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 the Application
2. Claims 1-20 have been examined in this application. This communication is the first action on the merits.
IDS Statements
3. The 1 Information Disclosure Statement (IDS) filed on 03/14/2026 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Claim Rejections - 35 USC § 112
4. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
5. Claims 4-7, 12-15 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
(A). Dependent Claims 4, 12 and 20 recite the following limitations: “wherein the personality data and the PsyCap pulse data are associated with an employee, the PsyCap result includes a PsyCap score of the employee, and providing the recommendation or the intervention based on the PsyCap result includes recommending an opportunity to the employee based on the PsyCap score of the employee.” There appears to be a lack of antecedent basis with respect to the 1st instance or 1st mentioning of “the PsyCap result” in Dependent Claims 4, 12 and 20 when referring back to the previous limitations of Independent Claims 1, 9 and 17 wherein there is no previous mentioning of “a PsyCap result”, these claims in Independent Claims 1, 9 and 17 only refer to “an assessment result”. For the purposes of examination, Examiner suggests to Applicant to amend the limitations of Dependent Claims 4, 12 and 20 to read as follows: “wherein the personality data and the PsyCap pulse data are associated with an employee, [[a PsyCap result includes a PsyCap score of the employee, and providing the recommendation or the intervention based on the PsyCap result includes recommending an opportunity to the employee based on the PsyCap score of the employee.”
(B). Dependent Claims 5 and 13 recite the following limitations: “wherein the personality data and the PsyCap pulse data are associated with employees in a team, the PsyCap result includes an aggregated PsyCap score of each employee in the team, and providing the recommendation or the intervention based on the PsyCap result includes recommending a team learning or training if the aggregated PsyCap score is lower than a previously stored score.” There appears to be a lack of antecedent basis with respect to the 1st instance or 1st mentioning of “the PsyCap result” in Dependent Claims 5 and 13 when referring back to the previous limitations of Independent Claims 1 and 9 wherein there is no previous mentioning of “a PsyCap result”, these claims in Independent Claims 1, 9 and 17 only refer to “an assessment result”. For the purposes of examination, Examiner suggests to Applicant to amend the limitations of Dependent Claims 5 and 13 to read as follows: “wherein the personality data and the PsyCap pulse data are associated with employees in a team, [[a PsyCap result includes an aggregated PsyCap score of each employee in the team, and providing the recommendation or the intervention based on the PsyCap result includes recommending a team learning or training if the aggregated PsyCap score is lower than a previously stored score.”
(C). Dependent Claims 6 and 14 recite the following limitations: “wherein the personality data and the PsyCap pulse data are associated with a team leader, the PsyCap result includes a PsyCap score of the team leader, and providing the recommendation or the intervention based on the PsyCap result includes: determining that the PsyCap score of the team leader is lower than a threshold; identifying a PsyCap driver from the one or more PsyCap drivers that cause low PsyCap score; and providing the recommendation or the intervention based on the identified PsyCap driver.” There appears to be a lack of antecedent basis with respect to the 1st instance or 1st mentioning of “the PsyCap result” in Dependent Claims 6 and 14 when referring back to the previous limitations of Independent Claims 1 and 9 wherein there is no previous mentioning of “a PsyCap result”, these claims in Independent Claims 1, 9 and 17 only refer to “an assessment result”. For the purposes of examination, Examiner suggests to Applicant to amend the limitations of Dependent Claims 6 and 14 to read as follows: “wherein the personality data and the PsyCap pulse data are associated with a team leader, [[a PsyCap result includes a PsyCap score of the team leader, and providing the recommendation or the intervention based on the PsyCap result includes: determining that the PsyCap score of the team leader is lower than a threshold; identifying a PsyCap driver from the one or more PsyCap drivers that cause low PsyCap score; and providing the recommendation or the intervention based on the identified PsyCap driver.”
