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, 6, 8-11, 13 and 15-16 are currently pending. Claims 3, 8-10 and 15-16 have been amended. Claims 1-4, 6, 8-11, 13 and 15-16 have been rejected.
Status of the Application
3. Claims 1-4, 6, 8-11, 13 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 01/20/2026 necessitated new grounds of rejection in this office action.
Continued Examination under 37 CFR 1.114
5. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/20/2026 has been entered.
Priority
6. The Examiner has noted the Applicants claiming Priority from Provisional Application 63/304,977 filed on 01/31/2022. Therefore, Examiner notes the effective filing date of this application examined on the record is 01/31/2022.
Response to Arguments
7. Applicant’s arguments, see page 6 filed on 01/20/2026, with respect to the Claim Objections for Claims 8-9 and 15-16 have been fully considered and are found to be persuasive. Therefore, the Claim Objections for Claims 8-9 and 15-16 have been withdrawn.
8. Applicant’s arguments, see page 6 filed on 01/20/2026, with respect to the 35 U.S.C. §112 (b) Claim Rejections for Claims 3-4 and 10-11 have been fully considered and are found to be persuasive. Therefore, the 35 U.S.C. § 112 (b) Claim Rejections for Claims 3-4 and 10-11 have been withdrawn.
9. Examiner adds a 35 U.S.C § 103 rejection for Claims 1-4, 6, 8-11, 13 and 15-16 shown below. The previous 35 U.S.C. § 103 rejection is moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Response to 35 U.S.C. § 101 Arguments
10. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-4, 6, 8-11, 13 and 15-16 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 9-12, dated 01/20/2026). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-4, 6, 8-11, 13 and 15-16 are not directed to a mental process. The Office asserts that the claims are directed at abstract mental process and/or instances of managing personal behavior or relationships or interactions between people (see Applicant Remarks, 1st ¶ of Page 7, dated 01/20/2026).
Examiner notes that Independent Claims 1 and 8 describes a system for managing personnel, identifying organizational needs, and recommending individuals for roles. According to the USPTO, this falls under the grouping of “Certain Methods of Organizing Human Activities”. This specifically is placed under (1) managing personal behavior or relationships or interactions between people -> The process of evaluating individual competencies, determining organizational needs, and recommending individuals for roles is a form of managing human interactions and professional relationships. Secondly, this is categorized under (2) fundamental economic principles or practices -> The “recommendation of individuals for roles” and matching “organizational needs” mirrors business and human resources practices that have been performed for decades. Additionally, or alternatively, that Independent Claims 1 and 8, detail steps such as “analyzing”, “identifying correlations”, “determining a set of individuals” and “generating recommendations”. These are often categorized as “Mental Processes” due to observations and evaluations -> The USPTO considers “evaluations, judgments, and opinions” regarding data to be mental process. Secondly, this is also representative as steps of evaluating a person’s skills (“user attributes”) against a job requirement (organizational need) to make a recommendation are evaluations and judgments that can practically be performed by a human, even if a computer does it faster or with more data.
Applicant argues that the USPTO Subject Matter Eligibility analysis follows this precedent and instructs Examiners to determine that a claim recites a mental process when it contains limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions. On the other hand, a claim does not recite a mental process when it contains limitations that cannot practically be performed in the human mind, for instance when the human mind is “not equipped” to perform the claim limitations (see Applicant Remarks, last ¶ of Page 7, dated 01/20/2026). Examiner respectfully disagrees. Under MPEP § 2106.04 (a) (2) and the USPTO’s updated guidance on AI and Mental Processes, the following rebuttal maintains the rejection by clarifying the legal distinction between qualitative nature and quantitative scale.
The Applicant’s argument that the claims (e.g., in particular Independent Claims 1 and 8) do not recite a “mental process” because the human mind is “not equipped” to perform the claim limitations to handle the scale of data is unpersuasive. This argument conflates the process (the type of activity) with the performance (the speed or volume of the activity). The Applicant incorrectly interprets the “practically performed in the human mind” test. Under MPEP 2106.04 (a) (2) (III), a limitation recites a mental process if it is a type of activity that can be performed mentally, such as “evaluating”, “determining”, or “identifying correlations”. The Federal Circuit in SAP Am., Inc. v. InvestPic, LLC clarified that if a process is “directed to” a mental or mathematical concept, it does not become a non-abstract simply because it is performed “at a speed or on a scale” that exceeds human capacity. The claim’s core – evaluating user attributes against organizational needs is a judgment and evaluation that humans have historically performed. Automating this judgment on a massive scale via “probabilistic model” does not change the fundamental character of the step from a mental process to a technical one. The “Equipped” standard is not a safe harbor for AI. The Applicant suggests that because the human mind is not “equipped” to process crowd-sourced tagging and historical data correlations in real-time, the claim is eligible. However, the USPTO 2019 Revised Guidance (and 2024 AI updates) states that a claim recites a mental process even if it is claimed as being performed on a computer. If the underlying logic is a “mental process” (e.g., assessing a “competency gap”), the use of a computer to execute it more efficiently is merely a “speed and efficiency” improvement. Even if the scale is “impractical” for a human, these claims remain ineligible because it does not provide a technical solution to a technical problem. In FairWarning IP, LLC v. Iatric Sys., Inc., the court held that “collecting and analyzing information” is abstract even if the data volume is high. To move beyond a mental process, these claims must recite a specific non-conventional technological implementation (e.g., a specific AI hardware accelerator or a novel way of optimizing memory for large-scale correlations). The current claims use a generic “analytics engine” to achieve a result that is fundamentally an evaluation of human data. The “practicality” test cited by the Applicant is intended to filter out claims that require physical hardware that precludes mental performance. It is not a loophole for data-heavy business methods. Matching individuals to roles based on correlations is an evaluation of information – a classic mental process – regardless of whether it is done for one person or one million people. These claims recite a mental process because its steps are evaluations and judgments. The fact that a computer performs these steps faster or using more data points does not transform the abstract idea into a patent-eligible invention.
