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 AIA .
Status of Claims
This communication is a Final Office action in response to communications received on 10/02/2025. Claims 1, 9-13, 17 and 20 have been amended. Claims 7 and 15 have been canceled. Claims 21-22 have been newly added. Therefore, claims 1-6, 8-14 and 16-22 are currently pending and have been addressed below.
Response to Amendment
Applicant has amended claim 9 to overcome the specification objection. Therefore Examiner withdraws the specification objection. Applicant has amended claims 1, 6, 10 and 11 to overcome the claim objections. Therefore Examiner withdraws the claim objections.
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 1-6, 8-14 and 16-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-6, 8 and 21 are directed to a system (i.e. a machine). Claims 9-14, 16-20 and 22 are directed to a method (i.e. a process). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-6, 8-14 and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1 recites: A system for tracking personnel engagement with distributed content, comprising: receive one or more behavioral data related to one or more employees and describing one or more employee actions related to an employer's; validate the one or more behavioral data ...on historical event data, generating one or more validated behavioral data; and organize the one or more validated behavioral data into time-indexed sequences of events; store one or more HR (Human Resources) data related to the one or more employees; a content distribution system
in content distribute the content to at least some of the one or more employees, and further detect at least one of the one or more employee actions in relation to the content and to communicate the at least one of the one or more employee actions; and present one or more quantitative metrics related to the one or more behavioral data and present one or more predictive retention metrics.
Independent claim 9 recites: A method for tracking employee retention data, comprising: one or more content for distribution and tracking of one or more employee actions; receiving the one or more employee actions; validating the one or more employee actions ... on historical event data, generating one or more validated employee actions; organizing the received one or more validated employee actions into one or more time- indexed sequences; and generating one or more predictive retention metrics by analyzing the one or more time- indexed sequences ... on historical engagement data
Independent claim 17 recites: A method for tracking employee retention data, comprising: receiving one or more events related to one or more employees and one or more interactions with one or more content distributed by a distribution system; validating the one or more events ... on historical event data, generating one or more validated events; receiving events related to the one or more employees; identifying a respective of the one or more employees associated with the one or more validated events and the one or more events; adding the one or more validated events and the one or more events to a respective one of one or more indexed sequences of events associated with the one or more employees; and generating one or more predictive retention metrics by analyzing the one or more time- indexed sequences on historical engagement data.
The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under at least the abstract groupings of:
Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations; (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)). As independent claims discuss tracking personnel engagement and employee retention data, which is a clear business relation and one of certain methods of organizing human activity.
Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (claim 1, recites for example: “receive one or more behavioral data related to one or more employees”; “describing one or more employee actions related to an employer's information system”; “distribute content to at least some of the one or more employees”; “detect at least one of the one or more employee actions in relation to the content and communicate the at least one of the one or more employee actions”; “present one or more quantitative metrics related to the one or more behavioral data.”). Concepts performed in the human mind as mental processes because the steps of receiving, describing, determining, detecting, distributing, presenting and analyzing data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)).
Dependent claims add additional limitations, for example: (claim 3) created by the employer's information system; (claim 6) wherein the one or more HR data comprise one or more of: personal information, name, age, gender, demographic information, employment information, position or role, group or subdivision, manager, hire date, salary, cost of replacement, status information, an indication that the employee has resigned or was terminated, past records of engagement with the employee, dates and results of reviews, stay interviews, exit interviews; (claim 10) providing retention to one or more users, the one or more retention illustrate the one or more retention metrics; (claim 11) providing one or more retention guidance related to the one or more employees; (claim 12) submitting a comment or other message in response to the content; (claim 13) using the received one or more employee actions used for predicting the one or more retention metrics; (claim 14) wherein the determining comprises analyzing the one or more employee actions with used for predicting one or more employee retention metrics; (claim 16) wherein the one or more retention metrics comprise a prediction of retention if an employee were to receive a salary increase or promotion; (claim 18) wherein the validating is performed with one or more of: on historic event data received from an organization's employees; manual annotation or curation of historic event data; (claim 19) discarding one or more events that are not related to the one or more employees; (claim 20) identifying one or more tags associated with the one or more validated events and/or the one or more validated events; and adding the one or more tags to the respective one of the one or more indexed sequences of events, but these only serve to further limit the abstract idea.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations of certain methods of organizing human activity and mental processes but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements (claim 1) event server(s) HRMS (Human Resource Management System) server(s), machine learning model trained, distribution server, behavior tracking functions, content distribution system, retention dashboard, user device(s); (claim 2) virtual, cloud, server(s); (claim 4) smartphone, computer, tablet; (claim 8) web-based content channels; APIs (application programming interfaces); webhooks; (claim 9) behavior tracking functions, machine learning model trained; (claim 12) menu link, "thumbs up" button; (claim 13 and 14) train a machine learning model; (claim 17) webhook(s); API (application programming interface); machine learning model trained; (claim 18) artificial intelligence; machine learning; neural network; machine learning model or neural network function trained; (claim 19) webhook; (claims 21 and 22) behavior tracking functions, webhooks, APIs. These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds the words “apply it” (or an equivalent) 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, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or machine learning, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 1-6, 8-14 and 16-22 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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 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 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-6, 8-14, 16 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Qamar et al. (US 2015/0269244 A1), hereinafter “Qamar”, over Ash et al. (US 2022/0292525 A1), hereinafter “Ash”, over Gordon et al. (US 2021/0133685 A1), hereinafter “Gordon”.
