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.
This application is a continuation application of U.S. application 18490849 filed on 10/20/2023. See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
37 CFR § 1.105 - Requirement for Information
Applicant and the assignee of this application are required under 37 CFR 1.105 to provide the following information that the examiner has determined is reasonably necessary to the examination of this application. Examiner’s search appears to suggest Applicant sold or publicly used the following product(s): PTO Optimizer, which includes Artificial Intelligence easily surfaces company-friendly opportunities for employees to take off and proactively encourages employees to use PTO, and Employee Pulse, including active monitoring and alert employees/departments at risk of burning out. These are demonstrated by at least the following publications:
Albinus Phil, 2022 Top HR Product PTO Genius, August 22, 2022 p.1, ¶1-¶2 extracted below:
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HR Technology Conference & Exposition, PTO genius profile, 2021, at p.2-p.3
Human Resource Executive HR Technology Conference and Exposition, globalwire webpages, August 23, 2022 at p.5
HR Tech 2022 PTO Genius, HR tech, 2022 at p.2
PTO Genius Named a Top HR Product of the Year PTOGenius webpages, August 24, 2022 p.2-3
Feffer Mark, Podcast What Burnout, Turnover and Time Off Have in Common, HCM Technology Report, May 27, 2022
PTOGenius, Do more with your paid time off, Oct 9th, 2021
Jessica Lin, It is Never Been More Critical To Utilize Your PTO, Here is The Startup Making It Happen, forbes, Aug 17, 2021, emphasis on the PTO genius platform at p.3 below:
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HR Tech 2022, PTO Genius co-founder discusses...burnout, excerpts, Human Resource Executive youtube channe, September 25th, 2023, emphasis on excerpts 1:13/2:36 and 2:23/2:36 below
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These findings are relevant to at least the current “processing the set of quantitative partial results through a trained optimization recommendation machine learning algorithm to generate the organizational exhaustion metrics and a set of recommendations for reducing organizational exhaustion amongst the set of employees, wherein the trained optimization recommendation machine learning algorithm is trained using historical recommendations for mitigating organizational exhaustion” of Claims 1,8,15.
The information is required to identify products and services embodying the disclosed subject matter of systems and methods for exhaustion mitigation and organization optimization and identify the properties of similar products and services found in the prior art.
In response to this requirement, please provide any additional citation and a copy of each publication that any of the applicants relied upon to develop the disclosed subject matter that describes the applicant’s invention, particularly as to developing the concept of exhaustion mitigation and organization optimization.
For each publication, please provide a concise explanation of the reliance placed on that publication in the development of the disclosed subject matter. Specifically, the examiner requests brochures, manuals, white papers, training materials, demos, sales presentations or the like related to the aforementioned product(s) software and/or other software directed to exhaustion mitigation and organization optimization.
In response to this requirement, please provide the citation and a copy of each publication that any of the applicants relied upon to draft the claimed subject matter. For each publication, please provide a concise explanation of the reliance placed on that publication in distinguishing the claimed subject matter from the prior art.
In response to this requirement, please provide the names of any products or services that have incorporated the disclosed prior art of exhaustion mitigation and organization optimization.
In response to this requirement, please provide the names of any products or services that have incorporated the claimed subject matter.
In responding to those requirements that require copies of documents, where the document is a bound text or a single article over 50 pages, the requirement may be met by providing copies of those pages that provide the particular subject matter indicated in the requirement, or where such subject matter is not indicated, the subject matter found in applicant’s disclosure. The fee and certification requirements of 37 C.F.R. § 1.97 are waived for those documents submitted in reply to this requirement. This waiver extends only to those documents within the scope of this requirement under 37 C.F.R. § 1.105 that are included in the applicant’s first complete communication responding to this requirement. Any supplemental replies subsequent to the first communication responding to this requirement and any information disclosures beyond the scope of this requirement under 37 C.F.R. § 1.105 are subject to the fee and certification requirements of 37 C.F.R. § 1.97. The applicant is reminded that the reply to this requirement must be made with candor and good faith under 37 CFR 1.56. Where the applicant does not have or cannot readily obtain an item of required information, a statement that the item is unknown or cannot be readily obtained will be accepted as a complete response to the requirement for that item. This requirement is an attachment of the enclosed Office action. A complete response to the enclosed Office action must include a complete response to this requirement.
The time period for reply to this requirement coincides with the time period for reply to the enclosed Office action, which is 3 months.
/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
DETAILED ACTION
The following NON-FINAL Office action is in response to application 19297711 filed 08/12/2025
Status of Claims
Claims 1-21 are currently pending and have been rejected as follows.
Priority
Examiner noted the Applicants claiming Priority from Application 18490849 filed 10/20/2023, which, at its turn, claims priority from Provisional 63380790 filled 10/25/2022.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1,8,15 are independent and each recite at 3rd processing limitation:
- “processing the set of quantitative partial results through a trained optimization recommendation machine learning algorithm to generate the organizational exhaustion metrics and a set of recommendations for reducing organizational exhaustion amongst the set of employees, wherein the trained optimization recommendation machine learning algorithm is trained using historical recommendations for mitigating organizational exhaustion”;
Claims 1,8,15 are rendered vague and indefinite because it is unclear if subsequently recited “organizational exhaustion” as in “mitigating organizational exhaustion”, relates back to antecedently recited “organizational exhaustion” as in “reducing organizational exhaustion amongst the set of employees”.
Claims 1,8,15 are recommended to be amended, as example only to recite among others:
- processing the set of quantitative partial results through a trained optimization recommendation machine learning algorithm to generate the organizational exhaustion metrics and a set of recommendations for reducing organizational exhaustion amongst the set of employees, wherein the trained optimization recommendation machine learning algorithm is trained using historical recommendations for the reducing of the organizational exhaustion amongst the set of employees;
Claims 2-7,9-14,16-21 are dependent and rejected based on rejected parent claims 1,8,15.
Clarification and/or correction is/are required.
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 1-21 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 grouping of certain method of organizing human activities because here, when tested per MPEP 2106.04(a)(2) II A, the independent Claims 1,8,15 recite, describe or set forth the fundamental practices or principles of: generat[ing] “the organizational exhaustion metrics and a set of recommendations for reducing organizational exhaustion amongst the set of employees, …using historical recommendations for mitigating organizational exhaustion” by “receiving a request to determine organizational exhaustion metrics corresponding to a set of employees associated with an organization”, taking into considerations abstract business relationships such as: “communications sources” [that] “include organization communication systems and third-party communications systems not associated with the organization”, “communications exchanged amongst the set of employees and other entities not associated with the organization” and by also “querying historical data associated with the organization to retrieve personal time-off data corresponding to the set of employees, wherein the historical data indicates amounts of personal time-off used amongst the set of employees”. Further, Claims 1,8,15 recites the equally abstract “quantitative partial results corresponding to the set of sentiments, the personal time-off data, the employee schedule deviations, and the employee events”, and the dependent Claims 3,10,17 similarly recite “employee states represent the organizational exhaustion, and wherein the employee states are used to define qualitative descriptors that provide indications of the organizational exhaustion metrics amongst the set of employees”. Similarly, dependent claims 5,12,19 narrow the “results” to a “subset corresponds to particular organizational exhaustion metrics amongst the set of employees and associated with the employee schedule deviations” and dependent claims 6,13,20 narrow the “results” to “the subset corresponds to particular organizational exhaustion metrics amongst the set of employees and associated with the personal time-off data”. Further dependent Claims 2,9,16 clarify that “the subset corresponds to particular organizational exhaustion metrics amongst the set of employees and associated with the set of sentiments”. Claims 5-6,12-13,19-20 clarify “the subset corresponds to particular organizational exhaustion metrics amongst the set of employees and associated with the employee schedule deviations” (claims 5,12,19) and “associated with the personal time-off data” (claims 6,13,20).
