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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 10, 2026, has been entered.
Response to Arguments
Applicant's arguments filed with respect to rejections under 35 USC 101 have been fully considered but they are not persuasive. Applicant argues the claims provide a practical application in which the prediction is used to automatically perform a retention-related intervention (automatically sending an electronic communication indicating a message of appreciation). Examiner notes, sending a message of appreciation is also an abstract idea. Using a computer to send an electronic communication amounts to using a computer as a tool to perform the abstract idea. The rejection under 35 USC 101 is upheld and has been updated in view of the added limitation.
Applicant’s arguments with respect to claim(s) rejected under 35 USC 102 and 35 USC 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-20 is/are directed to a method, system, and computer program product. Thus, all the claims are within the four potentially eligible categories of invention (a process, a machine and an article of manufacture, respectively), satisfying Step 1 of the Subject Matter Eligibility (SME) test.
As per Prong One of Step 2A of the §101 eligibility analysis set forth in MPEP 2106, the Examiner notes that the claims recite mental processes and certain methods of organizing human activity.
More specifically, independent claims recite:
accessing, by a database system, a set of historical employee data comprising characteristics of historical employees prior to departure of the historical employees from past employers;
generating, by the database system, a training set of data by performing one or more normalization operations on the accessed set of historical employee data and based on one or more geographic characteristics of each historical employee;
training, by the database system, a neural network in a first stage using the training set of data to predict when an employee will depart from a target employer;
applying, by the database system, the trained neural network to a set of target employees to predict dates when the target employees will leave the target employer;
responsive to determining that a predicted departure date for a target employee satisfies a departure threshold, automatically transmitting, by the database system, an electronic communication to the target employee including a message of appreciation for the target employee;
updating, by the database system, the training set of data to include the predicted dates of departure of the target employees and actual dates of departure of the target employees;
determining, by the database system, a performance metric for the neural network based on a comparison between the predicted dates of departure and the actual dates of departure, and improving the neural network by retraining the neural network in a second stage using the updated training set of data responsive to the performance metric satisfying a retraining condition.
The claims recite data analysis steps to access employee data, generate training set, train a neural network, apply the neural network to predict departure dates, transmit a message of appreciation based on departure date satisfies a threshold, update the training set of data and retraining the neural network. The concept data analysis to predict departure dates of employees and communicate appreciation is certain methods of organizing human activity as it relates to managing personal behavior. In addition, the claims include recitations of observations and evaluations to determine the predicted departure date which are mental processes. The nominal recitation of a database system in claim 1, a non-transitory computer readable storage medium storing executable instructions that cause processors to perform the method in claim 12, and a system comprising one or more processors and a non-transitory computer readable storage medium storing executable instructions that cause processors to perform the method by a database system in claim 20, does not necessarily preclude the claim from reciting an abstract idea as evidenced by the analysis at Prong 2 of Step 2A.
Regarding Prong Two of Step 2A, a claim reciting an abstract idea must be analyzed to determine whether any additional elements in the claim integrate the judicial exception into a practical application. Limitations that are indicative of integration into a practical application include: Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
In this case, the independent claims do not include limitations that meet the criteria listed above, thus the abstract idea is not integrated into a practical application. A database system in claim 1, a non-transitory computer readable storage medium storing executable instructions that cause processors to perform the method in claim 12, and a system comprising one or more processors and a non-transitory computer readable storage medium storing executable instructions that cause processors to perform the method by a database system in claim 20 amount to using a computer as a tool to perform the abstract idea. These additional elements taken alone or in combination with the abstract idea do not offer an improvement to the computer or technology. There is no integration into a practical application.
The dependent claims recite additional abstract ideas and some recite additional elements that do not integrate the abstract idea into a practical application.
Claims 2, 3, 13 and 14 recite processors generating recommendations. Generating recommendations is an abstract idea that falls within mental processes and certain methods of organizing human activity. The processors amount to using a computer as a tool to perform the abstract idea and do not integrate the abstract idea into a practical application.
Claims 4 and 15 recite identifying a subset of employees that are likely to depart within a time threshold which is an abstract idea that falls within mental processes and certain methods of organizing human activity. The processor and using a trained neural network amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 5-7 and 16-18 recite identify a level of behavior or treatment of employees and determine an employee churn rate which is an abstract idea that falls within mental processes and certain methods of organizing human activity. The processor and using a trained neural network amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 8 and 19 recite determine whether the churn rate is greater than a threshold and generating recommendations to reduce churn rate which is an abstract idea that falls within mental processes and certain methods of organizing human activity. The processor and using a trained neural network amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
The claims do not include limitations beyond generally linking the use of the abstract idea to a particular technological environment. When considered individually and in combination, the system and software claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. The invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense.
