DETAILED ACTION
1. 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
2. Claims 1-4, 8-11 and 15-16 are currently pending. Claims 1 and 8 have been amended. Claims 1-4, 8-11 and 15-16 have been rejected.
Status of the Application
3. Claims 1-4, 8-11 and 15-16 are currently pending and have been examined in this application. This communication is the first action on the merits.
Response to Amendments
4. Applicant’s amendment filed on 08/19/2026 necessitated new grounds of rejection in this office action.
Priority
5. The Examiner has noted the Applicants claiming from Provisional (PRO) Application # 63/304,977 filed on 01/31/2022. Therefore, the earliest effective filing date examined for this application is of 01/31/2022.
Continued Examination under 37 CFR 1.114
6. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/19/2026 has been entered.
Response to Arguments
7. Applicant’s arguments, see page 7 filed on 08/19/2026, with respect to the 35 U.S.C. § 112 (b) Claim Rejections for Claims 8-11 and 16 have been fully considered and is found to be persuasive. Therefore, the 35 U.S.C. § 112 (b) Claim Rejections for Claims 8-11 and 16 have been withdrawn due to Applicant’s claim amendment to Independent Claim 8 to address the lack of antecedent basis issue regarding changing “the server” to “a server”.
8. Applicant’s arguments, see pages 11-13 filed on 08/19/2026, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1-4, 8-11 and 15-16 have been fully considered and is found to be persuasive. Therefore, the 35 U.S.C. § 103 Claim Rejections for Claims 1-4, 8-11 and 15-16 are withdrawn. See Examining Claims with Respect to Prior Art Section shown below.
Response to 35 U.S.C. § 101 Arguments
9. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-4, 8-11 and 15-16 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 7-11 dated 08/19/2026). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-4, 8-11 and 15-16 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C § 101 analysis (see Applicant Remarks, Pages 7-9, dated 08/19/2026). Examiner respectfully disagrees.
Applicant asserts that the claims are not directed to a mental process because the claimed system analyzes a large amount of historical data, including crowdsourced information, and because the amount of information allegedly could not practically be processed by a human mind.
The argument is not persuasive. The August 4, 2025 USPTO memorandum correctly explains that a claim does not recite a mental process where a claimed limitation cannot practically be performed in the human mind. However, the inquiry is directed to the nature of the claimed activity, not merely the quantity of information that might be processed or the speed with which a computer performs the activity. Here, the claimed activities include: determining an organizational need; determining individuals who could thrive in a role; analyzing organizational needs and individual characteristics; comparing competencies and interests with organizational needs; considering historical information concerning persons who followed similar paths; generating a recommendation concerning suitability for a role; determining competency gaps; recommending pathways to address those gaps; and evaluating the extent of overlap between individual competencies/interests and organizational needs.
These are fundamentally evaluations, comparisons, judgments, and recommendations concerning people and organizational requirements. Independent Claims 1 and 8 do not recite a particular technological operation for performing those evaluations. Instead, it broadly instructs the computer system to obtain information about people and organizations, analyze the information, determine suitability, and provide recommendations. The Federal Circuit has repeatedly recognized that collecting information, analyzing information, and presenting the resulting information can constitute an abstract idea where the claim does not recite a particular technological improvement for performing those operations. Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1353–54 (Fed. Cir. 2016). Thus, Applicant's reliance on the quantity of information does not alter the nature of the claimed activity.
Applicant repeatedly argues that a human could not practically process the "large amount" or "sheer amount" of data.
This argument confuses the volume of information with the character of the claimed activity. For example, a human may not practically examine millions of employment records, but the underlying operation—reviewing information about a person, comparing that person's characteristics with job requirements, and determining whether the person is suitable—is nevertheless an evaluation or judgment. The computer merely permits the same type of evaluation to be performed on a larger quantity of information and at greater speed. The Federal Circuit has explained that claims directed to gathering and analyzing information remain abstract even when computers perform the analysis. Electric Power Group, 830 F.3d at 1353–54. The court characterized claims involving gathering information, analyzing the information, and displaying results as abstract where the claims did not identify a particular assertedly inventive technology for performing those functions. Accordingly, the fact that the computer performs the claimed evaluation on more information than a person could reasonably review manually does not establish that the claimed evaluation is technological.
Thirdly, the Office's characterization is not that the claim is merely directed to "mathematical and statistical analysis." Rather, the mathematical/statistical operations are part of a broader abstract process of evaluating individuals against organizational needs and making personnel recommendations. The claimed subject matter includes determining which individuals may thrive in a particular role, and generating recommendations of individuals suitable for roles targeted to an organizational need. The claimed statistical analysis is merely the mechanism by which information is evaluated to make the personnel recommendation. Accordingly, Independent Claims 1 and 8 implicates at least two recognized abstract-idea categories: certain methods of organizing human activity, including business relations and managing interactions between people; and mental processes, including evaluation, judgment, and opinion. The statistical/probabilistic portions additionally implicate mathematical concepts. The categories are not mutually exclusive, and the Office need not characterize every limitation as belonging to one and only one category. Indeed, SAP America, Inc. v. InvestPic, LLC (Fed. Cir. 2018) demonstrates that claims directed to performing statistical analysis can constitute an abstract idea even where the claims recite processors, databases, and computer implementation. Thus, the presence of statistical analysis does not remove the claim from the abstract-idea exception.
Applicant argues that "crowd sourcing tagging of skills and competencies" provides data that is broader than what a human would consider and therefore cannot constitute a mental process.
The argument is not persuasive. The claimed crowdsourced data merely supplies information about people's skills and competencies. Independent Claims 1 and 8 for example do not recite a new technological crowdsourcing protocol, a new consensus algorithm, a new data-verification architecture, or a new computer technology for obtaining or validating the crowdsourced information. Instead, the crowdsourced information is used as another input into the personnel evaluation. The relevant claimed operation remains identify competencies → compare competencies to organizational needs → evaluate suitability → recommend an individual. The fact that the information is obtained from multiple people or multiple sources does not change the character of the subsequent evaluation. Indeed, the Federal Circuit has explained that collecting information of a specified content does not cease to be abstract merely because the information concerns a particular subject matter. Electric Power Group, 830 F.3d at 1353–54. Thus, crowdsourcing merely increases the amount or source of information being considered; it does not transform the underlying personnel-evaluation process into a technological process.
Applicant argues that the Office improperly reviews individual limitations separately and fails to consider the claims as a whole. The Office agrees that the claims must be considered as a whole. The claims, however, remains abstract when considered as a whole. Considering the claims as a whole does not require the Office to disregard the character of each claimed operation. Rather, the question is what the claims are directed to as a whole. Here, the claimed combination is essentially: obtain organization and individual information; determine an organizational need; identify individuals based on characteristics and competencies; compare individual information with organizational needs and historical information; recommend individuals for roles; identify competency gaps; recommend pathways to address those gaps; and provide the recommendations and a confidence measure. The combination therefore remains focused on personnel selection, personnel evaluation, and organizational workforce management. The addition of more steps that implement the same personnel-evaluation concept does not prevent the claims from reciting an abstract idea. The Federal Circuit has explained that claims directed to collecting, analyzing, and displaying information can remain abstract when the claimed advance is the information-processing result rather than a particular technological improvement. Electric Power Group, 830 F.3d at 1353–54. Accordingly, the claims are being considered as a whole, and consideration of the claims as a whole confirms rather than defeats the § 101 rejection.
With respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. The Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (e.g., see Applicant’s Specification ¶ [0031]: “Each such computer program is preferably stored on a storage media or a device readable by a general or special purpose programmable computer for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein.”), or 2) in a computer environment (e.g., see Applicant’s Specification ¶ [0030]: “For example, and without limitation, the programmable computer may be a programmable logic unit, a mainframe computer, server, and personal computer, cloud based program or system, laptop, personal data assistance, cellular telephone, smartphone, or tablet device.”), or 3) is merely using a computer as a tool.
Additionally, according to MPEP § 2106.04 (a) (2) (III) (B): “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, 839 F.3d at 1139, 120 USPQ2d at 1474 (holding that claims to the mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").”