Accordingly, said claims set forth abstract employee communication and performance fatigue data, as part of entrepreneurial risk mitigation, which is especially relevant when reading the claims in light of the Invention’s Title: “Systems and Methods for Exhaustion mitigation and Organization optimization”, reflected at independent Claims 1,8,15 as “reducing organizational exhaustion amongst the set of employees” and “mitigating organizational exhaustion”. Yet, as stated by MPEP 2106.04(a)(2) II A risk mitigation1 falls within fundamental principles or practices of the abstract “Certain methods of organizing human activity” grouping, with MPEP 2106.04(a)(2) II A ¶2 clarifying that the term fundamental is not used in the sense of necessarily being old or well-known, but rather as a building block of modern economy. Based on such preponderance of legal evidence, the claims recite, or at minimum describe or set forth the abstract exception.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, the individual or combination of the additional, computer-based elements is found to merely apply the already recited abstract idea and/or narrow it to a field of sue or technological environment. Specifically, here, the additional computer-based elements are: the “interface” at Claims 1,8,15 the “memory storing thereon instructions executed by one or more processors” at system Claims 8-9,12,13, mere recitation of “computer implemented” at method Claims 1-7 and “instructions that, as a result of being executed by one or more processors” at non-transitory medium Claims 15-16,19,20 and the “trained sentiment analysis machine learning algorithm”, as and “trained optimization recommendation machine learning algorithm” of Claims 1,4,7,8,11, 14 , 15,18,21 and. Yet, when tested per MPEP 2106.05(f)(2)(i) such memory and instruct[ed] processor(s), would represent, along with machine learning algorithms mere use of computer to execute aforementioned business processes and underlining algorithms2 as part of applying the abstract idea. Further, MPEP 2106.05(f)(2) ¶1 corroborates that use of such additional computer-based elements to perform economic tasks [here identified above as human exhaustion identification and mitigation] and other associated tasks to receive and transmit data3 [recited here at receiving, querying, providing limitations etc.]. Similarly, the “interface” of Claims 1,8,15 would represent under MPEP 2106.05(f)(2)(iii),(v) a mere requirement to use software or other computer component to tailor information4 [here “the interface includes a set of elements for updating the interface in real-time to present subsets of recommendations and metrics corresponding to subsets of employees associated with the organization” at independent Claims 1,8,15] and to monitor audit log data executed on a computer5 [here “querying historical data associated with the organization to retrieve personal time-off data corresponding to the set of employees, wherein the historical data indicates amounts of personal time-off used amongst the set of employees”, “processing time series data associated with one or more employer systems and corresponding to the set of employees to detect employee schedule deviations and employee events occurring within the organization”; “simultaneously monitoring adherence to the set of recommendations and other recommendations provided to other organizations and fluctuations to different levels of organizational exhaustion associated with the organization and the other organizations” at independent Claims 1,8,15 and “employee states” of dependent Claims 3,10,17].
Returning now to the use of computer components such as “one or more processors” to execute the algorithms, the Examiner points to MPEP 2106.05(f)(2)(i), and finds such algorithms recited at the limitations: “processing the raw communications data through a trained sentiment analysis machine learning algorithm to determine a set of sentiments associated with the set of employees, wherein the trained sentiment analysis machine learning algorithm is trained using a dataset of sample communications and known indicators of task performance”; “processing the set of quantitative partial results through a trained optimization recommendation machine learning algorithm to generate the organizational exhaustion metrics and a set of recommendations for reducing organizational exhaustion amongst the set of employees, wherein the trained optimization recommendation machine learning algorithm is trained using historical recommendations for mitigating organizational exhaustion”, “continuously updating the trained optimization recommendation machine learning algorithm based on the adherence and the fluctuations” at independent Claims 1,8,15; “trained optimization recommendation machine learning algorithm generates the organizational exhaustion metrics over the time range to generate the set of recommendations” at dependent Claims 4,11,18; and “normalizing the set of sentiments to generate a subset of the set of quantitative partial results” at dependent Claims 2,9,16 and Claims 5,12,19; and similarly, “normalizing the personal time-off data to generate a subset of the set of quantitative partial results” at dependent Claims 6,13,20.
Yet, MPEP 2106.05(f)(2) is clear that invocation of such computer components or machinery, as tools to execute abstract or existing processes, does not integrate the abstract exception into a practical application. In a similar vein, MPEP 2106.05(h) cites Parker v. Flook, to state that calculating an updated value for a numerical limit on a process variable according to a mathematical formula, represented mere examples of limiting the abstract idea to a field of use limitation which did not integrate the abstract idea into a practical application. Looking closer at Parker v. Flook, 437 U.S. 584, 98 S. Ct. 2522,57 L. Ed. 2d 451,198 USPQ 193 (1978), the Examiner finds that the claims were directed to an analogous computational process repeated at selected time intervals, where in each updating computation, the most recently calculated alarm base and the current measurement of the process variable will be substituted for the corresponding numbers in the original calculation. Here too, independent Claims 1,8,15 similarly call for: “generating a set of quantitative partial results corresponding to the set of sentiments, the personal time-off data, the employee schedule deviations, and the employee events”; “processing the set of quantitative partial results through a trained optimization recommendation machine learning algorithm to generate the organizational exhaustion metrics and a set of recommendations for reducing organizational exhaustion amongst the set of employees”; “continuously updating the trained optimization recommendation machine learning algorithm based on the adherence and the fluctuations” with dependent Claims 4,11,18 further calling for “the trained optimization recommendation machine learning algorithm generates the organizational exhaustion metrics over the time range to generate the set of recommendations”. Since MPEP 2106.05(h) found calculating of updated value for a limit, did not integrate the abstract idea into a practical application, the Examiner reasons that here, “continuously updating the trained optimization recommendation machine learning algorithm based on the adherence and the fluctuations” at independent Claims 1,8,15, and the similar algorithms of dependent Claims 4,11,18 could also be argued not to integrate the abstract idea.