Argument #2:
(B). Applicant argues that Claims 1-4, 6, 8-11, 13 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, 1st ¶ of Page 8, dated 01/20/2026). Examiner respectfully disagrees.
In response to Applicant’s arguments contending that the Office has characterized the abstract idea too broadly and that the “technical function” of analyzing massive datasets exceeds human capability. These arguments fail to distinguish between commercial/computational utility and patent eligibility. The Office’s characterization of the abstract idea as “managing personal behavior or relationships” and “mental processes” is consistent with MPEP § 2106.04 (a). The core of the claims is the matching of human skills to organizational needs – a fundamental business practice. The fact that the claims recite specific data types (crowd-sourced data, historical profiles) does not change the nature of the activities. Identifying “correlations” between data points remains a mental process, even when performed on a computer. Under the “directed to” inquiry, the focus is on the principal purpose of the claims, which remains the abstract concept of workforce talent management.
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, 6, 8-11, 13 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 #3:
(C). Applicant argues that Claims 1-4, 6, 8-11, 13 and 15-16 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the USPTO 2019 Revised Patent Subject Matter Eligibility Guidance (see Applicant Remarks, Pages 8-9, dated 01/20/2026). Examiner respectfully disagrees.
In response to Applicant’s arguments here, Examiner notes that Complexity and “Scale” do not yield a technical improvement (Step 2a prong 2). The Applicant argues that the “sheer amount of data” makes the process a “technical improvement of an HR system.” However, the Federal Circuit has clarified in cases such as SAP Am., Inc. v. InvestPic LLC (Fed. Cir. 2018) and FairWarning IP, LLC v. Iatric Sys., Inc. (Fed. Cir. 2016) that an abstract idea does not become a technical solution simply because it is executed on a scale or at a speed that a human cannot replicate. A “technical improvement” must improve the functioning of the computer itself (e.g., a faster way to index data or a more efficient cooling system). An improvement in the result of an abstract process (e.g., a more accurate HR recommendation) is a non-technical business improvement. These claims lack any recitation of a novel algorithm or specialized hardware that solves a technical problem inherent in computer networking; it merely uses a network as a tool to automate an HR task. The Applicant’s argument that the human mind is not “properly equipped” to perform the limitations is a “speed and efficiency” argument. The USPTO and courts (e.g., Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (Fed. Cir. 2014)) have consistently ruled that the fact a human cannot manually perform a task as fast as a computer does not provide the “significantly more” required for an inventive concept. The “probabilistic model” is described in functional terms; it is the automation of a mental process (judging suitability) rather than a technological shift in how computers operate. With respect to reliance on (e.g., “artificial intelligence statistical model” & “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)). Moreover, Examiner notes requiring the use of software (e.g., “trained artificial intelligence statistical model”) to tailor information and provide the results to the user on a computer (see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115USPQ2d 1636, 1642 (Fed. Cir. 2015)).
Independent Claims 1 and 8: With respect to reliance on (e.g., “artificial intelligence statistical model” & “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)). 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). Therefore, in conclusion, Examiner maintains that Claims 1-4, 6, 8-11, 13 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 #4:
(D). Applicant argues that Examiner appears to review each limitation separately and notes that although the specification mentions a general or special purpose programmable computer. Applicant submits that such a statement is not determinative of whether the overall system and method are merely directed at mental processes. Although some aspects within the claims may be considered mathematical concepts or mental processes, when taken as a whole, Applicant submits that the system and method provide features that are not practically performable by a human mind and extend beyond simple mathematical concepts (see Applicant’s Remarks, 2nd ¶ of Page 8, dated 01/20/2026). Examiner respectfully disagrees.
The Applicant’s arguments have been considered but are not persuasive. The Applicant contends that the Office has performed a piecemeal analysis and that the “as a whole” system extends beyond a human mind’s capability. This argument fails to address the governing legal standards for Step 2A and Step 2B. First, the “As a whole” Inquiry Confirms the abstract focus (Step 2A). The Office has not improperly ignored these claims “as a whole”. Under Step 2A, the “directed to” inquiry requires the Office to look at the focus of the claimed invention. While the claims combine several steps, the integrated goal – analyzing user data to match individuals with roles – falls squarely within the category of Certain Methods of Organizing Human Activity (specifically, managing personnel interactions). As stated in Alice Corp v. CLS Bank, adding a “computer” or an “analytics engine” to a long-standing business practice does not change the character of the abstract idea, even when viewed as a combined system.
