Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
This Final Office Action is in response Applicant communication filled on 05/05/2026
Status of Claims
Claims 1,9,16 are independent and have been amended by Applicant.
Claims 1-20 are currently pending and have been rejected as follows.
Response to Amendments / Arguments
Applicant’s 05/05/2026 amendment necessitated the new grounds of rejection in this office action.
III. Response to Applicant’s subject matter eligibility arguments rebutting 101 rejection
SME argument 1: Remarks 05/05/2026 p.11 last ¶ - p.12 ¶1 cites Original Specification ¶ [0044], [0047] to argue the techniques integrate the abstract idea into a practical application since they recite a specific improvement to persona model training systems, where issue reporting and feedback functions work together to identify areas of improvement and determine adjustments to the persona model's training, to allegedly result in improved model performance for simulating group reactions to organizational changes. Specifically, it is argued that the issue reporting function analyzes outputs from the persona model to generate areas of improvement, such as where the model's responses are deficient or non-optimal. The feedback function interprets those identified areas of improvement to determine adjustments to the training of the persona model, such as weighting parameter adjustments, re-training data set recommendations, and/or model rule adjustments, that improve the persona model's accuracy in subsequent iterations.
Examiner fully considered the Applicant’s SME argument 1 but respectfully disagrees, by first noting that, while some features of the Original Specification ¶ [0044], ¶ [0047] appear to be reflected in the claims, other features for example, the intention to generate areas of improvement, such as where the model's responses are deficient or non-optimal, and the intention to determine adjustments to the training of the persona model, such as weighting parameter adjustments, re-training data set recommendations, and/or model rule adjustments [bolded emphasis added] to allegedly improve the persona model in subsequent iterations do not appear to be reflected in the claims. This finding is important because the “101 inquiry must focus on language of Asserted Claims themselves” as in “Synopsys, Inc. v Mentor Graphics Corp, U.S. Court of Appeals Federal Circuit, No 2015-1599, October 17 2016 2016 BL 344522 839 F3d 1138” citing “Accenture Global Servs., GmbH
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1336, 1345 108 USPQ2d 1173 Fed Cir. 2013: admonishing that the important inquiry for a 101 analysis is to look to the claim”, citing “Content Extraction & Transmission LLC
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1343, 1346 113 USPQ2d 1354 (Fed. Cir. 2014): We focus here on whether the claims of the asserted patents fall within the excluded category of abstract ideas”, cert. denied, 136 S Ct 119, 193 L. Ed. 2d 208 2015). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”. MPEP 2103 I C citing In re Hiniker Co 150 F3d 1362 1369 47 USPQ2d 1523, 1529 Fed Cir 1998.
This corroborates MPEP 2104.04(d)(1) and MPEP 2106.05(f)(1) establishing that the claim itself must reflect the technological improvement. Here, the improvement of weighting parameter adjustments, re-training data set recommendations, and/or model rule adjustments to address responses that are non-optimal do not appear to be reflected in the claim, nor does it appear to provide, the requisite technological detail to demonstrate it is an actual technological improvement, even if considered, in the arguendo, to be reflected in the claims. In support of such rationale, Examiner cites Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) as cited by PTAB on Appeal 2025-003304: “The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted…do not represent a technological improvement” at least because they are “incident to the very nature of machine learning”.
It then follows that here, the yet to be claimed re-training, as argued by Applicant above, is also an example of the ineligible iterative training in Recentive Analytics supra, while the yet to be claimed weighting parameter adjustments and model rule adjustments, as argued above, are also examples analogous to the ineligible dynamically adjusted in Recentive Analytics supra. Therefore, similar to Recentive Analytics, the yet to be claimed but still argued weighting parameter adjustments, re-training data set recommendations, and/or model rule adjustments to allegedly address responses that are non-optimal as raised by Remarks 05/05/2026 at p.11 last ¶-p.12 ¶1 do not represent an actual technological improvement because they are incident to the very nature of machine learning. As such, the SME argument 1 is found unpersuasive.
SME argument 2: Remarks 05/05/2026 p.12 ¶ 2 - p.13 ¶1 argues independent claim 1 recites a specific method for improving the persona model's training through issue reporting and feedback functions to identify areas of improvement for the persona model and determine adjustments to the training of the persona model, which is argued to parallel the type of model training improvements of precedential Ex Parte Desjardins as cited by MPEP 2106.04(d)(III). Specifically, it is argued that like the claims in Desjardins, which recited adjusting parameter values to optimize performance on a new task while protecting performance on a prior task, the current independent claim 1 recites a specific mechanism for improving model performance. The issue reporting function identifies areas of improvement from the persona model's outputs, and the feedback function determines adjustments to the persona model's training based on those identified areas. Applicant cites Original Specification ¶ [0047] to argue that these adjustments may address, instances in which the persona model has failed to optimally account for persistent issues across iterations, thereby refining the model's training to resolve identified deficiencies while preserving the model's existing ability to generate accurate responses for the group.
Examiner fully considered the Applicant’s SME argument 2 but respectfully disagrees, by again noting that while some features of Original Specification ¶ [0047] appear to be reflected in the claims, other features such as the adjustments may address, instances in which the persona model has failed to optimally account for persistent issues across iterations, thereby refining the model's training to resolve identified deficiencies while preserving the model's existing ability to generate accurate responses for the group [bolded emphasis added], do not appear to be not reflected in the claims. This finding is important because the “101 inquiry must focus on language of Asserted Claims themselves” as in “Synopsys, Inc. v Mentor Graphics Corp, U.S. Court of Appeals Federal Circuit, No 2015-1599, October 17 2016 2016 BL 344522 839 F3d 1138” citing “Accenture Global Servs., GmbH
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1336, 1345 108 USPQ2d 1173 Fed Cir. 2013: admonishing that the important inquiry for a 101 analysis is to look to the claim”, citing “Content Extraction & Transmission LLC
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1343, 1346 113 USPQ2d 1354 (Fed. Cir. 2014): We focus here on whether the claims of the asserted patents fall within the excluded category of abstract ideas”, cert. denied, 136 S Ct 119, 193 L. Ed. 2d 208 2015). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”. MPEP 2103 I C citing In re Hiniker Co 150 F3d 1362 1369 47 USPQ2d 1523, 1529 Fed Cir 1998.
This evidence has an impact on the subject matter eligibility tests of MPEP 2104.04(d)(1) and MPEP 2106.05(f)(1) that clearly establish that the claim itself must reflect the technological improvement. Here however, the alleged improvement of adjustments may address, instances in which the persona model has failed to optimally account for persistent issues across iterations, thereby refining the model's training to resolve identified deficiencies while preserving the model's existing ability to generate accurate responses for the group [bolded emphasis added] at Remarks 05/05/2026 p.13 ¶1 do not appear reflected in the claim.
Moreover, even before proceeding to Step 2A prong two of the analysis, an argument can be made, at Step 2A prong one, that such reporting and feedback functions are not meaningfully different than the abstract cognitive reporting and feedback functions of observation, evaluation and judgment, listed by MPEP 2106.04(a)(2) III ¶2. These reporting and feedback functions are argued by Applicant as further executed, in a computer environment. Yet, their execution, in a computer environment rather than the human mind, does unnecessarily preclude its underlining claims from reciting the abstract exception when tested per MPEP 2106.04(a)(2) III C #2.
Even when moving the analysis forward at Step 2A prong two, the claims do not provide additional elements capable to integrate the abstract exception into a practical application. Here,
Examiner notes that the legal findings of Ex Parte Desjardins as argued by Applicant above, are irreconcilably different with what is currently claimed and argued here, because, in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision, the improvement to machine learning technology explained how the machine learning model was trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of catastrophic forgetting, and that the claims reflected such improvement. For example, the enumerated improvements in the Desjardins included effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks to allow the system in Desjardins to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Here however, Remarks 05/05/2026 p.12 ¶ 2-p.13 ¶1 merely argues in favor of use of “an issue reporting function” allegedly identifying “areas of improvement for the persona model” and “issue feedback function” to “determine adjustments to the training of the persona model”.
Thus here, there is nothing remotely analogous to the technological details of reducing use of storage capacity and reducing complexity in the system as in Ex Parte Desjardins supra.
Rather here, given the high level of generality of the currently claimed and argued reporting and feedback functions, as tested per MPEP 2111, and also tested per the legal findings of Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025), the Examiner reasons that such functions are incident to the very nature of machine learning, namely the requirements that the machine learning model be iteratively trained and dynamically adjusted.
Based on the preponderance of legal evidence above, the SME argument 2 is unpersuasive.
SME argument 3: Remarks 05/05/2026 p.13 ¶2 argues that, as amended, claim 1 recites newly added wherein limitation, “wherein the response includes a recommended communication to convey the change data to the first group, the recommended communication being articulated in accordance with at least one of a recommended stylistic scheme or a recommended contextual scheme generated by the persona model based on the simulated reactions of the first group” that integrates the concepts recited in claim 1 into a practical application, because it recites a specific, tangible output of persona model (e.g. recommended communication tailored to the group's simulated reactions) that goes beyond abstract data analysis. Applicant cites Original Specification ¶ [0061]-¶ [0063] to argue that the persona model does not merely simulate group reactions in the abstract, but instead uses those simulated reactions to generate a communication articulated in a particular style and context suited to the group’s characteristics, representing a practical application of the simulation because the output directly tied to improving how organizational change is communicated to affected groups.
Examiner considered SME argument 3 but respectfully disagrees finding it unpersuasive.
Examiner first points to MPEP 2111.04, to test the patentable weight of the above argued “wherein” limitation and discovers that here, the “recommended communication”, is merely recited as includ[ed] in “the response” but not effecting any further limiting on the active method step of “generating”, the “response” at independent method Claim 1, or effecting a further limiting on the execution on the “stored instructions” “by the one or more processors” at independent system Claim 9 or the “machine” of independent Claim 16. Thus, it can perhaps be argued that the here, the expression “wherein the response includes a recommended communication” has limited patentable weight when tested per MPEP 2111.04. Examiner similarly points to USPTO’s training entitled USPTO’s Examining Functional Claim Limitations: Focus on Computer/Software-related Claims dated May 2015 slides 16-17,20-21, which cites MPEP 2111.04, on the patentable weight of intended use or result. Here, the expression “to convey the change data to the first group”, when tested per above, can perhaps be argued as an example of such an intended use or intended result that would similarly have limited patentable weight.
In any event, distinct from the above issue(s) of patentable weight in the claim interpretation or claim construction, the Examiner finds that the expression “the recommended communication being articulated in accordance with at least one of a recommended stylistic scheme or a recommended contextual scheme generated by the persona model based on the simulated reactions of the first group” does not render independent Claims 1,9,16 less abstract and eligible. This is because MPEP 2106.04(a)(2)IIIC#2 states that performing a mental process in a computer environment does not preclude the claims to recite the abstract exception. Similarly, MPEP 2106.04(a)(2)III C #3, finds that using a computer as a tool to perform a mental process, also not preclude the claims from reciting the abstract exception. In fact, MPEP 2106.04(a)(2) III ¶2 found that observations, evaluations, judgments, and opinions fall within the abstract grouping of mental processes. Based on legal findings of MPEP 2106.04(a)(2) III C #2,#3 and then MPEP 2106.04(a) (2) III ¶2 identified above, Examiner deduces that here, the computer aided judgments executed in a computer environment as equivalent of a cognitive environment, and/or using a computer as a tool based on observations, be it visual, auditory or both, on evaluated or simulat[ed] responses, in order to be able to convey opinions or “recommended communication”, would also not preclude the current claims to recite, describe or at least set forth the abstract exception. Also, the fact that the “recommended communication” [is] “being articulated in accordance with at least one of a recommended stylistic scheme” (i.e. short/long sentences, high numbers of colloquialism as read in light of Original Specification ¶ [0062]) “or a recommended contextual scheme” (i.e. subject matter of discussion or gravitas in light of Id [0062]) is not meaningfully different than the cognitive emotional intelligence or judgment, of one of ordinary skills in the art, to be brief, concise or short spoken in some situations, or more elaborated, or gravitas in other situations, according to the different anticipated reactions of the group or audience, i.e. employee affected by organizational change such as employees about to get fired or laid off as read in light of Original Specification ¶ [00109] 3rd sentence. Simply put, the expression “the recommended communication being articulated in accordance with at least one of a recommended stylistic scheme or a recommended contextual scheme generated by the persona model based on the simulated reactions of the first group” can be argued as equivalent of human emotional intelligence of how to communicate or break bad news, by use of compassion, gravitas, colloquialism, in hypothetical or simulated scenario, where employees affected by organizational change (i.e. downsizing, outsourcing etc.) are about to get fired. The use of “stylistic” tone or “scheme” (i.e. colloquialism) or “context” (i.e. gravitas) for such somber occasion does not in itself render the claims les abstract and eligible. It was legally tested as early as 2018 whether phonetic symbols in language, is abstract. In re Wang Fed. Appx. (Fed Cir. 2018) CAFC Appeal No. 2017-1827, June 20,2018
All of these facts corroborate, the findings of Non-Final Act 02/05/2026 p.3 ¶2- p.4 ¶1, showing that here, the improvement is not technological, but rather entrepreneurial or abstract in evaluating and judging of personal behaviors or relationships of people and conveying opinions based on the observation, evaluation and judgement. Applicant attests to such entrepreneurial improvement at Remarks 05/05/2026 p.13 ¶2, 3rd sentence, by characterizing the current claims as improving how organizational change is communicated to affected groups. Yet, as explained by Final Act 07/17/2025 p.3 ¶3-¶5, and then again by Non-Final Act 02/05/2026 p.3 ¶2- p.4 ¶1, even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the 101 inquiry” citing MPEP 2106.04 I, which at its turn cites Myriad, 569 U.S at 591, 106 USPQ2d at 1979. The “Myriad” rationale was further corroborated by “SAP Am Inc v InvestPic” cited by MPEP 2106.04(a)(2) I.C(i). Digging deeper into the Court’s rationale in SAP supra, Examiner finds the Court ruled that, “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”. That is, “no matter how much of an advance in the field the claims” [would] “recite the advance” [would still] “lie entirely in the realm of abstract ideas” with no plausibly alleged innovation in non-abstract application realm. Here, as in SAP Am Inc v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ.2d 1638 (Fed. Cir. 2018), no matter how much of an advance in the emotional intelligence the amended claims would recite in being brief, concise in one setting or stylistically lengthy, or gravitas, based on the sentiment or mood of the affected audience and characterized by Applicant at Remarks 05/05/2026 p.13 ¶2, 3rd sentence as “improving how organizational change is communicated to affected groups, said advance would still lie entirely within the realm of the behavior management of MPEP 2106.04(a)(2) II C of conveying information, within the broad abstract grouping of Organizing Human Activities at MPEP 2106.04(a)(2) II, and/or the computer aided cognitive and abstract functions of reporting and feedback as examples of observation, evaluation and judgement, as in MPEP 2106.04(a)(2) III ¶2, executed by, or executable in, a computer environment [MPEP 2106.04(a)(2) III C #2, #3 and, with no plausibly of the alleged innovation, from entering the non-abstract realm. The SAP findings were corroborated by Versata Dev Grp Inc v SAP Am Inc 115 USPQ2d 1681 Fed Cir 2015 again undelaying the difference between improvement to entrepreneurial goal objective versus improvement to actual technology. MPEP 2106.04. Also, per MPEP 2106.04(d)(1), “improvement in the judicial exception itself is not an improvement in technology”. Finally, MPEP 2106.04 I ¶3 is also clear that claims directed to narrow laws that have limited applications, remain ineligible.
Thus, it can be argued, right from onset (Step 2A prong one) that recitation at the “wherein” limitation of “the recommended communication being articulated in accordance with at least one of a recommended stylistic scheme or a recommended contextual scheme generated by the persona model based on the simulated reactions of the first group” remains abstract and ineligible.