As per the capabilities of the additional computer based elements such as: “the interface includes a set of elements for updating the interface in real-time to present subsets of recommendations and metrics corresponding to subsets of employees associated with the organization” at independent Claims 1,8,15, the Examiner points to MPEP 2106.05(h) citing Electric Power Group, LLC v Alstom S.A 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed Cir. 2016), where the claims were found ineligible despite accumulating and updating the measurements from the data streams and the dynamic stability metrics, grid data, and non-grid data in real time as to wide area and local area portions of the interconnected electric power. Also, the Federal Circuit ruled that a requirement of displaying concurrent visualization of two or more types of information, '710 patent, col. 31, line 37, even if understood to require time-synchronized display would offer anything but readily available computer components. As stressed by the Federal Circuit in Electric Power Group supra, “We have repeatedly held that such invocations of computers and networks that are not even arguably inventive are "insufficient to pass the test of an inventive concept in the application" of an abstract idea” further citing buySAFE, 765 F.3d at 1353,1355; Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324-25 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370 (Fed. Cir. 2015); Internet Patents, 790 F.3d at 1348-49; Content Extraction, 776 F.3d at 1347-48”.
Since accumulating and updating the measurements in real time, followed by displaying concurrent visualization of two or more types of information, and detecting and analyzing events in real-time from the plurality of data streams from the wide area did not save the claims from ineligibility in Electric Power Group, as cited in MPEP 2106.05(f),(h), the Examiner reasons that here, the capabilities of the additional elements (i.e. “one or more processors”) for “processing the raw communications data through a trained sentiment analysis machine learning algorithm to determine a set of sentiments associated with the set of employees, wherein the trained sentiment analysis machine learning algorithm is trained using a dataset of sample communications and known indicators of task performance”, “processing the set of quantitative partial results through a trained optimization recommendation machine learning algorithm to generate the organizational exhaustion metrics and a set of recommendations for reducing organizational exhaustion amongst the set of employees, wherein the trained optimization recommendation machine learning algorithm is trained using historical recommendations for mitigating organizational exhaustion”, “simultaneously monitoring adherence to the set of recommendations and other recommendations provided to other organizations and fluctuations to different levels of organizational exhaustion associated with the organization and the other organizations” and “continuously updating the trained optimization recommendation machine learning algorithm based on the adherence and the fluctuations” at independent Claims 1,8,15, would similarly not integrate the abstract idea into a practical application. In fact, MPEP 2106.05(a),(f),(h) each cite FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293,1296 (Fed. Cir. 2016), where despite FairWarning’s contention that its system allowed for the compilation and combination of disparate information sources and that the patented method "made it possible to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment, the Federal Circuit responded that mere combination of data sources, does not make the claims patent eligible. As we have explained, merely selecting information, by content or source, for collection, analysis, and [announcement] does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from 101 undergirds the information-based category of abstract ideas." citing Elec. Power, 830 F.3d 1350, [2016 BL 247416], 2016 WL 4073318, at *4.
Accordingly, the Examiner submits that there, there is a preponderance of legal evidence showing that that the additional computer-based elements identified above, do 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 as shown above, the additional computer-based elements merely apply the identified abstract idea and/or link use of abstract idea to a field of use or technological environment. Specifically, Examiner follows MPEP 2106.05 (d) II guidelines and carries over the findings tested per MPEP 2106.05 (f) and/or (h) to submit that the additional computer-based elements also do not provide significantly more. Yet assuming arguendo, further evidence would be required to demonstrate conventionality of the additional, computer-based elements, Examiner would also follow MPEP 2106.05(d) I.2.(a), and point as evidence for the conventionality of the additional computer-based elements as read in light of:
* Original Specification ¶ [0011] “Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations can be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and, such references mean at least one of the embodiments”.
* Original Specification ¶ [0014] 3rd sentence: “Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control”.
* Original Specification ¶ [0220]: “The processor 1304 can be a conventional microprocessor such as an Intel® microprocessor, an AMD microprocessor, a Motorola microprocessor, or other such microprocessors”.
* Original Specification ¶ [0236] 3rd sentence: “A processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine”
* Original Specification ¶ [0240] 2nd-3rd sentences: “These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result”.
* Original Specification ¶ [0246] last sentence: “Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure”.
* Original Specification ¶ [0251] “Moreover, while examples have been described in the context of fully functioning computers and computer systems, those skilled in the art will appreciate that the various examples are capable of being distributed as a program object in a variety of forms, and that the disclosure applies equally regardless of the particular type of machine or computer-readable media used to actually effect the distribution”.
* Original Specification ¶ [0272] “Some portions of this description describe examples in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof”.
* Original Specification ¶ [0274] 2nd sentence: “This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer”.
* Original Specification ¶ [0277] “Specific details were given in the preceding description to provide a thorough understanding of various implementations of systems and components for a contextual connection system. It will be understood by one of ordinary skill in the art, however, that the implementations described above may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments”.
* Original Specification ¶ [0278] “The foregoing detailed description of the technology has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the technology to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. The described embodiments were chosen in order to best explain the principles of the technology, its practical application, and to enable others skilled in the art to utilize the technology in various embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the technology be defined by the claim”.
Additionally, per MPEP 2106.05(d)(II), the additional computer-based elements, can perhaps also be viewed as performing the well-understood, routine or conventional functions of: gather statistics6 / electronic recordkeeping7 / store and retrieve information8 / [here “historical data” at Claims 1,8,15 as well as “sentiments” at Claims 1,2,7-9,14-16,21, “time range” at Claims 4,11,18] performing repetitive calculations9 [here “continuously updating the trained optimization recommendation machine learning algorithm based on the adherence and the fluctuations”.
at independent Claims 1,8,15] and arrange a hierarchy of groups [here “employee states are used to define qualitative descriptors” at dependent Claims 3,10,17] and sort information10 [here “normalizing” at Claims 2, 5,6,9,12,13,16,19,20].
With respect to the use of “machine learning algorithms at independent Claims 1,8,15, if necessary, the Examiner would further point to the high level of generality recited by:
* Original Specification ¶ [0078] 3rd sentence enumerating at high level of generality: “For example, the PTO conversion service may execute one or more 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 information for employees of the organization over the period of time. In some implementations, a recurrent neural network (RNN) or a 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 may 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”.
* Original Specification ¶ [0243] enumerating at high level of generality: “In some embodiments, one or more implementations of an algorithm such as those described herein may be implemented using a machine learning or artificial intelligence algorithm. Such a machine learning or artificial intelligence algorithm may be trained using supervised, unsupervised, reinforcement, or other such training techniques. For example, a set of data may be analyzed using one of a variety of machine learning algorithms to identify correlations between different elements of the set of data without supervision and feedback (e.g., an unsupervised training technique). A machine learning data analysis algorithm may also be trained using sample or live data to identify potential correlations. Such algorithms may include k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms, and the like. Other examples of machine learning or artificial intelligence algorithms include, but are not limited to, genetic algorithms, backpropagation, reinforcement learning, decision trees, liner classification, artificial neural networks, anomaly detection, and such. More generally, machine learning or artificial intelligence methods may include regression analysis, dimensionality reduction, metalearning, reinforcement learning, deep learning, and other such algorithms and/or methods. As may be contemplated, the terms "machine learning" and "artificial intelligence" are frequently used interchangeably due to the degree of overlap between these fields and many of the disclosed techniques and algorithms have similar approaches”.