Also, Examiner refers Applicant to MPEP § 2106.04 (a) (2) II which states that: “the sub-groupings encompass both activity of a single person (for example, “a respective employee”) and activity that involves multiple people (such as “plurality of employees”), and thus, certain activity between a person and a computer may fall within the "Certain Methods of Organizing Human Activities" groupings. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
Additionally, or alternatively, some of these claim limitations recited above for Independent Claim 1 for example can be performed as “Mathematical Concepts” which pertains to mathematical calculations or mathematical relationships. With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”
In conclusion, Examiner maintains that Claims 1-4, 8-11 and 15-16 are directed to abstract ideas under “Mental Processes” or “Certain Methods of Organizing Human Activities” or “Mathematical Concepts” Groupings under 35 U.S.C. § 101 Step 2A Prong 1.
Argument #2:
(B). Applicant argues that Claims 1-4, 8-11 and 15-16 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 9-10, dated 08/19/2026). Examiner respectfully disagrees.
Specifically, Applicant places particular emphasis on the limitation in Independent Claims 1 and 8 requiring: "the probabilistic model being a trained artificial intelligence statistical model configured to analyze and identify data correlations" and retraining the model: "over time using updated historical data ... to improve a recommendation accuracy of the probabilistic model." Applicant asserts that these limitations constitute more than use of a computer as a tool. The argument is not persuasive because the claim does not recite a technological improvement to the artificial-intelligence model itself.
Independent Claims 1 and 8 do not specify: a new machine-learning architecture; a particular neural-network configuration; a new training algorithm; a particular loss function; a new optimization technique; a new feature-extraction technique; a new model-update architecture; a new data structure; a new memory arrangement; reduced memory consumption; reduced processor utilization; improved training efficiency; improved model stability; improved model convergence; or any other specific technical mechanism that improves operation of the machine-learning technology. Instead, these claims state that the model is retrained "to improve a recommendation accuracy". The claimed improvement therefore concerns the accuracy of the personnel recommendation, not the technological operation of the computer or machine-learning model. This distinction is particularly significant in view of Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1202 (Fed. Cir. 2025). In Recentive, the Federal Circuit considered patents directed to using machine learning for scheduling and network mapping. The court held the claims ineligible because they used generic machine-learning techniques in a particular environment without claiming an improvement to the machine-learning technology itself. The same distinction applies here. The present claims use an AI/statistical model to make a more accurate workforce recommendation. These claims do not identify an improvement in how the AI model itself operates. Therefore, merely labeling the analytics engine as an "artificial intelligence statistical model" does not transform the underlying personnel-evaluation process into a technological invention.
To the extent Applicant relies upon Ex parte Desjardins, the decision is distinguishable.
The USPTO has designated Ex parte Desjardins, Appeal No. 2024-000567, as precedential and describes it as involving a technological improvement to a machine-learning model under Step 2A Prong Two. The relevant distinction is that Desjardins involved claimed improvements to the functioning of machine-learning technology itself, including reduced storage requirements, reduced system complexity, and the ability to learn successive tasks while protecting knowledge concerning previously learned tasks. The present claims do not recite such an improvement. Here, the claimed retraining is performed using updated historical data for the purpose of increasing the accuracy of the personnel recommendation. Thus: Desjardins showed an improvement to machine-learning technology whereas the present claims of the claimed invention per se resulted in an improved result of a personnel-matching/recommendation process. The distinction is legally significant.
The USPTO's subsequent MPEP update concerning Desjardins confirms that examiners must consider whether claims actually reflect an improvement to technology, including learning models, rather than treating every claim involving machine learning as automatically technological. Accordingly, Desjardins does not establish eligibility for the present claims.
Applicant argues that the model is "retrained over time using updated historical data" and that the automatic updating is allegedly beyond what a human can perform. Again, the relevant question is not whether a human can manually execute millions of machine-learning operations. The relevant question is whether the claimed retraining technique itself improves computer or machine-learning functionality. Independent Claims 1 and 8 for example do not recite a particular retraining mechanism. These claims merely provide the following of updated data → retraining → improved recommendation accuracy. There is no limitation specifying how the model is retrained or how the retraining technically improves the model. This is significant under Recentive. There, the Federal Circuit rejected reliance on machine-learning techniques that were described at a functional level and emphasized that applying generic machine learning to a particular environment does not become eligible merely because the machine-learning model is trained or updated. Accordingly, the retraining limitation does not integrate the abstract idea into a practical application.
Applicant argues that the confidence score provides a technical solution to employee fit and turnover. Independent Claims 1 and 8, however, does not actually claim a technological solution to employee turnover. It claims a confidence score indicating how likely an identified individual is to fill an organizational need. The confidence score is generated from: "a statistical analysis of extent of overlap between competencies and interests ... and the needs of the organization." Thus, the score merely quantifies the outcome of the personnel-matching evaluation. No particular technological mechanism is claimed for calculating or displaying the score. The score could conceptually be represented as: degree of candidate/organizational match → numerical confidence. The addition of a numerical value to an abstract judgment does not convert the judgment into patent-eligible subject matter. Indeed, SAP v. InvestPic confirms that statistical analysis remains an abstract mathematical concept where the claims do not add a sufficient technological application to the statistical analysis.
Applicant repeatedly characterizes employee fit and turnover as a "technical problem." The characterization of a problem as "technical" is not controlling. Independent Claims 1 and 8 itself must recite a technological solution to a technological problem. Here, the claimed problem is essentially: determining which individuals are appropriate for organizational roles and identifying competency gaps. That is a business/organizational problem, not a computer-technology problem. Likewise, employee turnover is a business or personnel-management consequence. These claims do not improve processor operation; network operation; database operation; computer memory; machine-learning architecture; model training efficiency; data-transfer efficiency; computer security; or another technical field. Instead, it improves the organization's ability to make personnel decisions. Accordingly, describing the business problem as a "technical problem" does not establish a technological improvement.
Applicant argues that the claimed system provides: "a technical solution to a technical problem." This conclusion is unsupported by the actual limitations. A claim does not become technological merely because it uses a server; uses a network; uses an analytics engine; processes data; uses artificial intelligence; produces a numerical confidence score; or presents information graphically. The August 4, 2025 USPTO memorandum specifically instructs examiners to distinguish between claims that improve computer/technical functionality and claims in which a computer is merely used as a tool to perform an abstract idea. Here, the computer is being used as a tool to perform the abstract process of evaluating individuals and organizational needs.
Applicant argues that: "presenting the data in as a graphical manner, converts the data from an abstract mathematical concept to a visual understanding of the data." This argument is not persuasive. Displaying the result of an abstract analysis does not transform the underlying abstract analysis into patent-eligible subject matter. The Federal Circuit addressed substantially this issue in Electric Power Group, explaining that gathering information, analyzing it, and displaying the results—without a particular assertedly inventive technology for accomplishing those functions—remains abstract. These claims here merely requires: "generate a graphical user interface element of the data" and "provide the graphical user interface element as the recommendation." These claims do not specify a particular technological improvement to graphical rendering. There is no claimed new rendering architecture; new graphical data structure; improved display protocol; new visualization algorithm; improved graphics processing; reduction in rendering resources; or new computer-human interface mechanism. The GUI therefore merely communicates the result of the abstract analysis.
Applicant also asserts that the GUI makes complex information easier for the user to understand. Even assuming this is true, improved human comprehension of information is not necessarily an improvement in computer technology. Independent Claims 1 and 8 for example do not recite a particular technological mechanism that makes the computer itself function better. Rather the computer performs the analysis → the computer produces a recommendation → the GUI displays the recommendation. This is analogous to the information-processing claims addressed in Electric Power Group, where displaying analyzed information did not supply the requisite technological improvement. The claimed GUI is therefore an output mechanism rather than an integration of the judicial exception into a practical technological application. Applicant argues that the claimed system is: "extensive, automatic and integrated into a practical application." Those characterizations do not establish eligibility. Automation of an abstract process does not necessarily make the process non-abstract. Indeed, the Federal Circuit has repeatedly recognized that implementing an otherwise abstract process on a computer does not make the process patent eligible merely because the computer performs it automatically. The claims remain directed to: automated evaluation and recommendation concerning organizational staffing. The automation simply changes who or what performs the evaluation; it does not change the nature of the evaluation.
Applicant asserts that the system can: "adapt to each user's knowledge and organizational needs in an automatic and real-time manner." This argument is not persuasive for two reasons.
First, "real-time" is not meaningfully claimed as a specific technical operation. Second, the claims do not recite a particular mechanism by which real-time processing improves the computer, network, database, or machine-learning technology. The asserted benefit is instead that organizational recommendations can be generated more quickly and updated more frequently. Speed or efficiency in performing an abstract process does not necessarily establish a technological improvement. The Federal Circuit's decisions distinguish between improving a computer or technical process and merely using computer technology to perform a non-technological task more efficiently.