Secondly, the Applicant argues that the system provides features “not practically performable by a human mind”, This is a computational efficiency argument, which the Federal Circuit has consistently rejected as a basis for eligibility. In SAP Am., Inc v. InvestPic, LLC, No. 17-2081 (Fed. Cir. 2018), the court held that even if a mathematical or statistical process is “too complex” for manual human calculation, it remains an abstract idea if it is not a technical improvement to the computer itself. Also, that “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not provide an inventive concept (see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d1636, 1639 (Fed. Cir. 2015)). The “complexity” here resides in the data and the logic of the HR evaluation, not in a novel configuration of computer hardware or a solution to a technical problem in computer science. While the Applicant is correct that a specification’s reference to a “general purpose computer” is not solely determinative, it is highly probative under Step 2B. Under MPEP § 2106.05 (a), when a specification describes the hardware in generic terms and the claims use that hardware to perform “well-understood, routine, and conventional” activities, the claims fail to provide an inventive concept. The Applicant has not identified any specific claim limitation that requires a specialized computer architecture or a non-conventional software modification that changes how the computer functions. Fourth Reason: Even taking the features as an ordered combination, Independent Claims 1 and 8 merely recites the automation of a career-pathing and recruitment process. The combination of (1) gathering crowd-sourced data, (2) applying a probabilistic model, and (3) generating a recommendation is the digital equivalent of a high-volume recruitment firm’s workflow. Automating this workflow using a generic “analytics engine” does not amount to “significantly more” than the abstract idea itself. Because the focus of Claims 1-4, 6, 8-11, 13 and 15-16 as a whole is an abstract business method, and the implementation relies on computing functions to handle data complexity, Claims 1-4, 6, 8-11, 13 and 15-16 are patent-ineligible under 35 U.S.C. § 101.
Argument #5:
(E). Applicant argues that for Independent Claims 1 and 8 the step of “the probabilistic model being a trained artificial intelligence statistical model configured to analyze and identify data correlations, wherein the probabilistic model is configured to be retained 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 Applicant Remarks, 3rd ¶ of Page 8, dated 01/20/2026). Examiner respectfully disagrees.
The Applicant agues that the “combination and correlations” exceed human capacity. However, the Federal Circuit has consistently held (e.g., SAP Am., Inc v. InvestPic, LLC, No. 17-2081 (Fed. Cir. 2018)) that the mathematical or statistical nature of an analysis does not change simply because it is performed on a larger scale or at a higher speed than a human could achieve. The standard remains that if the underlying steps (evaluating attributes and identifying correlations) are the digital versions of a mental process, the “sheer volume” of data is a matter of utility and efficiency, not a transformation into a patent-eligible technical improvement. The Applicant’s assertion that the AI model is “more than a tool” fails because these claims describe the model in terms of its functional results (improving recommendation accuracy) rather than a technical solution to a computing problem. Under Step 2a prong 2, an improvement to the business process of HR selection is not an improvement to the functioning of the computer. These claims do not recite a specific, novel AI architecture (e.g., a specific layer configuration or a unique loss function) that solves a technical problem like reducing memory overhead or improving training latency. Without these technical specifics, the AI remains a “black box” tool for performing a mental process.
Moreover, the Applicant points to the model being “retrained over time” as evidence of a specialized system. However, retraining is a known feature of nearly all machine learning models. Merely reciting that a model updates its parameters based on “updated historical data” does not provide an inventive concept; it is simply the automation of the human practice of “learning from experience” applied to a database. Then the Applicant argues that the process cannot be done with “pen and paper” in any practical way. The USPTO and courts have clarified that the “pen and paper” test is a threshold for identifying a mental process, not a safe harbor for eligibility. If the claim’s core innovation is the discovery of a relationship between data points (e.g., skills vs. organization needs), that is a discovery of an abstract correlation. Automating that discovery through AI is still directed to the correlation itself, which is an abstract idea under “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” groupings. These claims refer broadly to “identify data correlations” without reciting the specific rules or technical parameters that define how those correlations are identified. Because these claims are broad enough to cover any statistical correlation between user skills and job roles, it preempts the abstract idea of “matching people to jobs” rather than claiming a specific technical implementation. In conclusion, the Applicant’s argument conflates the computational difficult of the task with legal eligibility of the subject matter. Because these claims rely on AI functions to achieve a non-technical business result, it does not rise to the level of an inventive concept under Step 2B.
Argument #6:
(F). Applicant argues that for Independent Claims 1 and 8 the addition of “generate a pathway recommendation for the second set of individuals to meet the competency gap” is not a simple mental process, but given the complexity and the amount of data falls outside of the noted subject matter of abstract ideas (see Applicant Remarks, last ¶ of Page 8 and 1st ¶ of Page 9, dated 01/20/2026). Examiner respectfully disagrees.
Examiner responds by stating that the data volume and complexity does not negate “Mental Processes” category. The Federal Circuit has repeatedly held that an abstract idea does not become non-abstract simply because it is performed on a scale or at a speed that a human cannot replicate. In SAP Am., Inc v. InvestPic, LLC, No. 17-2081 (Fed. Cir. 2018), the court ruled that “mathematical calculations” and “statistical analyses” are abstract even if they are too complex for a human to perform manually. The Applicant’s argument that the “amount of data” precludes a mental process is a “speed and efficiency” argument, which the courts consistently reject as a basis for eligibility. Secondly point is the fundamental difference between functional results vs. technical improvements. While the Applicant submits the system is an “improved/modified human resources system”, the claim language describes an improvement in the result (a more accurate recommendation) rather than a technical improvement to the computer’s operation. Under MPEP § 2106.04 (a), an improvement to a business process (HR management) using computer tools (AI/probabilistic models) remains a “method of organizing human activity. The “pathway recommendation” is a functional output – what the system does – not a technical solution to a technical problem inherent in the computer itself.