Yet, Examiner submits, in the arguendo, that even if testing such newly added “wherein” limitation, at Step 2A prong two, as an additional computer-based element, its “communication” capability “in accordance with at least one of a recommended stylistic scheme or a recommended contextual scheme generated by the persona model based on the simulated reactions of the first group” would still not integrate the abstract exception into a practical application because it would represent a mere example of requiring the use of software or a computer component to tailor information and provide it to the user on a computer, which according to MPEP 2106.05(f)(2) v. represents mere invocation of computers or machinery as a tool to perform exiting or abstract processes which would not integrate the abstract exception into a practical application. Here, such tailoring is exemplified by the capabilities of the “recommended communication to convey the change data to the first group, the recommended communication being articulated in accordance with at least one of a recommended stylistic scheme or a recommended contextual scheme generated by the persona model based on the simulated reactions of the first group”. Applicant attests to such tailored communication at Remarks 05/05/2026 p.13 ¶2, 2nd sentence by stating:
“This element further integrates the concepts recited in claim 1 into a practical application because it recites a specific, tangible output of the persona model (e.g., a recommended communication tailored to the group's simulated reactions)”. Thus here, similar to MPEP 2106.05(f)(2) v., such communication tailored based on the generally recited “stylistic scheme” or “context”, would at most represent invocation of machinery that applies the abstract exception and does not integrate it into a practical application. Therefore, the SME argument # 3 is found unpersuasive.
In conclusion, the Examiner has provided a preponderance of legal and/or factual evidence showing that the claims still recite, describe or set forth the abstract exception with any alleged additional, computer-based elements, as argued by Applicant above, incapable to integrate such abstract exception into a practical application, and for same reason, also incapable to provide significantly more. Thus, Examiner reasons that the argued claims remain ineligible.
III. Response to Applicant’s rebuttal arguments of 35 USC 103 rejection
Applicant’s amendment necessitated new grounds of rejection in this action.
Prior art argument: Remarks 05/05/2026 p14-p.16 points to Original Specification ¶ [0077] to argue that Gordon et al US 20240135293 A1 does not teach or suggest simulating how a group would react to a proposed organizational change or generating communications tailored to group’s predicted reactions and communication preferences. Remarks 05/05/2026 p.16 ¶2 also cites Original Specification ¶ [0069] to exemplify change data as “planned, on-going, and/or a completed organizational change/transformation, such as an organizational merger, a physical office space move, an entity structural reorganization, a software/hardware transition" which is argued not taught by Gordon, nor rectified by Sagi et al, US 20230083781 A1. Further,
Remarks 05/05/2026 p.16 ¶3 also argues that Sagi does not teach generating a recommended communication articulated in accordance with a recommended stylistic scheme or contextual scheme based on simulated group reactions.
Examiner fully considered the Applicant’s prior art argument but respectfully disagrees, by noting that the bolded features of Original Specification ¶ [0069], ¶ [0077] upon which the Applicant relies, namely communication preferences and change data as “planned, on-going, and/or a completed organizational change/transformation, such as an organizational merger, a physical office space move, an entity structural reorganization, a software/hardware transition do not appear to be recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”. MPEP 2103 I C citing In re Hiniker Co 150 F3d 1362 1369 47 USPQ2d 1523, 1529 Fed Cir 1998.
First, as an issue of claim construction or claim interpretation, of the “change data”, the examiner applies the broadest reasonable interpretation test of MPEP 2111, and submits that
the current independent Claims 1,9,16 broadly exemplify “organizational change” [as] “including at least one of a change to an organizational structure, a change in location, or a software or hardware change anticipated to affect members of a first group”; which is clearly met by Gordon
= “a change to an organizational structure”
(Gordon ¶ [0056] 1st-2nd sentences: obtain real-time data of workforce reductions (e.g. layoffs, furloughs, etc.) affecting an employee's organization. Similarly, ¶ [0119] 2nd sentence: layoffs within organization and/or company at-large, etc. This is consistent when reading the change in organizational structure in light of the layoffs at Original Specification ¶ [00109] ),
= “a change in location”
(Examiner interprets the term change in location based on broadest reasonable interpretation MPEP 2111 as applying to any entity of the organization’s including a change in employee location, as a member or the organization. In a situation of sole proprietorship this would represent the organization in itself. Also, as another example, the Examiner interprets the claimed expression change in location, as a change in employee location relative to the location of the organization. Based on such broadest reasonable interpretation Gordon ¶ [0063] 2nd-3rd sentences teaches such change in location as: employees associated with the employer or organization may be provided with an opportunity to opt-in to provide location data that may be used to determine their relative daily commutes. For instance, an employee that has a significant commute time (as determined through time series data obtained from the employee's mobile device or application that tracks employee location) may experience increased organizational exhaustion as the addition of a lengthy commute to existing work schedule result in less time for employee downtime. Similarly, ¶ [0119] 2nd sentence: fluctuations [or changes] in …a personal event (e.g. … a move [or change] to a new location, etc.) may be indicative of an increase in the employee's organizational exhaustion over that period of time. Examiner interprets a lengthy commute (per Gordon ¶ [0063]) as part of move [or change] to a new location, etc. (per Gordon ¶ [0119]) as the claimed “change in location”) “or”
= “a software or hardware change anticipated to affect members of a first group”
(Gordon ¶ [0235] 1st sentence: techniques herein implemented in electronic hardware, computer software, firmware, or any combination thereof. For example, at ¶ [0045] identify, in real-time any communications that may be indicative of the quality and quantity of tools that an employee may use to perform assigned tasks. The machine learning algorithm or artificial intelligence may process these communications to identify employee sentiment with regard to the quality and quantity of these tools. As being provided with inferior or ineffective tools may result in a degradation of the employee's performance and, thus, increase organizational exhaustion, the machine learning algorithm or artificial intelligence may assign a negative polarity (e.g., score or other metric) to any communications where the employee expresses frustration, disappointment, or other similar sentiment with regard to these tools. Similar ¶ [0092] 2nd-4th sentence: noting employee provided with inferior or ineffective tools may experience a degradation in their performance and, thus, increase their organizational exhaustion).
Thus, Gordon teaches the change data as contested by at Remarks 05/05/2026 p.16 ¶2.
As per the “simulating” feature, as contested by Applicant at Remarks 05/05/2026 p.16 ¶2, the Non-Final Act 02/05/2026 p.18 ¶2-¶3, p.22 made it clear that while Gordon generates benchmarking on baseline or average sentiment or response on organizational change, Gordon does not explicitly equate its benchmarking on baseline or average sentiment to a simulating.
Sagi in analogous art of obtaining users responses for managerial plan purposes, easily remedies the Gordon’s lapse, by explicitly using the term “simulating” to teach or suggest:
- “responses simulating reactions of the first group to the organizational change” as claimed
(Sagi ¶ [0007]: g. obtaining observed responses during a simulation of the crisis scenario;
Sagi ¶ [0066] “Observed responses” (OX) refers to responses to a crisis scenario recorded during a simulation of the crisis scenario played out by members of the organization, wherein the observed member response data comprises at ¶ [0028] initiative, diversity, perception, responsiveness, comeback, perceived centrality, burn-out, and any combination thereof. One example is disclosed at ¶ [0090] #6: If your answer to question #5 is Yes how would you describe this experience? Answers: (1) Very good,(2) Good, (3) Not so good, (4) Bad, (5) Very bad).
Examiner reminds the Applicant that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
As per the newly added “a recommended communication to convey change data to a group that is articulated in accordance with a recommended stylistic scheme or contextual scheme based on simulated group reactions” not being taught by Gordon, as argued at Remarks 05/05/2026 p.14 last ¶ p.15¶2 3rd sentence, or by Sagi, as argued at Remarks 05/05/2026 at p.16 ¶3, such argument is moot in view new grounds of rejection necessitated by the amendment. Carolyn Peer US 20210201267 A1 is now relied by Examiner to teach or suggest:
- a recommended communication to convey the change data to the first group articulated in accordance with at least one of a recommended stylistic scheme or a contextual scheme based on the simulated group reactions (Peer teaches many examples as follows:
Peer ¶ [0004] 3rd-4th sentences first acknowledges that: A variety of factors influence employee sentiment, consequently affecting employee career satisfaction. When emotionally stimulated, people become generally less prepared or willing to respond in the best interest of a company; it is easy to imagine a scenario in which a conversation between human resources worker and dissatisfied employee results in worsening of employee satisfaction. In this case,
Peer ¶ [0028] 3rd sentence invention includes employee profiles managed by server where each employee profile includes a conversation history, a sentiment history, and decision history.
Peer ¶ [0005] 2nd sentence: the goal optimize employee communication to improve employee retention and job performance by employing each employee with accessible, personalized chatbot assistant. Peer ¶ [0028] 1st-2nd sentences similarly states: the invention optimizes employee communication to improve workplace engagement and retention for enhancing employee job satisfaction by facilitating employee communication accomplished by AI-driven messaging system that collects and analyzes data and subsequently returns appropriate conversational responses, in Figs.1,20,21. To this end at Peer ¶ [0005] 5th sentence: conversational data is collected and used to calculate optimal interactions with each employee. See Fig.20 below emphasis on the AI processing noting: Who gets connected. When. Why.
Peer ¶ [0036] 7th-8th sentences: The natural-language processing engine is executed by outputting context scores with the natural-language processing engine. The context scores are a set of key-value pairs of emotions or general attitudes and numeric values which can be analyzed to approximate [or simulate] the overall employee sentiment. Peer ¶ [0039] 6th-7th sentences: Each contextually-matching activity is compared to the conversation history, the sentiment history, and the decision history of the arbitrary profile to identify at least one personally-matching activity from the plurality of contextually-matching activities. this way, the server determine most effective response from a smaller [or shortest] set of allowable responses (read in light of the current Original Specification ¶ [0062] as an example of stylistic scheme), rather than from a larger set of all possible response.
Peer ¶ [0005] 3rd-4th sentences The chatbot assistant directly interacts with the employee to determine optimal solutions based on the employee's implicit or explicit needs and aspirations. The chatbot assistant makes suggestions [or recommendations] based on interactions with the employee, such as responding to employee dissatisfaction.
Peer ¶ [0029] 3rd - 4th sentences: the at least one conversation data relates to arrangement of alphanumeric and punctuation characters [as stylistic examples] which form the words of a response by the arbitrary employee profile. The at least one piece of conversation data may include full sentences, sentence fragments, spelling decisions [as stylistic examples], as well as metadata such as timing information. Indeed per, Peer ¶ [0042] 3rd sentence: The piece of conversation data, including any alphanumeric, punctuation, symbolic, hyperlink, image, or other data, is highly valuable in the process of learning how to optimize conversation with an arbitrary profile. Next, at Peer ¶ [0029] 5th- 9th sentences The piece of conversation data is then processed into context scores with the remote server (Step D). The plurality of context scores relates to the values associated with a variety of calculated social metrics, such as overall satisfaction, happiness, frustration, exhaustion, and more. Analysis of the plurality of context scores enables the present invention to approximate [or simulate] the mood of an employee and subsequently generate the optimal response. At least one available activity is assessed for the arbitrary profile in relation to the context scores, the conversation history, the sentiment history, and the decision history for the arbitrary profile with the remote server (Step E). The at least one available activity relates to a set of potentially-appropriate responses, including any text, rhetorical devices, pauses, unconventional characters, [interpreted as stylistic scheme examples] task-related actions, and more, that may be generated by the present invention as a potentially-appropriate response…. Peer ¶ [0030] 4th sentence: The confirmed activity is the response deemed most appropriate following processing by the suggested activity engine.
Peer ¶ [0032] 4th sentence: noting another example where the conversation-initiating message is generated to prevent the present invention from pestering an employee [as another example of context example or stylistic choice] who is perceived to be busy, annoyed, or otherwise unlikely to respond at a given date or time while simultaneously preventing conversational flow from devolving entirely [as another example of stylistic choice]. Also, per ¶ [0030] last sentence: the present invention develop future conversational replies based upon input from the pre-determined responses and the desired response).
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Peer Annotated Fig. 20 from Original Fig. 20 in support of rejection arguments
Thus, there is preponderance of evidence to show the contested features are taught by prior art.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) describe, or set forth the abstract “simulating transformation adoption” of the computer aided “Mental processes” grouping through physically aided or computer aided evaluation and judgment based on observation, followed by opinion based on said evaluation and judgment (MPEP 2106.04(a)(2) III) implemented using mathematical calculations and/or relationships expressed in words (MPEP 2106.04(a)(2) I)1 on what appear to be equally abstract personal behavior or relationships of people (MPEP 2106.04(a)(2) II C), which falls within the broader abstract grouping of Certain Methods of Organizing Human Activities. First, MPEP 2106.04(a)(2) III cites Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) holding that claims directed to a mental process of translating a functional description of a logic circuit into a hardware component description of the logic circuit are directed to the abstract mental processes. It then follows that here the analogous “simulating transformation adoption” as recited at the preamble of independent Claims 1,9,16 would analogously recite, describe or set forth the abstract mental processes. The same MPEP 2106.04(a)(2) III cites Electric Power Group v Alstom, SA, 830 F3d 1350,1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), to state that a claim directed to collecting information, analyzing it, and displaying certain results of the collection and analysis, describes or sets forth mental processes. Also, MPEP 2106.04(a)(2) I A iv cites Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech, to submit that generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form, describe an abstract process of organizing information and manipulating information through mathematical correlations. Similarly, MPEP 2106.04(a)(2) I, ¶4 and C i., cites SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018), to state that performing a resampled statistical analysis to generate resampled distribution, describes the abstract mathematical concepts.
- Here the computer-aided observation or collecting is set forth as: “receiving”, “change data associated with an organizational change including at least one of a change to an organizational structure, a change in location, or a software or hardware change anticipated to affect members of a first group”; “inputting”, “a portion of the change data into a persona model configured to generate responses simulating reactions of the first group to the organizational change” (independent Claims 1,9,16), “inputting” “the portion of the change data and the response to the portion of the change data into a tracking model” (dependent Claims 2,10,17); “receiving”, “updated change data associated with the organizational change”; “inputting” “a portion of the updated change data into the persona model” (dependent Claims 3,11,18); “inputting” “the portion of the change data into the plurality of persona models” (dependent Claims 5,13,20); “extracting” “the portion of the change data from the change data”, “inputting”, “the formatted input into the persona model as part of the portion of the change data” (dependent Claims 7,15); “receiving” “first verbal communication of the portion of the change data”; (dependent Claim 8).
- Here the physiologically computer-aided observation is set forth as: “converting” “second text string to a second verbal communication”; “and” “causing” “the second verbal communication to be conveyed to the user” (dependent Claim 8). Specifically, one of ordinary skills in the art would have been fully capable in [vocally] “converting” [paper written] “second text string to a second verbal communication”; “and” “causing” “the second verbal communication to be conveyed to the user” (Claim 8), or to substitute such morphological, lexical and speech cognitive functions with computer aids.
- Here, the computer-aided evaluation, analysis, manipulation / correlation is/are set forth as: “generate responses simulating reactions of the first group to the organizational change”, “generating”, “a response to the portion of the change data” (independent Claims 1,9,16), “generating”, “a performance value corresponding to the response generated by the persona model”; “comparing”, “the performance value with a prior performance value to track the performance of the persona model”; “and” “generating”, “a recommended adjustment to the persona model based on the comparing” (dependent Claims 2,10,17), “generating” “the persona model, an updated response to the portion of the updated change data, wherein the updated response represents an updated outlook of the first group with respect to the organizational change”; (dependent Claims 3,11,18), “aggregating”, “the plurality of group data that includes one or more of: (i) an internal document, (ii) an archived email, (iii) a recorded verbal conversation, or (iv) a recorded live chat”; “aggregating”, “the plurality of training change data” (dependent Claims 4,12,19), “generating”, “a plurality of responses to the portion of the change data” (dependent Claims 5,13,20), “generating”, “the response to the portion of the change data as a second text string” (dependent Claim 8)
- Also here, such observation/ collecting and evaluation/ analysis lead up to an equally abstract computer-aided judgment used for subsequent opinion. Such judgment and subsequent opinion are set forth here by “the persona model is trained using a plurality of training change data and a plurality of group data as inputs to output a plurality of training responses”, (independent Claims 1,9,16), “issue reporting function using outputs from the persona model” for “generating” [or cognitively judging], “areas of improvement for the persona model”; (independent Claims 1,9,16), and [cognitively] “interpreting” based on, “feedback”, “the areas of improvement to determine adjustments to the training of the persona model” (independent Claims 1,9,16), “using a plurality of training change data portions, a plurality of training responses, and a plurality of training performance values as inputs to output a plurality of training performance values and a plurality of training recommended adjustments” (dependent Claims 2,10,17), “training”, “the persona model with the plurality of training change data and the plurality of group data as inputs to generate the plurality of training responses as outputs” (dependent Claims 4,12,19); “each persona model of the plurality of persona models is trained using the plurality of change data and a plurality of subset group data as inputs to output a respective plurality of training responses” (dependent Claims 5,13,20); “the response includes at least one of: (i) a predicted subsequent response strategy to address the first group, (ii) a change receptiveness likelihood value, (iii) a predicted uptake time value, (iv) a best practices indication, (v) a successful adoption likelihood value, (vi) an estimated timeline for adoption, or (vii) a realization value” (dependent Claims 6,14)
and possibly emotional intelligence of having “the recommended communication being articulated in accordance with at least one of a recommended stylistic scheme” (short or more brief, or more lengthy response read in light of Original Specification ¶ [0062]) “or a recommended contextual scheme” (with or without gravitas as read in light of Original Specification ¶ [0062]) “based on the simulated reactions of” based on the affected group (as read in light of Original Specification
¶ [0005] - ¶ [0006] etc.)
