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 the Claims
The following is a non-final office action in response to the communication filed by applicant on 12/4/2025. Claims 1, 8, and 14 have been amended. Claims 1-20 are pending.
Response to Amendment
Applicant’s amendments are acknowledged.
Response to Arguments
With respect to Applicant’s arguments regarding 35 USC § 101:
Applicant argues that the claims are directed to a practical application of the judicial exception because they are directed to an improvement in the functioning of a computer itself, stating the claim provides a technical solution to a technical problem concerning reductions in computational accuracy and efficiency (see page 10 of the arguments and specification paragraph 0049 and 0069). The arguments further point to performing iterative analysis on sample data using machine learning to construct a predictive model, and improving an accuracy of a predictive model by iteratively modifying hyperparameters to control the rate of updating using reinforcement learning, and states the claims require the analysis of a large amount of data in a quick and efficient manner which would be ineffective without the computer components (eg machine learning models).
Examiner respectfully disagrees. First, the ability of a computer performing repetitive calculations is a basic function of a computer and does not impose meaningful limits on the scope of those claims in terms of eligibility. Further, iteratively modifying hyperparameters to control the rate of updating using reinforcement learning is claimed in a generic manner and sets forth the idea of a solution without details on how the solution is accomplished. Hyperparameters are discussed in 0069-70 of the specification, which states: if the model fails the test, the hyperparameters of the model are changed and/or the training and test data are re- randomized, and the iterative analysis of the training data is repeated (step 512). Hyperparameters are the settings of the algorithm that control how fast the model learns patterns and which patterns to identify and analyze. Once a model has passed the test stage, it is ready for application. See figure 5 and 0070. These sections do not describe how hyperparameters are modified and evaluated, just broadly describe that they can be modified. Thus, this is claimed in an apply it manner.
Applicant further states the claims solve technical problems and are patentable for similar reasons to the claims in Ex Parte Tovi Grossman et al. No. 2004-003196, 2025 WL 143914, at *8-9 (PTAB May 16, 2025) (“Grossman”).
Examiner respectfully disagrees. In Grossman, the appellant did argue that the claims are "directed towards the practical application of automatically analyzing and comparing a workflow of a user with workflows of other users for performing a design task, and automatically optimizing the workflow of the user based on the analysis and comparison" and further argued that the practical application "imparts the technological improvement of automatically analyzing, comparing, and optimizing workflows via a trained autoencoder and vector-based node representations of the workflows that enable more efficient and accurate comparisons between workflows relative to what can be achieved using conventional approaches." The Board emphasized that the inclusion of "machine learning" into the claims does not itself make the claims eligible, per Recentive Analytics, Inc. v Fox Corp. et al. (claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible) but felt in the instance of Grossman the claims do more. This was based on the specification describing the trained autoencoder and vector-based node representation of 3D data.
In the instant application, the fact pattern of the claims differ from that of Grossman with emphasis on the specification. The claims in the instant application perform iterative analysis using machine learning to construct a predictive model, where the machine learning modifies hyper parameters to control a rate of updating using reinforcement learning. Looking to the specification, figure 5 and 0069-70 are discussed above. Per MPEP 2106.04(d)(1) the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field. […] if the specification explicitly sets forth an improvement but only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field. Here, there are limited details regarding the modifying of hyperparameters. Thus, it appears the improvement is set forth in a conslusory manner and thus the claims do not improve technology or a technical field.
With respect to Applicant’s arguments regarding 35 USC § 103:
Applicant argues that Qamar, Lium Caouto and Vashisht do not teach converting, by the one or more processors, the predicted turnover for a plurality of selected employers into an index of employee turnover based at least in part on the predicted turnover relative to an observed voluntary employee turnover for employers with similar opportunity. This argument has been fully considered and is persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Qamar (US 2015/0269244) in view of Grindstaff et al. (US 2018/0285691) in further view of Parmar et al. (US 2013/0166358).
Allowable Subject Matter over the prior art
Claims 2, 9, and 15 are indicated as allowable over the cited prior art. There are still outstanding 35 USC 101 and 112 rejections, and these claims are also dependent upon a base claim rejected under 35 USC 103. The claims would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, as well as to overcome the 35 USC 101 rejections.