Applicant asserts that the invention is: "rooted" in technology. The phrase "rooted in technology" is not sufficient by itself. The claims must identify the specific technological improvement. The Federal Circuit has explained that the relevant inquiry for software claims is whether the claims focus on an improvement in computer capabilities or instead on a process for which computers are merely invoked as tools. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335–36 (Fed. Cir. 2016). Here, the claims focuses on the latter. The asserted advance is not a new way for computers to process information. It is a new way of evaluating individuals against organizational needs. The computer is therefore being invoked as a tool for performing the personnel-management process.
In summary, Applicant's arguments concerning "technical improvement," graphical presentation, automation, and efficiency are also insufficient under Step 2A Prong Two. The additional elements for Independent Claims 1 and 8 include: computing devices; a network; a server; data storage; a GUI; an analytics engine; an AI/statistical model; model retraining; statistical analysis; a confidence score; and graphical presentation. Considered individually and as an ordered combination, these elements do not integrate the abstract idea into a practical application. Examiner notes that for these claims there is no improvement to computer functionality. For example; Independent Claims 1 and 8 do not improve: processor operation; memory operation; network operation; database operation; computer architecture; or data-transfer functionality. There is also no improvement to machine-learning technology. These claims do not specify a new ML architecture or training mechanism. The model is merely used to improve the accuracy of personnel recommendations. There is no particular machine. Here, for Independent Claims 1 and 8, the computing devices and server are described generically. There is no transformation. These claims transform information, not a physical article. There is no particular treatment or application. These claims apply the abstract evaluation to the field of organizational staffing. Limiting an abstract idea to a particular field of use does not itself integrate the exception into a practical application.
With respect to reliance on (e.g., “artificial intelligence statistical model” & “a graphical user interface element” & “a graphical user interface (GUI)” & “at least one analytics engine”) as additional elements when considered individually and as an ordered combination (as a whole) for the claim limitations for Independent Claims 1 and 8, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) the claims as a whole are limited to a particular field of use or technological environment for recommending individuals for open job roles/positions using a computer in a business enterprise environment (see MPEP § 2106.05 (h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)).
Independent Claims 1 and 8 rely entirely on generic computer infrastructure ("store information," "analytics engine," "computing device") to execute the abstract idea. The specification does not disclose a novel hardware architecture or an improvement to the underlying operation of the computer itself. Rather, it uses the computer merely as a tool to execute data analysis faster than a human could. Automating an abstract business or mathematical method on a generic computer does not constitute a practical application (Alice Corp. v. CLS Bank). Storing organization, user, and historical data amounts to nothing more than insignificant extra-solution activity and data gathering. Collecting and organizing background information to feed into a statistical model is a necessary precursor to performing any data analysis and does not add an inventive practical application.
Determining a second set of individuals whose competency gap is "less than a pre-determined threshold" is a mathematical filtering step. Applying a quantitative threshold to data output is a mathematical exercise that does not transform the underlying abstract analysis into a concrete, practical application. The requirement to "generate a graphical user interface element" and "provide the graphical user interface element as the recommendation" is a post-solution activity. Transmitting and displaying the results of an abstract data analysis on a screen is a ubiquitous computer function. It does not solve a technical problem in the display technology itself, nor does it override standard user interface protocols.
Moreover, these claims define the “analytics engine” and “probabilistic model” using broad, functional language (e.g., configured to analyze and identify data correlations, “configurable to determine”). The Guidance states that functional descriptions of software performing an abstract idea on a computer without specifying how the computer is improved or how a technical problem is solved, do not constitute a practical application. The computer is merely a tool for applying the abstract idea of human resources (HR) management. The claim language is directed to the result of the abstract idea itself (“improve a recommendation accuracy”, “generate a pathway recommendation”). While the result is useful, the limitations focus on what the system does (manages HR data) rather than how it does so in a non-conventional, technical manner. The elements related to “user attributes, crowd sourcing tagging of skills and competencies” describe the data being used, not a technical solution to a technical problem inherent in the computer system. The data is simply input for the abstract idea (the evaluation / recommendation process).
Taken together as an ordered combination, the claim simply describes a basic workflow: gather workforce data -> analyze it mathematically via a statistical model -> filter the results by a threshold -> display the results on a screen. Because the elements collectively amount to nothing more than instructions to "apply" the abstract idea of talent matching using AI techniques and computer components, the claim fails Step 2A, Prong 2.
Accordingly, Applicant's arguments do not establish that the claims avoid the judicial exception. The additional computer, network, server, GUI, analytics, AI, and statistical elements do not integrate that exception into a practical application because they do not recite an improvement to computer functionality, machine-learning technology, or another technical field.
The claims remain directed to an abstract idea under Step 2A. Therefore, in conclusion, Examiner maintains that Claims 1-4, 8-11 and 15-16 do not recite additional elements that integrate the judicial exception into a practical application under step 2a prong 2 of the 35 U.S.C. § 101 analysis.
Argument #3:
(C). Applicant argues that Claims 1-4, 8-11 and 15-16 recite additional elements that amount to significantly more than the recited judicial exceptions under revised step 2B of the 35 U.S.C. 101 analysis (see Applicant Remarks, Pages 10-11, dated 08/19/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that Independent Claims 1 and 8 for example, as a whole, provide an improvement to technology of a human resource system and to the field of matching an individual to an appropriate organizational need and accurately and efficiently reporting matches with the confidence level of each match. The presently claimed combination of features demonstrates a technology rooted solution to a computer network-centric problem, specifically a networked human management system with analytics engine, and thus amounts to significantly more than mental processes (see Applicant’s Remarks, last 2 paragraphs of Page 10, dated 08/19/2026). Examiner respectfully disagrees.
Examiner refers Applicant to Examiner’s 35 U.S.C. § 101 analysis section (e.g., Claim Rejections - 35 U.S.C. § 101 section shown below) shown for step 2B particularly for Independent Claims 1 and 8. The claims do not recite additional elements that amount to significantly more than the recited judicial exceptions, because they are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exceptions. The limitations are directed to limitations referenced in MPEP § 2106.05I.A. that are not enough to qualify as significantly more when recited in these claims with the abstract idea which include: (1) adding the words “apply it” (or an equivalent) with the judicial exception, (2) or mere instructions to implement an abstract idea on a computer and providing the results to the user on a computer, and (3) generally linking the use of the judicial exception to a particular technological environment or field of use.
Even assuming arguendo that the additional elements are considered separately from the abstract idea, they do not amount to significantly more. The additional elements consist principally of generic computer/network components and functional data-processing operations.
Independent Claims 1 and 8 do not recite a particular unconventional technological mechanism for: training the probabilistic model; retraining the model; identifying correlations; calculating the confidence score; determining competency gaps; or generating the graphical presentation. Instead, these elements implement and refine the underlying personnel-evaluation process. Accordingly, the additional elements, considered individually and as an ordered combination, do not provide an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
The additional elements are computer components and generic information-processing functions. The server: receives information; stores information; processes information; generates results; and communicates results. The network communicates data. The GUI receives and displays information. The analytics engine processes information. The AI model analyzes correlations and generates a recommendation. None of these elements, as claimed, provides a particular technological implementation. Accordingly, the additional elements do not amount to significantly more than the abstract idea.
The Office has also considered the additional elements as an ordered combination. The ordered combination is essentially: collect organization and individual data → analyze the data → determine organizational need → identify suitable individuals → recommend individuals → identify competency gaps → recommend development pathways → calculate confidence → display the results. The ordered combination does not produce a technological result independent of the abstract personnel-management result. The computing components merely execute the steps of the abstract process. Thus, the ordered combination does not provide an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
Applicant argues that the invention allows HR personnel to: "quickly and efficiently access and pair individuals with organizational needs." Even accepting that assertion, improved efficiency in performing an abstract business process is insufficient whereas the claims does not specify how the computer itself is improved. The claimed efficiency is simply the expected result of automating a personnel-matching process and using computer processing to evaluate more information more quickly. These claims therefore does not establish an unconventional technological improvement.
Applicant argues that: "The analytics engine and probabilistic model can also be updated for all users automatically without human intervention." This argument does not establish eligibility. The removal of a human from the process does not transform an abstract business judgment into a technological invention. Otherwise, any business process could potentially become patent eligible merely by reciting that a computer performs the business process automatically. The relevant inquiry is whether the computer performs a specific technological operation that improves computer or technical functionality. No such operation is claimed here. Applicant argues that the claims do not preempt the alleged abstract idea. Lack of complete preemption is not dispositive.