Moreover, Independent Claims 1 and 8, for determining a “competency gap” and recommending a “pathway” to fill that gap is the digital equivalent of a career counselor reviewing a resume against a job description. The Applicant’s contention that the “amount of data” makes this non-abstract is refuted by the fact that the nature of the activity (mentoring and career planning) is a fundamental human activity. Automating this activity, even with “crowd-sourced tagging” and “statistical models” does not change its character as an abstract method of organizing human behavior. In conclusion, Applicant’s argument conflates commercial utility and computational speed with patent eligibility. Because these claims remain directed to the abstract concepts of data evaluation and human resource management without reciting a specific technical shift in computing technology, it fails Steps 2A and Step 2B of the 35 U.S.C § 101 analysis.
Argument #7:
(H). Applicant argues that Claims 1-4, 6, 8-11, 13 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 9-10, dated 01/20/2026). Examiner respectfully disagrees.
The Applicant argues that the claims provide an “improvement to technology of a human resource system” and address a “computer network centric problem.” However, the claims, as a whole, do not recite limitations that amount to “significantly more” than the abstract ideas identified previously (methods of organizing human activity and mental processes). The Applicant conflates a commercially desirable result with a technical improvement. The “improvement” is to the field of matching a user to the appropriate organizational need, which is an improvement per se in human resource management – a business practice. It is not an improvement to the operation of the computer or network itself. The claims do not describe how the computer’s hardware, memory, or network transmission is specifically enhanced, only that these generic components are used to achieve a better HR outcome. This aligns with holdings in cases like In re TLI Communications LLC, where claims directed to organizing information using conventional technology were found ineligible because the focus was on the information itself, not the technological solution to a technological problem.
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.
Independent Claims 1 and 8: With respect to reliance on (e.g., “artificial intelligence statistical model” & “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, particularly Independent Claims 1 and 8 relies on generic terms such as “communicate with the one or more computing device”, “store information for the system”, and “networked human management system”. The inclusion of standard computer components that perform WURC tasks in a network environment is insufficient to provide an inventive concept, as established in Alice Corp. v. CLS Bank Int’l. The system simply uses a standard network and database to execute the abstract logic of the analytics engine. The “analytics engine” with the “probabilistic model” is described in purely functional terms (“configured to analyze and identify data correlations”). The claims fail to specify any non-conventional technical implementation or algorithm. The fact that the process is “extensive, automatic and integrated” is merely a description of efficient automation using technology, which does not render the underly abstract idea patent-eligible. The Applicant’s argument that the claims do not preempt an alleged abstract idea is unpersuasive. The claims are broad enough to cover any statistical method of AI approach for correlating skills and organizational needs with a networked HR system. By broadly claiming the functional result of matching users to roles, the claims effectively preempt the entire abstract concept of using data correlations in a computerized system for workforce management. In conclusion, the claims, as a whole, are directed to the abstract ideas of mental processes and organizing human activity. Because the additional elements are generic computer components applied to achieve a business objective, they do not provide an inventive concept or amount to “significantly more” than the exceptions themselves. Claims 1-4, 6, 8-11, 13 and 15-16 remain patent-ineligible under 35 U.S.C. § 101.
Argument #8:
(I). Applicant argues that Independent Claims 1 and 8 recites data sourcing, scale, and automatic updates which provides an “inventive concept” or lift the claims out of the abstract realm (see Applicant Remarks, last ¶ of Page 9 and 1st ¶ of Page 10, dated 01/20/2026). Examiner respectfully disagrees.
The Applicant argues that features like “crowd sourcing”, “scale” and “automatic updates” and “real-time manner” move the claims beyond being WURC. However, these arguments merely describe the benefits of automating a human process using computing technology and fail to establish patent eligibility. Examiner refers Applicant to BSG Tech LLC v. Buyseasons Inc. decision (Aug. 15, 2018) court case noting that: “But the relevant inquiry is not whether the claimed invention as a whole is unconventional or non-routine. At Step two, we “search for an ‘inventive concept’… that is sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Alice, 134 S. Ct. at 2355 (internal quotation marks omitted) (quoting Mayo, 566 U.S. at 72-73). But this simply restates what we have already determined is an abstract idea. At Alice step two, it is irrelevant whether considering historical usage information while inputting data may have been non-routine or unconventional as a factual matter. As a matter of law, narrowing or reformulating an abstract idea does not add “significantly more” to it. See SAP Am., Inc. v. InvestPic, LLC. No. 2017-2081, slip op. at 14 (Fed. Cir. 2018). Applicant’s suggestion that specific limitations (or the claimed invention as a whole) must be shown to be well-understood, routine, and conventional to support the conclusion of subject matter ineligibility is not persuasive.
First, gathering data from various sources, including third-party crowd-sourcing inputs is a data-gathering step inherent in virtually all modern, data-driven computer applications. The specific data being gathered (skills, attributes) is conventional for HR management. The fact that the data is accessed “quickly and efficiently” is a natural result of using a computer system for data management, not an inventive technical feature that is “significantly more” than the abstract idea itself. The Applicant again relies on the argument that the scale and speed of the data processing make it infeasible for a human mind to perform. This is a speed and efficiency argument which does not make an abstract idea patent-eligible. Federal Circuit cases like SAP Am., Inc v. InvestPic, LLC, No. 17-2081 (Fed. Cir. 2018) confirm that complex mathematical or statistical operations, even if computationally intensive, remain abstract ideas when applied using generic computer components. The “ability to adapt” in “real-time” is simply the benefit of computer automation, not a unique technical solution. The feature that the analytics engine and probabilistic model “can also be updated for all users automatically without human intervention” is a description of a standard machine learning or software update process. This is a routine and conventional aspect of maintaining modern software systems and AI models. It is not an inventive concept that adds “significantly more” to the claims than the underlying abstract ideas. Moreover, the claims describe an improvement to the “field of matching a user to the appropriate organizational need”, which is a business goal. The Applicant has failed to identify any claim limitation that specifies how the underlying computer technology itself is improved in a non-conventional manner. The system uses conventional networking, conventional database storage, and AI modeling (which is treated as a mental process/mathematical concept in this context) to achieve its goal. In conclusion, the Applicant’s arguments highlight the commercial benefits and efficiency of their automated HR system. However, as the Claims 1-4, 6, 8-11, 13 and 15-16 use conventional technology to perform an abstract idea, they do not provide an inventive concept and remain patent-ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 101
11. 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.