If still necessary, the Examiner would rely on MPEP 2106.05(d)(I)2 (c) and further point to the conventionality of a secondary or ensemble machine learning as demonstrated by at least:
* US 20200134716 A1 ¶ [0088] “Generally, the MLAs that are trained and/or deployed as described herein may comprise any one, or some combination (e.g., in an ensemble), of a number of known machine learning techniques, which may include, without limitation: linear/logistic regression models, classification models, time-series models, clustering algorithms, nearest neighbor methods, decision trees, support vector machines, graphical models, neural networks, boosting, bagging, random forests, other ensemble methods, and/or any other type of function, algorithm, and/or model. In certain implementations, some of the noted algorithms might not use specifically engineered features”.
* US 20210192388 A1 ¶ [0070] last sentence: “There are several known techniques, including (but not limited to): convolutional neural networks (CNNs), recurrent neural networks (RNNs), ensemble learning methods such as adaptive boosting (e.g., Adaboost) learning, decision trees, support vector machines (SVMs), and other supervised learning techniques”.
* US 20200210894 A1 ¶ [0089] “The creation method for the prediction model by formula (3) is one example, and the prediction model may be derived by a generally known method such as regularization, decision tree, ensemble learning, neural networks, and Bayesian networks”.
* US 20200160460 A1 ¶ [0070] 4th sentence: “the machine learning classifier may include any machine learning classifier known in the art including, but not limited to, a conditional generative adversarial network (CGAN), a convolutional neural network (CNN) (e.g., GoogleNet, AlexNet, and the like), an ensemble learning classifier”
* US 20200134716 A1 ¶ [0088] “Generally, the MLAs that are trained and/or deployed as described herein may comprise any one, or some combination (e.g., in an ensemble), of a number of known machine learning techniques, which may include, without limitation: linear/logistic regression models, classification models, time-series models, clustering algorithms, nearest neighbor methods, decision trees, support vector machines, graphical models, neural networks, boosting, bagging, random forests, other ensemble methods, and/or any other type of function, algorithm, and/or model. In certain implementations, some of the noted algorithms might not use specifically engineered features”.
* US 20190289826 A1 ¶ [0115] system 100 may utilize one or more machine learning techniques to carry out one or more functions of the present disclosure. It is contemplated herein that system 100 may be configured to carry out any type of deep learning technique and/or machine learning algorithm/classifier known in the art including, but not limited to, a convolutional neural network, an ensemble learning classifier, a random forest classifier, an artificial neural network, and the like. In this regard, the one or more processors 12, 138, 146 may be configured to train one or more machine learning classifiers configured to carry out the one or more functions of the present disclosure.
* US 20210192388 A1 ¶ [0070] last sentence: “There are several known techniques, including (but not limited to): convolutional neural networks (CNNs), recurrent neural networks (RNNs), ensemble learning methods such as adaptive boosting (e.g., Adaboost) learning, decision trees, support vector machines (SVMs), and other supervised learning techniques”.
* US 20200210894 A1 ¶ [0089] “The creation method for the prediction model by formula (3) is one example, and the prediction model may be derived by a generally known method such as regularization, decision tree, ensemble learning, neural networks, and Bayesian networks”.
* US 20200143528 A1 ¶ [0051] 1st sentence: “the machine learning classifier generated in step 202 may include any type of machine learning algorithm/classifier and/or deep learning technique or classifier known in the art including, but not limited to, a random forest classifier, a support vector machine (SVM) classifier, an ensemble learning classifier, an artificial neural network (ANN), and the like”
All of these, fail to provide anything significantly more than what is already well-understood, routine and conventional in light MPEP 2106.05(d).
In conclusion, Claims 1-21 although directed to statutory categories (here “method” or process at Claims 1-7, “system” or machine at Claims 8-14, and “non-transitory, computer-readable storage medium” at Claims 15-21) they still recite, or at least set forth the abstract idea (Step 2A prong one), with their additional, computer-based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than abstract idea (Step 2B). Claims 1-21 are thus patent ineligible.
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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 1-4, 8-11, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over:
Anamandra et al US 20230131099 A1, hereinafter Anamandra, in view of
Jones et al, US 20210312468 A1 hereinafter Jones, and in further view of
Hardy et al, US 20180365619 A1 hereinafter Hardy. As per,
Claims 1, 8, 15 Anamandra teaches or suggests “A computer-implemented method comprising/ A system, comprising: one or more processors; and memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to: / A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to” (Anamandra ¶ [0014] ¶ [0021]-¶ [0022] ¶ [0082]-¶ [0089]):
- “
- “aggregating raw communications data associated with a set of communications sources” (Anamandra ¶ [0042] 2nd sentence: examples of productivity data include collaborative engagements, peer feedback. ¶ [0089] 3rd sentence: feedback provided can be any form of sensory feedback, such as for visual feedback, auditory feedback, or tactile feedback; and input from user including acoustic, speech, or tactile input. ¶ [0043] 4th sentence: Referring to Fig.3E, one example of data that may be aggregated for analysis by the trained machine learning models include multi-modal data such as text data from communication applications (e.g. instant messaging applications, short messaging service (SMS) applications, and/or the like), audio data (e.g., from meetings), and video data (e.g., from meetings), “wherein the set of communications sources include organization communication systems and third-party communications systems not associated with the organization” (Anamandra ¶ [0044] 3rd sentence: edge device 300 track screen time, categorized into quantities of time spent interacting with different types of applications installed on the edge device such as social media apps [interpreted as third-party communications]. For example, at ¶ [0042] 4th sentence: personal data include social situation, family situation, quantity of personal time), “and wherein the raw communications data includes communications exchanged amongst the set of employees and other entities not associated with the organization”; (Anamandra ¶ [0042] 4th sentence: personal data include social situation, family situation, quantity of personal time. For example, at ¶ [0043] 3rd sentence: edge device 300 may track … time spent interacting with … social media applications)
- “processing the raw communications data through a trained sentiment analysis machine learning algorithm to determine a set of sentiments associated with the set of employees”,
(Anamandra ¶ [0053] 3rd sentence by applying machine learning models to detect early signs of burnout diagnostic engine predicts concomitant decline in employee performance ahead of time such that proactive measures may be taken to avoid actual declines in employee performance. Specifically, ¶ [0043] 4th-7th sentence data aggregated for analysis by trained machine learning models include multi-modal data i.e. text data from communication apps (instant messaging and SMS apps), audio and video data from meetings. For example, text data include transcribed speech data from audio and/or video data, undergo sentiment analysis to flag the text data as being associated with positive, negative, or neutral sentiments. Audio data may be analyzed for tone of voice, used to determine a fatigue level and other emotions exhibited by the employees associated with the audio data. The video data may also be analyzed for fatigue and other emotions such as happiness, excitement, fear, anger, disgust. ¶ [0049] 1st sentence: local machine learning model 135 perform its inference at regular intervals e.g. hourly) “wherein the trained sentiment analysis machine learning algorithm is trained using a dataset of sample communications” (Anamandra ¶ [0049] 1st-2nd sentences: local machine learning model 135 perform its inference at regular intervals (e.g. hourly, weekly, monthly, and/or the like). the training data for local machine learning model 135 include data from earlier points in time… ¶ [0018] the data associated with the employee include a quantity of time spent interacting with different types of applications installed on an edge device of the employee) “and known indicators of task performance”; (Anamandra teaches several examples as follows ¶ [0047] 4th sentence: leading indicators are calculated relative to norm [or known] value for single or group of similar employees (e.g. mean, a median, a mode, etc.). ¶ [0042] 2nd sentence: examples of productivity data include workload…quantity of productive time. ¶ [0044] 3rd-4th sentences: in Figs.3E-F, the edge device 300 track screen time, which may be further categorized into quantities of time spent interacting with different types of applications installed on the edge device... Screen time data that has been categorized in accordance with the type of application provide insight how time utilization and management. ¶ [0045] 6th sentence noting another example where the lagging indicators serve as ground truth [or known] labels for a combination of leading indicators that are indicative of impending employee burnout. Also ¶ [0049] 2nd sentence: training data for local machine learning model 135 include data from earlier points in time, with the pervious output of the local machine learning model 135 (e.g., the confidence score indicative of the likelihood of burnout) being used as labels for the corresponding data).