The Supreme Court's § 101 framework does not require proof of complete preemption before claims may be determined to be directed to an abstract idea. Moreover, the fact that other methods of performing personnel selection may remain available does not establish that the claimed implementation is patent eligible. The proper inquiry remains whether the claims recites a judicial exception and, if so, whether the claims integrates that exception into a practical application or contains an inventive concept. The present claims fail those inquiries.
Applicant correctly observes that a statement in the specification referring to a "general or special purpose programmable computer" is not, standing alone, dispositive of eligibility. The Office does not rely solely on that statement. Rather, the specification's description of the computer environment may be considered together with the actual claim language in determining whether the claimed invention improves computer functionality or merely uses generic computer components. The relevant claim languages here does not identify a particular technical improvement. Accordingly, the specification's description is consistent with, but is not the sole basis for, the Office's determination.
Applicant characterizes the invention as an: "improvement to the technology of a human resource system." This characterization is not sufficient. The relevant distinction is between an improvement in the human-resource decision being made and an improvement in the technology used to make the decision. The claims recite the former. The model allegedly becomes more accurate at recommending individuals. The GUI allegedly makes recommendations easier to understand. The system allegedly processes more information and does so more quickly. These are improvements to the human-resource activity and its results, not improvements to the underlying computer technology. The claims do not identify a new technological mechanism responsible for those improvements. The USPTO's treatment of Ex parte Desjardins confirms that an AI claim can be eligible when the claims actually reflect a technological improvement to machine-learning functionality. That principle does not mean that all AI claims are eligible. The distinction is whether the claims identify a specific technological improvement.
For example, a claim that actually required a particular machine-learning architecture that reduced memory consumption or permitted successive learning while retaining prior knowledge could potentially demonstrate a technological improvement. The present claims contain no comparable limitation. Instead, the claims use a probabilistic/statistical model to accomplish the non-technological objective of identifying individuals suitable for organizational roles. Thus, Desjardins supports, rather than undermines, the need to determine whether an actual improvement to machine-learning technology is claimed. The Federal Circuit's decision in Recentive Analytics is particularly instructive. There, the claims used machine learning to generate optimized schedules and network maps. The court concluded that the claims were directed to the abstract idea of applying a generic machine-learning technique in a particular environment and lacked an inventive concept. The present claims similarly apply a generic probabilistic/artificial-intelligence model to a particular environment—human-resource staffing. The claimed inputs are organization data; user data; historical information; skills; competencies; interests; and organizational needs. The claimed output is a recommendation of individuals; a competency-gap determination; a development pathway; and a confidence score. The field has changed from entertainment scheduling in Recentive to human-resource management here, but the eligibility principle remains applicable: applying generic machine-learning functionality to a particular non-technological field does not, without more, constitute a technological improvement.
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-4, 8-11 and 15-16 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. In conclusion, Claims 1-4, 8-11 and 15-16 therefore remain rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 101
10. 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.
11. Claims 1-4, 8-11 and 15-16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-4, 8-11 and 15-16 are each focused to a statutory category namely a “apparatus” or a “system” (Claims 1-4 and 15) and a “method” or a “process” (Claims 8-11 and 16).
Step 2A Prong One: Independent Claims 1 and 8 recite limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“for providing data and outputting data to a user” (see Independent Claim 1);
“” (see Independent Claim 1);
“” (see Independent Claim 1);
“store information , the information including at least one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies”
“comprises a probabilistic model for recommending individuals to open roles, the probabilistic model being a trained statistical model configured to analyze and identify data correlations, wherein the probabilistic model is configured to be retrained over time using updated historical data, the historical data comprising representations of user attributes, crowd sourcing tagging of skills and competencies, and organizational needs to improve a recommendation accuracy of the probabilistic model, ” (see Independent Claims 1 and 8);
“determine an organizational need based at least on the organization data” (see Independent Claim 1);
“determine a set of one or more individuals that could thrive in a role targeted to the determined organization need based at least on the user data” (see Independent Claim 1);
“analyze the organizational need and the set of one or more individuals to generate a recommendation of individuals for roles based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies” (see Independent Claim 1);
“generate the recommendation of individuals suitable for roles targeted to the organizational need based in part on the analysis of the organizational need and the set of one or more individuals” (see Independent Claim 1);
“determining, an organizational need based at least on the organization data” (see Independent Claim 8);
“determining, , a set of one or more individuals that could thrive in a role targeted to the determined organization need based at least on the user data” (see Independent Claim 8);
“analyzing, , the organizational needs and the set of one or more individuals to generate a recommendation of individuals for roles based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies” (see Independent Claim 8);
“generating, , the recommendation of individuals suitable for roles targeted to the organizational need based in part on the analysis of the organizational need and the set of one or more individuals” (see Independent Claim 8);
“generating the recommendation of individuals based on the analysis of the organizational need and the set of one or more individuals” (see Independent Claim 8);
“generate of the data to provide to the user ” (see Independent Claims 1 and 8);
“provide as the recommendation ” (see Independent Claims 1 and 8);
“determining a second set of individuals having a competency gap that is less than a pre-determined threshold” (see Independent Claims 1 and 8);
“generating a pathway recommendation for the second set of individuals to meet the competency gap” (see Independent Claims 1 and 8);
“ output a confidence score indicating a confidence that the set of one or more individuals may fill a need of the organization based on a statistical analysis of extent of overlap between competencies and interests of the set of one or more individuals and the needs of the organization” (see Independent Claims 1 and 8).
Here, under step 2a prong 1, Independent Claims 1 and 8 recite the abstract idea of certain methods of organizing human activities by managing personnel relationships and professional development through the matching of organizational needs with individual competencies. Specifically, the core of these claims is managing workforce talent and matching individuals to roles. This constitutes “managing personal behavior, relationships, or interactions between people” or “fundamental economic principles” related to human resource management, which hereby encompass and reflect “Certain Methods of Organizing Human Activities”. Moreover, the steps such as “analyzing”, “determining an organizational need” and “identifying data correlations” are types of evaluations and judgements that can be performed in the human mind or via pen to paper as a physical aid. The use of a “probabilistic model” to find correlations is a high-level data processing task that humans have performed when reviewing resumes and organizational charts which hereby encompass and reflect “Mental Processes”.
Examiner analysis each of the claim limitation steps as follows: Store information for the system (organization data, user data, historical information...) -> Storing data falls under the abstract idea category of "certain methods of organizing human activity" (managing data) and generic computer functions. Implement at least one analytics engine (probabilistic model, trained AI statistical model...) -> A probabilistic or statistical model relies directly on mathematical concepts and algorithms, which are explicitly recognized as abstract ideas. Determine an organizational need based at least on the organization data -> Evaluating business or organizational needs is a mental process and a "method of organizing human activity" (commercial/management relationship). Determine a set of one or more individuals that could thrive in a role... -> Matching people to jobs based on user data is a mental process traditionally performed by human recruiters (organizing human activity). Analyze the organizational need and the set of individuals to generate a recommendation... -> Analyzing data and historical paths to make a recommendation is a mathematical concept combined with a mental process (comparing past experiences to current needs). Generate the recommendation of individuals suitable for roles... -> Outputting the result of a mental or mathematical process (the recommendation itself) is part of the underlying abstract idea. Generate a graphical user interface (GUI) element of the data... -> Displaying data on a GUI is a generic computer implementation element. Provide the graphical user interface element as the recommendation to the computing device -> this is a transmission step. Determine a second set of individuals having a competency gap that is less than a pre-determined threshold -> Filtering a list based on a mathematical threshold is a mathematical concept and a mental process (basic filtering/sorting data).
Mental Process & Mathematical Concepts: Evaluating a "competency gap," analyzing an "extent of overlap between competencies and interests," and generating a "confidence score" based on "statistical analysis" are mathematical and analytical steps. Historically, humans have evaluated resumes, interests, and corporate needs using mental steps or pen-and-paper calculations.
Commercial/Business Activity: Recommending a pathway or matching an individual's skills to an organization's needs is a fundamental method of structuring human activity (talent acquisition and workforce management).
Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) fundamental economic practices.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under the broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude these claims from reciting an abstract idea. Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under the broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mathematical Concepts” which pertains to (5) mathematical relationships or (6) mathematical calculations.
