DETAILED ACTION
Status of Claims
The following is a Final Office Action in response to amendments filed on 04/14/2025.
Claims 2, 3, 6, 7, 9, 10, 13, 14, 16, 17, and 20 are amended. Claims 1-20 are considered in this Office Action. Claims 1-20 are currently pending.
Response to Arguments
Applicant’s amendments necessitated the new ground(s) of rejections set forth in this Office Action.
Applicant’s arguments and amendments to claims 2, 3, 6, 7, 9, 10, 13, 14, 16, 17, and 20 are considered, and they overcome the 35 USC § 112(a) rejections to claims. Rejection is withdrawn.
Applicant’s arguments and amendments to claims 7 and 14 are considered, and they overcome the 35 USC § 112(b) rejections to claims. Rejection is withdrawn.
Applicant’s arguments with respect to the 35 USC § 101 rejections to claims have been considered, however are not persuasive.
Applicant argues that the features of the amended claims integrate any alleged abstract idea (i.e., a judicial exception) into a practical application, and thus are statutory under Step 2A, Prong Two of the current guidance. In particular, the recited features relate to a specific usage (identifying problem areas in an employee’s development and modifying the employee’s training plan as needed) in a specific situation (based on the employee’s performance over one or more time periods compared to a target employee performance threshold) to provide a particular practical application that improves the technical field of call center training (e.g., to modify an employee’s training plan based on problem areas for that specific employee based on the employee’s performance over one or more time periods). The claims provide specific improvements to the technical field so as to be limited to a practical application that improves over the prior systems.
The examiner respectfully disagrees. The claims fall under the “mental process” by reciting steps that can be performed in the human mind (e.g., observation, evaluation, judgment, opinion) of generating an improvement plan for employee based on performance score, wherein the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. (See MPEP 2106.04(a)(2)). The claims further recite an abstract idea by reciting concepts of managing personal behavior or interaction (including social activities, teaching, and following rules or instructions), which falls into the “certain methods of organizing human activity” group within the enumerated groupings of abstract. The claims, as drafted, merely recite generic steps of capturing performance data, assigning scores, inputting data into a performance model (mathematical model), and generating a training plan. The recited model and training plan are claimed in high level of generality and are not tied to any improvement in the function of a computer, network, or other technology. Furthermore, the additional elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification describes in paragraph [0018]- [0019] which describe high level computing environment) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: a system, a processor and computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, and a non-transitory computer-readable medium having stored thereon computer-readable instructions executable to implement the abstract idea. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification describes in paragraph [0018]- [0019] which describe high level computing environment) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
Accordingly, the 35 USC § 103 rejections to claims are maintained and an updated 35 USC § 103 rejections will address applicant’s amendments.
Applicant’s arguments with respect to the 35 USC § 103 rejections to claims have been considered, however are not persuasive.
Applicant argues in regard to independent claims 1, 8, and 15 that Dandan teaches a method of "provid[ing] users with performance summarizations over time," by providing a performance score for an employee generated using metrics collected for the employee at different times. Dandan Abstract; Paragraph [0013]. In particular, Dandan teaches using semantic meanings for text strings included in an employee's performance evaluations as input for a machine-learning model, to determine how the semantic meanings have changed over time. See Dandan Abstract. However, while Dandan focuses on the employee setting a goal (e.g., a sales goal) to be accomplished in a given time period, Dandan fails to disclose or even suggest modifying the employee's training plan in response to and/or using the employee's performance in comparison to their progress with achieving their set goals, as recited in the pending claims. See Dandan, Paragraph [0040].
The examiner respectfully disagrees. The examiner notes that the independent claims do not recite any of modifying the employee's training plan in response to and/or using the employee's performance in comparison to their progress with achieving their set goals. The independent claims broadly recite generating, using the performance model, a training plan for the employee using the target performance goal. Dandan is not relied on to teach “generating, using the performance model, a training plan for the employee using the target performance goal.”
