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 final office action in response to the communications filed by applicant on 8/19/2026. Claims 1-2, 8-9, and 14-15 have been amended. Claim 21 has been added. Claims 1-21 are pending.
Response to Amendment
Applicants’ amendments to claims 1, 8 and 14 are sufficient to overcome the 35 USC § 112 and 103 rejections set forth in the previous action. These rejections have been withdrawn.
Response to Arguments
With respect to Applicant’s arguments regarding 35 USC § 101:
Applicant argues that the claims do not recite subject matter that falls within certain methods of organizing human activity or mathematical concepts.
Specifically, with regards to mathematical concepts, applicant argues the characterization is not supported by the broadest reasonable interpretation of the claims as there are no formulas, equations or named mathematical operations recited.
Examiner respectfully disagrees. Per MPEP 2106.04(a)(2) I., the mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. Mathematical relationships may be expressed in words or using mathematical symbols, and thus the claim is not required to have a formula or equations to fall into this enumerated grouping. A mathematical operation takes one or more input values and transforms them into new output values based on rules or procedures. The instant claims include 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, and updating based on the comparing, the prediction model incorporating updated sample data collected for the second specified time period. These limitations reasonably fall within mathematical concepts, mathematical calculations because they determine values and convert values into an index using predictive models and mathematical means.
With respect to Applicants’ arguments regarding mental process, it is noted that this enumerated grouping of abstract ideas is not relied upon in the 35 USC § 101 rejection.
With respect to certain methods of organizing human activity, Applicant argues the claims recite specific computer-implemented operations for constructing, applying, comparing, and updating a machine learning predictive model and the fact the data on which the model operates relates to employee turnover does not transform the claimed computer operations into a method of organizing human activity since the claims recite what processors do, not what humans do.
Examiner respectfully disagrees. The claim limitations fall within the grouping of certain methods of organizing human activity because the claim involves identifying information about the employees of an organization and predicting turnover. Per MPEP 2106.04(a)(2) II., certain methods of organizing human activity encompasses both activity of a single person and activity that involves multiple people; further certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted the determination of whether a claim recites an abstract idea in this grouping is based on whether the activity itself falls within one of the sub-groupings.
Here, the claim is identifying sample data regarding a plurality of metrics associated with employee growth opportunity and voluntary employee turnover, performing iterative analysis on the sample data, determining an error rate and determining using the predictive model, responsive to the error rate satisfying a threshold, a predicted turnover for each individual based on the plurality of metrics associated with the employee growth opportunity, converting the predicted turnover for each individual employed by each of a plurality of 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 employee growth opportunity, transmitting data to cause a visual representation comprising at least a portion of the index, comparing a rank ordering of the predicted turnover for the employers to the observed voluntary employee turnover of the employers over a second specified period of time, and updating, based on the comparing, the prediction model incorporating updated sample data collected for the second specified time period. These recited limitations involve quantifying metrics about employee growth opportunities and predicting turnover based on analysis of information about an organization. This reasonably falls within the abstract idea grouping of certain methods of organizing human activity.
Applicant argues example 47, claim 3, that was found eligible is analogous to the present claims since they recite specific computer-executed actions taken based on model output which go beyond mere data processing or display.
Examiner respectfully disagrees. Example 47 is about anomaly detection and claim 3 was found eligible because, while it recites an abstract idea, the claim as a whole integrates this abstract idea into a practical application by improving network security. This improvement to the functioning of a computer or another technology or technical field was determined based on evaluating the sample specification and the example claim to ensure the claim reflects the improvement. Here, the specification asserted that the disclosed system detected network intrusions and took real-time remedial actions, including dropping suspicious packets and blocking traffic from specific source addresses, which achieves a benefit over prior systems by acting in real time to proactively prevent network intrusions-thus improving network security.
In the instant application, the claims perform predictive monitoring to measure and quantify metrics about employee growth opportunities and predict turnover based on analysis of information about an organization. While observed data is used to update the predictive model (and the computer executed action argued by applicant appears to be the updating of the model), this is not an improvement to the technical field of AI and does not appear equivalent to the improvement in the technical field of network security of the example. While the specification need not explicitly set forth the improvement to the functioning of the computer or the other technology or technical field, it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth the improvement in a conclusory manner, the examiner should not determine that the claim improves the computer, the technology or technical field. See MPEP 2106.04(d)(1).
The examiner reviewed the specification, and notes paragraphs such as 55, 60-62, 67, 69-71 and 76. However, it is respectfully submitted that these do not disclose an improvement to the computer, technology or technical field of machine learning or AI.