That is, other than reciting the additional elements of (e.g., “one or more computing devices”, “a graphical user interface element”, “at least one computing device”, “a server”, “the at least one analytics engine”, “system”, “a network” and “a graphical user interface (GUI)”, etc…), nothing in the claim elements precludes the steps from being performed as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) fundamental economic practices and additionally or alternatively as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical relationships or (6) mathematical calculations.
Therefore, at step 2a prong 1, Yes, Claims 1-4, 8-11 and 15-16 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1 and 8 recites additional elements directed to: (e.g., “one or more computing devices” & “a server” & “at least one computing device” & “system” & “a network”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h). Examiner notes that Independent Claims 1 and 8 recites communicating with “one or more computing devices” and “storing information for the system”. These elements are recited at a high level of generality. The guidance specifies that merely using a generic computer to perform an abstract idea is insufficient for eligibility.
Independent Claims 1 and 8: With respect to reliance on (e.g., “artificial intelligence statistical model” & “a graphical user interface element” & “a graphical user interface (GUI)” & “at least one analytics engine”) as additional elements when considered individually and as an ordered combination (as a whole) for the claim limitations for Independent Claims 1 and 8, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) the claims as a whole are limited to a particular field of use or technological environment for recommending individuals for open job roles/positions using a computer in a business enterprise environment (see MPEP § 2106.05 (h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)).
Independent Claims 1 and 8 rely entirely on generic computer infrastructure ("store information," "analytics engine," "computing device") to execute the abstract idea. The specification does not disclose a novel hardware architecture or an improvement to the underlying operation of the computer itself. Rather, it uses the computer merely as a tool to execute data analysis faster than a human could. Automating an abstract business or mathematical method on a generic computer does not constitute a practical application (Alice Corp. v. CLS Bank). Storing organization, user, and historical data amounts to nothing more than insignificant extra-solution activity and data gathering. Collecting and organizing background information to feed into a statistical model is a necessary precursor to performing any data analysis and does not add an inventive practical application.
Determining a second set of individuals whose competency gap is "less than a pre-determined threshold" is a mathematical filtering step. Applying a quantitative threshold to data output is a mathematical exercise that does not transform the underlying abstract analysis into a concrete, practical application. The requirement to "generate a graphical user interface element" and "provide the graphical user interface element as the recommendation" is a post-solution activity. Transmitting and displaying the results of an abstract data analysis on a screen is a ubiquitous computer function. It does not solve a technical problem in the display technology itself, nor does it override standard user interface protocols.
Moreover, these claims define the “analytics engine” and “probabilistic model” using broad, functional language (e.g., configured to analyze and identify data correlations, “configurable to determine”). The Guidance states that functional descriptions of software performing an abstract idea on a computer without specifying how the computer is improved or how a technical problem is solved, do not constitute a practical application. The computer is merely a tool for applying the abstract idea of human resources (HR) management. The claim language is directed to the result of the abstract idea itself (“improve a recommendation accuracy”, “generate a pathway recommendation”). While the result is useful, the limitations focus on what the system does (manages HR data) rather than how it does so in a non-conventional, technical manner. The elements related to “user attributes, crowd sourcing tagging of skills and competencies” describe the data being used, not a technical solution to a technical problem inherent in the computer system. The data is simply input for the abstract idea (the evaluation / recommendation process).
Taken together as an ordered combination, the claim simply describes a basic workflow: gather workforce data -> analyze it mathematically via a statistical model -> filter the results by a threshold -> display the results on a screen. Because the elements collectively amount to nothing more than instructions to "apply" the abstract idea of talent matching using AI techniques and computer components, the claim fails Step 2A, Prong 2.
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-4, 8-11 and 15-16 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1 and 8 recites additional elements directed to: (e.g., “one or more computing devices” & “a server” & “at least one computing device” & “system” & “a network”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (h) and See MPEP § 2106.05 (f). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification: ¶ [0031]: “Each such computer program is preferably stored on a storage media or a device readable by a general or special purpose programmable computer for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein.”). Therefore, Independent Claims 1 and 8 recite additional elements both individually and as an ordered combination in view of the claim limitations which fail to add significantly more to the judicial exception due to: reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or the claims as a whole are limited to a particular field of use or technological environment for recommending individuals for open job roles/positions using a computer in a business enterprise environment (see MPEP § 2106.05 (h)).
Independent Claims 1 and 8: With respect to reliance on (e.g., “artificial intelligence statistical model” & “a graphical user interface element” & “a graphical user interface (GUI)” & “at least one analytics engine”) as additional elements when considered individually and as a ordered combination (as a whole) for the claim limitations for Independent Claims 1 and 8, these additional elements do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment for recommending individuals for open job roles/positions using a computer in a business enterprise environment (see MPEP § 2106.05 (h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)). While these claims mention a “probabilistic model” and “trained AI statistical model”, it describes these elements in purely functional terms (e.g., “configured to analyze and identify data correlations”). These claims fail to recite a specific technological improvement to the way the AI operates – such as a novel neural network architecture or a more efficient mathematical algorithm – and instead focus on the high-level application of AI to achieve a business result (improving recommendation accuracy). The ordered combination of the steps – determining needs, analyzing individuals, and generating pathway recommendations merely mirrors the mental steps a human recruiter would take, only performed faster by a computer. Because these claims do not solve a problem rooted in computer technology (such as network latency or data security) but rather a business problem (matching people) to jobs, it lacks the “significantly more” required to overcome the judicial exception.
Moreover, with respect to Independent Claims 1 and 8, certain/particular limitations shown recite mere storing data such as (e.g., “store information for the system, the information including at least one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies” (see Independent Claim 1) and mere data outputting such as (e.g., “providing the graphical user interface element as the recommendation to the at least one computing device” (see Independent Claims 1 and 8)) wherein which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Furthermore, these certain/particular claim limitations as demonstrated above for Independent Claims 1 and 8 reflects Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). MPEP § 2106.05 (d) ii: See Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.,793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115USPQ2d at 1092-93.
Examiner notes that when looking at the additional elements in view of the claim limitations of Independent Claims 1 and 8 as a whole, the ordered combination of limitations simply describes the automated execution of a human resource or business process on a computer which lacks an inventive concept due to the following:
The steps—collecting candidate history, identifying organizational job openings, applying statistical matchmaking (the analytics engine), and determining competency thresholds—mimic mental operations historically performed by corporate recruiters.
These claims merely says "apply the abstract concept of candidate matching using AI tools." It does not detail any structural improvement to the underlying operation of the computer or network itself. Additionally, these claims lack any recitation of a specific improvement to computer hardware or software efficiency: It does not optimize database retrieval speeds, reduce computational memory overhead, or improve network security protocols. Instead, the stated improvement ("to improve a recommendation accuracy of the probabilistic model") is a purely business or mathematical optimization (i.e., making better employee recommendations), which cannot satisfy the technological improvement standard outlined in Enfish, LLC v. Microsoft Corp.
Furthermore, the additional elements of “artificial intelligence” in Independent Claims 1 and 8 does not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
For example, see US PG Pub (US 2017/0032298 A1) – “Methods and Systems for Visualizing Individual and Group Skill Profiles”, hereinafter de Ghellinck, et. al. De Ghellinck at ¶ [0021] notes that “The competencies to be included in the databank can vary from instance to instance. The competencies to be included in the databank can be extracted from public sources of information, open sources of information. The databank of competencies can be updated, managed and structured via an ongoing manual process, via Semantic Web analysis technologies, Linked Open Data technologies, other semantic intelligence technologies, other artificial intelligence technologies or via other automated processes.” For example, see US PG Pub (US 2018/0253989 A1) – “System and Methods that Facilitate Competency Assessment and Affinity Matching”, hereinafter Gerace. Gerace notes at ¶ [0017]: and at ¶ [0025]: “Artificial intelligence can be employed to match students and colleges based on competencies. The AI based system 100 can learn from student competencies and evidence (submitted work), learns from institutional messaging and from competencies of successful students at the college.”
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-4, 9-11 and 15-16 recite additional elements such as (e.g., “one or more computing devices”, “a server”, “at least one computing device”, “at least one analytics engine”, “system”, “a network” and “a graphical user interface (GUI)”), and when considered individually and as an ordered combination (as a whole) with the limitations recite the same abstract idea(s) as shown in Independent Claims 1 and 8 along with further steps/details that could be performed as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) fundamental economic practices and additionally or alternatively as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical relationships or (6) mathematical calculations.