(D). Dependent Claims 7 and 15 recite the following limitations: “wherein the personality data and the PsyCap pulse data are associated with employees in an organization, the PsyCap result includes a mean of organization employees' PsyCap scores, and providing the recommendation or the intervention based on the PsyCap result includes identifying one or more hidden boosters of motivation in the organization.” There appears to be a lack of antecedent basis with respect to the 1st instance or 1st mentioning of “the PsyCap result” in Dependent Claims 7 and 15 when referring back to the previous limitations of Independent Claims 1 and 9 wherein there is no previous mentioning of “a PsyCap result”, these claims in Independent Claims 1, 9 and 17 only refer to “an assessment result”. For the purposes of examination, Examiner suggests to Applicant to amend the limitations of Dependent Claims 7 and 15 to read as follows: “wherein the personality data and the PsyCap pulse data are associated with employees in an organization, [[a PsyCap result includes a mean of organization employees' PsyCap scores, and providing the recommendation or the intervention based on the PsyCap result includes identifying one or more hidden boosters of motivation in the organization.” Appropriate corrections are required.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-20 are focused to a statutory category namely, a “process” or a “method” (Claims 1-8), a “system” or an “apparatus” (Claims 9-16) and a “computer program product” or an “article of manufacture” (Claims 17-20).
Step 2A Prong One: Independent Claims 1, 9 and 17 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (see Independent Claim 9);
“” (see Independent Claim 9);
“” (see Independent Claim 17);
“obtaining psychological capital (PsyCap) driver data stored in a first data format, wherein the PsyCap driver data includes one or more PsyCap drivers” (see Independent Claims 1, 9 and 17);
“obtaining personality data stored in a second data format” (see Independent Claims 1, 9 and 17);
“obtaining PsyCap pulse data in a first format by providing that is superimposed over a in which a link is provided that redirects a user to provide the PsyCap pulse data” (see Independent Claims 1, 9 and 17);
“obtaining event data in a third data format, wherein the event data includes one or more events” (see Independent Claims 1, 9 and 17);
“accessing data from a , wherein the data includes at least one of growth opportunities, team assignments, projects, work activities, goals, or accomplishments” (see Independent Claims 1, 9 and 17);
“converting the PsyCap driver data, the personality data, the PsyCap pulse data, and the event data into a standardized format” (see Independent Claims 1, 9 and 17);
“processing the PsyCap driver data, the personality data, the PsyCap pulse data, the event data, and the data through a model to generate an assessment result” (see Independent Claims 1, 9 and 17);
“providing recommendation or intervention based on the assessment result includes a link to an opportunity managed by a third application” (see Independent Claims 1, 9 and 17).
Here, for Independent Claims 1, 9 and 17, these steps represent a computer-implemented method for employee performance and psychological analysis, which, under 35 U.S.C. § 101 Step 2A Prong 1, is directed to the abstract ideas of organizing human activity (personnel management) and methods of organizing human activity/mental processes (collecting/analyzing psychological and performance data), aimed at improving employee productivity. Moreover, the described steps represent an automated system for employee evaluation and career management, primarily directed to the abstract idea of "collecting, analyzing, and acting on employee data to make career recommendations" (a method of organizing human activity/business process).
For example; "Obtaining psychological capital (PsyCap) driver data...": This is a method of organizing human activity (collecting data on employee attitudes/emotions). It is a mental process or subjective gathering of data. "Obtaining personality data...": Similar to above, this is collecting personal behavioral data (organizing human activity). "Obtaining PsyCap pulse data... by providing a first GUI element... superimposed...": This is a method of organizing human activity via a user interface. Collecting data through UI interaction is a standard computer functionality used to gather human input. "Obtaining event data in a third data format...": This is a method of organizing human activity by collecting, formatting, and storing behavioral data (events). "Accessing data from a Human Resource Management System (HRMS)...": This is a method of organizing human activity by managing employee data (performance metrics).
Each of these steps represent "collecting information" and "gathering data," which are fundamental to human activity. Specifically, gathering and storing data from various sources (databases, applications) falls under "method of organizing human activity".
"Converting the... data into a standardized format...": Data manipulation, formatting, and normalization. This is a "mental process" analogous to a human classifying or organizing information into a standardized format for easier analysis.
"Processing... through a machine learning predictive model...": While ML can be technical, here it is used as a mental process tool (analyzing data) to generate an abstract output (assessment result). This is an "abstract idea" and a "mental process." It is the algorithmic manipulation of data to generate a result, similar to an HR professional reviewing files to form a conclusion.
