DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed on 12/30/2025.
Claims 1-3, 8-10, 13, 15-17, and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
Response to Amendment
The amendment to the claims filed on 12/30/2025 does not comply with the requirements of 37 CFR 1.121(c) because claim 8 line 9 has “predefined or” lined through as being removed when the immediately preceding version (dated 02/02/2024) of claim 8 read “analytical or”. In other words, claim 8 has been amended as if “predefined” was removed from the claim when “analytical” was actually removed. Claim 15 line 11 does not comply for similar reasoning. Amendments to the claims filed on or after July 30, 2003 must comply with 37 CFR 1.121(c) which states:
(c) Claims. Amendments to a claim must be made by rewriting the entire claim with all changes (e.g., additions and deletions) as indicated in this subsection, except when the claim is being canceled. Each amendment document that includes a change to an existing claim, cancellation of an existing claim or addition of a new claim, must include a complete listing of all claims ever presented, including the text of all pending and withdrawn claims, in the application. The claim listing, including the text of the claims, in the amendment document will serve to replace all prior versions of the claims, in the application. In the claim listing, the status of every claim must be indicated after its claim number by using one of the following identifiers in a parenthetical expression: (Original), (Currently amended), (Canceled), (Withdrawn), (Previously presented), (New), and (Not entered).
(1) Claim listing. All of the claims presented in a claim listing shall be presented in ascending numerical order. Consecutive claims having the same status of “canceled” or “not entered” may be aggregated into one statement (e.g., Claims 1–5 (canceled)). The claim listing shall commence on a separate sheet of the amendment document and the sheet(s) that contain the text of any part of the claims shall not contain any other part of the amendment.
(2) When claim text with markings is required. All claims being currently amended in an amendment paper shall be presented in the claim listing, indicate a status of “currently amended,” and be submitted with markings to indicate the changes that have been made relative to the immediate prior version of the claims. The text of any added subject matter must be shown by underlining the added text. The text of any deleted matter must be shown by strike-through except that double brackets placed before and after the deleted characters may be used to show deletion of five or fewer consecutive characters. The text of any deleted subject matter must be shown by being placed within double brackets if strike-through cannot be easily perceived. Only claims having the status of “currently amended,” or “withdrawn” if also being amended, shall include markings. If a withdrawn claim is currently amended, its status in the claim listing may be identified as “withdrawn—currently amended.”
(3) When claim text in clean version is required. The text of all pending claims not being currently amended shall be presented in the claim listing in clean version, i.e., without any markings in the presentation of text. The presentation of a clean version of any claim having the status of “original,” “withdrawn” or “previously presented” will constitute an assertion that it has not been changed relative to the immediate prior version, except to omit markings that may have been present in the immediate prior version of the claims of the status of “withdrawn” or “previously presented.” Any claim added by amendment must be indicated with the status of “new” and presented in clean version, i.e., without any underlining.
(4) When claim text shall not be presented; canceling a claim.
(i) No claim text shall be presented for any claim in the claim listing with the status of “canceled” or “not entered.”
(ii) Cancellation of a claim shall be effected by an instruction to cancel a particular claim number. Identifying the status of a claim in the claim listing as “canceled” will constitute an instruction to cancel the claim.
(5) Reinstatement of previously canceled claim. A claim which was previously canceled may be reinstated only by adding the claim as a “new” claim with a new claim number.
Since the reply filed on 12/30/2025 appears to be bona fide, and the changes are readily apparent, Examiner will proceed with examination on the merits. Examiner respectfully reminds Applicant that further issues with claim marking may result in an amendment being deemed non-compliant.
Response to Arguments
Applicant’s arguments, see page 8, filed 12/30/2025, with respect to the drawing objections have been fully considered and are persuasive. Applicant’s arguments and amendments to the drawings make the necessary changes and put an explanation on the record of the phrase used in Figs. 5, 11, 12, and 13. The drawing objections have been withdrawn.
Applicant’s arguments, see pages 8-9, filed 12/30/2025, with respect to the specification objections have been fully considered and are persuasive. The specification objections have been withdrawn.
Applicant’s arguments, see pages 9-17, filed 12/30/2025, with respect to the 35 U.S.C. 101 rejections of claims 1-20 have been fully considered but are generally not persuasive. The 35 U.S.C. 101 rejections of claims 1-20 have been maintained.
Applicant begins by arguing on pages 9-10 that the amended claims do not fall into the Mental Process Grouping of Abstract Ideas. Examiner generally agrees, specifically because of the machine learning algorithm now being required and trained over time in the amended claims. Accordingly, in the rejection below the claims are no longer classified under Mental Processes.
Next, Applicant argues on pages 10-11 that the claims also do not recite Certain Methods of Organizing Human Activity abstract ideas. Applicant particularly argues that the claims are allegedly directed to a technical solution for solving a metric inconsistency problem in multi-subsystem organizational platforms, which Applicant argues is a “technical data integration and consistency problem” and not a method of organizing human activity. Examiner respectfully disagrees.
First, Examiner notes that “transforming” step of claim 1 that Applicant appears to be referring to when discussing solving the metric inconsistency problem is part of the abstract idea. Specifically, the limitation of claim 1: “transforming the retrieved employment-related data by standardizing and aggregating the employment-related data into a unified dataset based on a common semantic model that aligns definitions and formats across the plurality of disparate systems” in view of at least specification paragraphs [0107]-[0114] is not a technical solution. Particularly, the broadest reasonable interpretation of the claim in view of the specification covers using a dictionary of terms to define how subsystem data corresponds to the standard model ([0108]), standardizing date formats and/or the number of decimal places used ([0109]), removing duplicate data ([0110]). These data manipulations are not rooted in technology or are a technical improvement as Applicant is arguing. Instead, the transformation of data from multiple subsystems into a standardized model covers abstract data manipulation that is not necessarily rooted in technology (i.e., given the definitions, required date formats, etc. for the standardized model, a human could standardize the data set offline/not on a computer). Accordingly, claim 1, as a whole recites the commercial interaction of an organization taking data from systems that its uses and combining the data into a single data set using common rules and terminology to identify patterns/trends. This is at least a business relation between an organization, the Applicant Tracking System/team/organization the organization uses, and the Human Resources Information System/team/organization the organization uses. See MPEP 2106.04(a)(2)II.B. for a business relation explicitly falling under the commercial interaction of the Certain Methods of Organizing Human Activity grouping of abstract ideas. Furthermore, Applicant’s specification [0034] recites that such a combination of data from the HRIS and ATS is done to allow “an organization [to] optimally improve its hiring practices for certain metrics related to employee performance and development, such as performance and/or retention”, which falls under the mitigation of risk in hiring practices. Accordingly, Applicant’s arguments that the claims do not recite a judicial exception are not persuasive.
Next, Applicant argues that the claims are patent eligible at Step 2A Prong Two across pages 11-15 of Remarks. Applicant argues on pages 11-13 that the claims amount to a technical improvement. Applicant points to passages of the specification and argues that the ordered limitations reflects the improvements outlined in the specification. Examiner respectfully disagrees. As discussed above, the aggregation and combination of data using a common semantic model covers abstract data manipulation that is not necessarily rooted in technology. Even in the specification portions Applicant cites to in Remarks, standardizing the definition of a one-on-one meeting across systems is not rooted in technology. Definitions can be changed and stats updated in an offline manner to ensure commonality across parts of an organization. The problem of “metric inconsistency” similarly exists outside of a technical environment as well. For example, if different regions of a business counted the number of sales leads differently, the business would run into the same assortment of problems discussed by Applicant (numbers seeming meaningless/useless or drawing incorrect conclusions as discussed in specification [0051], etc.) even if all the information was kept track of manually. However, once a standard definition of sales lead was adopted and communicated out to each region, the benefits a data with common semantic definitions would be realized, again without the data needing to be kept track of using technology. Therefore, instead of a technical solution to a technical problem, the claimed invention implementing a common semantic meaning to data across disparate systems is an improvement to the abstract idea (i.e. standardizing definitions used across multiple organizations to make better use of available data). MPEP 2106.05(a)II. recites “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”. Accordingly, the implementation of a common semantic model across organizations/systems is not a technical improvement.