As per the train[ed] persona and tracking models as recited throughout Claims 1,2,4,5,9,10,12, 13,16,17,19,20, and the one or more processors of Claims 1-5,7-13,15, and machine of Claims 16-20, the Examiner points to MPEP 2106.04(a)(2) III C and finds that #1. Performing a mental process on a generic computer, #2. Performing a mental process in a computer environment, or #3. Using a computer as a tool to perform a mental process, do not preclude the claims from reciting, describing or setting forth the abstract mental processes. It then follows that here performing the aforementioned mental processes using one or more processors (Claims 1-5,7-13,15) / machine (Claims 16-20) could perhaps also be argued here as, an example of performing a mental process on a generic computer and/or using a computer as a tool to perform a mental process, which per MPEP 2106.04(a)(2) III C #1, #3 would not preclude the claims from reciting, describing or setting forth the abstract idea. Similarly, it could also be argued that use of train[ed] persona and tracking models to output a plurality of training responses related to change/ recommend[ation] recited at Claims 1,2,4,5, 9,10,12,13,16,17,19,20, would correspond along with use of archived email, recorded verbal conversation, or recorded live chat for aggregating group data at dependent Claims 4,12,19, instances of performing a mental process in a computer environment, which according to MPEP 2106.04(a)(2) III C #2 would also not preclude the claims from reciting, describing or setting forth the abstract idea.
Such finding is further corroborated by MPEP 2106.04(a)(2) I, ¶4 and MPEP 2106.04(a)(2) I C i. each citing SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161,1163,127 USPQ2d 1597,1599 (Fed Cir 2018), to state that performing resampled statistical analysis to generate a resampled distribution, describes the abstract mathematical concepts. Here, as in SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ.2d 1638 (Fed. Cir. 2018), “no matter how much of an advance in the field the claims” [would] “recite the advance” [would still] “lie entirely in the realm of abstract ideas” with no plausibly alleged innovation in non-abstract application realm. Specifically, Examiner finds that the challenged patent in “SAP” proposed utilization of resampled statistical methods for analysis of financial data, which did not assume a normal probability distribution. One such method was found as a bootstrap method, which estimated distribution of data in a pool (sample space) by repeated sampling of the data in the pool, defined by selecting a specific investment or a particular period of time. Data samples are drawn from the sample space with replacement: samples are drawn from the sample space and then returned to the pool before next sample is drawn. Yet the Federal Circuit ruled that: “Dependent method claims 2-7 and 10 add limitations… [that] require the resampling method to be a bootstrap method." SAP, 260 F. Supp. 3d at 715. Likewise, "[c]laims 8 and 9 add limitations that the statistical method is a jackknife method and a cross validation method." Id. at 716. Because bootstrap, jack-knife, and cross-validation methods are all "particular methods of resampling," those features simply provide further narrowing of what are still mathematical operations. They add nothing outside the abstract realm. See Mayo, 566 U.S. at 88-89 (stating that narrow embodiments of ineligible matter, citing mathematical ideas as an example, are still ineligible); buySAFE, 765 F.3d at 1353 (same). Dependent method claims 12-21 are no different”.
Since implementation of a sample space, and the algorithmic properties of boot-strap, jackknife, cross validation, and resampling [bolded emphasis added] in SAP’s modeling did not save the claims in SAP from ineligibility, Examiner similarly reasons that here, the analogous recitations of “persona model is trained using a plurality of training change data and a plurality of group data as inputs to output a plurality of training responses”, “generating, by the one or more processors executing an issue reporting function using outputs from the persona model, areas of improvement for the persona model”; “interpreting, by the one or more processors executing a feedback function, the areas of improvement to determine adjustments to the training of the persona model” at independent Claims 1,9,16, “wherein the tracking model is trained using a plurality of training change data portions, a plurality of training responses, and a plurality of training performance values as inputs to output a plurality of training performance values and a plurality of training recommended adjustments” at dependent Claims 2,10,17; “aggregating, by the one or more processors, the plurality of training change data”; “and” “training, by the one or more processors executing a training module, the persona model with the plurality of training change data and the plurality of group data as inputs to generate the plurality of training responses as outputs” at dependent Claims 4,12,19; “each persona model of the plurality of persona models is trained using the plurality of training change data and a plurality of subset group data as inputs to output a respective plurality of training responses” and “generating” “a plurality of responses to the portion of the change data” at dependent Claims 5,13,20, should similarly not preclude the claims to recite, describe or set forth the abstract exception. As per recitation of “creating”, “a formatted input that includes a plurality of prompts for the persona model based on the portion of the change data” at dependent Claims 7,15, such feature is not meaningfully different than procedure for converting binary-coded decimal numerals into pure binary form2, found by MPEP 2106.04(a)(2) I ¶1 as falling within the abstract mathematical concepts and/or generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form3 found by MPEP 2106.04(a)(2) I A iv as falling within the abstract mathematical relationships. In an abundance of caution, the Examiner will more granularly test the train[ed] persona and tracking models of Claims 1,2,4,5,9,10,12, 13,16,17, 19,20 at subsequent steps below. For now, it is clear that given the preponderance of legal evidence above the claims’ character as whole is undeniably abstract. Step 2A prong one.
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This judicial exception is not integrated into a practical application because here, the Claims 1-20 appear to recite, for the most part, elements integral to the abstract exception itself as demonstrated above. For example, the train[ed] persona and tracking models as recited at Claims 1,2,4,5,9,10,12,13,16, 17,19,20, and the one or more processors of Claims 1-5,7-13, 15, and machine of Claims 16-20 could perhaps be argued as mere computer aids integral to abstract exception, as tested at the prior step under MPEP 2106.04(a)(2) III C #1,#2,#3. Yet, even if such train[ed] persona and tracking models recited at Claims 1,2,4,5,9,10,12,13,16, 17,19,20, and the processors of Claims 1-5, 7-13,15, machine of Claims 16-20, interface of Claim 9 would be construed, in the arguendo, as, additional, computer-based elements, they would still merely apply the aforementioned abstract processes along with their underlining mathematical, simulating and trained algorithms as components of a computer. This computerization, as tested per MPEP 2106.05(f)(2)(i)4, would simply represent a mere invocation of computers or machinery as tools to perform the aforementioned abstract processes, which would not integrate the abstract idea into a practical application. As per the capabilities of “tracking model” at Claims 2,10,17, and the capabilities of the “one or more processors” / “machine” “outputting” “response for display to a user” at independent Claims 1,9,16, to perform their respective computerized functions, Examiner points to MPEP 2106.05(f)(2) iii5,MPEP 2106.05(f)(2) v6 to assert that a computerized process for monitoring audit log data executed on a computer and requiring use of computer components to tailor information and provide it to the user on a computer, represent mere invocation of computers or machinery as a tool to perform an existing process, and thus merely apply the abstract exception without integrating it into a practical application. One example of tailoring information and providing on a computer it is exemplified here as “wherein the response includes a recommended communication to convey the change data to the first group, the recommended communication being articulated in accordance with at least one of a recommended stylistic scheme or a recommended contextual scheme generated by the persona model based on the simulated reactions of the first group” at independent Claims 1,9,16.
As it can be seen, other than aiming to better achieve the abstract result, at no point in their recitations do the additional elements provide any technological details of how an actual technological solution is performed to solve a technological problem as required by MPEP 2106.05(f)(1). This finding is especially important given the high level of generality of independent Claims 1,9,16 in “generating, by the one or more processors executing an issue reporting function using outputs from the persona model, areas of improvement for the persona model” and “interpreting, by the one or more processors executing a feedback function, the areas of improvement to determine adjustments to the training of the persona model”; and the high level of generality of Claim 8 in “converting, by the one or more processors, the first verbal communication to a first text string representing the portion of the change data”; “generating, by the one or more processors executing the persona model, the response to the portion of the change data as a second text string”; “converting, by the one or more processors, the second text string to a second verbal communication”; and “causing, by the one or more processors, the second verbal communication to be conveyed to the user”. Additionally, and/or alternatively, the fact that the Applicant narrows such computations to a simulating and machine learning environment could be also viewed as narrowing the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, to technological environment, as tested per, MPEP 2106.05(h)(vi)7 also does not integrate the abstract idea into a practical application. Step 2A prong two.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, the Examiner follows the guidelines of MPEP 2106.05 (d) II, bullet points #1 and #2, and carries over the findings of the MPEP 2106.05 (f) and/or (h) tests above to submit that the purported additional computer-based elements above, even if construed as additional computer-based elements would also not provide significantly more, without the need to rely on the well-understood, routine and conventional test of MPEP 2106.05(d). Yet, assuming arguendo that further evidence would be required to demonstrate conventionality of the additional, computer-based elements above, the Examiner would also point as evidence to the high level of generality of the additional elements in light of Original Disclosure:
- Original Specification ¶ [0074] 2nd,4th sentences: disclosing at high level of generality use of any trained large language algorithm/model (LLM) such as commercially available Chat GPT
- Original Specification ¶ [0060] reciting at high level of generality: “The persona model 134b2 may generally be an artificial intelligence (AI) trained large language algorithm/model (LLM) that is configured to interact with a user that is accessing the exemplary computing system 130. As a general example, when a user inputs a prompt into the central server 134 (e.g., via workstation 132), the user's inputs and any subsequent responses may be analyzed by the persona model 134b2 to generate outputs, such as predicted responses to such prompts. In particular, the persona model 134b2 may utilize the initial outputs (e.g., portions of change data 132b1, 134b6, group data 134b4, etc.) to generate subsequent responses that may be transmitted and displayed/conveyed to the user through the workstation 132. In certain embodiments, the central server 134 may train/re-train the persona model 134b2 during live communications across communications channels (e.g., a live webchat, a phone call, an email, a text message, a video call, etc.) to improve the subsequent iterations of the persona model 134b2”.
- Original Specification ¶ [00126] 2nd sentence reciting at high level of generality: “Generally, the exemplary GUI 300 may allow a user (e.g., an administrative user/operator) to interact with the central server 134, which may include receiving outputs from the central server 134 or sending inputs to the central server 134, as described in reference to the first exemplary workflow 200 of Figure 2A”.
- Original Specification ¶ [00133] 1st sentence reciting at high level of generality: “Moreover, it should be understood that any change data, group data, and/or any values determined, detected, calculated, and/or otherwise output by the central server 134 may be displayed generally in the exemplary GUI 300”.
- Original Specification ¶ [00146] 3rd sentence reciting at high level of generality: “A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations”.
- Original Specification ¶ [0147] 3rd-4th sentences reciting at high level of generality: “For example, where the hardware modules include a general-purpose processor configured using software, the general- purpose processor may be configured as respective different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time”.
- Original Specification ¶ [00153] reciting at high level of generality: “Unless specifically stated otherwise, discussions herein using words such as processing, computing, calculating, determining, presenting, displaying," or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information
- Original Specification ¶ [0038] reciting at high level of generality: “The Figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the invention described herein”.
- Original Specification ¶ [0080] reciting at high level of generality: “persona model 134b2 and/or the tracking model 134b3 may employ natural language processing (NLP) functions, which generally involves understanding verbal/written communications and generating responses to such communications. The persona model 134b2 and/or the tracking model 134b3 may be trained to perform such NLP functionality using a symbolic method, machine learning models, and/or any other suitable training method”
- Original Specification ¶ [00159] reciting at high level of generality combination or alternative combinations of the additional computer-based elements: “Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for evaluation properties, through the principles disclosed herein. Therefore, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims”.
Also, assuming arguendo that additional evidence would be require at dependent claim 8 to demonstrate conventionality of “converting, by the one or more processors, the first verbal communication to a first text string representing the portion of the change data”; “generating, by the one or more processors executing the persona model, the response to the portion of the change data as a second text string”; “converting, by the one or more processors, the second text string to a second verbal communication”; and “causing, by the one or more processors, the second verbal communication to be conveyed to the user” beyond the above computer-aided findings, and/or applying or narrow the abstract idea, the Examiner would further rely on MPEP 2106.05(d)(2)(a) to point out to use of commercially available Chat GPT admitted by Original Specification ¶ [0074] 2nd,4th sentences would represent well-understood, routine or conventional function. Additionally, and/or alternatively, if necessary, the Examiner would further rely on MPEP 2106.05(d)(2)(c) to point to conventionality of
* US 20230023645 A1 ¶ [0040] 4th sentence: training a GNN may occur in stages, first using a generic dataset, whether a publicly available NLP training dataset (e.g., an NLP training dataset, such as those available from commoncrawl or Wikipedia) or a generically pre-trained model (e.g., OpenAI GPT-3, Google BERT, Microsoft CodeBERT, Facebook RoBERTa).
* US 20220366145 A1 ¶ [0008] 1st sentence: Another conventional model is the Bidirectional Encoder Representations from Transformers (BERT) model.
* US 20220300708 A1 ¶ [0021] 4th sentence: A conventional BERT (Bidirectional Encoder Representation from Transformers) model is adopted in the main model.
* US 20220309089 A1 ¶ [0198] 1st sentence: Once a generic language model (e.g., a generic BERT model) has been created, the language model can be further fine-tuned on a specific domain by continuing training the generic language model on domain specific data.
* US 20220237682 A1 ¶ [0133] 3rd sentence: The evaluation follows the splitting strategy for a comparison with a conventional BERT model. Here each user behavior history is viewed as a single session.
* US 11232358 B1 column 6 lines 53-57: the generic language model may be implemented using a pre-trained language model, such as Google's BERT (Bidirectional Encoder Representations from Transformers) or OpenAI's GPT-3 (Generative Pretrained Transformer). column 7 lines 62-67: Generic language models such as BERT, ALBERT, RoBERTa, and DistilBERT, which employ deep bidirectional transformer architectures, perform well in both sentence-level and token-level tasks.
* US 20220222491 A1 ¶ [0026] 2nd sentence: Some conventional natural language processing techniques use a transformer-based language model (such as Bidirectional Encoder Representations from Transformers or “BERT”) for various language understanding tasks
* US 20230153533 A1 ¶ [0014] 3rd sentence: The conventional way to setup a model for entity extraction is to take a pre-trained language model like BERT (e.g., pre-trained on some generic dataset like Wikipedia) and fine-tune (train) it to handle a particular entity extraction task using labeled training data specific to the desired task. ¶ [0015] 3rd sentence: Some language models like BERT come pre-trained using masked language modelling (e.g., on a generic dataset like Wikipedia), and some techniques continue masked language modeling using data from a target domain.
* US 20220198327 A1 ¶ [0027] 3rd sentence: A conventional pre-training model is for example a Bidirectional Encoder Representations from Transformers (BERT) model
* US 20210357441 A1 ¶ [0049] 4th sentence:, the semantic machine learning model may include a pre-trained language model, which may be based on a Bidirectional Encoder Representations from Transformers (BERT) model or any other suitable conventional pre-trained language model.
* US 20210383068 A1 ¶ [0010] last sentence: …models and/or techniques may be implemented for the semantic machine learning model, including, but not limited to, generic language model(s) and/or natural language processing technique(s), FastText approach (based on subwords information), Bidirectional Encoder Representations from Transformers (BERT) model, Biomedical Bidirectional Encoder Representations from Transformers (BioBERT) model, and/or the like. Similarly, ¶ [0102] last sentence, ¶ [0143] last sentence.