Regarding Claim 1, Qamar teaches A system for tracking personnel engagement with …, (Qamar, Abstract and para 0050, the computer system calculates a performance metric for an employee based at least on the organization data. Additionally, the computer system may store the calculated performance metric. Note that the performance metric may include: productivity of the employee, overtime worked by the employee, adherence of the employee to a schedule, attendance of the employee, a number of employees that interact with the employee, activity of the employee (such as words typed per minute or keystrokes on user interface)) comprising: one or more event servers configured to receive one or more behavioral data related to one or more employees and describing one or more employee actions related to an employer's information technology system; (Qamar, Figure 2; Further, servers are taught throughout Qamar, see at least para 0061 and para 0085; Qamar, para 0008, teaches behavioral data including interaction among employees of the organization, … productivity of the employee, overtime worked by the employee, adherence of the employee to a schedule, attendance of the employee, a number of employees that interact with the employee, activity of the employee);
...
organize the one or more ... behavioral data into time-indexed sequences of events; (Qamar, para 0204, teaches organization data and/or optional external data at different time stamps (such as timestamp));
one or more HRMS (Human Resource Management System) servers communicatively coupled to the one or more event servers and configured to store one or more HR (Human Resources) data related to the one or more employees; (Qamar teaches storing data throughout, see at least para 0204, 0266, 0093, analysis module may receive, organization data, this information may be stored in a data structure (such as a database or another type of data structure));
… to at least some of the one or more employees, and further configured to detect at least one of the one or more employee actions in relation to the content ... and to communicate the at least one of the one or more employee actions to the one or more event servers; and (Qamar, para 0050, the computer system calculates a performance metric for an employee based at least on the organization data. Additionally, the computer system may store the calculated performance metric. Note that the performance metric may include: activity of the employee (such as words typed per minute or keystrokes on user interface); Qamar, para 0052, the external data may include: activity of the employee on a social network (such as posting by the employee on an employment forum or updates to the employee's profile on an employment-related social network));
a retention dashboard configured to present one or more quantitative metrics related to the one or more behavioral data to one or more user devices and present one or more predictive metrics (Qamar teaches predictive models and analytics throughout, Qamar, para 0006, analyzing employee value and retention risk, and providing a retention suggestion; Qamar, para 0052, the computer system determines retention risk for the employee based at least on the organization data; Qamar, Figure 4, para 0022, and para 0072, teaches retention dashboard presented on user interface(s)). Yet, Qamar does not appear to explicitly teach and in the same field of endeavor Ash teaches distributed content … a content distribution system comprising a distribution server configured to distribute the content (Ash, teaches servers throughout, see at least Ash, para 0300, teaches service system on one or more servers; para 0113, teaches the content development and management application or content strategy tool; para 0084, FIG. 2 provides a detailed functional block diagram of certain components and elements of a content development platform).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Qamar with distributed content … a content distribution system configured to distribute content as taught by Ash with the motivation for improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages (Ash, Abstract).