The “specific technical components” argued on pages 12-13 (HRIS system, ATS system, the trained machine learning algorithm, real-time API) are additional elements that are being used as tools to perform the judicial exception, and recite the idea of a solution or outcome (i.e., the claim fails to recite details of how a solution to a problem is accomplished.). Regarding the HRIS and ATS systems, while the type of system is recited in the claims, the claims provide no further structure for the systems or how the data is obtained from said systems. Claim 1 merely recites that “employment related-data” is retrieved from disparate systems including the ATS and HRIS. While types of data are recited in the claims, no further structure for the ATS or HRIS or methodology for how the data is retrieved from the systems. The ATS and HRIS are used as tools to hold the employment related data, and the claims recite the outcome that the data is retrieved from the HRIS and ATS. Even in the specification, the HRIS and ATS modules are simply recited as “providing one or more services” related to their respective data types in [0083]-[0084] along with examples of data items the modules may contain.
Regarding the newly added limitation of “the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations”, the trained machine learning algorithm is being used as a tool to perform the analysis of the unified dataset to identify patterns, trends, or correlations. Additionally, the idea of the outcome of analyzing the unified data set is recited without detail as to how the analysis is performed. The machine learning algorithm in the claim is being used as a black box that takes in the unified data set and outputs trends, correlations, and patterns. No detail as to the functioning or type of machine learning model is recited in the claims. Additionally, the specification similarly lacks details regarding the structure or type of machine learning model/algorithm, with the umbrella terms or machine learning model/algorithm being used throughout the disclosure at paragraphs [0072], [0089], and [0090]. Regarding the training of the model, while the type of data used to train the model is recited and the intended outcome of the training is recited, there is no detail regarding the training methodology recited in the claims. The specification likewise lacks detail regarding how the training of the machine learning model takes places, instead reciting the data used and what capabilities the training intends to give the model.
Finally, regarding the limitation of “updating the unified dataset or corresponding analyses in real-time through an application programming interface (API)”, the API call updating the dataset is again being used as a tool to update the data or analyses. Similar to the above, the goal of updating the unified dataset or analyses is recited with the API recited as accomplishing the goal without further detail. Even in claim 7 in which further detail of the API is given in the form of wrapping APIs of individual subsystem, nothing in the claims nor specification paragraphs [0055], [0096], [0101], [0103], [0106], and [0127] indicates to one of ordinary skill in the art that the wrapping of APIs is an improvement to the technology of APIs. See MPEP 2106.05(a) for the specification needing to convey to one of ordinary skill in the art the technical improvement and for the claims needing to reflect the technical improvement under the “improvements” consideration.
Even when viewed as an ordered combination, the additional elements, contrary to Applicant’s arguments on pages 12-13, fall into the “Mere Instructions to Apply” analysis of MPEP 2106.05(f) (whose individual considerations are listed by Applicant on page 13 of Remarks). The additional elements, as a whole, are being used as tools with an improved abstract idea being applied to them.
Applicant next argues on pages 13-14 that the instant claims are analogous to McRo. Applicant argues that the particular claimed architecture of the semantic model-based transformation and API-based synchronization and the recited detail makes the claims eligible for similar reasoning. Examiner respectfully disagrees. As discussed above, the additional elements are recited in the claims as tools with ideas outcomes recited without further detail. Regarding the common semantic model itself, claim 1 does not indicate any particulars about the common semantic model, and the specification recites a variety of potential definitions and number formatting that the common semantic model may take. This is in contrast with the citations from McRo and Example 48 in which detailed rules are recited to make improvements grounded in technology. Applicant’s arguments are not persuasive.
Applicant next argues that the amended recitation of machine learning goes beyond merely linking the judicial exception to the field of machine learning. Examiner respectfully disagrees. In contrast to the “specific machine learning algorithms” Applicant alleges are required by the claimed invention, the machine learning model in the claim is recited without any specificity of model type, structure, etc. The specification as-filed also does not recite any particular detail about how the machine learning model operates or is structured. Regarding the training process, neither the claims nor the specification recite details as to how the training is performed, just that it is done based on data from the subsystems and that the result is that the machine learning model can identify patterns, correlations, or trends. Applicant’s argument that the training being performed based on standardized data being a technical improvement is not persuasive. While the data is transformed, there are not specifics about the data being used to train the model. Further, while training based on standardized data may make finding patterns easier, the improvement is in the abstract data being fed into the model, not the model itself. While the amended machine learning model better falls into the “apply it” consideration, the machine learning model nonetheless does not integrate the claimed invention into a practical application.
Applicant argues across pages 14-15 that the API-based updating has not been properly considered. Applicant argues that the API architecture solves the metric inconsistency problem and cites to some features from the specification. Applicant argues that the API features are a technical improvement. Examiner respectfully disagrees. First, Examiner notes that the features argued by Applicant are not present in claim 1. As discussed above, the API in claim is merely recited as tool to update the unified dataset or the corresponding analyses without any detail as to how the API accomplishes the updating. While claim 7 does recite the argued point-in time feature, Examiner notes that the specification and claims do not indicate an improvement to technology to one of ordinary skill in the art. While the use of APIs communicates the standardized metrics across subsystems, the problem being solved as argued previously by Applicant and stated in [0034] of the specification is the combination of metrics from different systems. Applicant’s disclosure does not indicate that the dissemination of the standardized data is a technical hurdle being faced by the invention. In fact, specification paragraph [0036] recites that “the system may be configured to be integrated with external systems, such as Jira and Salesforce, such that goals and OKRs are synchronized across the organization’s systems” (emphasis added), indicating that synchronization of metrics across systems was not a technical challenge being tackled by the instant invention. Specification paragraphs [0054]-[0057] do recite that the APIs allow for metrics to be consistent across subsystems, but the specification does not indicate that the API architecture is a technical improvement over conventional APIs, just that the APIs are ensuring the improved data set is being used across subsystems. Similar to the machine learning model discussed above, the APIs are being used as a tool to ensure that the improved abstract data is being used, instead of the API architecture itself being a technical improvement over conventional APIs. Applicant’s arguments at Step 2A Prong Two are unpersuasive.
Applicant argues across pages 15-16 that the instant claims are eligible at Step 2B because they allegedly amount to significantly more than the judicial exception. Applicant argues that there is no evidence provided that the additional elements as a whole are well-understood, routine, and conventional activity. Applicant argues that without such evidence, the elements must amount to significantly more. Examiner respectfully disagrees.
MPEP 2106.05 II. recites that conclusions from MPEP 2106.05(f) (the “apply it” consideration) and MPEP 2106.05(h) (the “field of use” consideration) are carried over to Step 2B. MPEP 2106.05 II. further states that only elements that are considered to be insignificant extra-solution activity are re-evaluated at Step 2B to determine whether the elements are well-understood, routine, and conventional. As none of the additional elements in the previous or current 35 U.S.C. 101 rejections have been classified as insignificant extra-solution activity, no Berkheimer evidence is required for these additional elements. As MPEP 2106.05(f) states, “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more” (emphasis added). Therefore, Applicant’s arguments regarding the alleged lack of Berkheimer evidence at Step 2B are not persuasive.
Applicant argues that the instant claims should be eligible per the AI SME Update. Applicant argues that the metric consistency problem and alleged technical solution make the instant claims analogous to those in the Update. Examiner respectfully disagrees. As discussed above, the claimed machine learning algorithm and training process do not recite any technical detail, in contrast to the examples provided in the Update. Furthermore, the Update examples were improvements grounded in technology, while the instant claims are using machine learning algorithms as a tool. Finally, Examiner notes that, even if the metric consistency problem were to be considered a technical problem instead of a problem in the abstract idea of coordinating metrics across systems, the machine learning algorithm in the claimed invention does not provide the solution to metric consistency. The metrics have already been transformed into the unified data set in claim 1 before the machine learning algorithm analyzes them. Therefore, the machine learning algorithm in the instant invention does not represent an improvement to the functioning of a computer or improvements to other technology.