* US 20210375277 A1 ¶ [0019] 1st sentence: Several pre-trained language models, such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), have been used on many natural language processing tasks. ¶ [0040] 3rd sentence: conventional techniques included full BERT-based model, StateNet, GCE, GLAD, BERT-DST PS, Neural Belief Tracker-CNN, and Neural Belief Tracker-DNN. ¶ [0030] Fig.2 demonstrates an exemplary input representation of a multi-layer bidirectional transformer encoder (e.g., BERT model), according to one or more embodiments. BERT is a language representation model pre-trained on vast amounts of unlabeled text corpora, consisting of multiple transformer layers, each with a hidden size of 768 units and 12 self-attention heads. An input to BERT model is a sequence of tokens (i.e., words or pieces of words) and a corresponding output is a sequence of vectors, one for each input token. ¶ [0033] During pre-training, BERT model may be trained using two self-supervised tasks: masked language modeling (masked LM) and next sentence prediction (NSP). In masked LM, some tokens in the input sequence are randomly selected and replaced with a special token denoted [MASK], for the purpose of predicting the original vocabulary identifiers of the masked tokens. In NSP, BERT model may be configured to predict whether two input segments follow each other in the original text. Positive examples are created by taking consecutive sentences from the text corpus, whereas negative examples are created by picking segments from different documents. After the pre-training stage, BERT model can be applied to various downstream tasks such as question answering and language inference, without substantial task-specific architecture modifications.
* US 11361571 B1 column 5 lines 39-40: conventional systems often make use of multiple BERT models.
* US 20220382565 A1 ¶ [0116] 1st sentence: Similar to how BERT is used as a generic feature representation for different NLP downstream tasks, fine-tuning the interface prediction model for a variety of UI understanding tasks is relatively easy and does not require substantial task-specific architecture changes nor a large amount of task-specific data
* US 20230028664 A1 ¶ [0074] 1st sentence: Many deep learning models (e.g., the BERT architecture) are pre-trained on a big corpus of English, generic-domain texts so they can handle generic-domain applications well enough.
* US 20230029829 A1 ¶ [0051]: The first model may be a deep learning language model, such as BERT (Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” arXiv:1810.04805 [cs.CL], October 2018), or any other conventional language model. Although the following discussion utilizes certain BERT-related terminology for reasons of convenience, those of skill in the art will readily recognize how to adapt this discussion to any other conventional language model.
* US 20220237776 A1 claims 4,12: predetermined generic models include for said report texts and said structured data one or more chosen from a model of the Transformers family such as BERT, RoBERTa, DistillBert, Word2Vec and Elmo word embeddings, and for said images at least ImageNet.
* US 20200372341 A1 [0050] 1st sentence noting: conventional BERT and DFGN models.
Accordingly, there is a preponderance of legal and/or factual evidence showing that the claims do not have additional computer-based elements capable to provide significantly more.
In conclusion, Claims 1-20 although directed to statutory categories (“method” or process at Claims 1-8, “system” or machine at Claims 9-15, “medium” or article of manufacture at Claims 16-20) they still recite, or at least set forth the abstract idea (Step 2A prong one), with their computer-based elements, even as construed as additional elements, not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B). Therefore, the Claims 1-20 are not patent eligible.
Rejections under 35 § U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7 and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Gordon et al US 20240135293 A1 hereinafter Gordon, in view of
Sagi et al, US 20230083781 hereinafter Sagi, and in further view of
Carolyn Peer US 20210201267 A1 hereinafter Peer. As per,
Claims 1,9,16 Gordon teaches or suggests “A computer-implemented method for simulating transformation adoption, the method comprising:” / “A system for simulating transformation adoption, comprising: one or more processors; and a non-transitory computer-readable memory coupled to the one or more processors and the user interface, the memory storing instructions thereon that, when executed by the one or more processors, cause the one or more processors to:” / “A tangible machine-readable medium comprising instructions for simulating transformation adoption that, when executed, cause a machine to at least:” ([0213],[0220]-[0227],[0236],[0249])
- “receiving, at one or more processors, change data associated with an organizational change”
(Gordon ¶ [0123] 1st sentence: employer coordinator system 402 periodically, or in response to a triggering event (new employee onboarding, employee termination) obtain employee information including employee personal time-off balances, and the like. Then, ¶ [0058] 3rd-5th sentences: workforce event system 106 further measure the quantity of activities per employee while taking into consideration the employee's role within the organization. For instance, workforce event system 106 measure the number and complexity of these activities over a period of time given the employee's assigned role. For example, if the employee has been assigned a significant number of complex tasks not usually within the ambit of the employee's responsibilities, this may serve as indication that the employee is more likely to experience organizational exhaustion over time. To address this Gordon uses at ¶ [0078] 4th-5th sentences: a recurrent neural network (RNN) or convolutional neural network (CNN) to predict correlations between employee usage of personal time-off benefits within an organization and employee levels of organizational exhaustion within the organization. Optimization system 110 uses support vector machines (SVM), supervised, semi-supervised, ensemble techniques, or unsupervised machine-learning techniques to evaluate previous usage of personal time-off benefits within an organization and employee levels of organizational exhaustion within the organization to predict the effect of using personal time-off benefits within the organization towards reducing corresponding levels of organizational exhaustion. ¶ [0070] 3rd-5th sentences: through RNN or CNN, personal time-off system 108 determine whether a particular manager within an employee group or organization is favoring one or more employees over other employees within the employee group or organization by inordinately approving time-off requests from these one or more employees while continually refusing time-off requests from other employees. This detected favoritism result in increased organizational exhaustion amongst the other employees and, accordingly, the employee group or organization…. through the RNN or CNN, personal time-off system 108 determine whether a particular manager within an employee group or organization is rejecting time-off requests of employees associated with an affinity group. This may serve as an indication of possible discrimination within the employee group or organization, which may significantly increase the level of organizational exhaustion amongst these employees. ¶ [0148] 3rd- 4th sentences: if user has selected the Company Wide option from employee grouping panel 604, the optimization system automatically and in real-time update interface 610 provide a breakdown of employee organizational exhaustion amongst employees assigned to the different managers within the company. Thus, based on employee grouping selected from employee grouping panel, the optimization system automatically identify any internal organizations within selected employee grouping and provide organizational exhaustion breakdown according to these internal organizations. ¶ [0153] 3rd-4th sentences: in Fig.6B, the average organizational exhaustion for managers Aaron Rizzo and Tony Boone (88% and 70%) may exceed the average organizational exhaustion companywide (denoted through a vertical indicator). This indicate to user that teams managed by Aaron Rizzo and Tony Boone are experiencing heightened levels of organizational exhaustion compared to other teams within the company.
Gordon ¶ [0133] 1st-4th sentences: data combination sub-system 502 store new aggregated quantitative results and corresponding qualitative descriptors to a historical data repository 504 storing thereon entries corresponding to the different employees associated with an employer or organization. For example, an entry within the historical data repository 504 indicate, for a particular employee, the amount of organizational exhaustion that the employee may be experiencing over different periods of time (weekly, monthly). By maintaining employee's amount of organizational exhaustion (scores, metrics), any changes to the employee's organizational exhaustion may be tracked over time. Further
Gordon Fig.11 step 1108-> feedback again to -> step 1102 and ¶ [0202] At step 1108, the optimization system update the historical data according to obtained quantitative values corresponding to overall organizational exhaustion for each employee and corresponding qualitative descriptors for each of these quantitative values. For instance, the optimization system store the new aggregated quantitative results and corresponding qualitative descriptors to a historical data repository, which store thereon entries corresponding to the different employees associated with an employer or organization. An entry within this repository indicate, for a particular employee, the amount of organizational exhaustion that the employee may be experiencing over different periods of time, which allow for detecting any changes to the employee's organizational exhaustion over time. Thus, process 1100 may be continuously performed to obtain up-to-date and real-time aggregated quantitative results and corresponding qualitative descriptors for each employee) including at least one of
“a change to an organizational structure” (Gordon ¶ [0056] 1st-2nd sentences: obtain real-time data of workforce reductions (eg layoffs, furloughs, etc) affecting an employee's organization. Similarly, ¶ [0119] 2nd sentence: layoffs within organization and/or company at-large, etc.),
“a change in location” (Gordon ¶ [0063] 2nd-3rd sentences: employees associated with the employer or organization may be provided with an opportunity to opt-in to provide location data that may be used to determine their relative daily commutes. For instance, an employee that has a significant commute time (as determined through time series data obtained from the employee's mobile device or application that tracks employee location) may experience increased organizational exhaustion as the addition of a lengthy commute to existing work schedule result in less time for employee downtime. Similarly, ¶ [0119] 2nd sentence: fluctuations [or changes] in …a personal event (e.g. … a move [or change] to a new location, etc.) may be indicative of an increase in the employee's organizational exhaustion over that period of time), “or”
“a software or hardware change anticipated to affect members of a first group” (Gordon ¶ [0235] 1st sentence: techniques herein implemented in electronic hardware, computer software, firmware, or any combination thereof. For example, at ¶ [0045] identify, in real-time any communications that may be indicative of the quality and quantity of tools that an employee may use to perform assigned tasks. The machine learning algorithm or artificial intelligence may process these communications to identify employee sentiment with regard to the quality and quantity of these tools. As being provided with inferior or ineffective tools may result in a degradation of the employee's performance and, thus, increase organizational exhaustion, the machine learning algorithm or artificial intelligence may assign a negative polarity (e.g., score or other metric) to any communications where the employee expresses frustration, disappointment, or other similar sentiment with regard to these tools. Similar ¶ [0092] 2nd-4th sentence: noting employee provided with inferior or ineffective tools may experience a degradation in their performance and, thus, increase their organizational exhaustion)
- “inputting, by the one or more processors, a portion of the change data into a persona model configured to generate responses ”;
(Gordon ¶ [0093] 1st -2nd sentences: machine learning module process communications exchanged amongst employees associated with a particular role to determine a sentiment for each of these employees and a corresponding score or metric for the sentiment. As various employees have similar role within organization, sentiments associated with this role are used to determine a baseline sentiment amongst these employees for the particular role. Specifically, at ¶ [0093] 3rd sentence: machine learning module 210, thus identify any deviations from the baseline sentiment amongst employees having the particular role, as these deviations may be indicative of a change in an employee’s organizational exhaustion. Next, at ¶ [0093] 5th sentence: As scores or other metrics are determined, communications system 104, through a sentiment analysis system 204, may aggregate these scores or other metrics to identify the average sentiment score or other metric for the particular role. For example, at Fig.11 step 1108-> feedback again to -> step 1102->step 1104 and ¶ [0200] 1st-2nd sentence: optimization system aggregate the obtained quantitative partial results to obtain an overall organizational exhaustion score for each employee over a particular period of time. ¶ [0148] 4th sentence: Thus, based on the employee grouping selected from employee grouping panel 604, optimization system automatically identify internal organizations within selected employee grouping and provide organizational exhaustion breakdown according to these internal organizations)
- “generating, by the one or more processors executing the persona model, a response to the portion of the change data”; (Gordon Fig.11, 1108->feedback->1102->1104->1106->1108,
¶ [0202] 1st, 3rd-4th sentences: At step 1108, the optimization system update the historical data according to the obtained quantitative values corresponding to overall organizational exhaustion for each employee and the corresponding qualitative descriptors for each of these quantitative values. An entry within this repository indicate, for a particular employee, amount of organizational exhaustion that the employee may be experiencing over different periods of time, which allow detecting any changes to employee's organizational exhaustion over time. Thus, process 1100 may be continuously performed to obtain up-to-date and real-time aggregated quantitative results and corresponding qualitative descriptors for each employee. ¶ [0211] 1st sentence if optimization system provides recommendations generated using the machine learning algorithm or AI, the optimization system monitor the quantitative results and corresponding qualitative descriptors associated with any impacted employees to determine whether the provided recommendations (if adhered to) have resulted in the desired effect (reduction in organizational exhaustion). ¶ [0077] 2nd-5th sentences: For example, if a manager within organization consistently refuses to grant their employees requests for personal time-off and organizational exhaustion amongst these employees has been increasing over time as a result, optimization system 110 generate a recommendation for the manager to be provided with remedial training or with instructions to improve their rate of granting personal time-off requests. Further, if there are any employees within the manager's purview that are particularly experiencing elevated levels of organizational exhaustion as a result of other factors in addition to the continued rejection of personal time-off requests, optimization system 110 generate a recommendation for these particular employees to be granted their personal time-off requests immediately to address their elevated levels of organizational exhaustion. Further, the recommendation provide various instructions or steps for addressing these other factors. For example, if an employee’s level of organizational exhaustion is elevated as result of traumatic event, optimization system 110 through the recommendation, suggest providing additional support (counseling, communications providing condolences, etc.) to demonstrate employee value to the organization. Similarly, ¶ [0136] 2nd-3rd sentences)
“”
- “generating, by the one or more processors executing an issue reporting function using outputs from the persona model, areas of improvement for the persona model”;
(Gordon ¶ [0078] optimization system 110 further use employee’s level of organizational exhaustion as input to machine learning or artificial intelligence to provide recommendations to employee for usage their personal time-off benefits to reduce their level of organizational exhaustion. For example, the optimization system 110 use the employee's level of organizational exhaustion in conjunction with the employee's personal time-off benefit usage information and other employee performance information (e.g., overtime hours worked, workers' compensation claims received, etc.) to determine the effect of employee usage of their personal time-off benefits in reducing their level of organizational exhaustion. For example, the PTO conversion service clustering algorithms, such as K-means clustering, means-shift clustering, DBSCAN clustering, EM Clustering using GMM, and other suitable machine-learning algorithms, on datasets comprising levels of organizational exhaustion for employees of the organization over a period of time, personal time-off benefit usage for an organization over the period of time, and employee performance for employees of the organization over the period of time. In some implementations, a recurrent neural network (RNN) or convolutional neural network (CNN) may be used to predict correlations between employee usage of personal time-off benefits within an organization and employee levels of organizational exhaustion within the organization. In some implementations, the optimization system 110 use support vector machines (SVM), supervised, semi-supervised, ensemble techniques, or unsupervised machine-learning techniques to evaluate previous usage of personal time-off benefits within an organization and employee levels of organizational exhaustion within the organization to predict the effect of using personal time-off benefits within the organization towards reducing corresponding levels of organizational exhaustion.
Gordon ¶ [0084] 3rd-8th sentences: as an example of training of the machine learning algorithm or artificial intelligence, an evaluator of the machine learning algorithm or artificial intelligence may review the actions or recommendations identified by the machine learning algorithm or artificial intelligence to determine whether these actions or recommendations correspond to the level of organizational exhaustion for the employee or group of employees and characteristics of the employee or group of employees (e.g., any known events impacting their organizational exhaustion, rejection of personal time-off requests, extended periods without breaks or with overtime, etc.). To determine whether these actions or recommendations are appropriate, the evaluator may evaluate feedback corresponding to these actions or recommendations. This feedback include later levels of organizational exhaustion associated with the selected employee or group of employees. The later levels of organizational exhaustion indicate whether the actions or recommendations, if adhered to, led to a reduction or improvement in the levels of organizational exhaustion for the employee or group of employees. The evaluator, using these later levels of organizational exhaustion, determine whether the actions or recommendations provided are appropriate or otherwise consistent for addressing the original levels of organizational exhaustion and associated root causes. Accordingly, based on this evaluation, the evaluator re-train and/or improve the machine learning algorithm or artificial intelligence to improve the likelihood of the machine learning algorithm or artificial intelligence identifying appropriate actions or recommendations according to the levels of organizational exhaustion for an employee or groups of employees. Similarly, ¶ [0138] last 3 sentences. ¶ [0146] 2nd-3rd sentences: For example, in Fig.6A, through the historical burnout risk panel 606, the user may be presented with a histogram illustrating the level of organizational exhaustion company wide over the period of a year (e.g. January through December). This may allow the user to readily identify any changes to the organizational exhaustion amongst employees over this time period and, based on these changes, potentially identify trends that may be indicative of improvement or worsening of organizational exhaustion within the organization)
- “interpreting, by the one or more processors executing a feedback function, the areas of improvement to determine adjustments to the training of the persona model”;
(Gordon ¶ [0084] 4th-8th sentences: To determine whether these actions or recommendations are appropriate, the evaluator evaluate feedback corresponding to these actions or recommendations. This feedback may include later levels of organizational exhaustion associated with the selected employee or group of employees. The later levels of organizational exhaustion may indicate whether the actions or recommendations, if adhered to, led to a reduction or improvement in the levels of organizational exhaustion for the employee or group of employees. The evaluator, using these later levels of organizational exhaustion, may determine whether the actions or recommendations provided are appropriate or otherwise consistent for addressing the original levels of organizational exhaustion and associated root causes. Accordingly, based on this evaluation, the evaluator may re-train and/or improve the machine learning algorithm or artificial intelligence to improve the likelihood of the machine learning algorithm or artificial intelligence identifying appropriate actions or recommendations according to the levels of organizational exhaustion for an employee or groups of employees. ¶ [0146] 2nd-3rd sentences: For example, in Fig.6A, through the historical burnout risk panel 606, the user may be presented with a histogram illustrating the level of organizational exhaustion company wide over the period of a year (e.g. January through December). This may allow the user to readily identify any changes to the organizational exhaustion amongst employees over this time period and, based on these changes, potentially identify trends that may be indicative of improvement or worsening of organizational exhaustion within the organization)
- “outputting, by the one or more processors, the response for display to a user”.