Dependent Claims 2, 4, 9, 11 and 15-16 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and Step 2B for Independent Claims 1 and 8. Dependent Claims 3 and 10: With respect to reliance on (e.g., “trained AI statistical model”) as an additional element shown in Dependent Claims 3 and 10 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, this additional element does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment for recommending individuals for open job roles/positions using a computer in a business enterprise environment (see MPEP § 2106.05 (h)) or (2) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)).
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-4, 8-11 and 15-16 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-4, 8-11 and 15-16 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Examining Claims with Respect to Prior Art
12. Examiner deems that Independent Claims 1 and 8 are deemed allowable over the prior art only. Please note that Claims 1-4, 8-11 and 15-16 are still rejected under 35 U.S.C. § 101. Regarding Independent Claims 1 and 8, there is no disclosure in the existing prior art or any new art that either teaches and/or discloses the sequence operation of features either individually or in combination relating to:
generate the recommendation of individuals suitable for roles targeted to the organizational need based in part on the analysis of the organizational need and the set of one or more individuals; generate a graphical user interface element of the data to provide to the user corresponding the client device; provide the graphical user interface element as the recommendation to the at least one computing device; determine a second set of individuals having a competency gap that is less than a pre-determined threshold; and generate a pathway recommendation for the second set of individuals to meet the competency gap; wherein the server is further configured to output a confidence score indicating a confidence that the set of one or more individuals may fill a need of the organization based on a statistical analysis of extent of overlap between competencies and interests of the set of one or more individuals and the needs of the organization.
The closest prior arts are as follows:
#1) US PG Pub (US 2016/0379170 A1) – “Career Analytics Platform” hereinafter Pande;
#2) US PG Pub (US 2021/0264371 A1) – “Systems and Methods for Data-Driven Identification of Talent and Pipeline Matching to Role” hereinafter Polli, et. al.
#3) US PG Pub (US 2022/0004966 A1) – “Systems and Methods for a Professional Competency Framework” hereinafter Essafi, et. al.
#4) US PG Pub (US 2021/0089603 A1) – “Stacking Model for Recommendations” hereinafter Abbasi Moghaddam.
Regarding the Pande reference, Pande teaches and/or discloses the sequence operation of features of:
- store information for the system (see at least Pande: ¶ [0044-0045] & ¶ [0087] & ¶ [0091]. Pande notes stores data into a structured format. Storing of user data in structured data in database: All elements of a users data available are stored in a structured format. See also Pande at ¶ [0087]: “To provide a score on skills the analytics engine takes all data stored in the users profile and identifies what job role/function/industry the user fits into for job seekers, and for students—degree discipline, desired function/company.” See also Pande at ¶ [0091]: This is repeated for all skills stored in the database for that particular profile of user.), the information including at least one of: organization data, user data (see at least Pande: ¶ [0044-0045] & ¶ [0087] & ¶ [0091].) and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies (see at least Pande: ¶ [0144] & ¶ [0167] & ¶ [0173]. Pande teaches that Referring to FIGS. 10A & 10B, the career fit module enables the user to identify which career paths are the best fit for him based on matches with others who have entered those career paths. As an output, the user is provided with scores for top job roles that are a fit for him. See also Pande at ¶ [0167]: “As illustrated in FIG. 11, another manifestation of the career fit module is when the user is allowed to select and explore career paths. This module in the application allows the user to select a career goal, and identify what are the gaps in his/her career and how likely he is to be able to achieve that career goal.” See also Pande at ¶ [0173]: “In the next steps gaps in skills, trajectory, education, etc that user should target to achieve career paths.” See also Pande at ¶ [0184-0192]: “The profile is analyzed, and career path, soft and functional skills and competencies are identified in exactly the same manner as in the Resume Scoring and CareerFit processes.”);
- implement at least one analytics engine (see at least Pande: Fig. 1 & ¶ [0087] & ¶ [0247-0253]. Pande notes that to provide a score on skills the analytics engine takes all data stored in the user’s profile and identifies what job role/function/industry the user fits into for job seekers, and for students—degree discipline, desired function/company. See also Pande at ¶ [0247-0253] noting “analytics engine components”.), wherein the at least one analytics engine (see at least Pande: Fig. 1 & ¶ [0087] & ¶ [0247-0253]. Pande notes that to provide a score on skills the analytics engine takes all data stored in the user’s profile and identifies what job role/function/industry the user fits into for job seekers, and for students—degree discipline, desired function/company. See also Pande at ¶ [0247-0253] noting “analytics engine components”.) comprises a probabilistic model for recommending individuals to open roles (see at least Pande: ¶ [0118] & ¶ [0135] ¶ [0162-0166]. Pande teaches that the method includes receiving an input from the candidate regarding selection of at least one career path to be achieved within a timeframe and recommending the candidate at least one action to pursue the career in the at least one career path. The platform recommends who to send the request to. Network members are scored based on how relevant they are to the users. Members who are in similar desired roles, or companies, or HR managers within desired companies/industries/functions are also prioritized above those that do not match any of these criteria. The user however can bypass any of these recommendations and select whomsoever they want to send the request to. See also Pande at ¶ [0135] & ¶ [0162] noting “open opportunities”.), the probabilistic model (see at least Pande: ¶ [0013-0016] & ¶ [0018-0019]. Pande teaches Using intelligent algorithms, predictive models, context analysis using machine learning and natural language processing. See also Pande noting ¶ [0013-0016] noting “the invention applies criteria beyond skill assessment to analyze the likelihood of a candidate making it through the recruiting process.” See also Pande at ¶ [0115]: “A higher resume score increases likelihood of getting an interview.” See also Pande at ¶ [0164-0167] noting “The principle idea behind this is that the user is more likely to find a job if their network is likely to be close to the opportunity as a significant % of jobs are found through ones network.” See also Pande at ¶ [0246]: Higher candidate score implies higher likelihood of candidate getting selected and succeeding within the company.) being a trained artificial intelligence statistical model (see at least Pande: Fig. 3 & ¶ [0158] & ¶ [0253-0254]. Pande notes that the system is designed to be machine learning so that every new user profile that comes into the system improves all data sets, benchmarking, algorithms, etc. Components of machine learning are discussed in relevant sections. See also Pande at ¶ [0048-0051]: Summary level statistics/analysis of other users profiles are also shared with the user, which includes analytics possible on the users profile such as career background averages and statistics. See also Pande at ¶ [0158]: Training data or entire set of user profile is used as training set, skill patterns and also profile vectors are used to create relevant user clusters. Users closeness to other user profiles is calculated and based on match against elements of profile vector to determine match.) configured to analyze and identify data correlations (see at least Pande: ¶ [0054-0076] noting analyzing and identifying data correlations.), wherein the probabilistic model (see at least Pande: ¶ [0013-0016] & ¶ [0018-0019]. Pande teaches Using intelligent algorithms, predictive models, context analysis using machine learning and natural language processing. See also Pande noting ¶ [0013-0016] noting “the invention applies criteria beyond skill assessment to analyze the likelihood of a candidate making it through the recruiting process.” See also Pande at ¶ [0115]: “A higher resume score increases likelihood of getting an interview.” See also Pande at ¶ [0164-0167] noting “The principle idea behind this is that the user is more likely to find a job if their network is likely to be close to the opportunity as a significant % of jobs are found through ones network.” See also Pande at ¶ [0246]: Higher candidate score implies higher likelihood of candidate getting selected and succeeding within the company.) is configured to be retrained over time (see at least Pande: Figs. 8-9. Pande teaches noting “providing feedback from a network of the candidate.”) using updated historical data (see at least Pande: ¶ [0118-0119] & ¶ [0212-0218]. Pande teaches that similar logic is done for pastfunction and other matches, where harmonized data sets on job roles, company, function are leveraged to assess the “closeness” of a job role, function, industry, company, college on a normalized scale. See also