Yet, Qamar does not appear to explicitly teach and in the same field of endeavor Gordon teaches validate the one or more behavioral data using a machine learning model trained on historical event data, generating one or more validated behavioral data; and... validated (Gordon, para 0091, teaches Webhook; Examiner notes a webhook is a mechanism used as a behavior tracking function; Gordon, para 0285, service may perform human verification of the employee … prompt the employee to provide information regarding previous addresses, the employee's organization within the workforce (e.g., name of manager, name of organization, name of work group, etc.); Machine learning trained on historical employee data is taught throughout Gordon, see at least Gordon, para 0095; Gordon teaches employee authentication throughout, see at least Gordon, para 0287, teaches employee authentication tokens) embed one or more behavior tracking functions in content ... based on the one or more behavior tracking functions (Gordon, para 0091, teaches Webhook. Examiner notes a webhook is a mechanism used as a behavior tracking function.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Qamar with validate the one or more behavioral data using a machine learning model trained on historical event data, generating one or more validated behavioral data; and... validated... embed one or more behavior tracking functions in content ... based on the one or more behavior tracking functions as taught by Gordon with the motivation for a web-based platform that helps companies easily lower their vacation liability and helps employees self-direct how they use their paid time off (PTO) benefits (Gordon, para 0037). The Qamar invention now incorporating the Ash and Gordon invention, has all the limitations of claim 1.
Regarding Claim 2, Qamar, now incorporating Ash and Gordon, teaches The system of claim 1, wherein the one or more event servers comprise one or more of: physical, virtual, cloud, or other servers (Qamar, para 0099, Computer system, as well as electronic devices, computers and servers in system; Qamar, para 0098, such as cloud computing).
Regarding Claim 3, Qamar, now incorporating Ash and Gordon, teaches The system of claim 1, wherein the retention dashboard is created by the employer's information technology system (Qamar, para 0095, employee-management module 832 (such as human-resources software) provides, via communication module 828 and communication interface 812, one or more performance metric 844 and one or more retention risks 846 for the employee.)
Regarding Claim 4, Qamar, now incorporating Ash and Gordon, teaches The system of claim 1, wherein the one or more user devices comprise one or more of: a smartphone; a computer; a tablet (Qamar, para 0099, Computer system, as well as electronic devices, computers ).
Regarding Claim 5, Qamar, now incorporating Ash and Gordon, teaches The system of claim 1, wherein the one or more HRMS servers comprise the employer's information technology system (Qamar, para 0071, As shown in FIG. 3, which presents a drawing of a user interface 300, employee value 310 and retention risk 312 may be displayed graphically for one or more employees to user of the human-resources software, such as a manager at the organization or a representative of human resources.)
Regarding Claim 6, Qamar, now incorporating Ash and Gordon, teaches The system of claim 1, wherein the one or more HR data comprise one or more of: personal information, name, age, gender, demographic information, employment information, position or role, group or subdivision, manager, hire date, salary, cost of replacement, status information, an indication that the employee has resigned or was terminated, past records of engagement with the employee, dates and results of reviews, stay interviews, exit interviews (Qamar, para 0049, organization data may include human-resources data and/or operations data. In particular, the organization data may include: tenure of the employee at the organization (such as the hire date), attendance of the employee (such as how often the employee is sick or late for work), compensation of the employee (salary), satisfaction scores associated with the employee (such as rankings provided by a customer, a manager or other employees, a trainer or coach, etc.), skills of the employee, a supervisor of the employee, interaction among employees of the organization (such as email, telephone calls or text messages among the employees), metadata about the employee0.
Regarding Claim 8, Qamar, now incorporating Ash and Gordon, teaches The system of claim 1, wherein the one or more behavioral data comprise one or more of: one or more data from one or more web-based content channels; one or more data from one or more events tracking APIs (application programming interfaces); one or more data from one or more event tracking webhooks (Qamar, para 0052, the external data may include: activity of the employee on a social network (such as posting by the employee on an employment forum or updates to the employee's profile on an employment-related social network)).
Regarding Claim 9, Qamar teaches A method for tracking employee retention data, comprising: configuring one or more … and tracking of one or more employee actions; (Qamar, Abstract and para 0085, during the analysis technique a user of electronic device may use a software product, such as a software application that is resident on and that executes on electronic device. (Alternatively, the user may interact with a web page that is provided by computer via network, and which is rendered by a web browser on electronic device); Qamar, para 0086, During the analysis technique, the user of electronic device may provide, via network, the organization data);
receiving the one or more employee actions; (Qamar, para 0008, teaches behavioral data including interaction among employees of the organization, … productivity of the employee, overtime worked by the employee, adherence of the employee to a schedule, attendance of the employee, a number of employees that interact with the employee, activity of the employee);
organizing the received one or more ... employee actions into one or more time- indexed sequences; and (Qamar, para 0204, teaches organization data and/or optional external data at different time stamps (such as timestamp). As described further below, this information may be analyzed for different individuals to generate one or more predictive models; Qamar, para 0050, Then, the computer system calculates a performance metric for an employee based at least on the organization data. Additionally, the computer system may store the calculated performance metric);
generating one or more predictive retention metrics by analyzing the one or more time- indexed sequences using a machine learning model trained on historical engagement data (Qamar, para 0204, teaches organization data and/or optional external data at different time stamps (such as timestamp). As described further below, this information may be analyzed for different individuals to generate one or more predictive models; Qamar, para 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 … 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, and/or the ordering of the factors in these predictions may be used to select the polynomial (factors, exponents n and amplitude weights wi) to determine the retention risk.)