Finally, Applicant argues across pages 16-17 that the claimed invention is eligible for analogous reasoning as discussed in Desjardins. Applicant is appearing to tie the improvements to the training of a machine learning model to the alleged improvement of how organizational data platforms function by providing semantic alignment and API based synchronization. Examiner respectfully disagrees. First, Examiner notes that as discussed above, the instant claims and specification do not provide any technical detail as to how the machine learning model of the claims is trained. Accordingly, the instant claims are not analogous to Desjardins, which was eligible because of the specific improvements in the training process. Furthermore, aligning semantics and formats across datasets, as discussed above, is an improvement to the abstract idea, not a technical improvement. Finally, the API updating in the claims, for reasoning discussed in more detail above, is not a technical improvement but is being used as a tool to disseminate the unified metrics. As a whole, the claimed invention is not analogous to Desjardins, as the additional elements do not provide an improvement to the training of a machine learning model nor to the functioning of a computer. Applicant’s arguments against the eligibility of the pending are not persuasive. Claims 1-20 still stand rejected under 35 U.S.C. 101.
Applicant’s arguments, see pages 18-23, filed 12/30/2025, with respect to the 35 U.S.C. 103 rejections of pre-amended claims 1-20 have been fully considered but are generally not persuasive.
After summarizing the rejections, law, and claim 1 across pages 18-19, Applicant argues that the combination of Scarborough, Chapman, and Desai does not teach the pre-amended claim 1. Examiner respectfully disagrees.
Applicant first argues on page 20 that Scarborough does not teach transforming retrieved data by standardizing and aggregating the data into a unified dataset based on a common semantic model that aligns definitions and formats across disparate systems. Examiner agrees, but notes that the 07/30/2025 Office Action already stated as much (page 17, “Scarborough does not explicitly teach…the transformation of the pre- and post-hire data including standardizing the data based on a common semantic model that aligns definitions and formats of the data sets”). While Scarborough does teach the combining of pre-and post-hire data and handling missing/corrupted data, Scarborough does not teach a semantic model, and the 7/30/2025 Office Action did not claim as much.
Applicant then argues across pages 20-21 that Scarborough’s combining of pre-hire information and post-hire job performance is “fundamentally different” from the claimed invention retrieving data from an HRIS and ATS, transforming into a unified data set, and then analyzing the unified dataset to identify patterns, trends, or correlations. Examiner respectfully disagrees. While Scarborough does not explicitly use the terms “ATS” and “HRIS”, the pre- and post- hire data being obtained are the types of data Applicant’s invention is gathering from the HRIS and ATS. See in particular Applicant’s specification [0083]-[0084] reciting “any information gathered about an applicant during the hiring process” being provided by the ATS module and “any information pertaining to associated individuals” being provided by the HRIS. The pre- and post- hire information gathered by Scarborough are the kinds of data Applicant’s invention is retrieving, not fundamentally different. Additionally, Scarborough teaches in the cited paragraphs [0165]-[0177] the process of analyzing applicants to determine their similarity to former/current employees to determine the chances that the applicant would be eligible for rehire after termination or estimate the amount of sales an applicant is likely to make based on past sales employee performance. Not only is this analysis of the unified pre-and post-hire data in contrast to Applicant’s arguments, Scarborough also reads on a particular use case (Talent Acquisition Optimization) from Applicant’s specification [0116]. Therefore, while Scarborough does not explicitly teach all features of pre-amended claim 1, the merging of pre- and post-hire data into a transformed data set that is used to analyze patterns, trends, or correlations teaches portions of pre-amended claim 1 and is not “fundamentally different” as Applicant argues.
Next, Applicant argues on page 21 that Chapman does not cure deficiencies of Scarborough. Applicant argues that Chapman is directed to “fundamentally different” problems and solutions than what is claimed. Applicant argues that Chapman is directed towards aggregating profiles of a user across talent databases and not directed to analyzing combinations of applicant/employment data. Examiner respectfully disagrees. First, Examiner notes that Scarborough already teaches the retrieval of applicant and employment data. The deficiency Chapman is curing is that this pre- and post- hire data being retrieved from and ATS and HRIS in particular. Chapman explicitly teaches in [0082] that pre-hire data from an ATS and employee data from an HRIS are retrieved and correlated by an aggregation engine. This gathering of pre- and post-hire data and their subsequent aggregation is not “fundamentally different” from the claimed invention as Applicant argues. The goal of aggregating pre- and post-hire data of Chapman makes Chapman analogous art to the claimed invention and Scarborough.
Regarding Applicant’s arguments regarding the API, Examiner notes that Applicant appears to be overlooking the explicitly cited paragraph [0056], in which Chapman states "It should be understood that the functions attributed to the engines 322, 323, 324, 325, described herein are exemplary in nature, and that in alternative embodiments, any function attributed to any engine 322, 323, 324, 325, may be performed by one or more other engines 322, 323, 324, 325, or any other suitable processor logic". Examiner did not conflate the self-adjusting database updated via aggregation engine. Instead, Examiner cited to Chapman explicitly saying the functions, including updating of the database with information from the plurality of sources, performed by the aggregation engine (element 323 in Chapman) can be performed by the API (element 325 in Chapman). Accordingly, Applicant’s arguments that Chapman failed to teach the updating of the unified database via an API are not persuasive.
Next across pages 21-22, Applicant argues that Desai does not teach a common semantic model that aligns definitions and formats across disparate systems. Applicant particularly argues that Desai does not teach the definitions remaining consistent across subsystems, and appears to acknowledge that Desai teaches format conversion. Examiner notes that Desai recites in [0051] "The example aggregator 114 is configured to convert, supplement, standardize, map, or otherwise process service data from information sources into a generic or standardized format. The aggregator 114 is configured to access a service data model corresponding to the information source that provided the service data to determine how the service data is to be converted. The standardized format includes defined data fields of a data structure (e.g., a service data entry) corresponding to a generic service data model" and [0075] "It should be appreciated that the database 304 of FIG. 3 includes a different service data model for each different information source that provides service data. This configuration enables the aggregator 114 to map or convert service data from any known format of an information source into a uniform, standardized, generic structure" (emphasis added). The aggregation of Desai takes data from various information sources and converts it into a format with standardized definitions for fields. Therefore, Desai is taking in data from a variety of information sources and aggregating the data into a unified data set with a common format and definitions regardless of what information source the data originates from. This is aligning definitions and formats across a plurality of disparate sources. Examiner also notes the comparison between Applicant’s specification [0108] “Semantic Alignment Algorithm…Process: The algorithm maps each data point to a standardized semantic model. It uses a dictionary of terms and relationships that define how each term from the subsystems corresponds to the standardized model. Output: Semantically aligned data points that have a consistent meaning across the system” and Desai [0075] of “the aggregator 114 to map or convert service data from any known format of an information source into a uniform, standardized, generic structure” for the model mapping/converting data from any format of a data source to a standardized structure. Additionally, Desai aggregating data from a variety of information sources is not solving a fundamentally different problem than the claimed invention, as the claimed invention’s semantic model is also aggregating data from disparate sources into a standardized format. Accordingly, Applicant’s arguments that Desai does not teach transforming data using a common semantic model are not persuasive.