12. Claims 1-4, 6, 8-11, 13 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, 6, 8-11, 13 and 15-16 are each focused to a statutory category namely a “apparatus” or a “system” (Claims 1-4, 6 and 15) and a “method” or a “process” (Claims 8-11, 13 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);
“provide 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).
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”.
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 practice.
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”, “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, 6, 8-11, 13 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 (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)). 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).
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, 6, 8-11, 13 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 (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 recommendation to 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.
The additional element 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, 6, 9-11, 13 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, 6, 9, 11, 13 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, 6, 8-11, 13 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, 6, 8-11, 13 and 15-16 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 103
13. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
14. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
15. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
16. Claims 1-2, 8-9, 11 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2016/0379170 A1) hereinafter Pande, in view of US PG Pub (US 2021/0264371 A1) to Polli, and in further view of US PG Pub (US 2022/0004966 A1) to Essafi.
Regarding Independent Claim 1, Pande system for recommending individuals for open roles teaches the following:
- 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”.) to:
Moreover, Pande system for recommending individuals for open roles does not explicitly disclose, but Polli in the analogous art for recommending individuals for open roles does disclose the following:
- 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.)
- generate the recommendation of individuals (see at least Polli: ¶ [0124] & ¶ [0129-0131].) 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 at least Polli: ¶ [0013] & ¶ [0085] & ¶ [0122-0124]. Polli teaches that the fit score for a candidate may be an aggregate of the scores of the candidate on the neuroscience-based tasks. A fit score can range from 0-100%, and can be used to predict the likelihood that a candidate would be suitable for a specific position or career industry. By comparing the traits of the candidates with the traits of employees (e.g., top employees) in the company, an end user (e.g., a recruiter or human resource personnel) can determine whether a candidate is suitable for hiring to fill a specific job position. The candidate traits may be compared across multiple positions to determine which position(s), if any, are suitable for the candidates based on one or more employee models. There is a need for systems and methods that can be used by companies to (1) 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 candidate may be determined to be less suitable for the target position when the point lies in the first region, and the candidate may be determined to be more suitable for the target position when the point lies in the second region. A suitability of the candidate for the target position may be determined to decrease as a distance of the point from the decision boundary into the first region increases. Conversely, a suitability of the candidate for the target position may be determined to increase as a distance of the point from the decision boundary into the second region increases.)
- provide the recommendation to the at least one computing device (see at least Polli: ¶ [abstract] & ¶ [0015] & ¶ [0124] & Fig. 1. Polli notes that the systems and methods may employ an array of neuroscience-based tests to assess a user's career propensities, after which the systems and methods can provide career recommendations to the user or report on employment suitability of the user to a company, and data structures, such as a role directed graph with nodes and links. Polli at ¶ [0124]: 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.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Pande system for recommending individuals for open roles with the aforementioned teachings of: wherein the at least one analytics engine is configurable to: one or more computing devices that communicate over a network with the system, at least one computing device comprising a graphical user interface for providing data to the system and outputting data to a user; a server configured to; communicate with the one or more computing devices; determine an organizational need based at least on the organization data; 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; 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; and 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; and provide the recommendation to the at least one computing device, and in view of Polli, in order for the systems of Polli to be used by companies and different entities to: (1) 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 (see Polli: ¶ [0003]). Moreover, a user may use the sourcing models to identify candidates who are most similar to a target group of individuals. 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 (see Polli: ¶ [0169]).
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Polli, the results of the combination were predictable.
Moreover, Pande / Polli system for recommending individuals for open roles does not explicitly disclose, but Essafi in the analogous art for recommending individuals for open roles does disclose the following:
- 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”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Pande / Polli system for recommending individuals for open roles with the aforementioned teachings of: 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, and in further view of Essafi, wherein in the corporate field, a common problem is how to objectively determine the best fit (e.g., best employee,) for a role having a predefined target competency profile. A role can include a job position, a learning pathway or academic program in the context of education. Moreover, a pre-defined set of competencies (or assessment items) can be used to assess the performances of learners or respondents associated with different roles. For example, in companies or organizations, a fixed set of competencies, referred to as a competency framework, can be used to assess the performance of employees across different departments (see at least Essafi: ¶ [0285-0287].)
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Essafi, the results of the combination were predictable.
Regarding Independent Claim 8, Pande method for recommending individuals for open roles teaches the following:
- 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.):
Moreover, Pande method for recommending individuals for open roles does not explicitly disclose, but Polli in the analogous art for recommending individuals for open roles does disclose the following:
- determining, using the at least one analytics engine (see at least Polli: Fig. 2 & ¶ [0114]. Polli notes analytics engine 114 shown in Fig. 2.), 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).);
- determining, using the at least one analytics engine (see at least Polli: Fig. 2 & ¶ [0114]. Polli notes analytics engine 114 shown in Fig. 2.), 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].)
- analyzing, using the at least one analytics engine (see at least Polli: Fig. 2 & ¶ [0114]. Polli notes analytics engine 114 shown in Fig. 2.), 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.)