- “querying historical data associated with the organization to retrieve personal time-off data corresponding to the set of employees, wherein the historical data indicates amounts of personal time-off used amongst the set of employees”;
(Anamandra ¶ [0044] 3rd-5th sentences: …edge device 300 track screen time…Screen time data that has been categorized in accordance with the type of application may provide insight how time utilization and management… edge device 300 … provide insights into punctuality…
¶ [0045] 5th sentence: frequent leaves and tardiness. ¶ [0042] 1st,5th sentences: the trained machine learning models…applied to a variety of data corresponding to the leading indicators of employee burnout including…human resource data…. examples of human resource data include leave patterns… ¶ [0045] 5th sentence: frequent leaves and tardiness)
- “processing time series data associated with one or more employer systems and corresponding to the set of employees to detect employee schedule deviations and employee events occurring within the organization”;
(Anamandra ¶¶ [0011]-[0012],[0018]-[0019],[0043] 3rd sentence, [0044] 3rd-4th sentences, emphasis on ¶ [0045] 5th sentence: data associated with employee include delay in meeting deadlines. ¶ [0047] 2nd,4th sentences: in Fig.4, at least a portion of data corresponding to leading indicators of employee burnout be time series data that include a sequence of absolute and/or relative measurement values… relative measurements may include leading indicators that are calculated relative to a norm value for a single employee or a group of similar employees (e.g., mean, median, a mode, and/or the like). ¶ [0048] 3rd sentence: in the event the output of local machine learning model 135 indicates the employee as exhibiting signs of burnout more than a threshold quantity of time [as other example of deviation], a root cause analysis may be performed to determine, top k quantity of root causes for burnout and corrective actions addressing these root causes. ¶ [0053] 3rd sentence by applying machine learning models to detect early signs of burnout diagnostic engine predicts concomitant decline in employee performance ahead of time such that proactive measures may be taken to avoid actual declines in employee performance);
- “generating a set of quantitative partial results corresponding to the set of sentiments, the personal time-off data” (Anamandra ¶ [0043] 3rd sentence: aggregate, from the various sources, data corresponding to the leading indicators of employee burnout such as productivity data (i.e. missed deadlines at mid-¶ [0045] supra), human resource data (leave time-off mid-¶ [0042]), and personal and context data (i.e. current negative sentiments i.e. fear, anger, disgust per mid-¶ [0043] supra. Next, at ¶ [0048] 2nd sentence: the local machine learning model 135 may output a confidence score indicative of whether the employee is likely to experience burnout at various points in time, partially at time t, then at full time t+T. Then at ¶ [0048] 4th sentence: the information associated with at least a portion of employees, such as the results of performance prediction and root cause analysis, may be aggregated and shared with the employer.), “the employee schedule deviations” (Anamandra ¶ [0044] 3rd-4th sentences: in Figs.3E-F, edge device 300 track screen time, categorized into quantities of time spent interacting with different types of apps installed on the edge device such as productivity apps... Screen time data categorized in accordance with the type of app provide insight how time utilization and management. ¶ [0045] 5th sentence: examples of lagging indicators include missed deadlines. ¶ [0053] 3rd sentence: by applying machine learning models to detect early signs of burnout diagnostic engine 110 may be able to predict concomitant decline in employee performance ahead of time such that proactive measures may be taken to avoid actual declines in employee performance. ¶ [0054] 2nd sentence: information associated with at least a portion of employees, such as the results of performance prediction and root cause analysis, may be aggregated and shared with employer), “and the employee events” (Anamandra ¶ [0045] 5th sentence: frequent leaves and tardiness. ¶ [0042] 1st,5th sentences: the trained machine learning models…applied to a variety of data corresponding to the leading indicators of employee burnout including…human resource data…. examples of human resource data include leave patterns… ¶ [0045] 5th sentence: frequent leaves and tardiness);
- “processing the set of quantitative partial results through a trained optimization recommendation machine learning algorithm to generate the organizational exhaustion metrics and a set of recommendations for reducing organizational exhaustion amongst the set of employees, wherein the trained optimization recommendation machine learning algorithm is trained using historical recommendations for mitigating organizational exhaustion;
(Anamandra ¶ [0045] 6th - 7th sentences: the lagging indicators may serve as ground truth labels for a combination of leading indicators that are indicative of impending employee burnout. Thus, the training of the first local machine learning model 135 a and the second machine learning model 135 b may include adjusting the weights applied by each model when processing the first data such that a difference between the output of the models and the ground truths labels included in the second data is minimized. ¶ [0049] 4th-5th sentences: in Fig.4, the training of the global machine learning model 115 include propagating the weights applied at the local machine learning model 135 to the global machine learning machine model 115 such that the weights of the global machine learning model 115 may be updated accordingly. Doing so may train the global machine learning model 115 to include the insights learned at the local machine learning model 135 without sharing the absolute measurements, relative measurements, and context data available at client device 130. Similarly, ¶ [0059] 3rd-4th sentences. Also, ¶ [0052] 2nd sentence: 2nd machine learning model trained to detect in the measurements of indicators of burnout, outlier values indicative of root cause of impending decline in employee performance. ¶ [0047] 4th sentence: relative measurements include indicators calculated relative to a norm value for single employee or group of similar employees (mean, a median, a mode, and/or the like). For example, at ¶ [0048] 2nd-3rd sentences: the machine learning model output a confidence score indicative of whether employee is likely to experience burnout at various points in time (at t, t+T). In the event the output of the machine learning model indicates the employee as exhibiting signs of burnout > threshold quantity of time, a root cause analysis may be performed to determine, top k quantity of root causes for the burnout and corrective actions addressing these root causes. Moreover, info associated with at least a portion of employees, such as results of performance prediction & root cause analysis, may be aggregated and shared with employer. Specifically, per ¶ [0055] 1st-2nd sentences: at 508, diagnostic engine generate, based on the root causes, a corrective action for predicted decline in employee performance. For example, the diagnostic engine determine corrective actions for alleviating and eliminating the root causes of predicted decline in employee performance).