Claim Rejections - 35 USC § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8, and 14 claims recite the abstract idea of predicting turnover for employees and considering factors and data associated with employee growth opportunities and voluntary turnover. Using claim 1 as representative, the claim specifically recites:
identifying sample data regarding a plurality of metrics associated with employee growth opportunity and voluntary employee turnover;
performing iterative analysis on the sample data to construct a predictive model;
determining an error rate based on testing the accuracy of the predictive model using a test data split from the sample data;
determining using the predictive model, responsive to the error rate satisfying a threshold, for each individual of a plurality of individuals, a predicted turnover for each individual based on the plurality of metrics associated with the employee growth opportunity;
converting the predicted turnover for a plurality of selected employers into an index of employee turnover based at least in part on the predicted turnover relative to an observed voluntary employee turnover for employers with similar opportunity; and
transmitting data to cause a visual representation comprising at least a portion of the index.
These limitations fall within the abstract idea grouping of certain methods of organizing human activity, commercial interactions - marketing or sales activities or behaviors in that it is identifying information about the employees of an organization and predicting turnover.
Further, identifying sample data regarding a plurality of metrics, performing iterative analysis on the sample data to construct a predictive model, determining an error rate based on testing the accuracy of the predictive model using a test data split from the sample data, determining using the predictive model, responsive to the error rate satisfying a threshold, for each individual of a plurality of individuals, a predicted turnover, converting the predicted turnover into an index reasonably falls within mathematical concepts, mathematical calculations because it is determining a value and converting values into an index using predictive models and mathematical means.
This judicial exception is not integrated into a practical application. Claim 1 includes the additional elements of one or more processors; using machine learning, wherein the machine learning improves an accuracy of the predictive model by iteratively modifying hyperparameters of the predictive model to control a rate of updating the predictive model using reinforcement learning; and transmitting, by the one or more processors, to a computing device, data to cause the computing device to display, on a display device coupled with the computing device, a visual representation. Claim 8 additionally includes a computer system and claim 14 a non-transitory computer readable storage media comprising one or more instructions stored therein, wherein the instructions cause the processors to perform the functions. When considered in view of the claim as a whole, the claim is at a high level of generality and in a manner that describes how to generally apply the concept of predicting turnover for employees and considering factors and data associated with employee growth opportunities and voluntary turnover and performing mathematical calculations in a computer environment. Specifically, the processors, the computer system, and the non-transitory computer readable storage media are claimed at a high level of generality and merely invoked as tools to perform the recited abstract idea. Simply implementing the abstract idea in a generic computer environment is not enough. See Figure 12 and the associated paragraphs of Applicant’s specification, as well as MPEP 2106.05(f). With respect to iterative analysis using machine learning including modifying hyperparameters to control a rate of updating using reinforcement learning, this recitation still generally links the use of the abstract idea to a particular technological environment. It is also claimed at a high level of generality, and recites the idea of a solution or outcome without details of how the solution is accomplished. With respect to the transmitting and providing a digital representation, these limitations are mere data gathering/data exchange/data output and insignificant extrasolution activities which do not provide a practical application to the abstract idea (See MPEP 2106.05(g)). Further, when the additional elements are considered in combination, the additional elements are claimed at a high level of generality and generally applies the abstract idea in a computer environment. Thus, the additional elements do not integrate the recited abstract idea into a practical application and the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claim as a whole merely describes how to generally apply the exception. With respect to transmitting and providing a digital representation, these limitation is well-understood, routine, and conventional computer function. See 0081, Figure 12 and MPEP 2106.05(d)), where receiving or transmitting data over a network are elements that the courts have recognized as well-understood, routine, conventional activity in particular fields. These limitations are mere data gathering/data exchange and insignificant extrasolution activities which do not provide significantly more to the abstract idea (See MPEP 2106.05(g)); and these limitations involve necessary data gathering and outputting and are equivalent to receiving/transmitting data and are well-understood routine and conventional which do not provide significantly more to the abstract idea (See MPEP 2106.05(d)). Thus, when considering claims 1, 8 and 14 as a whole, and the additional elements alone and in combination, add nothing to the claims that is significantly more to the abstract idea. The claims are ineligible.
Claims 2-7, 9-13, and 16-20 further narrow the recited abstract idea recited above and are rejected for the same reasons. In addition, claim 2, 12, 15 include the use of machine learning to update the model and claims 5-7, 11-13, and 18-20 each recite types of machine learning (supervised, unsupervised, reinforcement). These additional elements, alone and in combination, are recited at a high level of generality and link the use of the abstract idea to a particular technological environment. Therefore, the additional elements, alone and in combination, that integrates the recited abstract idea into a practical application and adds nothing to the claims that is significantly more than the abstract idea.