Yet, Qamar does not appear to explicitly teach and in the same field of endeavor Ash teaches content for distribution (Ash, para 0113, teaches the content development and management application or content strategy tool; para 0084, FIG. 2 provides a detailed functional block diagram of certain components and elements of a content development platform).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Qamar with content for distribution as taught by Ash with the motivation for improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages (Ash, Abstract).
Yet, Qamar does not appear to explicitly teach and in the same field of endeavor Gordon teaches with one or more embedded behavior tracking functions (Gordon, para 0091, teaches Webhook. Examiner notes a webhook is a mechanism used as a behavior tracking function.) validating the one or more employee actions using a machine learning model trained on historical event data, generating one or more validated employee actions; ... validated (Gordon, para 0285, service may perform human verification of the employee … prompt the employee to provide information regarding previous addresses, the employee's organization within the workforce (e.g., name of manager, name of organization, name of work group, etc.); Machine learning trained on historical employee data is taught throughout Gordon, see at least Gordon, para 0095; Gordon teaches employee authentication throughout, see at least Gordon, para 0287, teaches employee authentication tokens). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Qamar with with one or more embedded behavior tracking functions... validating the one or more employee actions using a machine learning model trained on historical event data, generating one or more validated employee actions; ... validated as taught by Gordon with the motivation for a web-based platform that helps companies easily lower their vacation liability and helps employees self-direct how they use their paid time off (PTO) benefits (Gordon, para 0037). The Qamar invention now incorporating the Ash and Gordon invention, has all the limitations of claim 9.
Regarding Claim 10, Qamar, now incorporating Ash and Gordon, teaches The method of claim 9, further comprising providing one or more retention dashboards to one or more users, the one or more retention dashboards configured to illustrate the one or more retention metrics (Qamar, para 0022, FIG. 4 is a drawing of a user interface that provides information specifying employee value and retention risk).
Regarding Claim 11, Qamar, now incorporating Ash and Gordon, teaches The method of claim 10, further comprising providing one or more retention guidance related to the one or more employees (Qamar, para 0006, analyzing employee value and retention risk, and providing a retention suggestion; Qamar, para 0058, the retention suggestion may be to offer additional training opportunities to the employee to help them improve their skills).
Regarding Claim 12, Qamar, now incorporating Ash and Gordon, teaches The method of claim 9, wherein the one or more employee actions comprise one or more of: events indicating a piece of content was opened or accessed, events indicating the extent to which sub-content or sub-portions of a greater body of content are viewed or interacted with, the extent or depth to which a user scrolled downwards to view a sequence of sub-portions of content provided by a web or application based content feed or wall, user interactions with sub-portions of content such as clicking a menu link to jump to a particular sub portion, clicking a software control to expand and/or view further information on a sub portion, clicking a "thumbs up" button or other button to respond to the content, submitting a comment or other message in response to the content (Qamar, para 0050, Note that the performance metric may include… activity of the employee (such as words typed per minute or keystrokes on user interface; Qamar, para 0052, activity of the employee on a social network (such as posting by the employee on an employment forum or updates to the employee's profile on an employment-related social network)).
Regarding Claim 13, Qamar, now incorporating Ash and Gordon, teaches The method of claim 9, further comprising using the received one or more employee actions to train a machine learning model used for predicting the one or more retention metrics (Qamar, para 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 … 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 wi) using to calculate the performance metric and/or to determine the retention risk.)
Regarding Claim 14, Qamar, now incorporating Ash and Gordon, teaches The method of claim 9, wherein the determining comprises analyzing the one or more employee actions with a trained machine learning model used for predicting one or more employee retention metrics (Qamar, para 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 … 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 wi) using to calculate and/or to determine the retention risk.).