- “providing the set of recommendations and the organizational exhaustion metrics through an interface, wherein the interface includes a set of elements for updating the interface in real-time to present subsets of recommendations and metrics corresponding to subsets of employees associated with the organization”; (Anamandra ¶ [0083] 3rd-4th,6th sentences: processor 710 …processing instructions for execution within computing system 700. Such executed instructions can implement… the diagnostic engine 110. processor 710 is capable of processing instructions …to display graphical information for a user interface…. For example, per Fig.5, ¶ [0055] 1st-2nd sentences: the diagnostic engine generate, based at least on the root causes, a corrective action for predicted decline in employee performance. For example, the diagnostic engine determine corrective [or recommended] actions for alleviating and eliminating the root causes of predicted decline in employee performance).
- “”; “and”
- “continuously updating the trained optimization recommendation machine learning algorithm based on the adherence and the fluctuations” (Anamandra ¶ [0046] 1st-2nd sentences: with federated learning, training of the global machine learning model include updating parameter space of the global machine learning model with those trained 1st and 2nd machine learning models. The updated parameter space of global machine learning model may subsequently [or continually] be propagated to each of 1st and 2nd machine learning models. ¶ [0049] 2nd sentences: the training data for the machine learning model continues by including data from earlier points in time, with pervious output of the machine learning model (confidence score indicative of likelihood of burnout) as labels for the corresponding data. ¶ [0059] At 606, diagnostic engine 110 update, based at least on 1st parameter space of 1st trained local machine learning model and 2nd parameter space of 2nd trained local machine learning model, a global machine learning model. In some example embodiments, the global machine learning model may be trained by updating the parameter space of global machine learning model with those of trained 1st and 2nd machine learning models. For example, training global machine learning model include propagating the weights applied at 1st and 2nd machine learning models to the global machine learning machine model such that the weights of the global machine learning model may be updated accordingly. Doing so may train the global machine learning model to include insights learned at 1st and 2nd local machine learning models. ¶ [0060] At 608 diagnostic engine 110 update 1st and 2nd machine learning models by sending, to each of 1st and 2nd client device, the updated global machine learning model. The updated global machine learning model may be propagated to each of 1st and 2nd client devices such that 1st and 2nd machine learning models may be updated accordingly. For example, the parameter space of the global machine learning model, which has been updated based on those of the 1st and 2nd machine learning models, may be used to update the parameter spaces of the 1st and 2nd machine learning models. Doing so may allow an exchange of insights learned by each of the 1st and 2nd machine learning models from the locally available private data without an actual exchange of the private data between 1st and 2nd client device)
* While *
Anamandrau, as mapped above, still teaches:
- “…monitoring …organizational exhaustion …”;
Anamandrau does not explicitly recite as claimed:
- “simultaneously monitoring adherence to the set of recommendations and other recommendations provided to other organizations and fluctuations to different levels of organizational exhaustion associated with the organization and the other organizations”;
- “receiving a request to determine organizational exhaustion metrics corresponding to a set of employees associated with an organization”;
* However *
Jones in analogous art of company or organization tracking teaches or suggests:
- “simultaneously monitoring adherence to the set of recommendations and other recommendations provided to other organizations and fluctuations to different levels of organizational” [preparedness plan in the context of a healthcare organization] “associated with the organization and the other organizations”;
(Jones ¶ [0032] 1st sentence: Disclosed herein is a compliance tracking system and method that tracks, in real-time, compliance…. Specifically per ¶ [0018] providing a mechanism for selecting two or more organizations from a plurality of organizations; and displaying a compliance dashboard for each of the selected two or more organizations on a display device, for each standard of each plan, a compliance status indicator representing a compliance status for a respective standard. ¶ [0035] Fig.1 is a hierarchical diagram of aspects of a plan 100 that may be tracked for compliance within an organization 102. The plan 100 could be an emergency preparedness plan in the context of a healthcare organization 102, such as a hospital, long term care (LTC) facility, transplant center, or the like. However, other types of organizations 102 could benefit from the principles of the present disclosure. Thus, although healthcare is used as an example in the following embodiments, the disclosure is not limited to such.
Jones ¶ [0036] 2nd sentence: In Fig.1, two organizations 102 are shown, i.e., Organization #1 and Organization #2. ¶ [0052] In one embodiment, the GUI includes a compliance dashboard 300 for an organization 102, such as Organization #1. In other embodiments (not shown), the GUI may include multiple compliance dashboards 300 for multiple organizations 102).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Anamandra’s “method”/”system”/”non-transitory medium” to have included Jones’ teachings or suggestions to have better mitigated the daunting and complex task of compliance management, as further incentivized by market frugal forces to avoid fines or loss of right to participate in government programs (Jones ¶ [0003] in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art, as articulated by Jones ¶¶ [0031], [0039], [0042]-[0043], [0099]. Moreover, Anamandra would have been prime for such modification, since Jones recognized at ¶ [0035] 3rd-4th sentences that a vast number of organizations could have benefited from the principles of Jones’ disclosure. Thus, the fast paced and fatigue prone organizations or industries as disclosed by either Anamandra or Jones would have recognized the benefits of such teachings or suggestions, rendering the inclusion of Jones’ teachings to Anamandra predictable.
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with company or organization tracking. In such combination each element would have merely performed same or similar analytical, managerial, organizational, best practice and mitigative functions alone, 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 Anamandra in view of Jones, the to be combined elements would have fitted together, like puzzle pieces in logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination results would have been predictable (MPEP 2143 A).