As a result, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 recites “converting, by one or more processors, the predicted turnover for a plurality of selected employers into an index.” This limitation is unclear and appears to lack antecedent basis. The claim previously recited “determining [..] for each individual or a plurality of individuals, a predicted turnover for each individual…” The claim does not previously include a predicted turnover for a plurality of selected employers or how such turnover was generated. Per 0084 of the specification, it appears it is the plurality of individuals predicted turnover that is combined into an index for the employer, and it has been interpreted as such for examination purposes. Claims 8 and 14 contain substantially similar limitations. Appropriate correction is requested.
Claims 2-7, 9-13, and 15-20 depend from claims 1, 8, and 14 and are rejected for substantially the same reasons.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-8, 10-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Qamar (US 2015/0269244) in view of Grindstaff et al. (US 2018/0285691) in further view of Parmar et al. (US 2013/0166358).
Regarding Claim 1, Qamar discloses:
A computer-implemented method for predictive modeling, the method comprising: (Figure 8, 0090-0092)
identifying, by one or more processors, sample data regarding a plurality of metrics associated with employee growth opportunity and voluntary employee turnover (0054- using a training subset of organization data and external data to build regression models;
0008, 0049- …the organization data may include…compensation of the employee, satisfaction scores associated with the employee (employee satisfaction) (such as rankings provided by a customer, a manager or other employees, a trainer or coach, etc.);
0052 – …The external data may include…hiring trends in the region (such as a number of job postings or hiring by one or more competitors of the organization), retention of employees by competitors of the organization, proximity of the competitors of the organization (such as the opening nearby of a new factory), compensation offered by the competitors, (employee growth opportunities)….
performing, by the one or more processors, iterative analysis on the sample data using machine learning to construct a predictive model (0054- a series of regression models may be built and evaluated using a training subset of the organization data and/or the optional external data; In some embodiments, calculating the performance metric (operation 114) and/or determining the retention risk (operation 116) involves variance decomposition (into a portion of the variance associated with known sources and another portion of the variance associated with unknown sources) to select factors in the organization data, determine their impact, and to order or cluster the factors in regression models. For example, variance decomposition may perform regression to assess the importance and to order the factors in a polynomial, which may be a linear combination of the factors raised to associated exponents n and multiplied by associated amplitude weights w.sub.i (however, a wide variety of linear and nonlinear functions may be used). In particular, using the entropy, a set of factors may be identified in the organization data and/or the optional external data. Then, a series of regression models may be built and evaluated using a training subset of the organization data and/or the optional external data. In these regression models, factors may be removed one at a time, and the remaining factors may be reordered. These permutations and combinations on subsets of the set of factors may provide a table of predictions for the different regression models (i.e., statistical comparison between predictions of the regression models for a test subset of the organization data and/or optional external data relative to the training subset). The average model performance for the factors, the cross-correlations among the factors and/or the ordering of the factors in these predictions may be used to select the polynomial (factors, exponents n and amplitude weights w.sub.i) using to calculate the performance metric and/or to determine the retention risk.
determining, by the one or more processors, an error rate based on testing the accuracy of the predictive model using a test data split from the sample data, (0174-Note that the dataset used to generate the predictive model may include: 60% for training, 30% for validation or optimization, and 10% for testing or to confirm the results. When generating the predictive model, the analysis may be repeated 20 times and the best performing instance of the predictive model (ie. least error rate) may be used in method 1500 (FIGS. 15 and 16).
determining, by the one or more processors, using the predictive model, responsive to the error rate satisfying a threshold (See 0174- either the errors of the second best model or “20 repetitions” may be considered thresholds for the best performing model for the error rate), for each individual of a plurality of individuals, a predicted turnover for each individual based on the plurality of metrics associated with the employee growth opportunity; (Figure 9, 0054(bottom), 0093-determining retention risk based on the factors;
0093- As shown in FIG. 9, which presents a block diagram illustrating data structure 900, this information may be stored in a data structure (such as a database or another type of data structure) for subsequent analysis. In particular, data structure 900 includes entries 910, such as organization data 836 and/or optional external data 840 at different time stamps (such as timestamp 912). As described further below, this information may be analyzed one or more times for different employees 842 in subsets (such as subset 914) of organization 838 (FIG. 8) to determine one or more performance metrics 844, one or more retention risks 846 and/or one or more remedial actions 916 (such as one or more retention suggestions 852 and one or more cost-benefit analyses 854 in FIG. 8).
