevent shifters from being overwhelmed by a number of available shifts. For example, an order or arrangement in which the published shift is presented to each of the identified shifter profiles (e.g., among other shifts that are published to the shifter profiles) may be based on output of a machine learning model. For example, Fig. 2G depicts a flowchart of an exemplary process 280 for using a shift prioritization machine learning model to perform at least a portion of the publishing process at 212 of Fig. 2A. For example, at 282, for each of the shifter profiles identified for publishing, the shift data and information associated with the respective shifter profile may be provided as inputs to the shift prioritization machine learning model. At 284, a predicted probability that a shifter associated with the respective shifter profile accepts the shift may be provided as output of the shift prioritization machine learning model. At 286, based on the predicted probability, an order or arrangement in which to present the published shift among other published shifts to the respective shifter profile may be determined. For example, shifts that have a higher likelihood of being selected by a shifter may be presented or may be presented first to the shifter's profile. Additionally, and/or alternatively, a shifter may be able to parametrically filter attributes to narrow down a list of available shifts by, for example, time, duration, date, location, supervisor, co-shifter, or the like.
and executing the selected control action; Guiffre teaches in ¶ 0070, Based on an understanding that some shifts (e.g., day, time, length, etc.) jobs, and locations may be more difficult to fill than others, the adaptive pay machine learning model may be implemented to increase desirability/uptake of shifts and may normalize the amount made for more desirable shifts compared to less desirable shifts. For example, at 252, current shift data may be provided as inputs to the adaptive pay machine learning model. At 254, a predicted length of time between publishing and filling of the shift based on the current shift data may be received as output of the adaptive pay machine learning model. Based on the predicted length of time (e.g., if the length of time exceeds a predefined threshold), adjustments to at least a portion of the current shift data associated with a pay rate may be determined to decrease the length of time at 256. In some examples, the predefined threshold may be dependent on temporal aspects (e.g., if the shift is to start within a short time window of generation/publishing) or need (e.g., in an all hands on deck scenario) associated with the shift. At 258, the adjustments may be implemented automatically to modify the portion of the current shift data based on the adjustments or the adjustments may be provided as recommendations or suggestions to the shift owner to prompt the shift owner to increase shift uptake (e.g. by adjusting the wage rate or pay, real-time, for the shift). In some examples, an interface may be used to show shift owner's the probability a shift will be picked up based on the recommended adjustments. Guiffre teaches in ¶ 0071, once the shift is opened (e.g., at least the minimum shift data is generated), at 210, one or more of the shifter profiles generated at 204 may be identified for publishing the shift to, based on the shift data and one or more second attributes of the shifter profiles. Fig. 2E depicts a flowchart of an exemplary process 260 to perform at least a portion of the shifter profile identification process at 210 of Fig. 2A. For example, at 262, the shifter profiles may be queried, using the shift data, to identify an initial subset of the shifter profiles having one or more second attributes that at least partially match the shift data. In other words, attribute matching may be performed, where the first subset of the shifter profiles may meet a minimum matching criteria with the shift data.