"Providing recommendation or intervention... by providing a third GUI element...": This is a method of organizing human activity (delivering feedback/coaching) through a user interface. Moreover, this represents the "business method" aspect. Displaying a recommendation to a user for career opportunities is a method of managing human activities (performance management/staffing)
The claim steps, when read as a whole, do not improve the computer's functionality itself, but rather use the computer as a tool to automate conventional HR analysis and coaching, thus fitting within the "method of organizing human activity" abstract category.
Methods of Organizing Human Activities: Specifically, "inter-enterprise management," "managing human resources," and "tracking or improving employee performance" (by monitoring PsyCap and linking it to HR activities).
Fundamental Economic Practices: "Managing risk or peer-to-peer behavior" (by analyzing personality/events for recommendations).
Mental Processes: "Formulating a result" (the assessment) using a, potentially, human-like, albeit automated, evaluation process.
Therefore, other than reciting (e.g., “Human Resource Management System (HRMS)” & “first GUI element” & “second GUI element” & “third GUI element” & “software application” & “first database” & “second database” & “one or more computers” & “one or more processors”, etc…), nothing in the claim elements precludes the steps from being performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (4) fundamental economic principles or practices.
Therefore, at step 2a prong 1, Yes, Claims 1-20 recites 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 Claim 1 recites additional elements directed to: (e.g., “Human Resource Management System (HRMS)” & “first GUI element” & “second GUI element” & “third GUI element” & “software application” & “first database” & “second database”). Independent Claim 9 recites additional elements directed to: (e.g., “Human Resource Management System (HRMS)” & “first GUI element” & “second GUI element” & “third GUI element” & “software application” & “first database” & “second database” & “one or more computers” & “one or more processors”). Independent Claim 17 recites additional elements directed to: (e.g., “Human Resource Management System (HRMS)” & “first GUI element” & “second GUI element” & “third GUI element” & “software application” & “first database” & “second database” & “one or more processors”). 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).
Independent Claims 1, 9 and 17: With respect to reliance on (e.g., “machine learning predictive model”) as an additional element shown in Independent Claims 1, 9 and 17 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, this additional element does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to the following: (1) 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)) or (2) limiting a particular field of use or technological environment pertaining to organizing human activity/psychological assessment for evaluating employees through a subjective assessment process (evaluating employee psychology and performance) environment (see MPEP § 2106.05 (h)). Moreover, with respect to Independent Claims 1, 9 and 17, certain/particular limitations shown recite (1) mere data gathering (e.g., “obtaining psychological capital (PsyCap) driver data stored in a first database in a first data format, wherein the PsyCap driver data includes one or more PsyCap drivers” & “obtaining personality data stored in a second database in a second data format” & “obtaining PsyCap pulse data in a first format by providing a first graphical user interface (GUI) element that is superimposed over a second GUI element from a software application in which a link is provided in the first GUI element that redirects a user to provide the PsyCap pulse data” & “obtaining event data in a third data format, wherein the event data includes one or more events”) in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)).
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 1-20 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 Claim 1 recites additional elements directed to: (e.g., “Human Resource Management System (HRMS)” & “first GUI element” & “second GUI element” & “third GUI element” & “software application” & “first database” & “second database”). Independent Claim 9 recites additional elements directed to: (e.g., “Human Resource Management System (HRMS)” & “first GUI element” & “second GUI element” & “third GUI element” & “software application” & “first database” & “second database” & “one or more computers” & “one or more processors”). Independent Claim 17 recites additional elements directed to: (e.g., “Human Resource Management System (HRMS)” & “first GUI element” & “second GUI element” & “third GUI element” & “software application” & “first database” & “second database” & “one or more processors”). 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 (f) and MPEP § 2106.05 (h). 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 (e.g., see at Applicant’s Specification ¶ [0024]: “Indeed, the employee management system 102 may be any computer or processing device such as, a blade server, general-purpose personal computer (PC), Mac®, workstation, UNIX-based workstation, or any other suitable device. In other words, the present disclosure contemplates computers other than general-purpose computers, as well as computers without conventional operating systems.”).