* Further still *
Anamandra / Jones does not explicitly recite:
- “receiving a request to determine organizational exhaustion metrics corresponding to a set of employees associated with an organization”; as claimed
* Nevertheless *
Hardy in analogous performance tracking for company or organization teaches/ suggests:
- “receiving a request to determine organizational exhaustion metrics corresponding to a set of employees associated with an organization”;
(Hardy ¶ [0068] 3rd sentence: an organization editor utilize check-in schedule mixer to schedule specific dates and times for check-ins according to instructions from an authorized organization administrator. ¶ [0036] 3rd sentence: an organization or employer may set the predetermined times for check-ins where questions generated by question module 208 may be presented to users. Similarly, ¶ [0049] 1st sentence: organization administrator 330 or other authorized user edit, prioritize, or otherwise provide feedback regarding content of questionnaires 328 used for future check-ins. ¶ [0061] 2nd sentence: content and organization 502 of such questions 506 and follow-up questions 510,512a, 514a may be predetermined depending on authorization from organization administrator. To this end at [0121] 1st sentence, pluggable component generate organizational report 1200 about the workload, stress levels, and energy levels of the organization's workers over a period of time 1220. This may serve as a barometer to track the level of burnout among the organization's workers)
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Anamandra/ Jones’ “method”/”system”/”non-transitory medium” to have included Hardy’s teachings or suggestions to have provided a more effective assessment of worker engagement that would have substantially eliminated biases, data misinterpretation, and flawed correlations while, at the same time, have collected better data while still capturing the complexity of individual workers’ experiences. (Hardy ¶ [0006] in view of MPEP 2143 F and/or 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 performance tracking for company or organization. In such combination, each element would have merely performed same analytical, managerial, organizational or mitigative 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 Anamandra / Jones in further view of Hardy, 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 combination results would have been predictable (MPEP 2143 A).
Claims 2,9,16 Anamandra/Jones/Hardy teaches all limitations of claims 1,8,15 above. Further
Anamandra teaches: “wherein generating the set of quantitative partial results further includes”:
- “normalizing the set of sentiments to generate a subset of the set of quantitative partial results, wherein the subset corresponds to particular organizational exhaustion metrics amongst the set of employees and associated with the set of sentiments”.
(Anamandra ¶ [0047] 4th sentence: relative measurements include leading indicators calculated relative to a norm value for a single or group of similar employees e.g., an mean, a median, a mode. For example, ¶ [0043] 6th-8th sentences: text data include transcribed speech data from audio and video data, undergo sentiment analysis to flag text data as being associated with positive sentiments, negative sentiments, or neutral sentiments. Audio data may be analyzed for tone of the voice, which in turn may be used to determine a fatigue level, and other emotions exhibited by the employees associated with the audio data. The video data may also be analyzed for physical indicators (e.g., pupil movement and/or the like) of fatigue, alertness, engagement, and/or other emotions such as happiness, excitement, fear, anger, and disgust).
Claims 3,10,17. Anamandra / Jones / Hardy teaches all limitations in claims 1,8,15.
Anamandra/Jones does not teach: “wherein the set of quantitative partial results represents employee states, wherein the employee states represent the organizational exhaustion, and wherein the employee states are used to define qualitative descriptors that provide indications of the organizational exhaustion metrics amongst the set of employees”.
Hardy in analogous performance tracking for company or organization teaches/ suggest
“wherein the set of quantitative partial results represents employee states, wherein the employee states represent the organizational exhaustion, and wherein the employee states are used to define qualitative descriptors that provide indications of the organizational exhaustion metrics amongst the set of employees” (Hardy [0040] 2nd-3rd sentences: machine learning and artificial intelligence AI provide qualitative analyses to enable modeling of high-dimensional data, as discussed in more detail below. In some embodiments, the analysis module 216 may analyze answers [interpreted as states] and other data received from multiple users, and aggregate at least a portion of that info to provide contextual data for individuals or organizations. ¶ [0030] last sentence: Based on this info, real-time perception module 204 provide insights and personalized recommendations to workers and their organizations. ¶ [0099] The scope and nature of the data collection methods herein, as well as the recommendations and reports generated provide users and/or organizations with unique personal knowledge on how they function in workplace, and how they can develop or improve their capabilities at work. ¶ [0109] data include industry data aggregated by a system. For example, a system server aggregate data across several organizations and, as a result, may provide insights to companies on where they stand compared to organizations in their space, similar spaces, or different spaces. ¶ [0125] 2nd sentence: These data sets also enable companies and organizations to define operational measures and strategy improvements with high predictive utility. ¶ [0121] in Fig.12, a pluggable component generate a organizational report 1200 about workload, stress levels, and energy levels of the organization's workers over a period of time 1220. This serve as barometer to track level of burnout among the organization's workers. This organizational report 1200 display key indicators, such as a series of interactive charts representing the aggregate moods of the organization's workers, including but not limited to: stress/pressure 1214, tiredness 1212. The organizational report 1200 further include an interactive chart representing workload among the organization's members and/or a score representing predicted risk of burnout across the organization. In one embodiment, organizational report 1200 further include a summary of recommendations and insights for the organization, with hyperlinks to accompanying reports or suggested reading material).
Rationales to have modified/combined Anamandra/Jones with/and Hardy are above and reincorporated.
Claims 4,11,18 Anamandra/ Jones / Hardy teaches all limitations in claims 1,8,15. Furthermore,
Anamandra teaches or suggests
- “the set of quantitative partial results further corresponds to a time range for generating the organizational exhaustion metrics associated with the set of employees”
(Anamandra ¶ [0048] 2nd sentence: machine learning model may output a confidence score indicative of whether the employee is likely to experience burnout at various points in time (e.g., at time t, t+T, and/or the like); “and”
- “the trained optimization recommendation machine learning algorithm generates the organizational exhaustion metrics over the time range to generate the set of recommendations”.
(Anamandra mid-¶ [0044], ¶ [0047] 2nd, 5th sentences: in Fig.4 , at least a portion of the data corresponding to the leading indicators of the employee burnout be time series data that include sequence of absolute measurement values and/or relative measurement values. relative measurements may include leading indicators that are calculated relative to a norm value for a single employee or a group of similar employees (e.g., an mean, a median, a mode, and/or the like. ¶ [0048] 2nd - 3rd sentences: local machine learning model 135 may output a confidence score indicative of whether the employee is likely to experience burnout at various points in time (e.g., at time t, t+T, and/or the like). In the event the output of the local machine learning model 135 indicates the employee as exhibiting signs of burnout more than a threshold quantity of time, a root cause analysis may be performed to determine, for example, a top k quantity of root causes for the burnout and corrective actions addressing these root cause). ¶ [0049] 1st sentence: The local machine learning model 135 may be configured to perform its inference at regular intervals with adjustable granularity (e.g., hourly, weekly, monthly, and/or the like). In some cases, the training data for the local machine learning model 135 may include data from earlier points in time, with the pervious output of the local machine learning model 135 (e.g., the confidence score indicative of the likelihood of burnout) being used as labels for the corresponding data).
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Claims 5-6,12-13,19-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Anamandra / Jones / Hardy as applied to claims 1,8,15 above, in further view of
Kamura et al, US 20230196249 A1 hereinafter Kamura. As per,
Claims 5,12,19. Anamandra / Jones / Hardy teaches all the limitations in claims 1,8,15 above.
Anamandra recites at ¶ [0047] 4th sentences: Relative measurements include leading indicators calculated relative to a norm value for a single employee or group of similar employees (mean, median, mode).