Guiffre teaches a shift attractiveness machine learning model, trained demand forecasting machine learning model, shift not filled, shift attractiveness score, shift not selected, shift not yet published, and classifier model. Guiffre also teaches a machine learning model trained based on historical shift data for previous shift, and Guiffre is similar to Wayne, where Wayne teaches observing and evaluating work schedules with machine learning models and trained machine learning models. Wayne further teaches the following:
generating and training a classification model based on (i) the labels and (ii) the previous unallocated shifts; Wayne teaches in ¶ 0502, Embodiments may include learning external data source quality/reliability. In an unsupervised manner, the system may continually update an input quality model based on actual versus predicted results. If a given external data source provides predictive data (e.g., a weather forecast), the system learns to what degree a new input is reliable based on the actual weather once the predictive timeframe is reached. In certain embodiments, the system may also utilize direct feedback (labeling) from user(s) as a supervised learning input. Wayne teaches in ¶ 0523, the feedback influencer circuit 110108 generates the feedback influence command value based at least in part on machine learning. The machine learning may extract trends from the feedback data, and the trends are used to generate the feedback influence command value. In embodiments, the machine learning may be a neural network trained over a labeled training set that includes historical feedback data and associated trends. Wayne teaches in a connection module may be trained to determine the type of bias and/or apply a bias. In embodiments, one trained model may be configured to determine the type of bias and apply a bias to the data. In some embodiments, a plurality of trained models may be used such as one or more modules may be trained to determine the type of bias (such as determining if a bias can be directly applied to data or if biasing requires manipulation of input data to another module). Other modules may be trained to apply the bias to the data (such as by determining the magnitude and/or how to manipulate input data of other modules to obtain the desired output bias). The connection modules may be trained using training data using one or more supervised or unsupervised training methods. In one embodiment, a set of training data may include labeled data relating to a state of an agglomerate network and data that includes adjustments made to data by a user (manual biases made by a user). A machine learning model (such as a neural network) may be trained on the data to predict the type of bias that can be applied and magnitude of changes required to achieve the bias. In one example, a neural network may be a classifier that takes as input the state data of an agglomerate network (data including one or more data inputs, metadata, configuration data, etc.), and generates an output the identifies if the type of bias that can be applied. After training, the trained model may be used to automatically identify the type of biases that can be applied and/or the magnitude of bias. In embodiments, the trained modules may be refined and/or retrained based on changes from users (such as manual bias overrides, feedback from users regarding quality of schedules produced, etc.). Wayne teaches in ¶ 0579, certain further aspects of the method 160300 are described following, any one or more of which may be present in certain embodiments. For example, in embodiments, the employee data may include at least one of: a position, demographic information, a number of years in the position, an attendance rate, a residence, a work location, work commute data (e.g., distance, routes, or traffic congestion), education level, and the like. Matching of the first employee to the second employee may be based at least in part on a neural network 160402 that ranks 160404 a plurality of potential matches in a query result. In embodiments, the neural network may be trained over a training dataset of labeled query results each having a score corresponding to an initial employee profile (to which the results are matched). Extraction of the schedule trend may be based at least in part on a neural network. The extracted trend 160420 may be an attendance rate 160406 with respect to one or more of: a shift time, a number of shifts, a shift position within a workweek, a commute distance, a number of co-workers on a shift, or a number of managers on a shift. Wayne teaches in ¶ 0692, A bid evaluation circuit 100124 may evaluate each submitted bid 100122 and determine a bid quality value 100128 for each bid 100122 based on corresponding shift properties 100118, the bid 100122, and worker properties 100302 corresponding to the worker 100113 that submitted the bid 100122. Based on the submitted bids 100122 and bid quality values 100128, a winning bid 100130 is determined for a particular upcoming shift 100114. A shift allocation circuit 100132 may then allocate the upcoming shifts 100114 to different workers 100113 based on the winning bids 100130. The allocated shifts 100134 may then be provided to the different workers. Wayne teaches in ¶ 0739, in embodiments, a connection module may be trained to determine and/or apply a bias. A connection module may be trained using training data using one or more supervised or unsupervised training methods. In one embodiment, a set of training data may include labeled data relating to a state of an agglomerate network and data that includes adjustments made to data by a user (manual biases made by a user). A machine learning model (such as a neural network) may be trained on the data to predict when a bias is applied to the data. In one example, a neural network may be a classifier that takes as input the state data of an agglomerate network (data including one or more data inputs, metadata, configuration data, etc.), and generates an output the identifies if an output should be biases and/or the magnitude of the bias and/or a type of bias to be applied. Training data may be used to train the network (such as using back propagation) using the labeled data. After training, the trained model may be used to automatically identify if biases should be applied, the magnitude of bias, and/or the type of bias that should be applied. In embodiments, the trained modules may be refined based on changes from users (such as manual bias overrides, feedback from users regarding quality of schedules produced, etc.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine methods and systems for attribute-based generating and allocating of a shift opening of Guiffre with an apparatus and methods for enabling workers to compete for currently upcoming shifts of Wayne to assist businesses with input data used in building trained machine learning classifier models (Wayne, Spec. ¶ 0754).
6. Claims 3 – 5, 7 – 8, 11 – 13, and 15 – 16, are rejected under 35 U.S.C. § 103 as being unpatentable over Guiffre, Christopher et al. (U.S. Publication No. 2022/0180266) hereinafter “Guiffre” in view of Wayne, David Henry et al. (U.S. Publication No. 2024/0020756) hereinafter “Wayne” in view of Aslam, Sohail et al. (U.S. Patent Publication No. 2022/0198353) hereinafter “Aslam”.