Independent Claims 1, 9 and 17: With respect to reliance on (e.g., “machine learning predictive model”) as an additional element shown in Independent Claims 1, 9 and 17 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, this additional element does not amount to significantly more than the judicial exceptions under step 2B due to the following: (1) 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)) or (2) limiting a particular field of use or technological environment pertaining to organizing human activity/psychological assessment for evaluating employees through a subjective assessment process (evaluating employee psychology and performance) environment (see MPEP § 2106.05 (h)). Moreover, with respect to Independent Claims 1, 9 and 17, certain/particular limitations shown recite (1) mere data gathering (e.g., “obtaining psychological capital (PsyCap) driver data stored in a first database in a first data format, wherein the PsyCap driver data includes one or more PsyCap drivers” & “obtaining personality data stored in a second database in a second data format” & “obtaining PsyCap pulse data in a first format by providing a first graphical user interface (GUI) element that is superimposed over a second GUI element from a software application in which a link is provided in the first GUI element that redirects a user to provide the PsyCap pulse data” & “obtaining event data in a third data format, wherein the event data includes one or more events”) in 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, 9 and 17 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).
The additional elements of “machine learning” or “machine learning model” in Claims 1, 9 and 17 do not amount to significantly more than the judicial exceptions under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
See for example; US PG Pub (US 2024/0321453 A1) hereinafter Funahashi, et. al. Funahashi notes at ¶ [0086]: “A commonly used method may be employed as the machine learning method. For example, a method such as decision tree, random forest, eXtreme gradient boosting (XGBoost), support vector machine (SBM), or neural network may be employed to calculate the parameters of the prediction models and thereby create the health management indicator prediction models.” At ¶ [0196]: For each of a plurality of subjects, the calculation unit 404 acquires a data set including the condition indicators, the motivation indicators, the work information, and the health management indicators. Then, the calculation unit 404 creates the prediction models 414 through analysis with a method such as multivariate analysis, Bayesian network, or machine learning with the condition indicators, the motivation indicators, and the work information as the explanatory variables and the health management indicators as the objective variables. See for example; Foreign Patent Application (EP 4207016 A1) hereinafter Ahmadi, et. al. Ahmadi at ¶ [0021]: “The machine learning algorithms determine correlations between the communication data and the surveys using customized algorithms derived from known machine learning algorithms. Labels of data to input into the machine learning algorithms are derived from onboarding surveys.” Correlations from the survey data may be determined by extracting known COPSOQ survey questions and correlations known in the art.
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 2-8, 10-16 and 18-20 recite substantially the same or similar additional elements as addressed above and when considered individually and as an ordered combination (as a whole) with these limitations recite the same abstract idea(s) as shown in Independent Claims 1, 9 and 17 along with further steps/details pertaining to “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (4) fundamental economic principles or practices.
Dependent Claims 2-8, 10-16 and 18-20 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, 9 and 17. 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 1-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-20 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Examining Claims with Respect to Prior Art
8. Independent Claims 1, 9 and 17 have overcome the prior art rejections. However, Claims 4-7, 12-15 and 20 remain still rejected over 35 U.S.C. § 112 (b) and also Claims 1-20 are rejected under 35 U.S.C. § 101.
For Independent Claims 1, 9 and 17, there is no disclosure in the existing prior art or any new art that either teaches and/or discloses the sequence operation of each of these features either individually or in combination relating to:
obtaining PsyCap pulse data in a first format by providing a first graphical user interface (GUI) element that is superimposed over a second GUI element from a software application in which a link is provided in the first GUI element that redirects a user to provide the PsyCap pulse data;
converting the PsyCap driver data, the personality data, the PsyCap pulse data, and the event data into a standardized format;
processing the PsyCap driver data, the personality data, the PsyCap pulse data, the event data, and the data from the HRMS through a machine learning predictive model to generate an assessment result; and
providing recommendation or intervention based on the assessment result by providing a third GUI element, wherein the third GUI element includes a link to an opportunity managed by a third application.
The closest prior arts are as follows:
#1) US PG Pub (US 2024/0378519 A1) - Computer-Implemented Method and System for Assessing Compatibility of Human Resources within an Organization hereinafter Christine Drolet, et. al.;
#2) Foreign Patent Application (KR20180131755 A) – Service System for Reinforcing Positive Psychological Capital, hereinafter Kim, et. al.