Applicant next argues across pages 22-23 that the rationales for combining Scarborough, Chapman, and Desai are deficient. Regarding Scarborough and Chapman, Applicant argues that the motivation of a simple substitution of one known element for another producing a predictable result lacks rational underpinning as a motivation to combine references. Applicant argues that the technical problems, solutions, and use cases are different between Scarborough and Chapman, and that Chapman’s ATS and HRIS systems serve a different function. Examiner respectfully disagrees. MPEP 2141 II. recites “Examples of rationales that may support a conclusion of obviousness include:… (B) Simple substitution of one known element for another to obtain predictable results”. In the case of Scarborough and Chapman, the simple substitution rationale is for the substitution of the HRIS and ATS systems of Chapman in for the “variety of sources” of pre- and post-hire information of Scarborough. Chapman explicitly teaches that an ATS is an example of a data source for candidate data and a HRIS is an example data source for data regarding an individual post hire in [0082]. As both Scarborough and Chapman are teaching that pre- and post-hire data can be obtained and aggregated together, one of ordinary skill in the art would have recognized that the retrieval of pre-hire data from an ATS and post-hire data from an HRIS in the system of Scarborough would have yielded predictable results. The substitution of the ATS and HRIS in as the location from which to retrieve pre- and post-hire data would not affect downstream processing of Scarborough. Chapman not using the data from the HRIS and ATS in the exact same manner as Scarborough would not prevent one of ordinary skill in the art finding it obvious that the “variety of sources” of pre- and post-hir information of Scarborough being the ATS and HRIS of Chapman. The motivation to combine Scarborough and Chapman through a simple substitution is explicitly recited in the MPEP above as supporting a conclusion of obviousness. Applicant’s arguments are not persuasive.
Finally, Applicant argues on page 23 that the rationale for incorporating Desai lacks adequate reasoning. Applicant argues that one of ordinary skill in the art would not consult Desai when working in the field of HR and employment analytics. Applicant argues that the model of Desai is specifically designed for routing service requests. Finally, Applicant also argues that the rationale of the standardized data being easier to work with is insufficient because Applicant acknowledges that “Scarborough already teaches merging and standardizing data”. Examiner respectfully disagrees. First, Scarborough, Chapman, and Desai are analogous art. MPEP 2141.01(a) I. recites “A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). Note that "same field of endeavor" and "reasonably pertinent" are two separate tests for establishing analogous art; it is not necessary for a reference to fulfill both tests in order to qualify as analogous art… As for the "reasonably pertinent" test, the examiner should consider the problem faced by the inventor, as reflected - either explicitly or implicitly - in the specification. In order for a reference to be "reasonably pertinent" to the problem, it must "logically [] have commended itself to an inventor's attention in considering his problem." In re ICON Health and Fitness, Inc., 496 F.3d 1374, 1379-80 (Fed. Cir. 2007) (quoting In re Clay, 966 F.2d 656,658, 23 USPQ2d 1058, 1061 (Fed. Cir. 1992))”. While Desai may not be in the same field of endeavor as Chapman and Scarborough, they all reasonably pertinent to each other because they all address the problem of merging/aggregating data sets from different sources. One of ordinary skill in the art would recognize that how the field definitions are standardized across data sources of Desai would be reasonably pertinent to Scarborough and Chapman as they aggregate data from different sources. One of ordinary skill in the art would also recognize that data aggregation/standardization is not a unique problem to HR and employment data, and would not dismiss data aggregation teachings from other fields. Regarding the argument that the model is specifically designed for service requests, Examiner notes that the Desai [0051] teaching would be applicable and pertinent to aggregation of data from multiple sources. When viewing Scarborough, Chapman, and Desai together, one of ordinary skill in the art would have recognized the benefits of supplementing Scarborough’s data cleaning (addressing missing fields, corrupted data in [0150]) with the standardized field definitions taught by Desai’s model. Desai’s model being used for service request processing would not preclude one of ordinary skill in the art from applying the standardized field definition concept to data obtained from the multiple data sources in Scarborough and Chapman, as the data of Scarborough and Chapman is retrieved from multiple data sources and being combined into a unified data set just like in Desai. Regarding Applicant’s argument that Scarborough already teaches merging and standardizing data, Examiner notes that this argument appears to at least partially contradict Applicant’s earlier argument on page 20 of Remarks that Scarborough “does not teach or suggest transforming data by standardizing and aggregating based on a common semantic model that aligns definitions and formats” and instead merely teaches “data quality checks”. As discussed in the previous Office Action and in the rejections below, Scarborough does not already teach standardizing the data sets. Accordingly, one of ordinary skill in the art would have been motivated to incorporate the standardization of Desai. Applicant’s arguments against the pre-amended claims are therefore unpersuasive.
On pages 23-24, Applicant turns their arguments to the amended independent claims. Applicant particularly argues that the newly added limitation of “the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations” is distinguished over the prior art. Applicant also argues that the amended claims underscore that the retrieved employment related data comprises both applicant information and performance metrics to identify trends, patterns, or correlations, allegedly in contrast with Scarborough. Examiner respectfully disagrees.
First, Examiner notes that the limitation being argued lacks sufficient written description support, as will be discussed in the rejection below. Nevertheless the combination of Scarborough, Chapman, and Desai still teach these amended features.
Regarding the training of the machine learning model based on the transformed employment related data, Examiner notes that Scarborough [0150] "The program can use an expert system decision rule base to keep track of how many complete employee life cycle histories are in a reports database. In addition, the software can examine and partition individual records that may be unusable due to missing fields, corrupted data, or other data fidelity problems. Using pre-defined sample size boundaries, the software can merge available pre- and post-hire data transfer and transfer a file to the validation queue (e.g., the queue described below)" for the transformed data being sent to the validation queue, and Scarborough further teaches in [0191] that the validation queue is sent to the predictive model development environment in which predictive models are generated. Scarborough [0154] “the distribution of pre-hire variables (sometimes called "independent" or "predictor variables") can be analyzed in relation to the distribution of post-hire outcome data (sometimes called "dependent" or "criterion variables")” and [0159] “A training set can be used to train a neural network or neuro-fuzzy model to predict, classify, or rank the probable criterion value associated with each instance of predictor input variables” teach that the model development takes the transformed pre-and post-hire data from the validation queue and trains the models. Also see the example in Scarborough in paragraphs [0216] “pre-hire application data used to develop this exemplary model was collected over a period of a year and a half using an electronic employment application as administered using screen phones deployed in over 1800 stores across the United States. Termination records of employees hired via the system were received by download. Over 36,000 employment applications were received in the reporting period, of which approximately 6,000 resulted in employment. Complete hire to termination records were available for 2084 of these employees, and these records were used to develop the model” for a transformed data set combining pre and post hire data of 2084 individuals and [0239] “Once the set of predictor variables or inputs has been defined and the output criterion variable specified, a neural network model can be trained. For the tenure prediction model, 2084 cases were available. This sample was divided into training, test and verification sets. The training set contained 1784 cases and the verification and test sets contained 150 cases each” for the machine learning model being trained on the transformed/aggregated pre- and post-hire data. Accordingly, Scarborough explicitly teaches training the machine learning model on the transformed employment related data.
Regarding Applicant’s argument that the prior art does not teach the machine learning model being trained “over time”, Examiner notes that “over time” and “real-time” are used in Applicant’s argument as interchangeable have different breadths. Specifically, while “real-time” places some time constraint on how frequently something (in this case, model training) is performed, “over time” as used the claims can be read on without a particular required cadence. Applicant argues that Scarborough teaches model replacement with new models, and not the training over time. Examiner respectfully disagrees. Scarborough [0200] recites “Older predictive models can be replaced or re-trained to incorporate both new item content from the item rotation procedure and additional criterion variation resulting from the expanding number of employee histories contained in the validation database” (emphasis added). Scarborough [0200] therefore teaches that a models are re-trained over time as new employee histories are collected in the validation database. Accordingly, Scarborough teaches that the machine learning models are trained over time.