(Gordon Fig.12 step 1218, ¶ [0210] 4th sentence: at step 1218, optimization system provide the aggregated data and these recommendations [or responses] to the user for mitigation of organizational exhaustion associated with the indicated employee. ¶ [0211] 3rd-4th sentences: based on this feedback, optimization system determine whether provided recommendations have had the desired effect. based on this determination, the optimization system update or re-train the machine learning algorithm or artificial intelligence to provide the desired recommendations.
¶ [0165] last 2 sentences: For example, if employees associated with a particular manager indicated through manager field 644 have consistently exhibited a high risk of organizational exhaustion (as indicated through the organizational exhaustion risk field 640), the user determine that the manager or other supervisor may be a contributing factor to the organizational exhaustion associated with their employees. This guide the user to engage the manager or other supervisor in order to reduce the organizational exhaustion within the manager's employees. ¶ [0172] 3rd-5th,7th sentences: in Fig.7, the user opted to compare burnout rate for the employees assigned to manager Lisa Mathewson to average burnout rate for the entire company. Accordingly, through the linear plot, the user may readily determine the difference between the average burnout rate amongst Lisa Mathewson's employees and that of the company as a whole to identify any possible areas of concern with regard to Lisa Mathewson's group. For example, for the months of January, February, and April, the average burnout rate for Lisa Mathewson's team exceeded that of the company as a whole, which may denote an issue within those months. If corrective measures were taken to address burnout within this group, the user may use this information as an indication that these measures may be effective for other teams within the company).
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Gordon Fig.11 (shown left) and Fig.12(shown right) in support of rejection arguments
* While *
Gordon ¶ [0093] 1st -2nd sentences determines a baseline sentiment amongst these employees for the particular role, and then at ¶ [0093] 5th sentence: aggregate these scores or other metrics to identify the average sentiment score or other metric for the particular role
* Nevertheless *
Gordon does not equate such benchmarking on baseline or average sentiment to “simulating”
Gordon also does not teach “wherein the response includes a recommended communication to convey the change data to the first group, the recommended communication being articulated in accordance with at least one of a recommended stylistic scheme or a recommended contextual scheme generated by the persona model based on the simulated reactions of the first group”
* However *
Sagi in analogous obtaining users responses for managerial plan purposes teaches/suggest
-“responses simulating reactions of the first group to the organizational change”
(Sagi ¶ [0007]: g. obtaining observed responses during a simulation of the crisis scenario;
Sagi ¶ [0066] Observed responses (OX) refers to responses to a crisis scenario recorded during simulation of the crisis scenario played out by members of the organization, wherein the observed member response comprises at [0028] initiative, diversity, perception, responsiveness, comeback, perceived centrality, burn-out, and any combination thereof. One example is disclosed by Sagi at ¶ [0090] #6: If your answer to question #5 is “Yes,” how would you describe this experience? Answers: (1) Very good,(2) Good, (3) Not so good, (4) Bad, (5) Very bad)
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Gordon’s “method”/”system”/”non-transitory medium” to have included Sagi’s teachings to have allowed the member characteristics to have been combined with actual decisions made during the simulation, and analyzed to aid in helping predict likely future decision-making behaviors among the members. This info would then be aggregated, employing statistical tools to aid in forecasting the effectiveness of overall organizational crisis response (¶ [0232] in view of MPEP 2143 G and/or F). Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar managerial focused field of endeavor that takes into consideration user input. In such combination each element merely would have performed the same analytical and managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Gordon in view of Sagi, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
* Further *
Peer in analogous obtaining employee responses for managerial plan by optimizing employee communication to improve workplace engagement and retention teaches or suggests:
- “wherein the response includes a recommended communication to convey the change data to the first group, the recommended communication being articulated in accordance with at least one of a recommended stylistic scheme or a recommended contextual scheme generated by the persona model based on the simulated reactions of the first group” (Peer teaches several examples: Peer ¶ [0004] 3rd-4th sentences first acknowledges that: A variety of factors influence employee sentiment, consequently affecting employee career satisfaction. When emotionally stimulated, people become generally less prepared or willing to respond in the best interest of a company; it is easy to imagine a scenario in which a conversation between human resources worker and dissatisfied employee results in worsening of employee satisfaction. In this case,
Peer ¶ [0028] 3rd sentence: the invention includes employee profiles managed by a server, where each employee profile includes conversation history, sentiment history & decision history.
Peer ¶ [0005] 2nd sentence: the goal optimize employee communication to improve employee retention and job performance by employing each employee with accessible, personalized chatbot assistant. ¶ [0028] 1st-2nd sentences similarly states: the invention optimizes employee communication to improve workplace engagement and retention for enhancing employee job satisfaction by facilitating employee communication accomplished by AI-driven messaging system that collects and analyzes data and subsequently returns appropriate conversational responses, in Figs.1,20,21. To this end at Peer ¶ [0005] 5th sentence: conversational data is collected and used to calculate optimal interactions with each employee. See Fig.20 below emphasis on the AI processing noting: Who gets connected. When. Why.
Peer ¶ [0036] 7th-8th sentences: The natural-language processing engine is executed by outputting context scores with the natural-language processing engine. The context scores are a set of key-value pairs of emotions or general attitudes and numeric values which can be analyzed to approximate [or simulate] the overall employee sentiment.
Peer ¶ [0039] 6th-7th sentences: Each contextually-matching activity is compared to the conversation history, the sentiment history, and the decision history of the arbitrary profile to identify at least one personally-matching activity from the plurality of contextually-matching activities. this way, the server determine most effective response from a smaller [or shortest] set of allowable responses (read in light of Original Specification ¶ [0062] as an example of stylistic scheme), rather than from a larger set of all possible response.
Peer ¶ [0005] 3rd-4th sentences The chatbot assistant directly interacts with the employee to determine optimal solutions based on the employee's implicit or explicit needs and aspirations. The chatbot assistant makes suggestions [or recommendations] based on interactions with the employee, such as responding to employee dissatisfaction.
Peer ¶ [0029] 3rd - 4th sentences: the at least one conversation data relates to arrangement of alphanumeric and punctuation characters [as stylistic examples] which form the words of a response by the arbitrary employee profile. The at least one piece of conversation data may include full sentences, sentence fragments, spelling decisions [as stylistic examples], as well as metadata such as timing information. Indeed per, ¶ [0042] 3rd sentence: The piece of conversation data, including any alphanumeric, punctuation, symbolic, hyperlink, image, or other data, is highly valuable in the process of learning how to optimize conversation with an arbitrary profile. Next, at ¶ [0029] 5th- 9th sentences The piece of conversation data is then processed into context scores with the remote server (Step D). The plurality of context scores relates to the values associated with a variety of calculated social metrics, such as overall satisfaction, happiness, frustration, exhaustion, and more. Analysis of the plurality of context scores enables the present invention to approximate [or simulate] the mood of an employee and subsequently generate the optimal response. At least one available activity is assessed for the arbitrary profile in relation to the context scores, the conversation history, the sentiment history, and the decision history for the arbitrary profile with the remote server (Step E). The at least one available activity relates to a set of potentially-appropriate responses, including any text, rhetorical devices, pauses, unconventional characters, [interpreted as stylistic scheme examples] task-related actions, and more, that may be generated by the present invention as a potentially-appropriate response…. ¶ [0030] 4th sentence: The confirmed activity is the response deemed most appropriate following processing by the suggested activity engine.
Peer ¶ [0032] 4th sentence: noting another example where the conversation-initiating message is generated to prevent the present invention from pestering an employee [as another example of context example or stylistic choice] who is perceived to be busy, annoyed, or otherwise unlikely to respond at a given date or time while simultaneously preventing conversational flow from devolving entirely [as another example of stylistic choice]. Also, per ¶ [0030] last sentence: the present invention develop future conversational replies based upon input from the pre-determined responses and the desired response).
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Peer Annotated Fig. 20 from Original Fig. 20 in support of rejection arguments
It would have been obvious to one skilled in the art, to have further modified Gordon / Sagi’s “method”/”system”/”non-transitory medium” to have further included Peer’s teachings or suggestions to have allowed for an effective and efficient development of conversational material necessary to have facilitated communication between an AI and an employee (Peer ¶ [0029] 1st sentence in view of MPEP 2143 G) and having been capable of effectively deconstructing complex rhetoric, including language tools like sarcasm, potential local cultural influence, emojis, image or visual content, and more (Peer ¶ [0036] 1st sentence in view of MPEP 2143 G) as well as capable of being expeditive, when needed, by having determined the most effective response from a smaller set of allowable responses, rather than from a larger set of all possible responses (Peer ¶ [0099] 7th sentence in view of MPEP 2143 G)
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with obtaining employee responses for managerial plan purposes by optimizing employee communication such as to improve workplace engagement and retention. In such combination each element merely would have performed same analytical, decision making and communication function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Gordon / Sagi in further view of Peer, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claims 2,10,17 Gordon/Sagi/Peer teaches all the limitations in claims 1,9,16 above. Further,
Gordon teaches or suggests “further comprising”:
- “inputting, by the one or more processors, the portion of the change data and the response to the portion of the change data into a tracking model configured to generate performance values representative of an ability of the persona model to generate responses representative of the first group” (Gordon ¶ [0084] 2nd-3rd sentences: machine learning algorithm or artificial intelligence may be evaluated to determine, based on sample inputs supplied to the machine learning algorithm or artificial intelligence, whether the machine learning algorithm or artificial intelligence is producing accurate correlations between members of dataset (e.g. given level of organizational exhaustion for a particular employee or group of employees, the machine learning algorithm or artificial intelligence is accurately identifying the appropriate one or more actions for reducing the level of organizational exhaustion). As an example of the training of machine learning algorithm or artificial intelligence, an evaluator of the machine learning algorithm or artificial intelligence may review the actions or recommendations identified by the machine learning algorithm or artificial intelligence to determine whether these actions or recommendations correspond to the level of organizational exhaustion for the employee or group of employees and characteristics of the employee or group of employees (e.g. any known events impacting their organizational exhaustion, rejection of personal time-off requests, extended periods without breaks or with overtime, etc. Similarly, Fig.5, ¶ [0138] 2nd-3rd sentences).
- “wherein the tracking model is trained using a plurality of training change data portions, a plurality of training responses, and a plurality of training performance values as inputs to output a plurality of training performance values and a plurality of training recommended adjustments”;
(Gordon ¶ [0084] 4th-5th sentences: to determine whether these actions or recommendations are appropriate the evaluator evaluate feedback corresponding to these actions or recommendations. This feedback include later levels of organizational exhaustion associated with the selected employee or group of employees. For example, at ¶ [0053] 5th-6th sentences: If employee sentiment accounts for 30% of an employee's organizational exhaustion (sentiment may account for maxim of 300 points from the maxim possible organizational exhaustion score of 1,000 points, etc.), the communications system 104 apply a factor or weight to the normalized sentiment score or metric such that resulting quantitative partial result corresponding to an employee's sentiment is within range of -30% to 30% of the possible organizational exhaustion for the employee (e.g. -300 to 300 points). The resulting quantitative partial result may be negative, as a positive employee sentiment may provide a positive impact against organizational exhaustion, thereby reducing the employee's overall organizational exhaustion score or metric. Similarly ¶ [0064] 2nd sentence: if the workforce events above account for 20% of employee’s organizational exhaustion (e.g. workforce events may account for a maxim of 200 points from the maximum possible organizational exhaustion score of 1,000 points, etc.), the workforce event system 106 may apply a factor or weight to each normalized event score or metric such that the resulting quantitative partial result corresponding to events associated with an employee is within a range of 0% to 20% of the possible organizational exhaustion for the employee e.g. 0 to 200 points. Similarly
Gordon ¶ [0071] 2nd sentence: If lack of usage of allocated time-off benefits described above account for 50% of an employee's organizational exhaustion (e.g. lack of usage of time-off benefits account for maxim 500 points from maxim possible organizational exhaustion score of 1000), personal time-off system 108 apply a factor or weight to each normalized event score or metric such that resulting quantitative partial result corresponding to lack of usage of time-off benefits is within a 0%-50% of possible organizational exhaustion for employee e.g. 0-500 points.
Gordon ¶ [0199] 4th-7th sentences similarly recites: resulting quantitative partial result corresponding to employee’s sentiment within -30% to 30% of the possible organizational exhaustion for the employee (-300 to 300 points). The resulting quantitative partial result may be negative, as a positive employee sentiment provide a positive impact against organizational exhaustion, thereby reducing employee's overall organizational exhaustion score or metric. As other example, the resulting quantitative partial result corresponding to workforce events associated with an employee may be within 0% to 20% of the possible organizational exhaustion for the employee e.g. 0 to 200 points. As yet another example, the resulting quantitative partial result corresponding to the lack of usage of time-off benefits may be within 0% to 50% of possible organizational exhaustion for the employee e.g. 0 to 500 points.
Gordon ¶ [0084] 6th-8th sentences: The later organizational exhaustion levels indicate whether actions or recommendations if adhered led to reduction or improvement in organizational exhaustion levels for the employee or group of employees. The evaluator, using these later levels of organizational exhaustion, determine whether the actions or recommendations provided are appropriate or otherwise consistent for addressing the original levels of organizational exhaustion and associated root causes. based on this evaluation, the evaluator re-train and/or improve the machine learning algorithm or artificial intelligence to improve the likelihood of the machine learning algorithm or artificial intelligence identifying appropriate actions or recommendations according to the levels of organizational exhaustion for an employee or groups of employees. Similarly, Fig.5, ¶ [0138] 4th-8th sentences).
- “generating, by the one or more processors executing the tracking model, a performance value corresponding to the response generated by the persona model”; (Gordon ¶ [0084] 3rd sentence: evaluator of machine learning algorithm or artificial intelligence review the actions or recommendations identified by the machine learning or AI to determine whether these actions or recommendations correspond to the organizational exhaustion level for the employee or group of employees and characteristics of the employee or group of employees (any known events impacting their organizational exhaustion, rejection of personal time-off requests, extended periods without breaks or with overtime, etc. Similar ¶ [0138] 3rd sentence. For algorithmic details see ¶ [0053] 5th-6th sentences, [0064] 2nd sentence, [0071] 2nd sentence, [0199] 4th-7th sentences)
- “comparing, by the one or more processors executing the tracking model, the performance value with a prior performance value to track a performance of the persona model”; (Gordon ¶ [0084] 5th-6th sentences: this feedback include later levels of organizational exhaustion associated with the selected employee or group of employees. The later levels of organizational exhaustion may indicate whether the actions or recommendations, if adhered to, led to a reduction or improvement in the levels of organizational exhaustion for the employee or group of employees. Similarly, ¶ [0138] 5th-6th sentences. Also, ¶ [0133] 4th sentence: by maintaining the employee's amount of organizational exhaustion (scores, metrics, etc.), any changes to the employee's organizational exhaustion may be tracked over time. ¶ [0140] 3rd-4th sentences: optimization recommendation sub-system 506 may automatically monitor adherence to the recommendations and/or actions provided to these different companies, as well as any fluctuations to the levels of organizational exhaustion associated with these different companies. This data, as it is collected, may be used to continuously, and dynamically, update and re-train the machine learning algorithm or artificial intelligence implemented through the machine learning module 510. ¶ [0141] 1st sentence: If optimization recommendation sub-system 506 provides, through request processor 508, any recommendations generated using machine learning algorithm or artificial intelligence, the optimization recommendation sub-system 506 may monitor the quantitative results and corresponding qualitative descriptors associated with any impacted employees to determine whether the provided recommendations (if adhered to) have resulted in the desired effect e.g., reduction in organizational exhaustion. Similar ¶ [0211] 1st sentence) “and”
- “generating, by the one or more processors executing the tracking model, a recommended adjustment to the persona model based on the comparing” (Gordon ¶ [0084] 7th-8th sentences: The evaluator, using these later levels of organizational exhaustion, determine whether the actions or recommendations provided are appropriate or consistent for addressing original organizational exhaustion levels and associated root causes. Based on this evaluation, the evaluator re-train and/or improve the machine learning algorithm or artificial intelligence to improve the likelihood of the machine learning algorithm or artificial intelligence identifying appropriate actions or recommendations according to the levels of organizational exhaustion for an employee or groups of employees. ¶ [0141] 3rd-4th sentences: optimization recommendation sub-system 506 determine whether the provided recommendations have had the desired effect. Further, based on this determination, the optimization recommendation sub-system 506 update or re-train the machine learning algorithm or artificial intelligence to provide the desired recommendations. ¶ [0039] 3rd sentence: if employees categorized as being disengaged based on their determined quantitative values are actually exhibiting symptoms of burnout based on various factors (described in greater detail herein), the optimization system 110 may dynamically adjust the sub-ranges for each of the categories such that the minimum quantitative value corresponding to the burnout category is lowered to include these employees).