Lastly and in accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, and when considered individually and in combination, the additional elements amount to no more than mere instruction to apply the exception using generic computer component. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept.
Claim Rejections - 35 USC § 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.
Claim(s) 1, 12, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xia et al, US 2023/0028536, in view of Huang, US 2021/0383522, and Sandusky et al, US 2019/0347598.
As per claim 1, Xia et al discloses a method comprising: accessing, by a database system, a set of historical employee data comprising characteristics of historical employees prior to departure of the historical employees from past employers ([0012] – employee data gathered);
generating, by the database system, a training set of data by performing one or more normalization operations on the accessed set of historical employee data and based on one or more geographic characteristics of each historical employee ([0014-0017] – data processing, cleaning, etc. and normalizing to generate training set and based on various employee data including location);
training, by the database system, a neural network in a first stage using the training set of data to predict when an employee will depart from a target employer ([0013-0025, 0039] – training the model to predict turnover);
applying, by the database system, the trained neural network to a set of target employees to predict dates when the target employees will leave the target employer ([0033-0036 – predicting number resigning per day during a time period;
updating, by the database system, the training set of data to include the predicted dates of departure of the target employees and actual dates of departure of the target employees ([0032-0033] – comparison with actual number of resigning employees per day); and
improving, by the database system, the neural network by retraining the neural network in a second stage using the updated training set of data ([0032, 0039-0041] – model is updated and optimized).
While Xia et al discloses retraining and optimizing the neural network [0039], there is no explicit disclosure of retraining responsive to a performance metric for the neural network. Huang describes an accuracy rate evaluation of a neural network wherein it is retrained until the accuracy rate is greater than or equal to a threshold [0032-0034]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to retrain responsive to a performance metric as taught by Huang since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Xia et al and Huang fails to explicitly disclose responsive to determining that a predicted departure date for a target employee satisfies a departure threshold, automatically transmitting, by the database system, an electronic communication to the target employee including a message of appreciation for the target employee. Sandusky et al, in an analogous employee evaluation system, evaluates employees and scores them based on performance and value to a company ([0043] – with different thresholds of scoring, i.e., an Appreciate or A-player category]. When an employee is at risk for leaving or determines an employee is important to the company, the system recommends the organization take action to notify the employee they are valued and appreciated [0016, 0036, 0041-0046]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al and Huang the ability to acknowledge employee appreciation as taught by Sandusky et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 12, Xia et al in combination with Huang and Sandusky et al discloses a non-transitory computer readable storage medium storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform the operations of claim 1 [Xia et al - 0052].
As per claim 20, Xia et al in combination with Huang and Sandusky et al discloses a computing system comprising one or more processors; and a non-transitory computer readable storage medium storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform the operations of claim 1 [Xia et al 0050-0052].
Claim(s) 2-3 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xia et al, US 2023/0028536 and Huang, US 2021/0383522, and Sandusky et al, US 2019/0347598, in view of Howell et al, US 2018/0012187.
As per claims 2 and 13, Xia et al, Huang and Sandusky et al discloses claims 1 and 12, respectively, but fails to explicitly disclose while Howell et al, in an analogous retirement prediction tool, discloses further comprising generating one or more recommendations for the target employer to prevent the target employees from departing the target employer ([0018, 0037] – tool examines factors that can alter an employee’s retirement date and determines an offer that increases or decreases the probability that a user will retire). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to generate recommendations as taught by Howell et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claims 3 and 14, Xia et al fails to explicitly disclose while Howell et al discloses wherein the one or more recommendations comprise one or more of: a pay raise, a promotion, a change in job responsibilities, or an adjustment in workload ([0018, 0037] – determines an offer that increases or decreases the probability that a user will retire; offer may include pay, etc.). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to generate recommendations as taught by Howell et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 4-11 and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xia et al, US 2023/0028536, and Huang, US 2021/0383522, and Sandusky et al, US 2019/0347598, in view of Grady Smith et al, US 2017/0236081.