- generating, using the at least one analytics engine (see at least Polli: Fig. 2 & ¶ [0114]. Polli notes analytics engine 114 shown in Fig. 2.), the recommendation of individuals (see at least Polli: ¶ [0124] & ¶ [0129-0131].) 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 at least Polli: ¶ [0013] & ¶ [0085] & ¶ [0122-0124]. Polli teaches that the fit score for a candidate may be an aggregate of the scores of the candidate on the neuroscience-based tasks. A fit score can range from 0-100%, and can be used to predict the likelihood that a candidate would be suitable for a specific position or career industry. By comparing the traits of the candidates with the traits of employees (e.g., top employees) in the company, an end user (e.g., a recruiter or human resource personnel) can determine whether a candidate is suitable for hiring to fill a specific job position. The candidate traits may be compared across multiple positions to determine which position(s), if any, are suitable for the candidates based on one or more employee models. There is a need for systems and methods that can be used by companies to (1) 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 candidate may be determined to be less suitable for the target position when the point lies in the first region, and the candidate may be determined to be more suitable for the target position when the point lies in the second region. A suitability of the candidate for the target position may be determined to decrease as a distance of the point from the decision boundary into the first region increases. Conversely, a suitability of the candidate for the target position may be determined to increase as a distance of the point from the decision boundary into the second region increases.);
- generating the recommendation of individuals (see at least Polli: ¶ [0124] & ¶ [0129-0131].) based on the analysis of the organizational need (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.) and the set of one or more individuals (see at least Polli: ¶ [0013] & ¶ [0085] & ¶ [0122-0124]. Polli teaches that the fit score for a candidate may be an aggregate of the scores of the candidate on the neuroscience-based tasks. A fit score can range from 0-100%, and can be used to predict the likelihood that a candidate would be suitable for a specific position or career industry. By comparing the traits of the candidates with the traits of employees (e.g., top employees) in the company, an end user (e.g., a recruiter or human resource personnel) can determine whether a candidate is suitable for hiring to fill a specific job position. The candidate traits may be compared across multiple positions to determine which position(s), if any, are suitable for the candidates based on one or more employee models. There is a need for systems and methods that can be used by companies to (1) 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 candidate may be determined to be less suitable for the target position when the point lies in the first region, and the candidate may be determined to be more suitable for the target position when the point lies in the second region. A suitability of the candidate for the target position may be determined to decrease as a distance of the point from the decision boundary into the first region increases. Conversely, a suitability of the candidate for the target position may be determined to increase as a distance of the point from the decision boundary into the second region increases.)
- providing the recommendation to the at least one computing device (see at least Polli: ¶ [abstract] & ¶ [0015] & ¶ [0124] & Fig. 1. Polli notes that the systems and methods may employ an array of neuroscience-based tests to assess a user's career propensities, after which the systems and methods can provide career recommendations to the user or report on employment suitability of the user to a company, and data structures, such as a role directed graph with nodes and links. Polli at ¶ [0124]: 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.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Pande method for recommending individuals for open roles with the aforementioned teachings of: determining an organizational need based at least on the organization data; 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; 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; 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; generating the recommendation of individuals based on the analysis of the organizational need and set of one or more individuals and providing the recommendation to the at least one computing device, and in view of Polli, in order for the systems of Polli to be used by companies and different entities to: (1) 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 (see Polli: ¶ [0003]). Moreover, a user may use the sourcing models to identify candidates who are most similar to a target group of individuals. 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 (see Polli: ¶ [0169]).
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Polli, the results of the combination were predictable.
Moreover, Pande / Polli system for recommending individuals for open roles does not explicitly disclose, but Essafi in the analogous art for recommending individuals for open roles does disclose the following:
- determining 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.)
- generating 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”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Pande / Polli system for recommending individuals for open roles with the aforementioned teachings of: determining a second set of individuals having a competency gap that is less than a pre-determined threshold and generating a pathway recommendation for the second set of individuals to meet the competency gap, and in further view of Essafi, wherein in the corporate field, a common problem is how to objectively determine the best fit (e.g., best employee,) for a role having a predefined target competency profile. A role can include a job position, a learning pathway or academic program in the context of education. Moreover, a pre-defined set of competencies (or assessment items) can be used to assess the performances of learners or respondents associated with different roles. For example, in companies or organizations, a fixed set of competencies, referred to as a competency framework, can be used to assess the performance of employees across different departments (see at least Essafi: ¶ [0285-0287].)
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Essafi, the results of the combination were predictable.