Transmitting, by the one or more processors to a computing device, data to cause the computing device to display, on a display device coupled with the computing device, a visual representation comprising retention and turnover information (0047, 0066, 0071, 0074 – retention risks may be displayed graphically on the display. 0090, fig 23 – display and computing system).
Qamar does not explicitly disclose and Grindstaff et al. discloses the machine learning improves an accuracy of the predictive model by iteratively modifying hyperparameters of the predictive model to control a rate of updating the predictive model using reinforcement learning (0039, 0040 – data is split into training and validation datasets; the programmable device may further tune hyperparameters or training parameters to improve predictive performance of generalized model 430. In one embodiment, the candidate algorithms may be supervised, unsupervised or reinforcement machine learning algorithms depending on the characteristics of incoming data).
Qamar discloses predicative modeling using machine-learning techniques. Grindstaff et al. discusses assessing and improving the predictive performance of a model after it is created. It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify Qamar’s machine learning to include the tuning of hyperparameters using reinforcement machine learning algorithms in order to improve the predictive performance of the model. See 0040 of Grindstaff et al.
While Qamar discusses retention and predicted turnover, Qamar does not explicitly disclose and Parmar et al. discloses:
converting, by the one or more processors, the predicted turnover for a plurality of selected employers into an index of employee turnover based at least in part on the predicted turnover relative to an observed voluntary employee turnover for employers with similar opportunity, and transmitting, by the one or more processors, to a computing device, data to cause the computing device to display, on a display device coupled with the computing device, a visual representation comprising at least a portion of the index (0007, 0030, 0037, 0041 - calculating a numeric likelihood that an employee will voluntarily end employment with an employer, where the value is calculated using multiple numeric values and weighting factors. 0075, 0113 – outputting attrition predictions and using information about average attrition rate for a job or a department historically for the employer, such as by retrieving such information from one or more data stores maintained by an HR department. Further, the attrition predictor may obtain information on an average attrition rate for a job or a department in the industry in which the employer operates. The information on average attrition rates in the industry may be obtained from any suitable source. 0022, 0079, 0115 – charting potential growth of employees and providing more feedback or coaching to an employee, provide an employee with more opportunities at work, or otherwise attempt to increase an employee's satisfaction and prevent the employee from ending employment).
Both Qamar and Parmar et al. discuss retention analysis and predictors of retention and attrition. It would have been obvious to a person of ordinary skill in the art before the effective filing date to use the predicted turnover for each individual of Qamar to create a statistic about the employer and consider that in the industry in order to better understand whether the attrition rate is good or bad in the context of the employer's historic attrition or attrition in the industry. See 0113 of Parmar et al.
Regarding Claim 3, Qamar discloses: The method according to claim 1, wherein categories of data applied to the machine learning or the predictive modeling include at least one of: industry/sector for the employers; employment growth within the employers; manager-to-employee ratio within the employers; promotion rate within the selected group of employers; and promotion wage growth within the employers (0011- external data includes data related to competitors (sector); 0011 - retention of employees by competitors of the organization, proximity of the competitors of the organization, compensation offered by the competitors)
Regarding Claim 4, Qamar discloses: The method according to claim 1, further comprising:
responsive to the error rate failing to satisfy the threshold, identifying, by the one or more processors, sample data; and randomizing, by the one or more processors, a selection of the sample data used to construct the predictive model (Based on BRI-this is a first instance of randomizing (not re-randomizing like 0068, Spec); 10(3-20) - .....Specifically, the machine learning modeler 129 can input the testing set into the machine learning model and determine a measure of accuracy (e.g., the proportion of samples for which the machine learning model produced a correct output) and tune hyper-parameters of the machine learning model until a generalization error reaches a predetermine threshold value
9(35-45) - In some instances, machine learning modeler 129 can retrieve a set of datasets from data store 135 and randomly divide the retrieved set into three disjoint subsets of datasets. The three disjoint subsets of datasets can be used to build, train, and/or tune machine learning models. Specifically, a first subset can be used to train machine learning model's “training set,” a second subset can be used to validate machine learning model's “validation set,” and a third subset can be used to test machine learning model's “testing set.”