Claims 3,11,18. Gordon/Sagi/Peer, teaches all the limitations in claims 1,9,16 above. Further,
Gordon teaches “further comprising:
- “receiving, at the one or more processors, updated change data associated with the organizational change”; (Gordon ¶ [0123] 1st sentence: employer coordinator system 402 periodically, or in response to a triggering event (new employee onboarding, employee termination) obtain employee information including employee personal time-off balances, and the like. ¶ [0058] 3rd-5th sentences: workforce event system 106 further measure the quantity of activities per employee while taking into consideration the employee's role within the organization. For instance, the workforce event system 106 measure the number and complexity of these activities over a period of time given the employee's assigned role. For example, if the employee has been assigned a significant number of complex tasks not usually within the ambit of the employee's responsibilities, this may serve as indication that the employee is more likely to experience organizational exhaustion over time.
Gordon ¶ [0078] 4th-5th sentences: a recurrent neural network (RNN) or convolutional neural network (CNN) predict correlations between employee usage of personal time-off benefits within an organization and employee levels of organizational exhaustion within the organization. The optimization system 110 use support vector machines (SVM), supervised, semi-supervised, ensemble techniques, or unsupervised machine-learning techniques to evaluate previous usage of personal time-off benefits within an organization and employee levels of organizational exhaustion within the organization to predict the effect of using personal time-off benefits within the organization towards reducing corresponding levels of organizational exhaustion. ¶ [0070] 3rd-5th sentences: through RNN or CNN, personal time-off system 108 determine whether a particular manager within an employee group or organization is favoring one or more employees over other employees within the employee group or organization by inordinately approving time-off requests from these one or more employees while continually refusing time-off requests from other employees. This detected favoritism result in increased organizational exhaustion amongst the other employees and, accordingly, the employee group or organization…. through the RNN or CNN, personal time-off system 108 determine whether a particular manager within an employee group or organization is rejecting time-off requests of employees associated with an affinity group. This may serve as an indication of possible discrimination within the employee group or organization, which may significantly increase the level of organizational exhaustion amongst these employees. ¶ [0148] 3rd- 4th sentences: if user has selected the Company Wide option from employee grouping panel 604, the optimization system automatically and in real-time update interface 610 to provide a breakdown of employee organizational exhaustion amongst employees assigned to the different managers within the company. Thus, based on the employee grouping selected from employee grouping panel 604, the optimization system automatically identify any internal organizations within selected employee grouping and provide organizational exhaustion breakdown according to these internal organizations. ¶ [0153] 3rd-4th sentences: in Fig.6B, the average organizational exhaustion for managers Aaron Rizzo and Tony Boone (88% and 70%, respectively) exceed average organizational exhaustion companywide (denoted through vertical indicator). This indicate that teams managed by Aaron Rizzo and Tony Boone are experiencing heightened levels of organizational exhaustion compared to other teams within the company.
Gordon ¶ [0133] 1st-4th sentences: data combination sub-system 502 store new aggregated quantitative results and corresponding qualitative descriptors to a historical data repository 504 storing thereon entries corresponding to the different employees associated with an employer or organization. For example, an entry within the historical data repository 504 indicate, for a particular employee, the amount of organizational exhaustion that the employee may be experiencing over different periods of time (weekly, monthly, etc.). By maintaining employee's amount of organizational exhaustion (scores, metrics, etc.), any changes to the employee's organizational exhaustion may be tracked over time.
Further see Fig.11 step 1108-> feedback again to -> step 1102 and ¶ [0202] At step 1108, the optimization system update the historical data according to obtained quantitative values corresponding to overall organizational exhaustion for each employee and corresponding qualitative descriptors for each of these quantitative values. For instance, the optimization system store the new aggregated quantitative results and corresponding qualitative descriptors to a historical data repository, which may store thereon entries corresponding to the different employees associated with an employer or organization. An entry within this repository indicate, for a particular employee, the amount of organizational exhaustion that the employee may be experiencing over different periods of time, which may allow for detecting any changes to the employee's organizational exhaustion over time. Thus, the process 1100 may be continuously performed to obtain up-to-date and real-time aggregated quantitative results and corresponding qualitative descriptors for each employee)
- “inputting, by the one or more processors, a portion of the updated change data into the persona model”; (Gordon ¶ [0093] 1st -2nd sentences: machine learning module process communications exchanged amongst employees associated with a particular role to determine a sentiment for each of these employees and a corresponding score or metric for the sentiment. As various employees have similar role within organization, sentiments associated with this role are used to determine a baseline sentiment amongst these employees for the particular role. Specifically, at ¶ [0093] 3rd sentence: machine learning module 210, thus identify any deviations from the baseline sentiment amongst employees having the particular role, as these deviations may be indicative of a change in an employee’s organizational exhaustion. Next at ¶ [0093] 5th sentence: As scores or other metrics are determined, communications system 104, through a sentiment analysis system 204, may aggregate these scores or other metrics to identify the average sentiment score or other metric for the particular role. For example, at Fig.11 step 1108-> feedback again to -> step 1102-> step 1104 and ¶ [0200] 1st-2nd sentence: optimization system aggregate the obtained quantitative partial results to obtain an overall organizational exhaustion score for each employee over a particular period of time. ¶ [0148] 4th sentence: Thus, based on the employee grouping selected from employee grouping panel 604, optimization system automatically identify any internal organizations within selected employee grouping and provide organizational exhaustion breakdown according to these internal organizations)
- “generating, by the one or more processors executing the persona model, an updated response to the portion of the updated change data, wherein the updated response represents an updated outlook of the first group with respect to the organizational change”;
(Gordon Fig.11 step 1108-> feedback again to -> step 1102->1104 ->1106->1108 and
¶ [0202] 1st, 3rd-4th sentences: At step 1108, the optimization system update the historical data according to the obtained quantitative values corresponding to overall organizational exhaustion for each employee and the corresponding qualitative descriptors for each of these quantitative values. An entry within this repository indicate, for a particular employee, amount of organizational exhaustion that the employee may be experiencing over different periods of time, which allow detecting any changes to employee's organizational exhaustion over time. Thus, process 1100 may be continuously performed to obtain up-to-date and real-time aggregated quantitative results and corresponding qualitative descriptors for each employee. ¶ [0211] 1st sentence: if optimization system provides recommendations generated using the machine learning algorithm or artificial intelligence, the optimization system monitor the quantitative results and corresponding qualitative descriptors associated with any impacted employees to determine whether the provided recommendations (if adhered to) have resulted in the desired effect (e.g. reduction in organizational exhaustion. ¶ [0077] 2nd-5th sentences: For example, if a particular manager within organization consistently refuses to grant their employees' requests for personal time-off and the organizational exhaustion amongst these employees has been increasing over time as a result, optimization system 110 generate a recommendation for the manager to be provided with remedial training or with instructions to improve their rate of granting personal time-off requests. Further, if there are any employees within the manager's purview that are particularly experiencing elevated levels of organizational exhaustion as a result of other factors in addition to the continued rejection of personal time-off requests, the optimization system 110 may generate a recommendation for these particular employees to be granted their personal time-off requests immediately in order to address their elevated levels of organizational exhaustion. Further, the recommendation may provide various instructions or steps for addressing these other factors. For example, if an employee's level of organizational exhaustion is elevated as a result of a traumatic event, the optimization system 110, through the recommendation, may suggest providing additional support (counseling, communications providing condolences, etc.) in order to demonstrate employee value to the organization. Similarly, ¶ [0136] 2nd-3rd sentences) “and”
- “outputting, by the one or more processors, the updated response for display to the user”
(Gordon Fig.12 step 1218, ¶ [0210] 4th sentence: at step 1218, optimization system provide the aggregated data and these recommendations to the user for mitigation of organizational exhaustion associated with the indicated employee. ¶ [0211] 3rd-4th sentences: based on this feedback, optimization system determine whether provided recommendations have had the desired effect. based on this determination, the optimization system update or re-train the machine learning algorithm or artificial intelligence to provide the desired recommendations. ¶ [0165] last 2 sentences: For example, if employees associated with a particular manager indicated through manager field 644 have consistently exhibited a high risk of organizational exhaustion (as indicated through the organizational exhaustion risk field 640), the user determine that the manager or other supervisor may be a contributing factor to the organizational exhaustion associated with their employees. This guide the user to engage the manager or other supervisor in order to reduce the organizational exhaustion within the manager's employees. ¶ [0172] 3rd-5th,7th sentences: in Fig.7, the user opted to compare burnout rate for the employees assigned to the manager Lisa Mathewson to the average burnout rate for the entire company. Accordingly, through the linear plot, the user may readily determine the difference between the average burnout rate amongst Lisa Mathewson's employees and that of the company as a whole to identify any possible areas of concern with regard to Lisa Mathewson's group. For example, for the months of January, February, and April, the average burnout rate for Lisa Mathewson's team exceeded that of the company as a whole, which may denote an issue within those months. If corrective measures were taken to address burnout within this group, the user may use this information as an indication that these measures may be effective for other teams within the company).
Claims 4,12,19 Gordon/Sagi/Peer, teaches all the limitations in claims 1,9,16 above. Further,
Gordon teaches “further comprising”:
- “aggregating, by the one or more processors, the plurality of group data that includes one or more of: (i) an internal document, (ii) an archived email, (iii) a recorded verbal conversation, or (iv) a recorded live chat”; (Gordon ¶ [0085] 2nd sentence: communications aggregator 202 automatically, in real-time, aggregate raw communications from communications sources 208. For example ¶ [0086] 1st-2nd sentences: communications aggregator 202 obtains, in real-time, raw communications data from communications sources 208 that include communications exchanged through electronic mail servers, chat sessions, voice conversations (telephonic and/or VoIP etc.)
- “aggregating, by the one or more processors, the plurality of training change data”;
(Gordon ¶ [0093] 3rd-6th sentence: machine learning module 210, thus identify any deviations from the baseline sentiment amongst employees having the particular role, as these deviations may be indicative of change in employee's organizational exhaustion. In some instances, machine learning module 210 alternatively provide a score or other metric for the sentiment expressed by an employee associated with a particular role. As scores or other metrics are determined, communications system 104, through a sentiment analysis system 204, aggregate these scores or other metrics to identify the average sentiment score or other metric for the particular role. For each employee associated with the particular role, the sentiment analysis system 204 consider any statistical deviation from the average sentiment score or metric as this may be indicative of a change in sentiment for the employee and, thus indicative of a change in the employee's organizational exhaustion) “and”
- “training, by the one or more processors executing a training module, the persona model with the plurality of training change data and the plurality of group data as inputs to generate the plurality of training responses as outputs” (Gordon ¶ [0089] machine learning module 210 implements a machine learning algorithm or artificial intelligence dynamically trained to perform a semantic analysis of communications data from communications datastore 206. As noted above, this data correspond to communications exchanged via communications channels provided by communications sources 208 and associated with the workforce or organization and/or communication channels that are not associated with the workforce or organization (subject to employee approval). The machine learning algorithm or artificial intelligence implemented by the machine learning module 210 may be dynamically trained to identify, from communications exchanged through different communications channels, keywords, sentence structures, repeated words, punctuation characters and/or non-article words and the like in order to identify employee sentiments expressed through these communications.
Gordon ¶ [0090] 1st-4th sentences: As noted above the machine learning algorithm or artificial intelligence used to determine employee sentiment from communications exchanged between the employee and other employees or other entities not associated with the employer or organization may be trained using supervised learning techniques. Machine learning module 210 select dataset of input communications and known sentiments expressed in input communications for training of machine learning algorithm or artificial intelligence. Machine learning algorithm or artificial intelligence may be evaluated to determine, based on the input sample communications supplied to the machine learning algorithm or artificial intelligence, whether the machine learning algorithm or artificial intelligence is extracting the expected sentiments from each of these communications. Based on this evaluation, the machine learning algorithm or artificial intelligence may be modified or re-trained to increase the likelihood of machine learning algorithm or artificial intelligence generating the desired results.
Gordon ¶ [0093] 5th-6th sentences: As scores or other metrics are determined, communications system 104, through sentiment analysis system 204, may aggregate these scores or other metrics in order to identify the average sentiment score or other metric for the particular role. For each employee associated with the particular role, the sentiment analysis system 204 may consider any statistical deviation from the average sentiment score or metric as this may be indicative of a change in sentiment for the employee and, thus, may be indicative of a change in the employee's organizational exhaustion.
Gordon ¶ [0098] 2nd-3rd sentences: sentiment analysis system 204, aggregates the array of sentiment punctuations corresponding to employee sentiments expressed in the exchanged communications over a particular period of time to determine an average sentiment score or metric that may be used to determine each employee's organizational exhaustion over this particular period of time. By determining an average sentiment score or metric that may be used to determine each employee's organizational exhaustion over this particular period of time, the sentiment analysis system 204 may normalize each employee's sentiment over the particular period of time and obtain quantitative partial results corresponding to the contribution that each employee's sentiment has on their organizational exhaustion. ¶ [0099] As noted above, the normalized sentiment score or metric for an employee may be adjusted according to a factor or weight determined based on the contribution of the employee’s sentiment to overall organizational exhaustion for the employee. For example, if employee sentiment accounts for 30% of employee's organizational exhaustion, sentiment analysis system 204 apply factor or weight to the normalized sentiment score or metric such that the resulting quantitative partial result corresponding to an employee's sentiment is within a range of -30% to 30% of the possible organizational exhaustion for the employee. The resulting quantitative partial result may be negative, as a positive employee sentiment provide a positive impact against organizational exhaustion, thereby reducing the employee's overall organizational exhaustion score or metric. The sentiment analysis system 204 may provide these quantitative partial results to the optimization system 110, which may aggregate these quantitative partial results with other quantitative partial results corresponding to workforce events and requested personal time-off data in order to generate the quantitative values used to determine an amount of organizational exhaustion for the workforce or organization and for each employee associated with the workforce or organization.
Gordon ¶ [0105] noting another example where if event aggregator 302 implements a machine learning algorithm or artificial intelligence to detect when an employee is performing any tasks outside of their regular/formal schedule or during a scheduled time off, the event aggregator 302 dynamically train the machine learning algorithm or artificial intelligence to perform a semantic analysis of exchanged communications to identify any indications of an employee performing tasks outside of their regular/formal schedule or during a scheduled time off. Similarly, ¶ [0152]-¶ [0154], ¶ [0164], ¶ [0172], ¶ [0179], ¶ [0200]).