#3) US PG Pub (US 2023/0058835 A1) – Systems and Methods for Media-Based Personality Analysis, hereinafter Ahluwalia, et. al.;
#4) US PG Pub (US 2024/0321453 A1) - Information Processing Apparatus, Information Processing System, and Information Processing Method, hereinafter Funahashi, et. al.;
#5) US PG Pub (US 2020/0057976 A1) – Organization Analysis Platform for Workforce Recommendations, Prakash, et. al.
Regarding the Christine Drolet reference, Christine Drolet teaches or suggests the sequence of operations comprising the following:
Christine Drolet notes at ¶ [0074] an exemplary result portal is shown, for a resource named Jason Smith. In this example, the general cognitive ability 132 and the personality traits 134 have been classified based on the Stanine standard. The result portal displays the score obtained for general cognitive ability (in this case, the score is 1), as well as the score obtained for several fields of the personality traits.
Christine Drolet teaches at ¶ [0106] that fitting score determined for each dimension of the personality traits, the AI recommender system 70 can provide an indication of strengths and challenges 450 of two resources within the same team, at a same hierarchical level.
Christine Drolet teaches at ¶ [0157] that the user interface 60 of the virtual coach platform will provide means for users to provide feedback or field data 600 on the output generated by the machine learning model-based AI-recommender system. For example, the one or more machine learning models of AI recommender system, having been trained entirely on synthetic data, may output competencies, alerts and recommendations that end users (such as team managers and/or HR manager) will not entirely agree with. The graphical user interface of the virtual coach platform can thus include means for the manager to provide feedback on the predictions and/or recommendations, such as boxes to checks, priority tag, usefulness/relevancy of suggestions, comments that can be entered, etc. The graphical user interface of the virtual coach platform can also include means for the resource to provide feedback, such as satisfaction following onboarding process.
However, Christine Drolet, et. al. specifically, does not teach or suggest the sequence of operations comprising:
obtaining PsyCap pulse data in a first format by providing a first graphical user interface (GUI) element that is superimposed over a second GUI element from a software application in which a link is provided in the first GUI element that redirects a user to provide the PsyCap pulse data;
converting the PsyCap driver data, the personality data, the PsyCap pulse data, and the event data into a standardized format;
processing the PsyCap driver data, the personality data, the PsyCap pulse data, the event data, and the data from the HRMS through a machine learning predictive model to generate an assessment result; and
providing recommendation or intervention based on the assessment result by providing a third GUI element, wherein the third GUI element includes a link to an opportunity managed by a third application.
Regarding the Kim reference, Kim teaches or suggests the sequence of operations comprising the following:
Kim teaches that Positive Psychological Capital (PsyCap) is a positive psychological capital needed for individuals and organizations, and is a resource that can realize their potential. In addition, positive psychological capital is the expanded capital of Human Capital, Social Capital, and Financial Capital, a psychological capital that enables the development and development of individual capabilities. By strengthening positive psychological capital, it can help achieve personal and organizational goals and achieve performance. The present invention provides an item measuring self-efficacy (self-efficacy), hope, optimism, and resiliency, which measure positive psychological capital, and provides a response to the question A service providing server for receiving and providing positive psychological capital measurement results;
A response to the positive psychological capital measurement item from the user is received and transmitted to the service providing server, and the positive psychological capital measurement result for the response from the service providing server and the reinforcement plan for strengthening the positive psychological capital according to the measurement result A user terminal provided with a user terminal.
However, Kim, et. al. specifically, does not teach or suggest the sequence of operations comprising:
obtaining PsyCap pulse data in a first format by providing a first graphical user interface (GUI) element that is superimposed over a second GUI element from a software application in which a link is provided in the first GUI element that redirects a user to provide the PsyCap pulse data;
converting the PsyCap driver data, the personality data, the PsyCap pulse data, and the event data into a standardized format;
processing the PsyCap driver data, the personality data, the PsyCap pulse data, the event data, and the data from the HRMS through a machine learning predictive model to generate an assessment result; and
providing recommendation or intervention based on the assessment result by providing a third GUI element, wherein the third GUI element includes a link to an opportunity managed by a third application.