Applicant’s argues that Morrissey does not disclose or even suggest modifying the employee's training plan in response to and/or using the employee's performance in comparison to their progress with achieving their set goals, as recited in the pending claims. At most, Morrissey discuses using its Al model's assessment to "figure out which employees to send to a training seminar with limited seats," or "suggesting methods of improving the user's [performance] score." Morrissey, Paragraphs [011] and [0078]. However, such methods for user improvement are described as including "predicting a user's optimal career track, suggesting which technical or developmental training options the user might find most useful" [0078], suggesting "a cue that might impact the user's performance, such as a timed reminder to support punctuality" [0081], or "suggesting that the user might participate more in office chats and emails" [0091]. Morrissey, Paragraphs [0078], [0081], and [0091].
The examiner respectfully disagrees. The examiner notes that the independent claims do not recite any of modifying the employee's training plan in response to and/or using the employee's performance in comparison to their progress with achieving their set goals. The independent claims broadly recite generating, using the performance model, a training plan for the employee using the target performance goal. Morrissey is relied on to teach “generating, using the performance model, a training plan for the employee using the target performance goal,” as described in paragraph [0078] In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop.
Accordingly, the 35 USC § 103 rejections to claims are maintained and an updated 35 USC § 103 rejections will address applicant’s amendments.
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 non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “Patent Subject Matter Eligibility Guidance” (MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-7), the system (claim 8-14), and the non-transitory computer-readable medium (claims 15-20) are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One of MPEP 2106, it is next noted that the claims fall under the “mental process” by reciting steps that can be performed in the human mind (e.g., observation, evaluation, judgment, opinion) of generating an improvement plan for employee based on performance score, wherein the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. (See MPEP 2106.04(a)(2)). The claims further recite an abstract idea by reciting concepts of managing personal behavior or interaction (including social activities, teaching, and following rules or instructions), which falls into the “certain methods of organizing human activity” group within the enumerated groupings of abstract. The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 8, are: A system of determining personalized goals for staff performance, which comprises: a processor and computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to generate personalized goals for staff performance by: capturing, over a first time period, a first time period performance of an employee; assigning a first score to the first time period performance; capturing, over a second time period, a second time period performance of the employee; assigning a second score to the second time period performance; receiving a target employee performance threshold; inputting, into a performance model, the first score, the second score, and the target employee performance threshold; computing, using the performance model, a target performance goal for the employee based on one or more of the first score, the second score, and the target employee performance threshold, wherein the target performance goal comprises a time of when the employee will reach the target employee performance threshold; and generating, using the performance model, a training plan for the employee using the target performance goal. Claim 1 and 15 recite substantially the same limitations as claim 8 and therefore subject to the same rationale.
With respect to Step 2A Prong Two of MPEP 2106, the judicial exception is not integrated into a practical application. The additional elements are directed to a system, a processor and computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, and a non-transitory computer-readable medium having stored thereon computer-readable instructions executable to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification describes in paragraph [0018]- [0019] which describe high level computing environment) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: a system, a processor and computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, and a non-transitory computer-readable medium having stored thereon computer-readable instructions executable to implement the abstract idea. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification describes in paragraph [0018]- [0019] which describe high level computing environment) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well (i.e., claims 5-7, 12-14, 19, and 20 recite the use of neural network model (recited at high level of generality amount to a mathematical model). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification describes in paragraph [0018]-[0019] which describe high level computing environment) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s Specification describes in paragraph [0018]- [0019]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo), however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of mental process and certain method of organizing human activity, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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.
Claims 1, 5, 8, 12, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Michael John Serrano Dandan (US 20220318716 A1, hereinafter “Dandan”) in view of Ryan Francis Morrissey (US 20230237416 A1, hereinafter “Morrissey”).