Finally, Applicant argues on pages 24-25 that the amended claims underscore that the employment-related data comprises both applicant and employee metrics and that the claimed system identifies trends, patterns, or correlations. Applicant argues that the cited references lack this capability to “identify organizational trends regarding the relationship between hiring practices and employee outcomes”. Examiner respectfully disagrees. First, Examiner notes that the claims recite “trends, patterns, or correlations within the employment-related data”. The claims do not recite a particular trend between hiring practices and employment outcomes that Applicant is arguing here. The claims only require trends, patterns, or correlation within the employment-related data. The scope of the claim is not limiting what such a trend, pattern, or correlation must be. Regardless, Scarborough, using the unified dataset as discussed above, determines correlations between within the employment-related data at least in the form of correlating pre-hire attributes with employment tenure and chance for rehire eligibility in the future in cited paragraphs [0165]-[0177]. This correlation within the employment-related data reads on the “trends, patterns, or correlations” recited in the claimed invention. Accordingly, Applicant’s arguments regarding both the pre-amended and amended claim set and the Scarborough, Chapman, and Desai references are not persuasive. Independent claims 1, 8, and 15 still stand rejected under 35 U.S.C. 103.
Applicant’s arguments on page 25 that the dependent claims are distinguished over the prior art by virtue of their dependence on independent claims 1, 8, and 15 are not persuasive because claims 1, 8, and 15 still stand rejected under 35 U.S.C.103 for the reasons discussed above. Dependent claims 2-7, 9-14, and 16-20 still stand rejected under 35 U.S.C. 103.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor had possession of the claimed invention.
Regarding claim 1, the claim has been amended to recite “the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations” (emphasis added) in lines 13-15. This limitation lacks written description support. The specification discusses training the machine learning model in paragraphs [0089] and [0090], particularly in the lines “a machine-learning model may be trained to generate a value predicting a performance of the organization with respect to one or more objectives, an action to take to improve the performance, and/or an indicator of one or more data items that will have the biggest impact on satisfaction of the one or more objectives” (from [0089]) and “a machine-learned model may be trained (e.g., with ATS data items and/or HRIS data items) to predict one or more employee scores, such as employee scores associated with OKRs (e.g., quality of hire, employee performance, and/or employee retention rate)” (from [0090]). While the specification supports training the machine learning model based on ATS and HRIS data, the specification does not provide support for training the machine learning model on transformed employment-related data from the HRIS and ATS.
Furthermore, nowhere in the specification is the training of the machine learning model recited as being performed “over time” as Applicant has amended into claim 1. Paragraphs [0089]-[0090] instead recite training a machine learning model, then applying the machine learning model to novel data. The specification makes no mention of repeated training or training over time.
Accordingly, one of ordinary skill in the art would not have recognized based on Applicant’s disclosure as-filed that Applicant had possession of training the machine learning model based on transformed employment-related data over time. Therefore, claim 1 does not have sufficient written description support and is rejected under 35 U.S.C. 112(a).
Independent claims 8 and 15 have been amended similarly to claim 1 and lack written description support for similar reasoning as claim 1. Dependent claims 2-7 are rejected by virtue of their dependence on claim 1, dependent claims 9-14 are rejected by virtue of their dependence on claim 8, and dependent claims 16-20 are rejected by virtue of their dependence on claim 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite identifying trends and patterns between applicant tracking data and human resource data.
As an initial matter, claims 1-7 fall into at least the machine category of statutory subject matter. Claims 8-14 fall into at least the process category of statutory subject matter. Finally, claims 15-20 fall into at least the manufacture category of statutory subject matter. Therefore, all claims fall into at least one of the statutory categories. Eligibility analysis proceeds to Step 2A.
Claim 1 recites the concept of identifying trends and patterns between applicant tracking data and human resource data which is a certain method of organizing human activity including commercial interactions between business data systems and the fundamental economic practices of mitigating the risk of hiring applicants who underperform as employees. A set of instructions, the set of instructions to perform operations, the operations comprising: retrieving employment-related data, wherein the employment-related data comprises applicant information and employee performance metrics; transforming the retrieved employment-related data by standardizing and aggregating the employment-related data into a unified dataset based on a common semantic model that aligns definitions and formats; analyzing the unified dataset to identify trends, patterns, or correlations within the employment-related data using one or more algorithms; and updating the unified dataset or corresponding analyses all, as a whole, fall under the category of commercial interactions and fundamental economic practices. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
These judicial exceptions are not integrated into a practical application. In particular, the claim recites the additional elements of a system; one or more computer processors; one or more computer memories; a plurality of disparate systems including a human resource information system (HRIS) and an applicant tracking system (ATS); one or more machine learning algorithms, the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations; and updating in real-time analyses or data through an application programming interface (API). The recited 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. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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, the additional elements of a system; one or more computer processors; one or more computer memories; a plurality of disparate systems including a human resource information system (HRIS) and an applicant tracking system (ATS); one or more machine learning algorithms, the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations; and updating in real-time analyses or data through an application programming interface (API) amount to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-5 further limit the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 6 further limits the abstract idea of claim 1 while introducing the additional element of using a caching layer. The claim does not integrate the abstract idea into a practical application because the element of using a caching layer is 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. Adding this new additional element into the additional element from claim 1 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 7 further limits the abstract idea of claim 1 while introducing the additional element of the API being configured to wrap APIs of individual subsystems contributing standardized point-in-time data APIs. The claim does not integrate the abstract idea into a practical application because the element of the API being configured to wrap APIs of individual subsystems contributing standardized point-in-time data APIs is 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. Adding this new additional element into the additional element from claim 1 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 8 recites the concept of identifying trends and patterns between applicant tracking data and human resource data which is a certain method of organizing human activity including commercial interactions between business data systems and the fundamental economic practices of mitigating the risk of hiring applicants who underperform as employees. A method comprising: retrieving employment-related data, wherein the employment-related data comprises applicant information and employee performance metrics; transforming the retrieved employment-related data by standardizing and aggregating the employment-related data into a unified dataset based on a common semantic model that aligns definitions and formats; analyzing the unified dataset to identify trends, patterns, or correlations within the employment-related data using one or more algorithms; and updating the unified dataset or corresponding analyses all, as a whole, fall under the category of commercial interactions and fundamental economic practices. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
These judicial exceptions are not integrated into a practical application. In particular, the claim recites the additional elements of a plurality of disparate systems including a human resource information system (HRIS) and an applicant tracking system (ATS); one or more machine learning algorithms, the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations; and updating in real-time analyses or data through an application programming interface (API). The recited 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. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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, the additional elements of a plurality of disparate systems including a human resource information system (HRIS) and an applicant tracking system (ATS); one or more machine learning algorithms, the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations; and updating in real-time analyses or data through an application programming interface (API) amount to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 9-12 further limit the abstract idea of claim 8 without adding any new additional elements. Therefore, by the analysis of claim 8 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 13 further limits the abstract idea of claim 8 while introducing the additional element of using a caching layer. The claim does not integrate the abstract idea into a practical application because the element of using a caching layer is 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. Adding this new additional element into the additional element from claim 8 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 14 further limits the abstract idea of claim 8 while introducing the additional element of the API being configured to wrap APIs of individual subsystems contributing standardized point-in-time data APIs. The claim does not integrate the abstract idea into a practical application because the element of the API being configured to wrap APIs of individual subsystems contributing standardized point-in-time data APIs is 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. Adding this new additional element into the additional element from claim 8 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 15 recites the concept of identifying trends and patterns between applicant tracking data and human resource data which is a certain method of organizing human activity including commercial interactions between business data systems and the fundamental economic practices of mitigating the risk of hiring applicants who underperform as employees. A set of instructions that, when executed, perform operations, the operations comprising: retrieving employment-related data, wherein the employment-related data comprises applicant information and employee performance metrics; transforming the retrieved employment-related data by standardizing and aggregating the employment-related data into a unified dataset based on a common semantic model that aligns definitions and formats; analyzing the unified dataset to identify trends, patterns, or correlations within the data using one or more algorithms; and updating the unified dataset or corresponding analyses all, as a whole, fall under the category of commercial interactions and fundamental economic practices. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
These judicial exceptions are not integrated into a practical application. In particular, the claim recites the additional elements of a non-transitory computer-readable storage medium storing a set of instructions; one or more computer processors; a plurality of disparate systems including a human resource information system (HRIS) and an applicant tracking system (ATS); one or more machine learning algorithms, the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations; and updating in real-time analyses or data through an application programming interface (API). The recited 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. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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, the additional elements of a non-transitory computer-readable storage medium storing a set of instructions; one or more computer processors; a plurality of disparate systems including a human resource information system (HRIS) and an applicant tracking system (ATS); one or more machine learning algorithms, the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations; and updating in real-time analyses or data through an application programming interface (API) amount to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 16-19 further limit the abstract idea of claim 15 without adding any new additional elements. Therefore, by the analysis of claim 15 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 20 further limits the abstract idea of claim 15 while introducing the additional element of using a caching layer. The claim does not integrate the abstract idea into a practical application because the element of using a caching layer is 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. Adding this new additional element into the additional element from claim 15 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 8-10, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Scarborough et al. (U.S. Pre-Grant Publication No. 2005/0246299, hereafter known as Scarborough) in view of Chapman et al. (U.S. Pre-Grant Publication No. 2019/0129996, hereafter known as Chapman) and Desai (U.S. Pre-Grant Publication No. 2014/0250166, hereafter known as Desai).