Regarding Claim 16, Qamar, now incorporating Ash and Gordon, teaches The method of claim 9, wherein the one or more retention metrics comprise a prediction of retention if an employee were to receive a salary increase or promotion (Qamar, para 0058, the retention suggestion may include an action that may keep the employee from leaving (such as: a one-time bonus, a pay increase, a promotion)).
Regarding Claim 21, Qamar, now incorporating Ash and Gordon, teaches The system of claim 1, wherein the one or more behavior tracking functions comprise at least one of the following: one or more web-based event tracking webhooks or one or more event tracking APIs (application programming interfaces) (Gordon, para 0091, teaches Webhook. Examiner notes a webhook is a mechanism used as a behavior tracking function.)
Regarding Claim 22, Qamar, now incorporating Ash and Gordon, teaches The method of claim 9, wherein the one or more behavior tracking functions comprise at least one of the following: one or more web-based event tracking webhooks or one or more event tracking APIs (application programming interfaces) (Gordon, para 0091, teaches Webhook. Examiner notes a webhook is a mechanism used as a behavior tracking function.)
Claims 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gordon et al. (US 2021/0133685 A1), hereinafter “Gordon”, over Qamar et al. (US 2015/0269244 A1), hereinafter “Qamar”,
Regarding Claim 17, Gordon teaches A method for tracking employee retention data, comprising: (Gordon, para 0171, Through the interface, the PTO conversion service may further provide an employer with various metrics with regard to the impact of PTO usage to key performance indicators, such as retention);
receiving one or more webhook events related to one or more employees ... ; (Gordon, para 0084, If requested through the employer via a human resources systems provider, the employer's human resources software may automatically send a notification to the PTO conversion service via an application programming interface (API), Webhook);
validating the one or more webhook events; using a machine learning model trained on historical event data, generating one or more validated webhook events; (Gordon, para 0285, service may perform human verification of the employee … prompt the employee to provide information regarding previous addresses, the employee's organization within the workforce (e.g., name of manager, name of organization, name of work group, etc.); Machine learning trained on historical employee data is taught throughout Gordon, see at least Gordon, para 0095; Gordon teaches employee authentication throughout, see at least Gordon, para 0287, teaches employee authentication tokens; Gordon, para 0091, teaches Webhook);
receiving one or more API (application programming interface) events related to the one or more employees; (Gordon, para 0084, If requested through the employer via a human resources systems provider, the employer's human resources software may automatically send a notification to the PTO conversion service via an application programming interface (API), Webhook, that paid time off conversion has been requested.);
identifying a respective of the one or more employees associated with the one or more validated webhook events and the one or more API events; (Gordon, para 0091, If the employee requests conversion of paid time off through the employer via an HR systems provider, the employer's HR software may automatically send a notification to the PTO conversion service via an application programming interface (API) or Webhook that paid time off conversion has been requested by a particular employee);
adding the one or more validated webhook events and the one or more API events to a respective one of one or more indexed sequences of events associated with the one or more employees; and (Gordon, para 0084, If requested through the employer via a human resources systems provider, the employer's human resources software may automatically send a notification to the PTO conversion service via an application programming interface (API), Webhook, that paid time off conversion has been requested; Gordon, para 0256, under the factoring process, the PTO conversion service may tag the transaction as being in progress and provide the employee with a transaction confirmation number. This confirmation number may be used to track the progress of the PTO conversion request submitted by the employee via the PTO conversion service. The PTO conversion service may submit an invoice to the factor or to an API associated with the factor that can be used to obtain the requisite funds from the factor.)
Yet, Gordon does not appear to explicitly teach and in the same field of endeavor Qamar teaches and one or more interactions with one or more content distributed by a distribution system; (Qamar, para 0049, interaction among employees of the organization (such as email, telephone calls or text messages among the employees) generating one or more predictive retention metrics by analyzing the one or more time- indexed sequences using a machine learning model trained on historical engagement data (Qamar, para 0204, teaches organization data and/or optional external data at different time stamps (such as timestamp). As described further below, this information may be analyzed for different individuals to generate one or more predictive models; Qamar, para 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 … 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, and/or the ordering of the factors in these predictions may be used to select the polynomial (factors, exponents n and amplitude weights wi) to determine the retention risk.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gordon with and one or more interactions with one or more content distributed by a distribution system; ... generating one or more predictive retention metrics by analyzing the one or more time- indexed sequences using a machine learning model trained on historical engagement data as taught by Qamar with the motivation for analyzing employee value and retention risk, and providing a retention suggestion and an associated cost-benefit analysis for an employee (Qamar, para 0006). The Gordon invention now incorporating the Qamar invention, has all the limitations of claim 17.