Regarding Dependent Claims 2 and 9, Pande / Polli / Essafi system / method for recommending individuals for open roles teaches the limitations of Independent Claims 1 and 8 above, and Pande further teaches the system / method for recommending individuals for open roles comprising:
- wherein the at least one analytics engine (see at least Pande: Fig. 1 & ¶ [abstract]. Pande teaches that “an analytics engine configured to retrieve a score from the plurality of scores for the at least one parameter identified within the career profile.”) comprises a trained model (see at least Pande: ¶ [0158] & ¶ [0254] & Fig. 3. Pande notes that skills from the user database can serve as 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. See also Pande at ¶ [0254]: “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.”) that is trained using at least one of: personal profile of an individual, including role (see at least Pande: ¶ [0094-0096]. Pande teaches that all possible parameters of user profile including education degree, college, job role, experience, company name, can be applied as filters to determine the relevant user cluster.), interests (see at least Pande: ¶ [0194] & ¶ [0256]. Pande teaches an analysis of what resume is projecting as users career interest, vs. what linkedin is showing as career interest, based on careerfit match of both. See also Pande ¶ [0256]: “interest 1” and “interest 2”.), background education (see at least Pande: ¶ [0173-0176]. Pande teaches in the next steps gaps in skills, trajectory, education, etc that user should target to achieve career paths are illustrated. Gaps are showcased on the following dimensions: such as “educational background”.), competencies (see at least Pande: ¶ [0184] & ¶ [0226-0230]. Pande notes that 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.), competency gaps (see at least Pande: ¶ [0173-0176]. Pande teaches in the next steps gaps in skills, trajectory, education, etc that user should target to achieve career paths are illustrated. Gaps are showcased on the following dimensions: such as “competency levels”.); information from third parties including universities, the information indicating what programs lead into certain skills; internet sources using semantic analysis; and information pertaining to skill gaps at industry level (see at least Pande: ¶ [0173-0177] & ¶ [0186-0187]. Pande refers to information pertaining to skill gaps at industry level shown at ¶ [0173-0177].).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Pande / Polli / Essafi system / method for recommending individuals for open roles with the aforementioned teachings of: wherein the at least one analytics engine comprises a trained model that is trained using at least one of: personal profile of an individual, including role, interests, background education, competencies, competency gaps; information from third parties including universities, the information indicating what programs lead into certain skills; internet sources using semantic analysis; and information pertaining to skill gaps at industry level, and in further view of Pande, whereby A Career Coach possesses experience having dealt with a variety of other students and professionals, know-how to read from a profile/resume, understand personality issues and individual preferences to create a career roadmap including developmental interventions. Using intelligent algorithms, predictive models, context analysis using machine learning and natural language processing (see at least Pande: ¶ [0018]). The system in Pande is designed to be machine learning so that every new user profile that comes into the system improves all data sets, benchmarking, algorithms (see at least Pande: ¶ [0254]).
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Pande, the results of the combination were predictable.
Regarding Dependent Claim 11, Pande / Polli / Essafi method for recommending individuals for open roles teaches the limitations of Claims 8-9 above, and Pande further teaches the method for recommending individuals for open roles comprising:
- 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 adapted to recommend individuals for roles based on characteristics pertaining to the individual (see at least Pande: ¶ [0118] & ¶ [0166].), and historical information pertaining to others (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).”) that followed similar paths or developed similar competencies (see at least Pande: ¶ [0077] & ¶ [0162-0163]. Pande notes that each of the parameters above is also assigned a weight based on importance of parameter for user profile, like for example for students quantification weight may be lower than for a sales professional. Again benchmarking of similar user profiles is done to determine what level is appropriate for each group. See also Pande noting “similar desired roles, or companies” at¶ [0118-0119] and “similarities scores” at ¶ [0230-0236].).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Pande / Polli / Essafi method for recommending individuals for open roles with the aforementioned teachings of: wherein the at least one analytics engine comprises a trained model that is trained using at least one of: wherein the probabilistic model is adapted to recommend individuals for roles based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies, and in further view of Pande, whereby a Career Coach possesses experience having dealt with a variety of other students and professionals, know-how to read from a profile/resume, understand personality issues and individual preferences to create a career roadmap including developmental interventions. Using intelligent algorithms, predictive models, context analysis using machine learning and natural language processing (see at least Pande: ¶ [0018]). The system in Pande is designed to be machine learning so that every new user profile that comes into the system improves all data sets, benchmarking, algorithms (see at least Pande: ¶ [0254]).
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Pande, the results of the combination were predictable.
Regarding Dependent Claims 15-16, Pande / Polli / Essafi system / method for recommending individuals for open roles teaches the limitations of Independent Claims 1 and 8 above, and Pande further teaches the system / method for recommending individuals for open roles comprising:
- wherein the at least one analytics engine (see at least Pande: Fig. 1 noting “analytics engine”) comprises at trained model that is trained (see at least Pande: Fig. 3 & ¶ [0158] & ¶ [0254]. Pande noting a training model to “serve as 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.”) using organizational competencies, including needs versus competencies of current personnel (see at least Pande: ¶ [0270] & ¶ [0276]. Pande notes that 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 ¶ [0231]: “comparing career paths of the at least one employee and the at least one candidate to assist the recruiter in selection of the at least one candidate for the at least one job.” See also Pande at Skills/Competencies at ¶ [0223-0226].)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Pande / Polli / Essafi system / method for recommending individuals for open roles with the aforementioned teachings of: wherein the at least one analytics engine comprises a trained model that is trained using at least one of: wherein the probabilistic model is adapted to recommend individuals for roles based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies, and in further view of Pande, whereby a Career Coach possesses experience having dealt with a variety of other students and professionals, know-how to read from a profile/resume, understand personality issues and individual preferences to create a career roadmap including developmental interventions. Using intelligent algorithms, predictive models, context analysis using machine learning and natural language processing (see at least Pande: ¶ [0018]). The system in Pande is designed to be machine learning so that every new user profile that comes into the system improves all data sets, benchmarking, algorithms (see at least Pande: ¶ [0254]).
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Pande, the results of the combination were predictable.
17. Claims 3-4 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2016/0379170 A1) hereinafter Pande, in view of US PG Pub (US 2021/0264371 A1) hereinafter Polli, et. al., in view of US PG Pub (US 2022/0004966 A1) hereinafter Essafi, et. al., and in further view of US PG Pub (US 2020/0302564 A1) hereinafter Varga, et. al.