Anamandra / Jones / Hardy nevertheless does not go so far to explicitly teach:
- “normalizing the employee schedule deviations to generate a subset of the set of quantitative partial results, wherein the subset corresponds to particular organizational exhaustion metrics amongst the set of employees and associated with the employee schedule deviations”
Kamura however in analogues art of measuring exhaustion teaches or suggests
- “normalizing the employee schedule deviations to generate a subset of the set of quantitative partial results, wherein the subset corresponds to particular organizational exhaustion metrics amongst the set of employees and associated with the employee schedule deviations”
(Kamura ¶ [0004] 4th sentence: calculating stress value in accordance with job stress questionnaire, a simple total score a standardized score using a raw score conversion table. For example, at ¶ [0072] 4th sentence: stress check result record 23a includes results such as job burden determination, a physical burden etc. ¶ [0121] 2nd sentence: work execution efficiency is obtained from each employee who compares work execution ability and productivity before any disease or symptom occurs that is set to 100% with the work execution ability and productivity efficiency in a predetermined period. ¶ [0026] In the system for calculating cost of labor productivity loss, the absenteeism loss cost calculation unit calculate absenteeism loss cost with Equation 5 below on basis of number of paid leave days due to poor health of an employee and number of absence days obtained from the employment attendance information storage unit, and an average individual daily unit price that is average amount of salary paid to one employee per day, in which the average individual daily unit price is included in the setting data. Absenteeism loss cost = Number of absence days + Number paid Leave days due to poor health x Average individual daily unit price).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Anamandra / Jones / Hardy’s “method” / “system” / “non-transitory medium” to have further included Kamura’s teachings or suggestions to have effectively detected excessive overtime hours as well as have better identified the increase in number of people in poor health, and increase in the number of people absent from work on the day, while, at the same time having improved the working environment and implement improvement, as well as to have improved employees’ lifestyle diseases and mental health disorders (Kamura ¶ [0029] 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 field of endeavor dealing with performance tracking for company or organization. In such combination, each element merely would have performed similar managerial analytical and mitigative 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 Anamandra/Jones/Hardy in further view of Kamura, 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 6,13,20 Anamandra / Jones / Hardy teaches all the limitations in claims 1,8,15 above.
Anamandra recites at ¶ [0047] 4th sentences: relative measurements include leading indicators calculated relative to a norm value for a single employee or group of similar employees (mean, median, mode).
Anamandra/Hardy nevertheless does not go as far to explicitly teach:
- “normalizing the personal time-off data to generate a subset of the set of quantitative partial results, wherein the subset corresponds to particular organizational exhaustion metrics amongst the set of employees and associated with the personal time-off data”.
Kamura in analogues art of measuring exhaustion teaches or suggests:
- “normalizing the personal time-off data to generate a subset of the set of quantitative partial results, wherein the subset corresponds to particular organizational exhaustion metrics amongst the set of employees and associated with the personal time-off data”.
(Kamura ¶ [0004] 4th sentence: calculating stress value in accordance with job stress questionnaire, a simple total score a standardized score using a raw score conversion table. For example, at ¶ [0072] 4th sentence: stress check result record 23a includes results such as job burden determination, a physical burden etc. ¶ [0121] 2nd sentence: work execution efficiency is obtained from each employee who compares work execution ability and productivity before any disease or symptom occurs that is set to 100% with the work execution ability and the productivity efficiency in a predetermined period. Moreover in addition to ¶ [0026] as mapped above, it is further noted at ¶ [0057] 3rd sentence, that the target reasons for leave 308 also include unexpected leaves such as, overwork, burnout syndrome, and the like. ¶ [0172] 2nd sentence: When calculating the absenteeism loss cost, the absenteeism loss cost calculation unit 45 may obtain the number of leave-of-absence days or the hours for lateness and early leave in addition to the number of absence days and the number of paid leave days taken).
Rationales to have modified/combined Anamandra/Hardy/Kamura are above and reincorporated.
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Claims 7,14,21 are rejected under 35 U.S.C. 103 as being unpatentable over
Anamandra / Jones / Hardy as applied to claims 1,8,15 above, in further view of
Tryfon et al, US 20150206156 A1 hereinafter Tryfon. As per,
Claims 7,14,21 Anamandra / Jones / Hardy teaches all the limitations in claims 1,8,15 above.
Anamandra / Jones / Hardy does not explicitly recite: “wherein the set of sentiments is determined based on an array of sentiment punctuations generated through the trained sentiment analysis machine learning algorithm, and wherein the array corresponds to sentimental states corresponding to the set of employees and the raw communications data”.
Tryfon in analogous assessing employee behavior for a company per ¶ [0030] 6th-7th sentences teaches or suggests: “wherein the set of sentiments is determined based on an array of sentiment punctuations generated through the trained sentiment analysis machine learning algorithm, and wherein the array corresponds to sentimental states corresponding to the set of employees and the raw communications data”
(Tryfon ¶ [0048] In addition to keywords the system may determine punctuation marks that are indicative of sentiment such as an exclamation point. The system 105 may also identify non-standard characters input into a text interface such as smiley faces or other emotion conveying characters such as ":)", ":(", ": |", or a whole host of additional characters. ¶ [0055] last two sentences: n addition to keywords the system may determine punctuation marks that are indicative of sentiment such as an exclamation point. The system 105 may also identify non-standard characters input into a text interface such as smiley faces or other emotion conveying characters such as ":)", ":(", ": |", or a whole host of additional characters).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Anamandra / Jones / Hardy “method”/”system”/”non-transitory medium” to have further included Tryfon’s teachings or suggestions in order to have allowed the company to have better assessed both quantitatively or qualitatively as measured any subject of one or more questions in a survey (Tryfon ¶ [0030] last sentence in view of MPEP 2143 G and/or F). The predictability of such modification would have been further corroborated by the broad level of skill of one of ordinary skills in the art as further articulated by Tryfon ¶ [0008], ¶ [0017], ¶ [0041] 2nd sentence, ¶ [0070] 3rd-4th sentences, ¶ [0075] 5th sentence.
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar assessing employee behavior. In such combination each element merely would have performed same analytical, organizational or assessment 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 Anamandra / Jones / Hardy in further view of Tryfon, the to be combined elements would have fitted together like puzzle pieces in 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
This Office action has an attached requirement for information under 37 C.F.R. § 1.105. A complete response to this Office action must include a complete response to the attached requirement for information. The time period for reply to the attached requirement coincides with the time period for reply to this Office action.
Following art is made of record and considered pertinent to Applicant’s disclosure:
Grzadzielewska M, Using machine learning in burnout prediction, a survey, Child and Adolescent Social Work Journal, 38 n2, p175-p180, Apr, 2021
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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
September 17th, 2026
1 Alice Corp. v. CLS Bank,573 U.S. 208, 218, 110 USPQ2d 1976, 1982 (2014);
2 “Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014)” and “Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972)”; and “Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
3 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016),
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)
4 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
5 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
6 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93
7 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014)
Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755
8 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93
9 Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values);
Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)
10 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015)
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