Regarding Claim 18, Gordon, now incorporating Qamar, teaches the method of claim 17, wherein the validating is performed with one or more of: artificial intelligence; machine learning; a neural network; a machine learning model or neural network function trained on historic event data received from an organization's employees; manual annotation or curation of historic event data (Gordon, para 0095, the PTO conversion service may use a machine learning algorithm, trained using supervised learning techniques; 0285, the PTO conversion service may perform human verification of the employee, such as through a chat session or other communications channel (e.g., telephone call, etc.). As another example, if the information provided by the employee is valid but a phone number provided by the employee is not valid or was otherwise not provided by the employer, the PTO conversion service may prompt the employee to provide additional information that may be used to authenticate the employee.)
Regarding Claim 19, Gordon, now incorporating Qamar, teaches The method of claim 17, further comprising discarding one or more webhook events that are not related to the one or more employees (Gordon, para 0184, Thus, when an employee submits a travel option query to identify travel options, the PTO conversion service may discard any results corresponding to the prohibited destinations).
Regarding Claim 20, Gordon, now incorporating Qamar, teaches The method of claim 17, further comprising; identifying one or more data tags associated with the one or more validated webhook events and/or the one or more validated API events; and (Gordon, para 0244, The confirmation may include a unique identifier for the PTO conversion request, which the employee may use to track the PTO conversion and determine when it has been fulfilled. In some instances, the transaction is further tagged as being “in progress.”);
adding the one or more data tags to the respective one of the one or more indexed sequences of events (Gordon, para 0244, The confirmation may include a unique identifier for the PTO conversion request, which the employee may use to track the PTO conversion and determine when it has been fulfilled. In some instances, the transaction is further tagged as being “in progress.”; para 0256, under the factoring process, the PTO conversion service may tag the transaction as being in progress and provide the employee with a transaction confirmation number. This confirmation number may be used to track the progress of the PTO conversion request submitted by the employee via the PTO conversion service.)
Response to Arguments
Applicant’s arguments filed on 10/02/2025 have been fully considered but they are not persuasive.
Regarding 35 U.5.C. § 101 rejections: Examiner has updated the 101 rejections in light of the most recent claim amendments. Applicant’s arguments have been fully considered but are found unpersuasive and Examiner maintains the 101 rejection.
With respect to Applicant’s remarks on Step 2A- Prong I, Examiner respectfully notes Applicant is arguing computing elements “machine learning”, “machine learning model trained”, “behavior tracking functions”, “event server”. Examiner respectfully notes these are additional elements and are considered in Step 2A – Prong Two and Step 2B, not in Step 2A, Prong One.
With respect to Applicant’s remarks on Step 2A – Prong 2, Examiner respectfully disagrees. As an initial matter, with respect to the limitation “validate the one or more behavioral data using a machine learning model trained on historical event data”, this is only mentioned once in Applicant’s specification, para 0041, at a high level with no specifics or details. The specification does not illustrate any details on how training may be implemented, or describe what machine learning is used other than “a machine learning or neural network” which is generic and high level. While the claims recite machine learning, this merely establishes an environment of use which as outlined by MPEP 2106.05(h) the Field of Use and Technological Environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
With respect to the behavior tracking function, Applicant’s claim and specification includes webhooks, however, webhooks are described and used in their ordinary and expected capacity. Further, the claims are not rooted in web tracking technologies, and the claims do not solve a technical problem that only arises in tracking technology. MPEP § 2106.05(a). The generically implemented tracking function being referred to does nothing more than generally link the abstract idea to the technological environment and/or merely apply a generic tracking technology, which is not sufficient to integrate the judicial exception into a practical application, or significantly more. Further, Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Each step does no more than require a generic
computer to perform generic computer functions. The claims do not, for example, purport to improve the functioning of the computer itself. In addition, the claims do not affect an improvement in any other
technology or technical field. The specification spells out different generic equipment and parameters that might be applied using the concept and the particular steps such conventional processing would entail based on the concept of information access. Thus, the claims at issue amount to nothing significantly more than instructions to apply the abstract idea using some unspecified, generic computer(s). Therefore, Applicants remarks are found unpersuasive and Examiner maintains the 101 rejection.
Regarding 35 U.S.C. § 102/103 rejections. With respect to the prior art rejections, Applicants arguments have been considered but are moot in light of the most recent claim amendments as the Examiner has updated the rejections with the Qamar and Gordon references.
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.
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/R.R.N./Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629