Regarding the Ahluwalia reference, Ahluwalia teaches or suggests the sequence of operations comprising the following:
Ahluwalia teaches at ¶ [0067] that the psychology section 507 includes one or more graphs indicating the user's personality data or personality scores, such as the personality graph 401, the graph 451, or another graph indicating personality data or scores. The graphs may also be compared to graphs of a team, group, etc., that the user belongs to. The team section 509 includes an indication of one or more of the teams which the user belongs to. In some embodiments, interacting with an interface element that indicates one of the teams adjusts a graph in the psychology section 507 to overlay the team's personality score(s) on top of the user's personality score(s).
Ahluwalia teaches at ¶ [0132] that the personality factor determination system may use recommendations and peer reviews as part of a credibility assessment. The content of these narratives may be analyzed using NLP, which allows for decomposition of the narrative into a set of elements which can form a foundation for clustering methods and predictive models. Examiner notes that Ahluwalia does not explicitly teach or suggest how that the GUI element (e.g., whether it be a button, drop-down menu or some other element) includes or incorporate a software application in which a link (e.g., whether it be a URL link or a hyperlink) is provided in the first GUI element that redirects a user to provide the PsyCap pulse data. Furthermore, Ahluwalia does not explicitly teach or suggest specifically obtaining Psychological Capital data from a pulse survey/poll/questionnaire.
Therefore, Ahluwalia, et. al. specifically, in summary, does not teach or suggest the sequence of operations comprising:
obtaining PsyCap pulse data in a first format by providing a first graphical user interface (GUI) element that is superimposed over a second GUI element from a software application in which a link is provided in the first GUI element that redirects a user to provide the PsyCap pulse data;
converting the PsyCap driver data, the personality data, the PsyCap pulse data, and the event data into a standardized format;
processing the PsyCap driver data, the personality data, the PsyCap pulse data, the event data, and the data from the HRMS through a machine learning predictive model to generate an assessment result; and
providing recommendation or intervention based on the assessment result by providing a third GUI element, wherein the third GUI element includes a link to an opportunity managed by a third application.
Regarding the Funahashi reference, Funahashi teaches or suggests the sequence of operations comprising the following:
Funahashi shows at ¶ [0040] that the health management indicators may be indicators described in the guidelines for health investment management accounting set by the METI, such as indicators related to the progress of health investment measures, indicators related to changes in awareness and behavior of employees, and ultimate goal indicators for health-related issues, or may be other indicators recommended by the METI or ACOEM.
Funahashi teaches accessing data from a Human Resource Management System (HRMS), wherein the data from the HRMS includes at least one of growth opportunities, team assignments, projects, work activities, goals or accomplishments (see at least Funahashi: ¶ [0040-0042]. Funahashi notes that the hygiene factors are factors that bring dissatisfaction in work, such as company policies and management, working conditions, interpersonal relations, supervision, status, fair payment, job security, human resource system, and private life, for example. Ultimate goal indicators for health-related issues, or may be other indicators recommended by the METI or ACOEM. The improvement plan may be providing or referring to related information or service, such as the referral to an occupational physician, the referral to a specialist, work visualization, manager training, team building training. The motivator factors are factors that bring satisfaction in work, such as work fulfilment, sense of achievement in work, recognition for achievement, recognition from supervisors and colleagues, growth opportunities, and responsibility, for example.
Funahashi at ¶ [0053] teaches that the health management indicators are obtained by census survey or pulse survey, for example. The census survey includes many questions, which increases the accuracy of the survey. The census survey, however, takes places infrequently (e.g., once a year). Meanwhile, the pulse survey includes less questions and thus may be carried out more frequently (e.g., once a week or month). The pulse survey, however, obtains subjective evaluations alone, which reduces the accuracy of the survey. Funahashi at ¶ [0083] notes that the health management indicator prediction models are created with the condition indicators and the motivation indicators as the explanatory variables and the health management indicators as the objective variables. The health management indicator prediction models may be created with a method such as multivariate analysis, Bayesian network, or machine learning, for example.
Funahashi at ¶ [0196] notes that for each of a plurality of subjects, the calculation unit 404 acquires a data set including the condition indicators, the motivation indicators, the work information, and the health management indicators. Then, the calculation unit 404 creates the prediction models 414 through analysis with a method such as multivariate analysis, Bayesian network, or machine learning with the condition indicators, the motivation indicators, and the work information as the explanatory variables and the health management indicators as the objective variables.