Applicant argues that the claims are directed to a practical application because they improve how the machine learning predictive model operates over time through the validation and updating based on observed outcomes (further pointing to the Desjardins Memo and arguing a disclosed improvement to a technical problem in paragraphs 70 and 76 of the specification that is reflected in the claims). It is again noted that 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 or if it explicitly sets forth an improvement but only in a conclusory manner. Here, the specification provides limited details about reinforcement learning (and does not provide an improvement to such algorithm). While it states constructing accurate, complex predictive models in a timely manner as empirical data changes rapidly, there is not a nexus formed between the additional elements of the claims and how these benefits are achieved. The claim seems to only mention accuracy with respect to modifying hyperparameters, but the claims and specification do not provide details of how this is accomplished. See paragraph 69.
When considering the additional elements in terms of them being more than a generic tie to the technical environment, it is noted that the additional elements appear to generally link the abstract idea to a technological environment and are also claimed at a high level of generality, reciting the idea of a solution or outcome without details of how the solution is accomplished. Please note per MPEP 2106.05(f)(2), claiming the improved speed or efficiency inherent with applying the abstract idea on a computer or other machinery (e.g. tool) does not integrate a judicial exception into a practical application or provide an inventive concept.
Applicant argues that the amended claims recite specific steps of comparing the model's rank ordering of predicted employer turnover to observed voluntary turnover over a second specified time period and updating the predictive model, based on the comparing, using machine learning incorporating updated sample data collected for that period, which provides a concrete mechanism of comparing predictions against observed outcomes and updating the model based on that comparison that the claims specify.
In response, per MPEP 2106.04 I. and Ultramercial, new and narrow abstract ideas and judicial exceptions are still abstract ideas and judicial exceptions. Here, comparing the model's rank ordering of predicted employer turnover to observed voluntary turnover over a second specified time period and updating the predictive model, based on the comparing, incorporating updated sample data collected for that period are all considered part of the recited abstract idea, as discussed below in the 35 U.S.C. 101 rejection. The use of machine learning in the updating limitation was considered an additional element. However, this is claimed at a high level of generality and describes generally applying 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. Further, updating using machine learning generally links the use of the abstract idea to a particular technological environment and recites the idea of a solution or outcome without details of how the solution is accomplished. Thus, it is respectfully submitted that this argument is not persuasive.
Finally, Applicant argues that the amended claims do not merely recite generic processors performing generic functions, but a specific ordered combination that provides an inventive concept: (1) constructing the predictive model using reinforcement learning; (2) generating individual predictions and an employer-level index; (3) comparing a rank ordering of predicted turnover against observed voluntary employee turnover over a second specified time period; and (4) updating the predictive model, based on the comparing, using machine learning incorporating updated sample data collected for that period.
Again, as stated above, performing iterative analysis on the sample data to construct a predictive model, generating individual predictions and converting these into an index of employee turnover, comparing a rank ordering of the predicted turnover for the employers to the observed voluntary employee turnover of the employers over a second specified period of time and updating, based on the comparing, the prediction model incorporating updated sample data collected for the second specified time period are considered to be part of the recited abstract idea, as discussed in the 35 U.S.C. 101 rejection below. Using reinforcement learning and using machine learning when updating are considered additional elements; however, the claim (when considered as a whole and these elements alone and in combination) is determined to be claimed at a high level of generality and claimed in a manner that generally applies the recited abstract idea in a computer environment. Using reinforcement learning and updating using machine learning generally links the use of the abstract idea to a particular technological environment (artificial intelligence). Thus, it is respectfully submitted that this argument is not persuasive.
Applicants’ arguments regarding 35 USC § 103, in light of the amendments to claims 1, 8, and 14, have been fully considered and are persuasive. The 35 USC § 103 rejections have been withdrawn.
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 1-21 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 each individual of the plurality of individuals employed by each of a plurality of 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 employee growth opportunity; and
transmitting data to cause a visual representation comprising at least a portion of the index;
comparing a rank ordering of the predicted turnover for the employers to the observed voluntary employee turnover of the employers over a second specified period of time; and
updating, based on the comparing, the prediction model incorporating updated sample data collected for the second specified time period.
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, and updating based on the comparing, the prediction model incorporating updated sample data collected for the second specified time period 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; 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, and updating using machine learning. 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 and updating using machine 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 limitations are 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 16-21 further narrow the recited abstract idea recited above and are rejected for the same reasons. In addition, claims 5-7, 11-13, and 18-21 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, do not integrate the recited abstract idea into a practical application and do not provide significantly more than the abstract idea.
As a result, claims 1-21 are ineligible.
Conclusion
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.
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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