Claims 5,13,20 Gordon/Sagi/Peer teaches all the limitations in claims 1,9,16 above. Further,
Gordon teaches “wherein”:
- “the persona model includes a plurality of persona models”; (Gordon ¶ [0046] 1st sentence: the communications system 104, through the machine learning algorithm or artificial intelligence, may process communications exchanged amongst employees associated with a particular role (e.g. product team, internal organization, business unit, etc.) to determine a sentiment for each of these employees and a corresponding score or other metric for the sentiment. ¶ [0070] In an embodiment, personal time-off system 108 evaluate the obtained data for a set of employees associated with a particular group or organization to identify any possible inequities within the group or organization that may serve as drivers for increased organizational exhaustion. For instance, the personal time-off system 108 process the obtained data through a recurrent neural network (RNN) or a convolutional neural network (CNN) to predict correlations between employee usage of personal time-off benefits within an employee group or organization and possible inequities whereby one or more employees may be favored over others when granting time-off requests. For example, through the RNN or CNN, the personal time-off system 108 may determine whether a particular manager within an employee group or organization is favoring one or more employees over other employees within the employee group or organization by inordinately approving time-off requests from these one or more employees while continually refusing time-off requests from other employees. This detected favoritism may result in increased organizational exhaustion amongst the other employees and, accordingly, the employee group or organization. As another illustrative example, through the RNN or CNN, the personal time-off system 108 may determine whether a particular manager within an employee group or organization is rejecting time-off requests of employees associated with an affinity group. This may serve as an indication of possible discrimination within the employee group or organization, which may significantly increase the level of organizational exhaustion amongst these employees)
- “each persona model of the plurality of persona models is configured to generate responses representative of a respective subset of the first group”
(Gordon ¶ [0093] 1st -2nd sentences: machine learning module process communications exchanged amongst employees associated with a particular role to determine a sentiment for each of these employees and a corresponding score or metric for the sentiment. As various employees have similar role within organization, sentiments associated with this role are used to determine a baseline sentiment amongst these employees for the particular role.
Gordon ¶ [0077] 2nd-5th sentences: if a particular manager within organization consistently refuses to grant their employees' requests for personal time-off and the organizational exhaustion amongst these employees has been increasing over time as a result, optimization system 110 generate a recommendation for the manager to be provided with remedial training or with instructions to improve their rate of granting personal time-off requests. Further, if there are any employees within the manager's purview that are particularly experiencing elevated levels of organizational exhaustion as a result of other factors in addition to the continued rejection of personal time-off requests, the optimization system 110 may generate a recommendation for these particular employees to be granted their personal time-off requests immediately in order to address their elevated levels of organizational exhaustion. Further, the recommendation may provide various instructions or steps for addressing these other factors. For example, if an employee's level of organizational exhaustion is elevated as a result of a traumatic event, the optimization system 110, through the recommendation, may suggest providing additional support (counseling, communications providing condolences, etc.) in order to demonstrate employee value to the organization. Similarly, ¶ [0136] 2nd-3rd sentences
Gordon ¶ [0172] 3rd -5th,7th sentences: in Fig.7, the user opted to compare burnout rate for the employees assigned to the manager Lisa Mathewson to the average burnout rate for the entire company. Accordingly, through the linear plot, the user may readily determine the difference between the average burnout rate amongst Lisa Mathewson's employees and that of the company as a whole to identify any possible areas of concern with regard to Lisa Mathewson's group. For example, for the months of January, February, and April, the average burnout rate for Lisa Mathewson's team exceeded that of the company as a whole, which may denote an issue within those months. If corrective measures were taken to address burnout within this group, the user may use this information as an indication that these measures may be effective for other teams within the company. ¶ [0153] 3rd sentence: in Fig. 6B, the average organizational exhaustion for managers Aaron Rizzo and Tony Boone (88% and 70%, respectively) may exceed the average organizational exhaustion company wide (denoted through a vertical indicator). This may indicate, to the user, that the teams managed by Aaron Rizzo and Tony Boone are experiencing heightened levels of organizational exhaustion compared to other teams within the company.
Gordon ¶ [0162] 3rd-6th sentences, ¶ [0163] 3rd-4th sentences: in Fig.6C, because employee Otto Leon has corresponding 92% risk of organizational exhaustion, highest amongst all employees associated with selected employee grouping, the optimization system present Otto Leon and their corresponding metrics above all other employees associated with the selected employee grouping. In some instances, the optimization system, through the organizational exhaustion risk field 640, provide the user with an option to sort the set of employees presented through the interface 634 according to their risk of organizational exhaustion)
- “each respective subset of the first group representing a different perspective with respect to the organizational change from every other respective subset of the first group”; (Gordon ¶ [0106] 4th - 6th sentences: event aggregator 302 measure the quantity of activities per employee while taking into consideration the employee role within the organization. For instance, event aggregator 302 measure number and complexity of these activities over a period of time given the employee's assigned role. For example, if the employee has been assigned a significant number of complex tasks that are not usually within the ambit of employee’s responsibilities, this may serve as indication that the employee is more likely to experience organizational exhaustion over this period of time. Similarly, ¶ [0112] event aggregator 302 may further identify, from the time series data and/or from other data obtained from the employer systems, the amount of time each employee has been assigned to a particular role and/or has held their current job title. For instance, if a particular employee has held their current job title for a significant period of time, this may be an indicator of a lack of advancement in the particular employee's career. This lack of advancement may result in a greater level of organizational exhaustion for the particular employee, as the particular employee (over time) may become disillusioned with their current role. As another illustrative example, if a particular role is associated with a higher level of stress (e.g., greater responsibilities, greater overtime requirements, greater number of direct reports, etc.), the level of organizational exhaustion for an employee assigned to this particular role may increase at greater rate over time. ¶ [0113] 3rd,5th-6th sentences: an employee nearing retirement age may be more susceptible to organizational exhaustion as they experience greater fatigue resulting from overwork, assignment of a greater number of tasks or roles, inability to take advantage of their personal time-off benefits, etc. Factors that lead to greater organizational exhaustion may be generational, whereby employees within certain age groups may be more susceptible to experiencing greater levels of organizational exhaustion compared to their peers in other age groups. For example, millennial and Generation Z employees may be more susceptible to organizational exhaustion resulting from overwork and/or lack of personal time-off when compared to Generation X employees. ¶ [0153] 3rd sentence: in Fig. 6B, average organizational exhaustion for managers Aaron Rizzo and Tony Boone (88% and 70%, respectively) may exceed the average organizational exhaustion company wide (denoted through a vertical indicator). This may indicate, to the user, that the teams managed by Aaron Rizzo and Tony Boone are experiencing heightened levels of organizational exhaustion compared to other teams within the company. ¶ [0162] 3rd-6th sentences, ¶ [0163] 3rd-4th sentences: in Fig.6C, because employee Otto Leon has a corresponding 92% risk of organizational exhaustion, the highest amongst all employees associated with selected employee grouping, the optimization system present Otto Leon and their corresponding metrics above all other employees associated with the selected employee grouping. In some instances, the optimization system, through the organizational exhaustion risk field 640, provide the user with an option to sort the set of employees presented through the interface 634 according to their risk of organizational exhaustion)
- “each persona model of the plurality of persona models is trained using the plurality of training change data and a plurality of subset group data as inputs to output a respective plurality of training responses”; (Gordon ¶ [0243] 3rd sentence: the set of data may be analyzed using a variety of machine learning algorithms to identify correlations between different elements of the set of data. Specifically, at ¶ [0039] while aforementioned quantitative value range, normalization table categories, and corresponding sub-ranges for each of these categories are described extensively herein for the purpose of illustration, other parameters may be used to denote a level of organizational exhaustion for employees associated with the workforce or organization. For example, the quantitative value sub-ranges for each of aforementioned categories may be dynamically changed based on an evaluation of the actual organizational exhaustion of the workforce over time. For instance, if employees categorized as being disengaged based on their determined quantitative values are actually exhibiting symptoms of burnout based on various factors (described in greater detail herein), the optimization system 110 may dynamically adjust the sub-ranges for each of the categories such that the minimum quantitative value corresponding to the burnout category is lowered to include these employees. As another example, while organizational exhaustion may be described according to the aforementioned categories, additional and/or alternative categories of organizational exhaustion may be introduced to provide a more granular qualitative description of employee’s level of organizational exhaustion. These additional and/or alternative categories may have corresponding quantitative value sub-ranges to allow for normalization of quantitative values according to these additional and/or alternative categories. ¶ [0058] workforce event system 106 further evaluate the workforce associated with each employee (e.g. product team, internal organization, business unit, etc.) to measure any discrepancies between the number of current, active employees within the workforce and the required number of active employees. For example, if a particular product team is understaffed, an employee within this particular product team may be more prone to experiencing organizational exhaustion as opposed to another employee within a different product team that is fully or adequately staffed. The workforce event system 106 further measure the quantity of activities per employee while taking into consideration the employee's role within the organization. For instance, workforce event system 106 may measure the number and complexity of these activities over a period of time given the employee's assigned role. For example, if the employee has been assigned a significant number of complex tasks that are not usually within the ambit of the employee's responsibilities, this may serve as an indication that the employee is more likely to experience organizational exhaustion over this period of time.
Gordon ¶ [0093] 3rd-5th sentences: machine learning module 210, thus identify any deviations from the baseline sentiment amongst employees having the particular role, as these deviations may be indicative of a change in an employee’s organizational exhaustion. The machine learning module 210 alternatively provide a score or other metric for the sentiment expressed by an employee associated with a particular role. As scores or other metrics are determined, communications system 104, through a sentiment analysis system 204, aggregate these scores or other metrics in order to identify average sentiment score or other metric for the particular role. For each employee associated with the particular role, sentiment analysis system 204 may consider any statistical deviation from the average sentiment score or metric as this may be indicative of a change in sentiment for the employee and, thus, may be indicative of a change in the employee's organizational exhaustion. ¶ [0098] 2nd - 3rd sentences: sentiment analysis system 204, in an embodiment, aggregates the array of sentiment punctuations corresponding to employee sentiments expressed in the exchanged communications over a particular period of time to determine an average sentiment score or metric used to determine each employee's organizational exhaustion over this particular period of time. By determining an average sentiment score or metric that may be used to determine each employee's organizational exhaustion over this particular period of time, the sentiment analysis system 204 may normalize each employee's sentiment over the particular period of time and obtain quantitative partial results corresponding to the contribution that each employee's sentiment has on organizational exhaustion.
Gordon ¶ [0111] event aggregator 302 further identify, from the time series data and/or from other data obtained from the one or more employer systems, the number of job titles and number of direct reports (supervisors, managers, etc.) associated with each employee within the organizations being evaluated. For instance, as the number of job titles increases for an employee, the organizational exhaustion associated with this employee may increase as the number of job titles may be correlated with the number of tasks and responsibilities assigned to the particular employee at any given time. Further, as the number of job titles increases, there is a higher likelihood that an employee may be required to perform disparate and unrelated duties across these different job titles, which may have a negative impact on the employee's level of organizational exhaustion. Similarly, there may be a correlation between the number of direct reports and an employee's level of organizational exhaustion. For instance, a greater number of direct reports may be indicative of an employee having to satisfy a greater number of requirements associated with their duties and tasks, as these duties and tasks may be supervised by a greater number of entities that may have different requirements that the employee may be required to satisfy. This greater level of scrutiny may result in a greater level of organizational exhaustion for the employee. ¶ [0112] event aggregator 302 further identify, from the time series data and/or from other data obtained from the one or more employer systems, the amount of time each employee has been assigned to a particular role and/or has held their current job title. For instance, if a particular employee has held their current job title for a significant period of time, this may be an indicator of a lack of advancement in the particular employee's career. This lack of advancement may result in a greater level of organizational exhaustion for the particular employee, as the particular employee (over time) may become disillusioned with their current role. As another illustrative example, if a particular role is associated with higher level of stress (e.g. greater responsibilities, greater overtime requirements, greater number of direct reports, etc.), the level of organizational exhaustion for an employee assigned to this particular role may increase at a greater rate over time. ¶ [0113] demographic information associated with the particular organization being evaluated may be used by the event aggregator 302 to determine their contribution to the level of organizational exhaustion for each employee associated with the particular organization. As an illustrative example, an employee's age may serve as a contributing factor to the employee's level of organizational exhaustion. For instance, an employee that may be nearing retirement age may be more susceptible to organizational exhaustion, as they may experience greater fatigue resulting from overwork, assignment of a greater number of tasks or roles, inability to take advantage of their personal time-off benefits, and the like. Further, if an employee's company or organization has been the subject of age-based discrimination claims in the past, an employee's age may be a contributing factor to their organizational exhaustion as their age may create an apprehension towards the company or organization. In some instances, factors that may lead to greater organizational exhaustion may be generational, whereby employees within certain age groups may be more susceptible to experiencing greater levels of organizational exhaustion compared to their peers in other age groups. For example, millennial and Generation Z employees may be more susceptible to organizational exhaustion resulting from overwork and/or lack of personal time-off when compared to Generation X employees.
Gordon ¶ [0152] 4th-5th sentences: as noted above the optimization system generate quantitative values used to determine an amount of organizational exhaustion for the workforce or organization and for each employee associated with the workforce or organization. Using these quantitative values, the optimization system determine an average quantitative value corresponding to the average level of organizational exhaustion for each sub-group. ¶ [0153] In an embodiment, the optimization system indicates, through organizational exhaustion rate field 618, the average organizational exhaustion for the selected employee grouping in addition to the average organization exhaustion for each sub-group. This may allow the user to readily determine whether the average organizational exhaustion for a sub-group exceeds that of the selected employee grouping as a whole. For example, as illustrated in FIG. 6B, the average organizational exhaustion for managers Aaron Rizzo and Tony Boone (88% and 70%, respectively) may exceed the average organizational exhaustion company wide (denoted through a vertical indicator). This may indicate, to the user, that the teams managed by Aaron Rizzo and Tony Boone are experiencing heightened levels of organizational exhaustion compared to other teams within the company. ¶ [0154] last two sentences: Similar to the organizational exhaustion rate field 618 described above, the churn rate for each sub-group may be represented as a percentage corresponding to the amount of turnover amongst employees within each sub-group. Further, the churn rate field 620 may further indicate the average churn rate for the selected employee grouping in addition to the churn rate for each sub-group) “the method further comprises”:
- “inputting, by the one or more processors, the portion of the change data into the plurality of persona models” (Gordon [0093] 1st-2nd sentences machine learning module process communications exchanged amongst employees associated with a particular role to determine a sentiment for each of these employees and a corresponding score or metric for the sentiment. As various employees have similar role within organization, sentiments associated with this role are used to determine a baseline sentiment amongst these employees for the particular role. Specifically, at ¶ [0093] 3rd sentence: machine learning module 210, thus identify any deviations from the baseline sentiment amongst employees having the particular role, as these deviations may be indicative of a change in an employee’s organizational exhaustion. Next at ¶ [0093] 5th sentence: As scores or other metrics are determined, communications system 104, through a sentiment analysis system 204, may aggregate these scores or other metrics to identify the average sentiment score or other metric for the particular role. For example, at Fig.11 step 1108-> feedback again to -> step 1102-> step 1104 and ¶ [0200] 1st-2nd sentence: optimization system aggregate the obtained quantitative partial results to obtain an overall organizational exhaustion score for each employee over a particular period of time. ¶ [0148] 4th sentence: Thus, based on the employee grouping selected from employee grouping panel 604, optimization system automatically identify any internal organizations within selected employee grouping and provide organizational exhaustion breakdown according to these internal organizations)
- “generating, by the one or more processors executing the plurality of trained persona models, a plurality of responses to the portion of the change data” (Gordon Fig.11 step 1108-> feedback again to -> step 1102->1104-> 1106->1108, ¶ [0202] 1st, 3rd-4th sentences: At step 1108, the optimization system update the historical data according to obtained quantitative values corresponding to overall organizational exhaustion for each employee and the corresponding qualitative descriptors for each of these quantitative values. An entry within this repository indicate, for a particular employee, amount of organizational exhaustion that the employee may be experiencing over different periods of time, which allow detecting any changes to employee's organizational exhaustion over time. Thus, process 1100 is continuously performed to obtain up-to-date and real-time aggregated quantitative results & corresponding qualitative descriptors for each employee. ¶ [0211] 1st sentence: if optimization system provides recommendations generated using the machine learning algorithm or artificial intelligence, the optimization system monitor the quantitative results & corresponding qualitative descriptors associated with any impacted employees to determine whether the provided recommendations (if adhered to) have resulted in desired effect (reduction in organizational exhaustion). ¶ [0077] 2nd-5th sentences: For example, if a particular manager within organization consistently refuses to grant their employees' requests for personal time-off and the organizational exhaustion amongst these employees has been increasing over time as a result, optimization system 110 generate a recommendation for the manager to be provided with remedial training or with instructions to improve their rate of granting personal time-off requests. Further, if there are any employees within the manager's purview that are experiencing elevated levels of organizational exhaustion as a result of other factors in addition to the continued rejection of personal time-off requests, the optimization system 110 generate a recommendation for these particular employees to be granted their personal time-off requests immediately in order to address their elevated levels of organizational exhaustion. Further, the recommendation may provide various instructions or steps for addressing these other factors. For example, if an employee's level of organizational exhaustion is elevated as a result of a traumatic event, the optimization system 110, through the recommendation, may suggest providing additional support (counseling, communications providing condolences, etc.) in order to demonstrate employee value to the organization. Similarly, ¶ [0136] 2nd-3rd sentences) “and”
- “outputting, by the one or more processors, the plurality of responses for display to the user” (Gordon Fig.12 step 1218, ¶ [0210] 4th sentence: at step 1218, optimization system provide the aggregated data and these recommendations to the user for mitigation of organizational exhaustion associated with the indicated employee. ¶ [0211] 3rd-4th sentences: based on this feedback, optimization system determine whether provided recommendations have had the desired effect. based on this determination, the optimization system update or re-train the machine learning algorithm or artificial intelligence to provide the desired recommendations.