Qamar does not explicitly state the identifying and selection of the data is “by rows.” Grindstaff et al. discloses rows in the test and validation datasets (0039, After pre-processing, splitting known data 405 into training dataset 410, validation dataset 415, and test dataset 420, in order to evaluate the model to estimate the quality of its pattern generalization for data the model has not been trained on. That is, since future data instances have unknown target values that cannot be used for checking accuracy of predictions of the model, some of the data from known data 405 for which we already know the answer of the prediction target for each row is used to evaluate the accuracy of the model (and underlying algorithm).
Qamar discloses predicative modeling using machine-learning techniques. Grindstaff et al. discusses assessing and improving the predictive performance of a model after it is created. It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify Qamar’s machine learning to include the tuning of hyperparameters using reinforcement machine learning algorithms in order to improve the predictive performance of the model. See 0040 of Grindstaff et al.
Regarding Claim 5, Qamar discloses: The method according to claim 1, wherein the machine learning uses supervised learning to construct the predictive model. . (0276 –supervised)
Regarding Claim 6 Qamar in discloses: The method according to claim 1, wherein the machine learning uses unsupervised learning to construct the predictive model. (0276 – unsupervised)
Regarding Claim 7, Qamar discloses: The method according to claim 1, wherein the machine learning uses the reinforcement learning to construct the predictive model. [0076] In an exemplary embodiment, the analysis technique generates and maintains an econometric regression model. This regression model uses consistent and high-velocity data streams that are repeatedly updated to conduct analyses and to maintain calibration. For example, the regression model may be updated in near real-time (such as hourly, daily or weekly).
Claims 8 and 14 stand rejected based on the same citations and rationale as applied to Claims 1, respectively.
Claims 10 and 17 stand rejected based on the same citations and rationale as applied to Claims 4.
Claims 11-13 stand rejected based on the same citations and rationale as applied to Claims 5-7, respectively.
Claims 16 and 18-20 stand rejected based on the same citations and rationale as applied to Claims 3, 5-7, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zatlukal (US 2004/0010459) discloses voluntary employee turnover and the industry statistics comprising annual voluntary employee turnover 502 for the industry of the employer, and calculating a return on investment for an employee.
Taylor (US 2008/0140473) discloses industry related data used with key indicators including staff turnover percentage and staff training indicators.
Giannakakis et al. (US 10,810,524) discloses a resource prediction system with initial planning input including dwell time parameter applied to employees, an attrition rate parameter determining a termination (e.g., voluntary or involuntary) rate for employees, a promotion rate indicating a predicted rate of promotion for employees within a time period, an external hires parameter predicting the number of external hires at a point in the future, and a total number of resources for a particular granularity, job level, or region at a time period in the future.
Grady Smith et al. (2017/0236081) discusses retention and renewal rates, and calculating the churn risk or a related metric relating to the possibility of an employee voluntarily resigning. Historical employee data that provides both cases of long tenured employees and cases of attrition for analysis can be used to train machine learning algorithms and to predict possible cases that would benefit from further analysis or investigation.
MacArthur (US 2012/0078803) discloses a job seeker being able to see statistics related to employers including turnover of employees over a period of time and attrition rate, and includes employer characteristics comprising at least one of: employer ranking.
Bassuk (US 2008/0294539) discusses constructing indexes, ranking companies, and converting the ranking into a composite score.
Miller et al. (US 2014/0278826) teaches use of turnover KPIs to analyze the human capital of a company.
Newman et al. (US 2020/0050982) discusses creating indices of employee tenure and using this to rank order employers.
Beck (US 20160180264) teaches analyzing retention and predicting retention risk using AI models.
Glover et al. (Your Firm's Employee Turnover: How to Calculate it and How it Compares) teaches calculating turnover and comparing industries and employers with regards to calculated turnover.
Singh et al. (An Analytics Approach for Proactively Combating Voluntary Attrition of Employees) teaches an analytics approach to proactively tackle voluntary attrition of employees.
Sela et al., (Big Data Analysis of Employee Turnover in Global Media Companies, Google, Facebook and Others) discusses analysis of job satisfaction and employee turnover at companies using an analysis of a large data set to look for patterns.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BETH V BOSWELL whose telephone number is (571)272-6737. The examiner can normally be reached M-F 8AM - 4:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tariq Hafiz can be reached at (571) 272-5350. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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