Pande at ¶ [0212-0218] noting “job-role parameters (across past 3 experiences).”), the historical data (see at least Pande: ¶ [0118-0119] & ¶ [0212-0218]. Pande teaches that similar logic is done for pastfunction and other matches, where harmonized data sets on job roles, company, function are leveraged to assess the “closeness” of a job role, function, industry, company, college on a normalized scale. See also Pande at ¶ [0212-0218] noting “job-role parameters (across past 3 experiences).”) comprising representations of user attributes (see at least Pande: ¶ [0153] & ¶ [0275-0276]: Pande teaches that the database is structured such that each users attributes along with scores for each element are stored. Every new user adds to this database dynamically and this database is used to provide customized benchmarking to all users. Benchmarking can be customized on any attribute of a user including, past college, tier of college, job role, company, years of experience, skills, competencies to show relative positioning with respect to each or a combination of these elements. See also Pande at ¶ [0153]: Skill is pulled from the table in database where job role, and match attributes are stored. The data is normalized on a 0 to 1 scale with 1 being tightest match and 0 being no match.), crowd sourcing (see at least Pande: ¶ [0019]. Pande teaches that Models based on crowdsourcing of career paths. This SMART CAREER COACH does not stop just at that i.e. getting you an entry into a company, but also suggests how to succeed in the role, who to target as mentor and continues to help for the next career move.) tagging of skills and competencies (see at least Pande: ¶ [0098] & ¶ [0270]. Pande teaches bullet samples tagged to industry, function, job role, skills, competencies, years of experience, education, type of experience (e.g. awards, extracurricular, etc). The samples are pulled from the samples library matched to the users profile to ensure they are relevant for the user using tags already in the system.), and organizational needs (see at least Pande: ¶ [0265]. Pande notes that the job role mapping database —job roles mapped to functions, industries with corresponding skills needed in the role, along with weights for each skill by relative importance.), to improve a recommendation accuracy (see at least Pande: ¶ [0016] & ¶ [0049] & ¶ [0114]. Pande teaches “positions for which they have the highest likelihood of success further improving the chances of success both in the recruiting process as well as during their career journey at the company.” See also Pande at ¶ [0049] noting “Score improvements of others” and Pande at ¶ [0114] noting “The user follows a process of dynamic score-based Score Improvement of their profile where they follow a process leveraging feedback from the system to dynamically score their improvements and also see the score improve dynamically.”) of the probabilistic model (see at least Pande: ¶ [0013-0016] & ¶ [0018-0019]. Pande teaches Using intelligent algorithms, predictive models, context analysis using machine learning and natural language processing. See also Pande noting ¶ [0013-0016] noting “the invention applies criteria beyond skill assessment to analyze the likelihood of a candidate making it through the recruiting process.” See also Pande at ¶ [0115]: “A higher resume score increases likelihood of getting an interview.” See also Pande at ¶ [0164-0167] noting “The principle idea behind this is that the user is more likely to find a job if their network is likely to be close to the opportunity as a significant % of jobs are found through ones network.” See also Pande at ¶ [0246]: Higher candidate score implies higher likelihood of candidate getting selected and succeeding within the company.) wherein the at least one analytics engine is configurable (see at least Pande: Fig. 1 & ¶ [0087] & ¶ [0247-0253]. Pande notes that to provide a score on skills the analytics engine takes all data stored in the user’s profile and identifies what job role/function/industry the user fits into for job seekers, and for students—degree discipline, desired function/company. See also Pande at ¶ [0247-0253] noting “analytics engine components”.
However, Pande reference does not teach or disclose the sequence operation of features either individually or in combination relating to:
generate the recommendation of individuals suitable for roles targeted to the organizational need based in part on the analysis of the organizational need and the set of one or more individuals; generate a graphical user interface element of the data to provide to the user corresponding the client device; provide the graphical user interface element as the recommendation to the at least one computing device; determine a second set of individuals having a competency gap that is less than a pre-determined threshold; and generate a pathway recommendation for the second set of individuals to meet the competency gap; wherein the server is further configured to output a confidence score indicating a confidence that the set of one or more individuals may fill a need of the organization based on a statistical analysis of extent of overlap between competencies and interests of the set of one or more individuals and the needs of the organization.
Regarding the Polli reference, Polli teaches and/or discloses the sequence operation of features of:
- one or more computing devices that communicate over a network with the system (see at least Polli: Fig. 1 & ¶ [0091-0092]. Polli teaches that a user device may be, for example, one or more computing devices configured to perform one or more operations.), at least one computing device (see at least Polli: Fig. 1 & ¶ [0102]. Polli teaches that a server may include a web server, an enterprise server, or any other type of computer server, and can be computer programmed to accept requests (e.g., HTTP, or other protocols that can initiate data transmission) from a computing device (e.g., a user device) and to serve the computing device with requested data.) comprising a graphical user interface (see at least Polli: ¶ [0165]. Polli notes that the reporting engine may be configured to generate a plurality of graphical user interfaces (GUIs) for displaying data on a user device.) for providing data to the system and outputting data to a user (see at least Polli: ¶ [0113] & ¶ [0353] & Figs. 1-4. Polli teaches that FIG. 2 illustrates a schematic block diagram of exemplary components in a screening system and inputs/output of the screening system. See also Polli at ¶ [0115]: An end user may use the screening system to match candidates with the company, by analyzing the candidates' behavioral output using an employee model. See also Polli at ¶ [0353]: “outputting by the output module the identified career propensity to a hiring officer.”).
- a server configured to (see at least Polli: ¶ [0005] & ¶ [0091] & Fig. 1. Polli notes that the system may comprise a server in communication with a plurality of computing devices associated with a plurality of participants. Polli teaches a server 104 shown in Fig. 1.)
- communicate with the one or more computing devices (see at least Polli: ¶ [0005]. Polli teaches that the system may comprise a server in communication with a plurality of computing devices associated with a plurality of participants.);
- determine an organizational need based at least on the organization data (see at least Polli: ¶ [0115] & ¶ [0201] & ¶ [0209]. Polli notes that for sourcing, a user (e.g., a recruiter) may use the sourcing models to identify candidates who are most similar to a target group of individuals (i.e., identify candidates who match closely to an employee model), and present those candidates to a company for its hiring needs. An end user may submit a request (e.g., via a user device) to the screening system to identify top candidates who may be tailored to a company's needs for a specific position. An end user may use the screening system to match candidates with the company, by analyzing the candidates' behavioral output using an employee model. The employee model may be representative of ideal (or exemplary) employee for a specific position in the company. Accordingly, a user can adjust the decision boundary depending on the screening and hiring needs of a company (for example, whether the company is willing to accept a larger pool of candidates, or requires only a select number of candidates).);
- determine a set of one or more individuals that could thrive in a role targeted to the determined organization need based at least on the user data (see at least Polli: ¶ [0169-0171] & ¶ [0218] & ¶ [0333-0335]. Polli notes that a user may use the sourcing models to identify candidates who are most similar to a target group of individuals (i.e., candidates who match closely to an employee model). Accordingly, sourcing models can be used by companies and recruiters to ‘source’ for talent. A user may use the sourcing models to identify candidates who meet a cut-off threshold, and present those candidates to a company for its hiring needs. A target group 416 of top employees of a company may include eight employees, and an employee model of the target group may be contrasted against a baseline group 418. A number of candidates (e.g., four) 420 may be compared against the employee model to determine how well the candidates fit or match the employee model. The data from the tests can then be applied to the trained analytics engine to create a fit score for the candidate. These predictive models can be used to assess factors including, for example, how likely a potential hire would be to succeed in a particular role at the company. See also Polli at ¶ [0089-0090].)