Claims 3 and 11:
Guiffre and Wayne teach claims 1 and 9. Neither Guiffre nor Wayne explicitly teach random forest classification. However Aslam teaches the following:
wherein generating the classification model includes: generating a random forest classification model configured to output a label for an unallocated shift record; Aslam teaches in ¶ 0129, the ML model may be a random forest model.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine methods and systems for attribute-based generating and allocating of a shift opening of Guiffre and an apparatus and methods for enabling workers to compete for currently upcoming shifts of Wayne with methods and systems to identify problem shifts by programmatically analyzing the published shift schedule and the corresponding time and attendance record to determine unscheduled shifts of Aslam to assist businesses with implementing machine learning classifying models using random forest modeling (Aslam, Spec. ¶ 0129).
Claims 4 and 12:
Guiffre and Wayne teach claims 1 and 9; and Guiffre further teaches trained shift machine learning model, shift prioritization and a shift attractiveness score and Wayne further teaches identifier for one or more shifts, required skill set, location performance of a task, neither Guiffre nor Wayne explicitly teach task priority. However, Aslam teaches the following:
wherein generating the impact indicator includes: retrieving a task priority corresponding to the task identifier; Aslam teaches in ¶ 0183, a shift schedule may be finalized (published) based on the received shift bids, eligibility verified, regulatory, overtime, etc. prioritization based on performance, other factors when finalizing; See also ¶ 0153.
and generating a component of the impact indicator based on the task priority; Aslam teaches in ¶ 0231, prioritization may be based on the skillset of the employee and/or experience in the relevant field. See also ¶ 0153.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine methods and systems for attribute-based generating and allocating of a shift opening of Guiffre and an apparatus and methods for enabling workers to compete for currently upcoming shifts of Wayne with methods and systems to identify problem shifts by programmatically analyzing the published shift schedule and the corresponding time and attendance record to determine unscheduled shifts of Aslam to assist businesses with evaluating and implementing schedules based on prioritization of task performance (Aslam, Spec. ¶ 0183).
Claims 5 and 13:
Guiffre and Wayne teach claims 1 and 9. Guiffre further teaches a shift start time, a shift stop time, a type of job function or specialty associated with the shift, a location identifier, a date and time the shift was posted, a length of time between publishing and filling the shift, a year, a day of year, a day of week, a holiday; and Guiffre relates to Aslam where Aslam teaches assigning identifiers to schedules, and Aslam further teaches the following:
wherein the unallocated shift record includes a portion of the time period corresponding to the task identifier; Aslam teaches in ¶ 0170, additional notifications may be provided to users/employees based on detection of a problem shift in a shift schedule template or a published shift schedule (e.g., due to an employee calling-off a scheduled shift, etc.). A notification of availability of a special shift may be displayed on a user device, along with details of the shift, and a type of incentive offer associated with the special shift. The user may request a special shift via the user interface;
and wherein generating the component of the impact indicator is further based on the portion of the time period; Aslam teaches in ¶ 0231, the prioritization may be based on a time at which a bid for received (first come first serve basis), as an incentive for employees to submit their bids early.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine methods and systems for attribute-based generating and allocating of a shift opening of Guiffre and an apparatus and methods for enabling workers to compete for currently upcoming shifts of Wayne with methods and systems to identify problem shifts by programmatically analyzing the published shift schedule and the corresponding time and attendance record to determine unscheduled shifts of Aslam to assist businesses with evaluating and implementing shift schedules based on shift identifiers (Aslam, Spec. ¶ 0005).