Regarding claim 1, Scarborough teaches:
A system comprising: one or more computer processors; one or more computer memories (see Fig. 13 and [0189]-[0194] for overall system, Fig. 6 and [0053]-[0056] for detail portion of the system for collecting pre-hire information, and Fig. 12 and [0109]-[0110] for detail portion of the system for collecting post-hire information. For processors, see servers 622 from [0053], servers 1222 and 1242 from [0109], and the transaction monitor 1318 from [0190]. For memories, see [0044] "The pre-hire information 112 can be stored in electronic (e.g., digital) form in a computer-readable medium (e.g., RAM, ROM, magnetic disk, CD-ROM, CD-R, DVD-ROM, and the like)" and see [0139]-[0144] for both pre-and post-hire data being stored in databases)
a set of instructions stored in the one or more computer memories, the set of instructions configuring the one or more computer processors to perform operations, the operations comprising: retrieving employment-related data from a plurality of disparate systems (see various software in [0130] for receiving in-bound communications, [0150] for merging datasets, [0081] and [0121] for AI for making hiring predictions, and [0179] for software for rotating out ineffective predictors from pre-hire data used in making predictions. See [0044]-[0045] for collecting pre- and post-hire information, see [0063] and [0067] for collecting application information via a computer. See [0074]-[0076] for collecting post-hire employee performance metrics. See [0115]-[0116] for specific pre- and post-hire metrics collected)
transforming the retrieved employment-related data by across the plurality of disparate systems (see [0150] "The program can use an expert system decision rule base to keep track of how many complete employee life cycle histories are in a reports database. In addition, the software can examine and partition individual records that may be unusable due to missing fields, corrupted data, or other data fidelity problems. Using pre-defined sample size boundaries, the software can merge available pre- and post-hire data transfer and transfer a file to the validation queue (e.g., the queue described below)")
analyzing the unified dataset to identify trends, patterns, or correlations within the employment-related data using one or more machine learning algorithms (see [0152] for the validation queue, which the unified data set has been sent to per [0150] above, sending datasets to model development. See [0153] "Model development can result in the creation of a model that represents observed functional relationships between pre-hire data and post-hire data. Artificial intelligence technologies can be used to define and model such relationships" and [0165]-[0177] for the model determining the applicant's predicted service days, sales amounts, etc. based on a relationship between applicant data being similar to former employee data and the former employee's performance. See [0121]-[0129], particularly [0121] "There are at least three approaches to machine intelligence: expert systems, neural networks, and fuzzy logic systems" and [0129] "An employee selection system can include adaptive learning technology. Such a system can be constructed as a hybrid artificial intelligence application, based in part on various (or all) of the above artificial intelligence technologies" for the analysis being done based on machine learning algorithms)
the one or more machine learning algorithms trained on the transformed employment-related data from the plurality of disparate systems over time to predict the trends, patterns, or correlations (see [0150] for the transformed data being sent to the validation queue and [0191] that the validation queue is sent to the predictive model development environment in which predictive models are generated. Also see [0154] and [0159] “A training set can be used to train a neural network or neuro-fuzzy model to predict, classify, or rank the probable criterion value associated with each instance of predictor input variables” for the model development taking the transformed pre-and post-hire data from the validation queue and training the models. Also see [0216] “pre-hire application data used to develop this exemplary model was collected over a period of a year and a half using an electronic employment application as administered using screen phones deployed in over 1800 stores across the United States. Termination records of employees hired via the system were received by download. Over 36,000 employment applications were received in the reporting period, of which approximately 6,000 resulted in employment. Complete hire to termination records were available for 2084 of these employees, and these records were used to develop the model” for a transformed data set combining pre and post hire data of 2084 individuals and [0239] “Once the set of predictor variables or inputs has been defined and the output criterion variable specified, a neural network model can be trained. For the tenure prediction model, 2084 cases were available. This sample was divided into training, test and verification sets. The training set contained 1784 cases and the verification and test sets contained 150 cases each” for the unified data set being used to train and test the machine learning model. See [0200] “Older predictive models can be replaced or re-trained to incorporate both new item content from the item rotation procedure and additional criterion variation resulting from the expanding number of employee histories contained in the validation database” for the model being re-trained over time, and see the [0165]-[0177] citations above for the models identifying correlations within the unified data set)
and updating the unified dataset or corresponding analyses in real-time (see [0098] "it was determined that IN.sub.4 and IN.sub.5 were ineffective predictors, so the content (e.g., question) related to IN.sub.4 and IN.sub.5 was removed from the corresponding employment application. Based on the finding that IN.sub.4 and IN.sub.5 were not effective predictors, they were not included in the model deployed at that time" for updating the unified dataset and corresponding model analysis based on the determination that particular predictors were ineffective. See [0096] "Model refinement can also be achieved through increased sample size, improvements to model architecture, changes in the model paradigm, and other techniques" for updating the models with relationships between applicant and employee data. See [0213] for model refinement being performed in real-time)
As discussed above Scarborough teaches the unifying of the pre-and post-hire data collected from disparate applicant and employee data sources, and further teaches the updating of the data sets to exclude pre-hire data types that are ineffective predictors of future performance as an employee and updating/refining models correlating pre-hire information with post-hire attributes. However, Scarborough does not explicitly teach the disparate sources specifically being HRIS and ATS systems, the updating of the unified dataset or analyses being done through an API, and the transformation of the pre- and post-hire data including standardizing the data based on a common semantic model that aligns definitions and formats of the data sets. Chapman teaches:
retrieving employment-related data from a plurality of disparate systems including a human resource information system (HRIS) and an applicant tracking system (ATS) (see [0082] "an individual may have a profile having a feature set 120A with data describing the individual as a candidate in a first data source 111 (e.g. applicant tracking system or ATS), the individual may have a profile having feature set 120B data describing the individual as hired and being an employee in a second data source 112 (e.g. human resource information system or HRIS), and the individual may have a profile having feature set 120C data describing the individual as being a potential prospect in a third data source 113 (e.g. Candidate Relationship Management or CRM)" for pre-hire data originating from an ATS and post-hire data originating from a HRIS)
and updating the unified dataset or corresponding analyses in real-time through an application programming interface (API) (see [0072] "an application program interface (API) engine 325 may provide a self-adjusting application program interface (API)...The self-adjusting API may enable any number of consumers to search, query, and model insights into the data model of the system database 330" and for the self-adjusting API and [0082] "the aggregation engine 323 and/or database engine 324 may automatically self-adjust the aggregated system database 330 by adjusting, updating, or otherwise modifying the aggregated features 151-161 of an individual in their aggregated profile record 150 using data from feature sets 120 extracted from one or more data sources 111, 112, 113, in step 553" and [0056] "It should be understood that the functions attributed to the engines 322, 323, 324, 325, described herein are exemplary in nature, and that in alternative embodiments, any function attributed to any engine 322, 323, 324, 325, may be performed by one or more other engines 322, 323, 324, 325, or any other suitable processor logic" for the API updating the unified dataset)
Regarding the ATS and HRIS of Chapman, since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of collecting pre-and post- hire data from an ATS and HRIS of Chapman for the collection of pre- and post- hire data from a variety of data sources of Scarborough.
Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding the use of the API, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include an API to update analysis and data sets as taught by Chapman in the system of Scarborough, 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. Specifically, one of ordinary skill in the art would have found it obvious to perform the updating recited in Scarborough using an API, and the use of an API to execute the Scarborough updating would have had predictable results.
The combination of Scarborough and Chapman still does not explicitly teach the transformation of the pre- and post-hire data including standardizing the data based on a common semantic model that aligns definitions and formats of the data sets. However, Desai teaches:
transforming the retrieved employment-related data by standardizing and aggregating the employment-related data into a unified dataset based on a common semantic model that aligns definitions and formats across the plurality disparate systems (see [0051] "The example aggregator 114 is configured to convert, supplement, standardize, map, or otherwise process service data from information sources into a generic or standardized format. The aggregator 114 is configured to access a service data model corresponding to the information source that provided the service data to determine how the service data is to be converted. The standardized format includes defined data fields of a data structure (e.g., a service data entry) corresponding to a generic service data model" and [0075] "It should be appreciated that the database 304 of FIG. 3 includes a different service data model for each different information source that provides service data. This configuration enables the aggregator 114 to map or convert service data from any known format of an information source into a uniform, standardized, generic structure" for a model that allows data from various information sources to be aggregated in a standardized format with standard field definitions. In combination with Scarborough and Chapman, the data being standardized is the pre- and post-hire employment-related data)
One of ordinary skill in the art would have recognized that applying the known technique of standardizing and aggregating data from disparate sources using a common semantic model that aligns definitions and formats of Desai to the combination of Scarborough and Chapman would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Desai to the teaching of the combination of Scarborough and Chapman would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such standardizing and aggregating data from disparate sources using a common semantic model that aligns definitions and formats. Further, applying standardizing and aggregating data from disparate sources using a common semantic model that aligns definitions and formats to the combination of Scarborough and Chapman would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient data analysis of patterns and correlations between pre-hire information and post-hir performance. As Desai states in [0040], “This standardization enables the example system to apply the same service rules and/or service fulfillment rules service data regardless of the information source”. One of ordinary skill in the art would have recognized that, by standardizing the data received from the plurality of sources in Scarborough and Chapman, the resulting unified data would be easier to work with to find the job performance predictions of applicants as desired by Scarborough.
Regarding claim 2, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 1 above. Scarborough further teaches:
wherein the retrieving comprises accessing data items from the HRIS and the ATS that includes information pertaining to employee goals, compensation, engagement, or applicant data gathered during a hiring process (see [0107]-[0110] for employee payroll data being included in post-hire data collection. See [0142] "post-hire data about the job performance of employees after being hired can be stored. Such data can include, for example, supervisor opinion ratings about the employee's overall job performance or specific aspects of the employee's job effectiveness. Quantitative indicators about attendance, sales or unit production, disciplinary records and other performance measures may also be collected" for attendance data which is being interpreted as engagement data. See [0141] "An arrangement of three basic types of data can be used for the applicant database. First, standard pre-hire application information (e.g., name, address, phone number, job applied for, previous experience, references, educational background, and the like) can be stored. Also, included can be applicant responses to psychological or other job-related assessments administered via an external data collection device (e.g., the electronic device 124 of FIG. 1)" for applicant data gathered during the hiring process being collected)
Regarding claim 3, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 1 above. While Chapman teaches the receiving of data from an HRIS and ATS as discussed above, the combination of Scarborough and Chapman does not explicitly teach standardizing the retrieved pre- and post- hire data based on a common semantic model that aligns definitions and formats of the data sets. Similarly, the combination of Scarborough and Chapman also does not explicitly teach normalizing the data items to a common format or resolving semantic differences between data representations in the HRIS and the ATS. However, Desai further teaches:
wherein the transforming comprises normalizing the employment-related data to a common format or resolving semantic differences between data representations in the HRIS and the ATS (see [0051] "The example aggregator 114 is configured to convert, supplement, standardize, map, or otherwise process service data from information sources into a generic or standardized format. The aggregator 114 is configured to access a service data model corresponding to the information source that provided the service data to determine how the service data is to be converted. The standardized format includes defined data fields of a data structure (e.g., a service data entry) corresponding to a generic service data model" for the transforming comprising normalizing the data to a common format)
One of ordinary skill in the art would have recognized that applying the known technique of normalizing data to a common format and resolving semantic differences between data sets of Desai to the combination of Scarborough and Chapman would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Desai to the teaching of the combination of Scarborough and Chapman would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such normalizing data to a common format and resolving semantic differences between data sets. Further, applying normalizing data to a common format and resolving semantic differences between data sets to the combination of Scarborough and Chapman would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient data analysis of patterns and correlations between pre-hire information and post-hir performance. As Desai states in [0040], “This standardization enables the example system to apply the same service rules and/or service fulfillment rules service data regardless of the information source”. One of ordinary skill in the art would have recognized that, by standardizing the data received from the plurality of sources in Scarborough and Chapman, the resulting unified data would be easier to work with to find the job performance predictions of applicants as desired by Scarborough.
Regarding claim 8, Scarborough teaches:
A method comprising (see Figs. 5 and 9 and [0052] and [0078]-[0085] for the overall method)
Regarding the remaining limitations of claim 8, see the rejection of claim 1 above.
Regarding claim 9, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 8 above. Regarding the limitations introduced in claim 9, see the rejection of claim 2 above.
Regarding claim 10, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 8 above. Regarding the limitations introduced in claim 10, see the rejection of claim 3 above.
Regarding claim 15, Scarborough teaches:
A non-transitory computer-readable storage medium storing a set of instructions that, when executed by one or more computer processors, causes the one or more computer processors to perform operations, the operations comprising (see [0046] "the model can be implemented as computer-executable code stored in a computer-readable medium" and various software in [0130] for receiving in-bound communications, [0150] for merging datasets, [0081] and [0121] for AI for making hiring predictions, and [0179] for software for rotating out ineffective predictors from pre-hire data used in making predictions. See [0310] for this software being implemented in a non-transitory computer-readable medium)
Regarding the remaining limitations of claim 15, see the rejection of claim 1 above.
Regarding claim 16, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 15 above. Regarding the limitations introduced in claim 16, see the rejection of claim 2 above.
Regarding claim 17, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 15 above. Regarding the limitations introduced in claim 17, see the rejection of claim 3 above.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Scarborough in view of Chapman, Desai, and Horseman (U.S. Pre-Grant Publication No. 2013/0013327, hereafter known as Horseman).