- analyze the organizational need and the set of one or more individuals (see at least Polli: ¶ [0003] & ¶ [0086] & ¶ [0115]. Polli notes that identify talent that is tailored to a company's needs for a specific job position, and (2) identify top employees and recommend placement of those employees in positions that optimize their potential. The systems and methods can match candidates with companies, based on the candidates' behavioral output obtained from one or more neuroscience-based tasks (or tests). The candidates' behavioral output may be compared against an employee model that is representative of an ideal employee for a specific position in the company. An end user may submit a request (e.g., via a user device) to the screening system to identify top candidates who may be tailored to a company's needs for a specific position. An end user may use the screening system to match candidates with the company, by analyzing the candidates' behavioral output using an employee model. The employee model may be representative of ideal (or exemplary) employee for a specific position in the company.) to generate a recommendation of individuals for roles based on characteristics pertaining to the individual (see at least Polli: ¶ [0124] & ¶ [0220]. Polli notes that the reporting engine may receive the fit score for each candidate from the model analytics engine, and provide the fit score and a recommendation to the end user. The recommendation may include whether a particular candidate is suitable for hiring to fill a specific job position, and the likelihood of the candidate's success in that position. The comparison of the subject's trait with a database of test subjects can also be used to generate a model of the subject. The results of the comparison can be outputted to a hiring officer. The results of the comparison can further be used to recommend careers for the subject.), and historical information (see at least Polli: ¶ [0121] & ¶ [0159] & ¶ [0292]. Polli notes that the neuroscience-based games (that were previously played by employees to generate the employee model) may now be provided to one or more candidates. The screening system may be configured to obtain the candidates' behavioral output from their performance on the neuroscience-based games. For example, the traits extraction engine may be configured to extract emotional and cognitive traits about each candidate based on each candidate's gameplay data. The traits extraction engine can determine whether a user has correctly selected, placed, and/or used different objects in the game to complete a required neuroscience-based task. The traits extraction engine can also assess the user's learning, cognitive skills, and ability to learn from previous mistakes. Using the model previously built for Company C in EXAMPLE 21, the system compared average fit scores for those individuals who accepted an offer from the company to fit scores of those individuals who rejected an offer from the company.) pertaining to others that followed similar paths or developed similar competencies (see at least Polli: ¶ [0129-0131] & ¶ [0374]. Polli notes that the models may be associated with different fields (e.g., banking, management consulting, engineering, etc.). Alternatively, the models may be associated with different job functions within a same field (e.g., software engineer, process engineer, hardware engineer, sales or marketing engineer, etc.). An end user (e.g., a recruiter or a career advisor) may use the results of the traits comparison to recommend one or more suitable careers to the subject. The screening system can use the fit score to determine the subject's career propensity and recommend suitable career fields to the subject. Non-limiting examples of the fields (or industries) that can be recommended by the screening system may include consulting, education, healthcare, marketing, retail, entertainment, consumer products, entrepreneurship, technology, hedge funds, investment management, investment banking, private equity, product development, or product management. The first assessment based on similarity of the measurements to first group measurements of a first group of persons having the first role; generating a second assessment for suitability of the person for a second role based on similarity of the measurements to a second group of measurements of a second group of persons having the second role; outputting an alert indicating suitability of the person for the first role and for the second role.)
However, Polli reference does not teach or disclose the sequence operation of features either individually or in combination relating to:
generate the recommendation of individuals suitable for roles targeted to the organizational need based in part on the analysis of the organizational need and the set of one or more individuals; generate a graphical user interface element of the data to provide to the user corresponding the client device; provide the graphical user interface element as the recommendation to the at least one computing device; determine a second set of individuals having a competency gap that is less than a pre-determined threshold; and generate a pathway recommendation for the second set of individuals to meet the competency gap; wherein the server is further configured to output a confidence score indicating a confidence that the set of one or more individuals may fill a need of the organization based on a statistical analysis of extent of overlap between competencies and interests of the set of one or more individuals and the needs of the organization.
Regarding the Essafi reference, Essafi teaches and/or discloses the sequence operation of features of:
- determine a second set of individuals having a competency gap that is less than a pre-determined threshold (see at least Essafi: ¶ [0272-0273] & ¶ [0290] & ¶ [0294-0296]. Essafi notes that in a company or an organization, the target competency level profile for each position can be maintained by a performance evaluation database. Assuming k different roles with k corresponding target competency level profiles denoted as C1, . . . , Ck, and m different assessment items (or competencies), the target competency level profiles C1, . . . , Ck can be represented in matrix form as depicted below in Table 10. When θi≈θtl, the respondent ri is expected to have performance scores equal to or very close to those of the target competency level profile Cl. If θi>θtl, the respondent ri is expected to have performance scores higher than those of the target competency level profile Cl (e.g., above exceeding targets), and if θi<θtl, the respondent ri is expected to have performance scores below the target scores of the target competency level profile Cl (e.g., below targets). See also Essafi at ¶ [0288]: “These methods described herein include an objective approach to measure gaps (also referred to herein as ability gaps) between respondents' abilities and a target ability that corresponds to a target competency level profile. Also, a similarity metric based on an aggregation function approach is described for objectively measuring similarities (or differences) between performance scores (or competency levels) of respondents across the predefined set of competencies and the target competency levels specified for any given role.” See also Essafi at Figs. 14A, 14B and 14C.)
- generate a pathway recommendation for the second set of individuals to meet the competency gap (see at least Essafi: ¶ [0067] & ¶ [0178] & ¶ [0287]. Essafi notes that the knowledge base of respondents can serve as a bank of information about the respondents that can be used for various purposes, such as generating learning paths, making recommendations to respondents or grouping respondents, among other applications. A competency or skill can include one or more competency items. For example, communication skills can include writing skills, oral skills, client communications and/or communication with peers. The assessment with respect to each competency or each competency item can be based on a plurality of performance or proficiency levels, such as “Significantly Needing Improvement,” “Needing Improvement,” “Meeting Target/Expectation,” “Exceeding Target/Expectation” and “Significantly Exceeding Target/Expectation.” Also Essafi teaches at ¶ [0287]: “The performances of respondents associated with a given role can be compared to the target competency level profile for that role to determine, for example, whether each respondent is on track (or meeting target), below track (or below target) or above track (or exceeding target).” See also Essafi at (Dependent Claim 3) teaches “determining, for each respondent of the plurality of respondents, a corresponding ability gap representing a difference between the ability level of the respondent and the target ability level of the first target competency level profile”).
However, Essafi reference does not teach or disclose the sequence operation of features either individually or in combination relating to:
generate the recommendation of individuals suitable for roles targeted to the organizational need based in part on the analysis of the organizational need and the set of one or more individuals; generate a graphical user interface element of the data to provide to the user corresponding the client device; provide the graphical user interface element as the recommendation to the at least one computing device; determine a second set of individuals having a competency gap that is less than a pre-determined threshold; and generate a pathway recommendation for the second set of individuals to meet the competency gap; wherein the server is further configured to output a confidence score indicating a confidence that the set of one or more individuals may fill a need of the organization based on a statistical analysis of extent of overlap between competencies and interests of the set of one or more individuals and the needs of the organization.
Regarding the Abbasi Moghaddam reference, Abbasi Moghaddam teaches and/or discloses the sequence operation of features of:
- wherein a server (see at least Abbasi Moghaddam: Fig. 4.) is further configured to output a confidence score indicating a confidence that the set of one or more individuals may fill a need of the organization based on a statistical analysis of extent of overlap between competencies and interests of the set of one or more individuals and the needs of the organization (see at least Abbasi Moghaddam: ¶ [0028] & ¶ [0041-0042] & ¶ [0050-0051]. Abbasi Moghaddam teaches that the euclidean distances, and/or other measures of similarity, distance, or overlap between standardized versions of all of the candidate's attributes and all of the job's corresponding attributes. Candidate-job features also, or instead, include measures of similarity or overlap between text in the candidate's profile and the description or posting of the job. Candidate-job features also, or instead, include other measures of similarity and/or compatibility between one attribute of the candidate and another attribute of the job (e.g., a match percentage between a candidate's “Java” skill and a job's “C++” skill). Examiner notes that the overlap may just be a euclidean distance between the applicant's metrics and the job rqmt standards. The component(s) may recommend jobs to a candidate based on the predicted relevance or attractiveness of the jobs to the candidate and/or the candidate's likelihood of applying to the jobs. After machine learning models 238 are trained, machine learning models 238 generate match scores 240 ranging from 0 to 1. Each match score represents the likelihood of a positive outcome between a candidate and a job. The positive outcome includes, but is not limited to, the candidate applying to the job, given the candidate's impression of the job; the candidate receiving a response to the job application; adding of the candidate to a hiring pipeline for the job; interviewing of the candidate for the job; and/or hiring of the candidate for the job.).
However, Abbasi Moghaddam reference does not teach or disclose the sequence operation of features either individually or in combination relating to:
generate the recommendation of individuals suitable for roles targeted to the organizational need based in part on the analysis of the organizational need and the set of one or more individuals; generate a graphical user interface element of the data to provide to the user corresponding the client device; provide the graphical user interface element as the recommendation to the at least one computing device; determine a second set of individuals having a competency gap that is less than a pre-determined threshold; and generate a pathway recommendation for the second set of individuals to meet the competency gap; wherein the server is further configured to output a confidence score indicating a confidence that the set of one or more individuals may fill a need of the organization based on a statistical analysis of extent of overlap between competencies and interests of the set of one or more individuals and the needs of the organization.
Therefore, when taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor does the available art suggest or otherwise render obvious further modification of the evidence at hand. Such modification would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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/DERICK J HOLZMACHER/ Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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