Claims 7 and 15:
Guiffre and Wayne teach claims 1 and 9. Guiffre does not explicitly teach visual indicators; however Wayne teaches a visual indicator and is related to Aslam where Aslam teaches a visualization of a shift attribute(s) and Aslam further teaches the following:
wherein selecting the control action includes: (i) maintaining, at the computing device, a plurality of visual indicators associated with respective thresholds; Aslam teaches in ¶ 0241, a shift slot with a high likelihood of becoming unscheduled (e.g., a night shift during a holiday weekend), a reception level of for one or more incentive offers(s) met a predetermined threshold (e.g., exceeded a predetermined number of users who viewed the message), but still did not receive a threshold number of valid shift bids;
(ii) selecting one of the visual indicators by comparing the impact indicator to the thresholds; Aslam teaches in ¶ 0010, determining an updated incentive offer; Aslam teaches in ¶ 0010, and displaying the updated incentive offer on the user devices;
and wherein executing the selected control action includes: controlling a display to present the unallocated shift record in association with the selected one of the visual indicators; Aslam teaches in ¶ 0010, the reception level of one or more incentive offers may be compared to a first threshold level, and a count of received shift bids compared to a second threshold level.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine methods and systems for attribute-based generating and allocating of a shift opening of Guiffre and an apparatus and methods for enabling workers to compete for currently upcoming shifts of Wayne with methods and systems to identify problem shifts by programmatically analyzing the published shift schedule and the corresponding time and attendance record to determine unscheduled shifts of Aslam to assist businesses with evaluating and implementing shift schedules based on visual attributes (Aslam, Spec. ¶ 0132).
Claims 8 and 16:
Guiffre and Wayne teach claims 1 and 9; and Guiffre further teaches a predicted probability that the shift will be accepted based on the current shift data may be received as output from the shift attractiveness machine learning model. In some examples, the probability (e.g., a percentage) may be visually displayed as a shift attractiveness score to the shift owner during the shift generation process and machine learning model techniques to identify performance optimizers and/or incentives, including pay incentives and is related to Aslam where Aslam teaches displaying shift incentives to users for bidding, and Aslam further teaches the following:
wherein selecting the control action includes: determining, at the computing device, an incentive value based on the impact indicator; Aslam teaches in ¶ 0007, a method supporting evaluation of an incentive structure for resolving problem shifts;
and transmitting the unallocated shift record and the incentive value to a plurality of client devices; Aslam teaches in ¶ 0011, determining an incentive offer for each problem shift; instantiating a graphical user interface (GUI) portion on a plurality of user devices; displaying the incentive offer for each of the identified problem shifts via the GUI.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine methods and systems for attribute-based generating and allocating of a shift opening of Guiffre and an apparatus and methods for enabling workers to compete for currently upcoming shifts of Wayne with methods and systems to identify problem shifts by programmatically analyzing the published shift schedule and the corresponding time and attendance record to determine unscheduled shifts of Aslam to assist businesses with evaluating and implementing shift schedules based on displaying incentive offers to the user device (Aslam, Spec. ¶ 0009).
7. Claims 6 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Guiffre, Christopher et al. (U.S. Publication No. 2022/0180266) hereinafter “Guiffre” in view of Wayne, David Henry et al. (U.S. Publication No. 2024/0020756) hereinafter “Wayne” in view of Westland, Dina et al. (U.S. Patent No. 10,535,024) hereinafter “Westland”.
Claims 6 and 14:
Guiffre and Wayne teach claims 1 and 9. Guiffre further teaches a shifter's profile may include scheduled travel such that a shifter based in Arizona that calendars a trip to Georgia may see the published shift in Georgia if the shift date and the travel date overlap and is related to Westland where Westland teaches shift conflicts and Westland further teaches the following:
wherein generating the impact indicator includes: retrieving a reference time period, and generating a component of the impact indicator based on an overlap between the time period and the reference time period; Westland teaches in col. 15, lines 53 – 61, the shift management service module may automatically identify conflicts with selected shifts for other employers, and as indicated may present a conflict indicator, such as the word “conflict” or other visual indication in association with these shifts. As one example, the shift manager service module may determine that a conflict with another shift exists based at least in part on an overlap of a time of day of a first shift and a time of day of a second shift on the same day.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine methods and systems for attribute-based generating and allocating of a shift opening of Guiffre and an apparatus and methods for enabling workers to compete for currently upcoming shifts of Wayne with determining from a shift schedule that a first employee is scheduled to work a particular shift at a business within a threshold period of time is unlikely to arrive at the business in time for the start of the particular shift of Westland to assist businesses receiving an employee request to be scheduled for a shift in a graphic user interface (Westland Spec. col. 20, lines 1 – 3).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Hunter, Bane et al. (U.S. Publication No. 2022/0092519) discloses a method of generating a schedule for a company including obtaining profile data for one or more workers associated with the company.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
02/17/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625