Regarding claim 4, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 1 above. Scarborough further teaches in at least [0055] and [0188] that hiring managers receive results of the modeling and analysis discussed above. However, the combination of Scarborough, Chapman, and Desai does not explicitly teach utilizing a set of predefined analytical tools that are accessible based on roles assigned to users. Horseman teaches:
wherein the analyzing comprises utilizing a set of predefined analytical tools that are accessible based on roles assigned to users (see [0240] "The database 108 may include...an employee access database that stores credential data and permissions data for verifying user's right to access the system 100 based on the credentials and/or restricting access to the system 100 based on corresponding permissions" and [0306] "the permissions may grant some employees permission to access tables aggregating employee profile data, while other employees can only access their own profiles". In combination with Scarborough, only some employees have permission to access aggregate data models based on their credentials)
One of ordinary skill in the art would have recognized that applying the known technique of verifying a user’s access permissions before granting access to aggregated employee data of Horseman to the combination of Scarborough, Chapman, and Desai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Horseman to the teaching of the combination of Scarborough, Chapman, and Desai would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such verifying a user’s access permissions before granting access to aggregated employee data. Further, applying verifying a user’s access permissions before granting access to aggregated employee data to the combination of Scarborough, Chapman, and Desai would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more secure data processing in the system. For example, by only allowing administrators to access data for multiple employees like in Horseman [0306], one of ordinary skill in the art would have recognized that the combined system would offer enhanced data security by only allowing access to applicant and employee data to a select few individuals.
Regarding claim 11, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 8 above. Regarding the limitations introduced in claim 11, see the rejection of claim 4 above.
Regarding claim 18, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 15 above. Regarding the limitations introduced in claim 18, see the rejection of claim 4 above.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Scarborough in view of Chapman, Desai, and Barulli et al. (U.S. Pre-Grant Publication No. 2021/0209558, hereafter known as Barulli).
Regarding claim 5, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 1 above. Scarborough further teaches in Fig. 16 and [0212] and [0213] the effectiveness of the models over time. Scarborough also teaches in [0091] that expected job tenure is a predicted value by the model for a job applicant. However, the combination of Scarborough, Chapman, and Desai does not explicitly teach applying time series analysis techniques to the unified dataset. Barulli teaches:
wherein the analyzing comprises applying time series analysis techniques to the unified dataset (see [0023] "For simple historical data such as an employee's past tenure history for example, Survival Analysis methods might be applied to find the expected current tenure in order to approximate the timeframe in which employees may become less than satisfied with their current position. However these estimates might be further refined with the inclusion of seasonality curves via Time Series analysis, which can modify the estimated or expected duration based on other factors endemic to seasonality (e.g. holidays, employment anniversaries, or broader economic employment trends)" and [0032] "The tenure of each individual person-job pair is transformed into a time series object, where it is de-seasonalized in order to model the effects on it of a variety of other variables, including changes in recent online activity (e.g. to GitHub, StackOverflow, or Meetup profiles), shifts in sentiment scores derived from sites like Twitter, job anniversaries, changes to company evaluation, etc" for applying time series analysis to data to determine a predicted tenure for a person. See [0036] for this prediction being used to determine the recruitability of a person to a role)
One of ordinary skill in the art would have recognized that applying the known technique of using time series analysis to predict a person’s tenure at a company of Barulli to the combination of Scarborough, Chapman, and Desai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Barulli to the teaching of the combination of Scarborough, Chapman, and Desai would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such using time series analysis to predict a person’s tenure at a company. Further, applying using time series analysis to predict a person’s tenure at a company to the combination of Scarborough, Chapman, and Desai would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient prediction of an applicant’s expected tenure if they were to become an employee, which is one of the characteristics Scarborough desires to predict per at least [0101].
Regarding claim 12, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 8 above. Regarding the limitations introduced in claim 12, see the rejection of claim 5 above.
Regarding claim 19, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 15 above. Regarding the limitations introduced in claim 19, see the rejection of claim 5 above.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Scarborough in view of Chapman, Desai, and Yueh (U.S. Pre-Grant Publication No. 2012/0016839, hereafter known as Yueh).
Regarding claim 6, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 1 above. However, the combination of Scarborough, Chapman, and Desai does not explicitly teach the updating comprises using a caching layer to enhance performance or responsiveness of the system. Yueh teaches:
wherein the updating comprises using a caching layer to enhance performance or responsiveness of the system (see Fig. 2(a) and [0021]-[022] for a file system storing data from multiple disparate servers and [0048] "In some embodiments added storage caching layers can improve the performance of the backup file system, especially if multiple blocks are shared across different systems accessing different points in time or different file systems")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the caching layer of Yueh into the combination of Scarborough, Chapman, and Desai. As Yueh states in [0048] “added storage caching layers can improve the performance of the backup file system, especially if multiple blocks are shared across different systems accessing different points in time or different file systems”. The backup system of Yueh takes in and stores updated data from a plurality of disparate systems into a single file system, analogous to the Scarborough validation queue holds merged data from a plurality of disparate sources. One of ordinary skill in the art would therefore recognize that the benefits to system performance described in Yueh would similarly be experienced in the combination of Scarborough, Chapman, and Desai. Accordingly, one of ordinary skill in the art would have recognized that adding caching layers to the combination of Scarborough, Chapman, and Desai would provide improved system performance over the combination of Scarborough, Chapman, and Desai alone.
Regarding claim 13, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 8 above. Regarding the limitations introduced in claim 13, see the rejection of claim 6 above.
Regarding claim 20, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 15 above. Regarding the limitations introduced in claim 20, see the rejection of claim 6 above.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Scarborough in view of Chapman, Desai, and Dragomirescu (U.S. Pre-Grant Publication No. 2018/0246886, hereafter known as Dragomirescu).
Regarding claim 7, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 1 above. Scarborough further teaches in [0131] that the received data is logged with a date/time record. As discussed above regarding claim 1, Chapman teaches using an API to update the dataset. However, the combination of Scarborough, Chapman, and Desai does not explicitly teach the API being configured to wrap APIs of individual subsystems contributing standardized point-in-time data APIs that expose subsystem data. Dragomirescu teaches:
wherein the API is configured to wrap APIs of individual subsystems contributing standardized point-in-time data APIs that expose subsystem data (see [0032] "The applications 112, 120 can make use of an application protocol interface (API) 118, interface features such as custom (or tenant-specific) user interfaces 106a, 108a, standard (or universal) user interfaces or the like. The API 118 can be configured to facilitate various interactive functions between the local computing device and the multi-tenant environment such as, for example, the creation, deletion, updating, retrieval, searching, sorting, and reporting of files and other data objects" for the source server and target server all use APIs to update data. See [0027] " the source database 114 and the target database 122 can be implemented using separate physical and/or virtual (e.g., federated) database server (e.g., a container for components used to integrate data from multiple data sources, so that the multiple data sources can be accessed in an integrated manner through a single, uniform API 118 to view and query several databases as if they were a single entity) that communicates with the source server 102 and the target server 104" and [0032] " the API 118 includes a bulk API wrapper, configured to insert, update, and delete large numbers of data asynchronously by submitting them in batches to the target server" for the API wrapper is configured to interact with the APIs of individual systems that report/retrieve data. In combination with Scarborough, the data is point-in-time data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the API wrapper of Dragomirescu into the combination of Scarborough, Chapman, and Desai. As Dragomirescu states in [0032] “The API 118 can be configured to transfer a large number of records between the source server and the target server by using a minimum amount of API calls”. One of ordinary skill in the art would have recognized that incorporating the API features of Dragomirescu into the combination of Scarborough, Chapman, and Desai would minimize the number of API calls needed to be performed to update and retrieve data from the disparate systems of the combination of Scarborough, Chapman, and Desai. Therefore, by incorporating the API features of Dragomirescu, the combined system would be able to update and retrieve data across disparate systems more efficiently.
Regarding claim 14, the combination of Scarborough, Chapman, and Desai teaches all of the limitations of claim 8 above. Regarding the limitations introduced in claim 14, see the rejection of claim 7 above.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Alexander (U.S. Pre-Grant Publication No. 2023/0127725) teaches using a trained machine learning model to predict a likelihood that an employee will be a successful candidate
Fang (U.S. Pre-Grant Publication No. 2017/0193394) teaches training a machine learning model to predict the likelihood of a success of a job candidate using employee data from other similar organizations to supplement employee data from an organization
Gomes et al. (U.S. Pre-Grant Publication No. 2020/0327505) teaches progressively training machine learning models based on accumulating resume data and employment data from an employer
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/M.C.M./Examiner, Art Unit 3628
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
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