Regarding Dependent Claims 3 and 10, Pande / Polli / Essafi system method for recommending individuals for open roles does not explicitly disclose, but Varga in the analogous art for recommending individuals for open roles does disclose the following:
- wherein the trained model (see at least Varga: ¶ [0003] & ¶ [0072] & ¶ [0088-0090]. Varga notes that the user profile module 107 and/or may apply statistical machine learning to the grammar rules to determine the meaning behind the collected user data 130. Once the user profile module 105 has analyzed and understood the user data 130, portions of the user data 130 can be characterized using keywords or tags applied to the user data 130 and the user-defined parameters to understand the preferences, habits, activities, geolocation and interests of the user and may generate a plurality of user parameter values corresponding to the plurality of parameters of the set of user data set and user-defined parameters. Varga also created a statistical model comprising a set of predictors or known features and applying one or more probabilistic techniques to predict a likely outcome using the predictive model.) comprises at least one of: a regression model (see at least Varga: ¶ [0089] & ¶ [0091-0092]. Varga teaches that machine learning techniques that may use semi-supervised learning may include classification, regression and prediction models. A support-vector machine (SVM) model algorithm may be used. SVM models may use classification and/or regression analysis to create correlations between careers, vocations and educational opportunities and weights for the different types/values of gathered and stored by the knowledge base 117.), or a stochastic model
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Pande / Polli / Essafi system / method for recommending individuals for open roles with the aforementioned teachings of: wherein the trained artificial intelligence statistical model comprises at least one of: a regression model, or a stochastic model, and in further view of Varga, whereby the knowledge base may determine which vocational action to present to each through the use of one or more machine learning techniques. The machine learning techniques may be used to analyze user profiles, user-data, vocational data and user-defined parameters to arrive at the vocational action that will be presented to the user and may include supervised learning, unsupervised learning and/or semi-supervised learning techniques (see at least Varga: ¶ [0088]). During the training phase, the knowledge base may learn the correct outputs by analyzing and describing well known historical data and information that may be stored by the knowledge base. Examples of data modeling include classification, regression, prediction and gradient boosting (see at least Varga: ¶ [0089]).
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Varga, the results of the combination were predictable.
Regarding Dependent Claim 4, Pande / Polli / Essafi / Varga system for recommending individuals for open roles teaches the limitations of Claims 1-3 above, and Pande further teaches the system for recommending individuals for open roles comprising:
- 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 adapted to recommend individuals for roles based on characteristics pertaining to the individual (see at least Pande: ¶ [0118] & ¶ [0166].), and historical information pertaining to others (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).”) that followed similar paths or developed similar competencies (see at least Pande: ¶ [0077] & ¶ [0162-0163]. Pande notes that each of the parameters above is also assigned a weight based on importance of parameter for user profile, like for example for students quantification weight may be lower than for a sales professional. Again benchmarking of similar user profiles is done to determine what level is appropriate for each group. See also Pande noting “similar desired roles, or companies” at ¶ [0118-0119] and “similarities scores” at ¶ [0230-0236].).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Pande / Polli / Essafi / Varga system for recommending individuals for open roles with the aforementioned teachings of: wherein the at least one analytics engine comprises a trained model that is trained using at least one of: wherein the probabilistic model is adapted to recommend individuals for roles based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies, and in further view of Pande, whereby a Career Coach possesses experience having dealt with a variety of other students and professionals, know-how to read from a profile/resume, understand personality issues and individual preferences to create a career roadmap including developmental interventions. Using intelligent algorithms, predictive models, context analysis using machine learning and natural language processing (see at least Pande: ¶ [0018]). The system in Pande is designed to be machine learning so that every new user profile that comes into the system improves all data sets, benchmarking, algorithms (see at least Pande: ¶ [0254]).
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Pande, the results of the combination were predictable.
18. Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2016/0379170 A1) hereinafter Pande, in view of US PG Pub (US 2021/0264371 A1) hereinafter Polli, et. al., in view of US PG Pub (US 2022/0004966 A1) hereinafter Essafi, et. al., and in further view of US PG Pub (US 2021/0089603 A1) to Abbasi Moghaddam.
Regarding Dependent Claims 6 and 13, Pande / Polli / Essafi system / method for recommending individuals for open roles does not explicitly disclose, but Abbasi Moghaddam further teaches the system / method for recommending individuals for open roles does disclose the following:
- 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.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the teachings of Pande / Polli / Essafi system / method for recommending individuals for open roles with the aforementioned teachings of: wherein the server is further configured to output a confidence score indicating a confidence that a 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 one or more individuals and the needs of the organization, and in view of Abbasi Moghaddam, in order for interactions to be inputted into one or more additional machine learning models that generate recommendations and/or other output related to the users and jobs. In turn, machine learning models are able to execute with higher performance and/or lower latency, computational overhead, and/or memory consumption than a conventional technique that applies a machine learning model to the full set of untransformed features (see at least Abbasi Moghaddam: ¶ [0057].)
Further, the claimed invention is merely a combination of old elements in a similar field for recommending individuals for open roles, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Abbasi Moghaddam, the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Patents and/or US PG Publication Documents
US PG Pub (US 2015/0135043 A1) – “Method and System for Generating and Modifying Electronic Organizational Charts”;
US PG Pub (US 2019/0340945 A1) – “Automatic Generation and Personalization of Learning Paths”;
US PG Pub (US 2020/0051178 A1) – “Dynamic Modification of User Skill Profile Using Determined Crowdsourced Social Presence”.
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.
Foreign Patent Documents
WO 2015/136555 A2 – Career Analytics Platform
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625