However, Funahashi, et. al. specifically, does not teach or suggest the sequence of operations comprising:
obtaining PsyCap pulse data in a first format by providing a first graphical user interface (GUI) element that is superimposed over a second GUI element from a software application in which a link is provided in the first GUI element that redirects a user to provide the PsyCap pulse data;
converting the PsyCap driver data, the personality data, the PsyCap pulse data, and the event data into a standardized format;
processing the PsyCap driver data, the personality data, the PsyCap pulse data, the event data, and the data from the HRMS through a machine learning predictive model to generate an assessment result; and
providing recommendation or intervention based on the assessment result by providing a third GUI element, wherein the third GUI element includes a link to an opportunity managed by a third application.
Regarding the Prakash reference, Prakash teaches or suggests the sequence of operations comprising the following:
Prakash teaches that at ¶ [0039] that the organization analysis platform may use a predetermined format designed to convert text received from an organization data storage device, into particular strings of text that are standardized, within the organization analysis platform, for use with a variety of machine learning models or other types of analysis. At ¶ [0043]: The organization analysis platform may use a predetermined format designed to convert text received from a workforce psychology data storage device, into particular strings of text that are standardized, within the organization analysis platform, for use with a variety of machine learning models or other types of analysis. At ¶ [0051]: The organization analysis platform may use a predetermined format designed to convert text received from an industry trend data storage device into particular strings of text that are standardized, within the organization analysis platform, for use with a variety of machine learning models or other types of analysis.
Examiner notes that although the human resource system takes in psychology data and converts the data into a standardized format, the conversion does not incorporate specifically also PsyCap pulse data and PsyCap driver data into this process as well, since the conversion factors all of the elements of PsyCap driver data, the personality data, the PsyCap pulse data and the event data into a standardized format.
However, Prakash, et. al. specifically, does not teach or suggest the sequence of operations comprising:
obtaining PsyCap pulse data in a first format by providing a first graphical user interface (GUI) element that is superimposed over a second GUI element from a software application in which a link is provided in the first GUI element that redirects a user to provide the PsyCap pulse data;
converting the PsyCap driver data, the personality data, the PsyCap pulse data, and the event data into a standardized format;
processing the PsyCap driver data, the personality data, the PsyCap pulse data, the event data, and the data from the HRMS through a machine learning predictive model to generate an assessment result; and
providing recommendation or intervention based on the assessment result by providing a third GUI element, wherein the third GUI element includes a link to an opportunity managed by a third application.
Therefore, when taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor do the available art suggest or otherwise render obvious further modification of the evidence at hand. Such modification would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Patents and/or US PG Pub Documents
US PG Pub (US 2023/0058835 A1) – “Systems and Methods for Media-Based Personality Analysis”, hereinafter Ahluwalia, et. al.;
US PG Pub (US 2016/0203432 A1) – “Assessment System and Method”, hereinafter Shaw, et. al.;
US PG Pub (US 2024/0378519 A1) – “Computer-Implemented Method and System for Assessing Compatibility of Human Resources within an Organization”, hereinafter Christine Drolet, et. al.;
US PG Pub (US 2024/0119422 A1) – “Work Profile Alignment”, hereinafter Olson, et. al.;
US PG Pub (US 2024/0321453 A1) – “Information Processing Apparatus, Information Processing System, and Information Processing Method”, hereinafter Funahashi, et. al.;
US PG Pub (US 2020/0057976 A1) – “Organization Analysis Platform for Workforce Recommendations”, hereinafter Prakash, et. al.
US PG Pub (US 2024/0185991 A1) – “System and Method for Humanizing the Onboarding Buddy Recommendation Process”, hereinafter Ganesh, et. al.
Foreign Patent Documents
Foreign Patent Document (EP 4207016 A1) – “Predicting Employee Wellness Using Passively Collected Data”, hereinafter Ahmadi, et. al.;
Foreign Patent Document (KR 2018/0131755 A) – “Service System for Reinforcing Positive Psychological Capital”, hereinafter Kim, et. al.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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