As per claims 4 and 15, Xia et al discloses training the neural network but fails to explicitly disclose training to identify disparities in pay among employees performing similar jobs or in similar roles, and the method of further comprises identifying a subset of target employees that are likely to depart the target employer within a time threshold based on the identified disparities. Grady Smith et al discloses the machine learning algorithm uses disparities in pay wherein employees who did not have a raise in salary in the past 2 years are more likely to churn while considering employees with the same title [0289, 0293]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to generate recommendations as taught by Grady Smith et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5 and 16, Xia et al discloses the method of claim 1, but fails to explicitly disclose while Grady Smith et al discloses wherein the neural network is further trained [0155] to: identify a level of behavior or treatment of employees at the target employer compared to that of employees at one or more other employers; and determine an employee churn rate of the target employer based on the identified level ([0166, 0230, 0275, 0289] – machine learning algorithms predict churn based on evaluating factors including salary, performance, and looks at trends or correlations wherein the algorithm is trained based on past churn behavior to predict churn risk for the future). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to determine churn as taught by Grady Smith et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claims 6 and 17, Xia et al fails to explicitly disclose, while Grady Smith et al discloses wherein the level of behavior of employees comprises absenteeism, tardiness, attendance, overtime, productivity, work quality, or attitude towards work ([0012, 0051, 0052, 00660] – considers attendance, productivity). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to determine churn as taught by Grady Smith et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claims 7 and 18, Xia et al fails to explicitly disclose, while Grady Smith et al discloses wherein the level of treatment of employees comprises salary, bonus, paid time off, health benefit, professional development, group retreat, or recognition ([0275, 0289-0292] – considers salary, vacation time). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to determine churn as taught by Grady Smith et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claims 8 and 19, Xia et al fails to explicitly disclose, while Grady Smith et al discloses the method further comprising: determining whether the employee churn rate of the target employer is greater than a threshold; and generating one or more recommendations for the target employer to reduce employee churn rate ([0289-0292] – comparison to determine churn rate is greater than average churn rate and generating actions to reduce/prevent churn in the near term). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to determine churn and generate recommendations as taught by Grady Smith et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 9, Xia et al fails to explicitly disclose while Grady Smith et al discloses wherein the neural network is further trained to determine a trend in employee behavior over a period of time, and the method further comprises: responsive to identifying a trend in employee behavior, generating one or more recommendations to the target employer to remediate or encourage the trend ([0231-0234] – examines trends and determines ways to address employees with high churn risk). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to determine churn and generate recommendations as taught by Grady Smith et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 10, Xia et al fails to explicitly disclose while Grady Smith et al discloses wherein the trend in employee behavior comprises an absenteeism trend or an overtime trend over the period of time ([0232] – common factors such as patterns in vacation and paid time off). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to determine churn and generate recommendations as taught by Grady Smith et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 11, Xia et al fails to explicitly disclose while Grady Smith et al discloses wherein the neural network is further trained to identify a trend in churn rate over a period of time, and the method further comprises responsive to determining the trend in churn rate, generating one or more recommendations to the target employer to remediate or encourage the trend ([0231-0234] – examines trends and determines ways to address employees with high churn risk; [0289-0292] – comparison to determine churn rate is greater than average churn rate and generating actions to reduce/prevent churn in the near term). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Xia et al the ability to determine churn and generate recommendations as taught by Grady Smith et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Menon, US 2009/0307025 - Attrition Warning And Control System - An attrition warning and control system informs an employer as to the risk of attrition for an employee. The employee is assigned a discrete attrition category. The discrete attrition category is determined from an employee satisfaction behavior
Raza, US 2015/0294257 – Techniques for Reducing Employee Churn - identify a disgruntled employee within the organization that may potentially leave the organization. Furthermore, recommendations can be provided to help alleviate the employee's discontent. This can prevent the employee from leaving the organization, thus reducing employee churn rate. The techniques described can be used to generate a baseline model that represents the organization and apply the baseline model to a particular employee to determine whether the employee is likely to leave the organization. In some embodiments, company resources can also be examined to determine whether a remedial action exists to reduce the employee's discontent, thus improving the chances that the employee will stay with the organization
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNA LOFTIS whose telephone number is (571)272-6736. The examiner can normally be reached M-F 7:00am-3:30pm.
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JOHNNA LOFTIS
Primary Examiner
Art Unit 3625
/JOHNNA R LOFTIS/Primary Examiner, Art Unit 3625
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