Gordon ¶ [0165] last 2 sentences: For example, if employees associated with a particular manager indicated through manager field 644 consistently exhibited a high risk of organizational exhaustion (as indicated through the organizational exhaustion risk field 640), the user determine that the manager or other supervisor may be a contributing factor to the organizational exhaustion associated with their employees. This guide the user to engage the manager or other supervisor to reduce the organizational exhaustion within the manager's employees.
Gordon ¶ [0172] 3rd-5th,7th sentences: in Fig.7, the user opted to compare burnout rate for the employees assigned to the manager Lisa Mathewson to the average burnout rate for the entire company. Accordingly, through the linear plot, the user may readily determine the difference between the average burnout rate amongst Lisa Mathewson's employees and that of the company as a whole to identify any possible areas of concern with regard to Lisa Mathewson's group. For example, for January, February, and April, the average burnout rate for Lisa Mathewson's team exceeded that of the company as a whole, which may denote an issue within those months. If corrective measures were taken to address burnout within this group, the user may use this information as indication that these measures may be effective for other teams within company).
Claims 6,14 Gordon/Sagi/Peer teaches all the limitations in claims 1,9 above. Furthermore,
Gordon teaches wherein the response includes at least one of:
(i) a predicted subsequent response strategy to address the first group,
(ii) a change receptiveness likelihood value,
(iii) a predicted uptake time value,
(iv) a best practices indication, (Gordon ¶ [0081] 2nd-3rd sentences: optimization system 110 provide a recommendation to employee experiencing a significant level of organizational exhaustion to use their personal time-off benefits for vacation. In addition to the recommendation, the optimization system 110 provide an indication of an expected reduction to the employee's level of organizational exhaustion should the employee use their personal time-off benefits for the recommended vacation. ¶ [0136] 4th sentence if there are any employees within the manager's purview that are particularly experiencing elevated levels of organizational exhaustion as a result of other factors in addition to the continued rejection of personal time-off requests, the optimization recommendation sub-system 506 may generate a recommendation for these particular employees to be granted their personal time-off requests immediately in order to address their elevated levels of organizational exhaustion)
(v) a successful adoption likelihood value, (Gordon ¶ [0080] 3rd sentence: A confidence score may be a percentage value where the higher the percentage, the more likely the identified impact is a good correlation for the employee. ¶ [0084] last sentence: based on this evaluation, the evaluator re-train and/or improve the machine learning algorithm or artificial intelligence to improve likelihood of the machine learning algorithm or artificial intelligence identifying appropriate actions or recommendations according to the levels of organizational exhaustion for an employee or groups of employees)
(vi) an estimated timeline for adoption, or
(vii) a realization value (Gordon ¶ [0136] 3rd sentence: if a particular manager within the organization consistently refuses to grant their employees' requests for personal time-off and the organizational exhaustion amongst these employees has been increasing over time as a result, the optimization recommendation sub-system generate a recommendation for the manager with remedial training or with instructions to improve their rate of granting personal time-off requests)
Claims 7,15 Gordon/Sagi/Peer teaches all the limitations in claims 1,9 above. Furthermore,
Gordon teaches “further comprising”:
- “extracting, by the one or more processors, the portion of the change data from the change data”; (Gordon ¶ [0149] 1st-2nd sentences: in Fig.6B, interface 610 include a date range drop down menu 612, through which user select a time range for presentation of organizational exhaustion within the selected employee grouping. For example, in Fig.6B, the user has used the date range drop down menu 612 to select an option to present the organizational exhaustion amongst employees within the selected employee grouping for the year up to present day. Similarly, ¶ [0171] 3rd sentence: in Fig.7 the user use a drop down menu 706 to select the particular metric for which the user would like to compare the selected manager or other supervisor to the selected employee grouping)
- “creating, by the one or more processors, a formatted input that includes a plurality of prompts for the persona model based on the portion of the change data” (Gordon Fig.6B, ¶ [0151] 1st sentence Through interface 610, the optimization system provide various fields, through which the user review organizational exhaustion amongst different sub-groups within elected employee grouping and other metrics that correspond to the organizational exhaustion amongst these different sub-groups. ¶ [0152] interface 610 include organizational exhaustion rate field 618, to denote organizational exhaustion level within each sub-group indicated through sub-group field 616. in Fig.6B, the rate of organizational exhaustion is provided as % corresponding to average level of organizational exhaustion within the sub-group. Further, a higher % value denote a higher level of organizational exhaustion within the sub-group. As noted, the optimization system generate quantitative values used to determine an amount of organizational exhaustion for the workforce or organization and for each employee associated with the workforce or organization. Using these quantitative values, the optimization system determine average quantitative value corresponding to the average level of organizational exhaustion for each sub-group. As the quantitative value may be defined as a scalar [format] value within a particular range, the optimization system define the % as function of determined average quantitative value for the sub-group and the maxim possible quantitative value for denoting organizational exhaustion.
Gordon ¶ [0117] 3rd-4th sentences noting a different example where event aggregator 302 prompt each employee to provide their authorization for obtaining biometric data from their fitness devices and/or applications. In response to this prompt, a user provide their authorization and indicate which fitness devices and/or applications are to be monitored in real-time in order to obtain their biometric data. If employee authorization is provided, event aggregator 302, through the API automatically, and in real-time, pulls employee's biometric data from the employee's designated fitness devices and/or applications as this biometric data is generated.
Gordon ¶ [0119] 1st-2nd sentences: using employee biometric data, event aggregator 302 automatically detect any fluctuations in employee's health indicative of a change in the employee's organizational exhaustion. For example, fluctuations in an employee's heart rate, blood pressure, weight, etc. over the course of a period of time that corresponds to a workplace event (e.g. increased overtime, additional responsibilities assigned to the employee, layoffs within the organization and/or company at-large, etc.) or a personal event (e.g., death in the family, birth of a new child, a divorce, a move to a new location, etc.) may be indicative of an increase in the employee's organizational exhaustion over that period of time.
Gordon ¶ [0120] 1st, 3rd-4th sentences: the event aggregator 302 stores data corresponding to the detected events and any calculated scores or metrics corresponding to these events in an event datastore 304. Thus, an entry corresponding to a particular detected event indicate the involved employees, as well as any timestamps corresponding to when the particular detected event occurred. This information may be used by normalization module 306 implemented by the workforce event system 106 to aggregate and normalize [or format] any employee scores or metrics corresponding to the detected events over a particular period of time (weekly, monthly, etc) to determine contribution of these events to the employee's overall organizational exhaustion.
Gordon ¶ [0121] normalization module 306 processes the various scores and metrics from event datastore 304 and corresponding to the measurements above using linear normalization to generate a quantitative partial result corresponding to the impact these particular workforce events have had on employees' organizational exhaustion over a period of time. For example, if workforce events above account for 20% of employee's organizational exhaustion, normalization module 306 may apply a factor or weight to each normalized event score or metric such that the resulting quantitative partial result corresponding to events associated with an employee is within a range of 0% to 20% of the possible organizational exhaustion for the employee. Similarly
Gordon ¶ [0149] 3rd - 4th sentences: selection of a time range within date range drop down menu 612 cause the optimization system to dynamically, and in real-time, update the interfaces described herein to present the organizational exhaustion amongst employees within the selected employee grouping over the selected time range. For example, if the user selects a time range from date range drop down menu 612, the optimization system may additionally update the historical burnout risk panel 606 and organizational exhaustion breakdown panel 608 above in connection with Fig.6A to present the organizational exhaustion amongst employees within the selected employee grouping over the selected time range. The normalization module 306 may provide these quantitative partial results to optimization system 110, which may aggregate these quantitative partial results with other quantitative partial results corresponding to employee sentiments and requested personal time-off data in order to generate the quantitative values used to determine an amount of organizational exhaustion for the workforce or organization and for each employee associated with the workforce or organization); “and”
- “inputting, by the one or more processors, the formatted input into the persona model as part of the portion of the change data” (Gordon ¶ [0078] 1st sentence: optimization system uses employee level of organizational exhaustion as input to machine learning algorithm or artificial intelligence to provide recommendations to the employee for usage of their personal time-off benefits to reduce their level of organizational exhaustion. Normalization module 306 provide these quantitative partial results to optimization system 110, which may aggregate these quantitative partial results with other quantitative partial results corresponding to employee sentiments and requested personal time-off data to generate the quantitative values used to determine an amount of organizational exhaustion for the workforce or organization and for each employee associated with the workforce or organization).
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Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over
Gordon/Sagi/Peer as applied to claim 1, and in further view of
Sarkar el al US 20230073916 A1 hereinafter Sarkar. As per,
Claim 8 Gordon teaches all the limitations in claim 1 above. Furthermore,
Gordon teaches “further comprising”:
- “receiving, at the one or more processors, a first verbal communication of the portion of the change data”; (Gordon ¶ [0041] 2nd-4th sentences: communications system 104 automatically, and in real-time, monitors employee communications to identify indicators of organizational exhaustion. The employee communications include any communications exchanged within the workforce or organization by an employee through the communications channels provided by the organization. For example, communications system monitor communications exchanged through electronic mail servers, chat sessions, voice conversations (e.g. telephonic and/or Voice over Internet Protocol (VoIP)). ¶ [0086] 1st sentence: communications aggregator 202 obtains communications data from communications sources that include communications exchanged through voice conversations)
* While *
Gordon recites at ¶ [0091] 1st sentence: machine learning module 210 may implement the machine learning algorithm or artificial intelligence using NLP, which can process both textual and audial communications to identify the keywords, sentence structures, repeated words, punctuation characters and/or non-article words and the like expressed within each communication in real-time, and similarly at ¶ [0176] 2nd, 6th sentences,
* However *
Gordon/Sagi/Peer does not teach:
- “converting, by the one or more processors, the first verbal communication to a first text string representing the portion of the change data”;
- “generating, by the one or more processors executing the persona model, the response to the portion of the change data as a second text string;
- “converting, by the one or more processors, the second text string to a second verbal communication”; “and”
- “causing, by the one or more processors, the second verbal communication to be conveyed to the user”.
Sarkar in analogues managing organizational changes vis-à-vis fatigue teaches/suggest
- “converting, by the one or more processors, the first verbal communication to a first text string representing the portion of the change data”; (Sarkar ¶ [0067] 2nd-4th sentences: in Fig.1, operator's response 102 comprises: “Hey Bro! Say 6! Today is cold and working too fast!”. The operator's response 102 may first perform suitable speech recognition to convert the audio signals into an appropriate (e.g. text-based) format for analysis)
- “generating, by the one or more processors executing the persona model, the response to the portion of the change data as a second text string”; (Sarkar ¶ [0067] 5th - 10th sentences: natural language-based processing 103 thus perform semantic analysis in order to extract the relevant information. For example, in a first step, the operator's response may be processed in order to remove any extraneous or linking words to thereby extract the actual information content of the response. In the example in Fig.1, this would involve extracting the operator's perceived fatigue level, i.e. ‘6’, as well as the relevant key words/phrases ‘cold’ and ‘too fast’. This can be done in various ways, e.g. using any suitable and desired natural language processing algorithms. Typically this will involve training the algorithm based on the system in question in order to learn key words/phrases. For example, the natural language processing unit (NLP) may be trained using historic data, or during dedicated testing sessions, to understand the relationships between the operator's response and the perceived fatigue level and environmental causes of fatigue, and so on. ¶ [0068] Once operator's response 102 has been processed to determine a current operator fatigue level, as well as the contextual information relating to the causes of the operator fatigue, this information can then be processed further in order to determine a corresponding set of actions 104 to take to try to reduce the operator's fatigue level. For instance, in Fig.1, the actions 104 comprises increasing the temperature and/or slowing down the robotic arm. ¶ [0069] These actions 104 can then be implemented appropriately. The monitoring apparatus 100 then validate that the actions 104 have had the desired effect by issuing another suitable operator query 105, e.g. Are you feeling better now?’. Assuming the operator responds positively 106, the obtained information can then be combined into a new rule that can be subsequently, automatically applied when similar conditions to those which causes the operator fatigue are encountered in the future. For example, in Fig.1, a rule is generated as follows: “If <operator_name> is working and if 10° C.<ambient temperature<16°C, then reduce robotic arm speed for 20 minutes and turn on heater).
- “converting, by the one or more processors, the second text string to a second verbal communication”; (Sarkar ¶ [0068] Once operator's response 102 has been processed to determine a current operator fatigue level and the contextual information relating to the causes of operator fatigue, this info can then be processed further to determine a corresponding set of actions 104 to reduce the operator's fatigue level. For example, at ¶ [0069] 4th sentence in Fig.1, a rule is generated as follows: “If <operator_name> is working and if 10° C.<ambient temperature<16° C., then reduce robotic arm speed for 20 minutes and turn on heater”. ¶ [0069] 1st sentence: these actions 104 can then be implemented appropriately. The monitoring apparatus 100 then validate that actions 104 have had the desired effect by issuing another suitable operator query 105, e.g. Are you feeling better now?) “and”
- “causing, by the one or more processors, the second verbal communication to be conveyed to the user” (Sarkar ¶ [0069] These actions 104 can then be implemented appropriately. the monitoring apparatus 100 then validate that the actions 104 have had the desired effect by issuing another suitable operator query 105 e.g Are you feeling better now? Assuming the operator responds positively 106, the obtained info can then be combined into a new rule that can be subsequently, automatically applied when similar conditions to those which causes the operator fatigue are encountered in the future. For example, in Fig.1, a rule is generated as follows: “If <operator_name> is working and if 10° C.<ambient temperature<16° C., then reduce robotic arm speed for 20 minutes and turn on heater)
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Sarkar Fig.1 in support of rejection arguments
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Gordon / Sagi / Peer “method”/system/medium” to have further included Sarkar’s teachings in order to have provided improved systems and methods for monitoring operator fatigue in highly stressed environments (Sarkar ¶ [0002]- ¶ [0004] in view of MPEP 2143 G). The predictability of such modification would have been corroborated by the broad level of skills of one of ordinary skills in the art as articulated by Gordon ¶ [0011] 2nd sentences, ¶ [0271], ¶ [0272], ¶ [0277], ¶ [0278], in view of Sagi ¶ [0077] and in further view of Sarkar ¶ [0058], ¶ [0084] and Peer ¶ [0046]. Moreover, Gordon ¶ [0044] ¶ [0057] 1st sentence, ¶ [0091], ¶ [0102] last sentence, ¶ [0176] last sentence teaches Natural Language Processing (NLP) and Sagi ¶ [0235] processes answers to the respective question and Peer ¶ [0036], ¶ [0043] also teaches natural language processing. Thus the language centric modeling of Gordon in view of Sagi and Peer would have been prime to have been modified by the natural language processing (NLP) teachings of suggestions as further provided by Sarkar ¶ [0030] -¶ [0031], ¶ [0033], ¶ [0045], ¶ [0067], ¶ [0080], ¶ 0081] 4th sentence.
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar managing organizational changes field of endeavor. In such combination each element would have merely performed same analytical, predictive and organizational / managerial function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Gordon Peer / Sagi in further view of Sarkar, the to be combined elements would have fitted together, like puzzle pieces in a logical, complementary, technologically feasible and or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
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Conclusion
Following art is made of record and considered pertinent to Applicant’s disclosure:
- Zhernova, et al, Detection and Prevention of Professional Burnout Using Machine Learning Methods, 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), pp. 218-221, 2020,
- WO 2021173519 A1 teaching Severance event modeling and management system
- US 20210272216 A1 teaching Severance event modeling and management system
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624 A
June 2nd, 2026
1 MPEP 2106.04(a) last ¶ which states that: “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible, if a claim limitation(s) is determined to fall within multiple groupings…”.
2 Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972)
3 Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014).
4 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014);
Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972);
Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
5 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
6 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
7 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
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