Claim 1/8/15
Dandan teaches:
A method of determining personalized goals for staff performance, which comprises: capturing, over a first time period, a first time period performance of an employee (fig. 2 illustrates a captured a first time period (Q1) performance of an employee over a first time period);
assigning a first score to the first time period performance (fig. 2 illustrates an assigned a first score to the first time period performance e.g., a score of 4 at Q1);
capturing, over a second time period, a second time period performance of the employee; (fig. 2 illustrates a captured a second time period (Q2) performance of an employee over a second time period);
assigning a second score to the second time period performance (fig. 2 illustrates an assigned a second score to the second time period performance e.g., a score of 3 at Q2);
receiving a target employee performance threshold ([0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses);
the first score, the second score (fig. 2 illustrates an assigned a first score to the first time period performance e.g., a score of 4 at Q1 and an assigned a second score to the second time period performance e.g., a score of 3 at Q2);
computing, using the performance model, a target performance goal for the employee based on one or more of the first score, the second score, and the target employee performance threshold, wherein the target performance goal comprises a time of when the employee will reach the target employee performance threshold([0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter (a time of when the employee will reach the target employee performance threshold), and the enterprise system 106).
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses, Dandan does not explicitly teach the following, however analogous reference Morrissey teaches:
inputting, into a performance model, [performance data] and the target employee performance threshold ([0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data. All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop);
and generating, using the performance model, a training plan for the employee using the target performance goal ([0078] In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan with Morrissey to include inputting, into a performance model, [performance data] and the target employee performance threshold and generating, using the performance model, a training plan for the employee using the target performance goal, because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan(Morrissey [0001]).
Claim 5/12/19
Dandan teaches:
The method of claim 1, wherein the performance model includes a neural network, wherein the neural network([0014] The machine-learned model may include one or more supervised models (e.g., classification, regression, similarity, or other type of model), unsupervised models (e.g., clustering, neural network, or other type of model), and/or semi-supervised models configured to determine performance over time);
the first score, the second score, and the target performance goal([0039] the enterprise system 106 may receive one or more goals for the employee, and metrics associated with the goal(s) over time. The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0043] and fig. 2 the user 102(1) may provide the first performance evaluation and the second performance evaluation for an employee according to a schedule, such as at the culmination of a first quarter and a second quarter of the enterprise, respectively).
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses, Dandan does not explicitly teach the following, however analogous reference Morrissey teaches:
receives, as input, [performance data e.g., first score and second score], the target employee performance threshold, ([0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data. All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop);
and generates, as output, the training plan for the employee ([0078] In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan with Morrissey to include receives, as input, [performance data e.g., first score and second score], the target employee performance threshold and generates, as output, the training plan for the employee, because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan(Morrissey [0001]).
Claims 2, 3, 6, 7, 9,10, 13, 14, 16 ,17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dandan in view of Morrissey, as applied in claim 1, 8, and 15, and further in view of Mayumi Akatsuka (US 20200074361 A1, hereinafter “Akatsuka”) in view of Jamie Delo (US 20210110329 A1, hereinafter “Delo”).
Claim 2/9/16
Dandan teaches:
The method of claim 1, which further comprises: capturing, over a third time period, a third time period performance of the employee (fig. 2 illustrates a captured a third time period (Q3) performance of an employee over a third time period);
assigning a third score to the third time period performance (fig. 2 illustrates an assigned a third score to the third time period performance e.g., a score of 2.5 at Q3);
the time associated with the target performance goal ([0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter (a time of when the employee will reach the target employee performance threshold), and the enterprise system 106).
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses, Dandan does not explicitly teach the following, however analogous reference Morrissey teaches:
inputting, into the performance model, the third time period performance and the third score ([0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop);
wherein the comparison of the third time period performance of the employee based on the third score and the expected score comprises making a determination whether the employee is on target for achieving the target performance goal, is exceeding expectations for achieving the target performance goal, or is not on target for achieving the target performance goal ([0073] In step 6.04, a score is calculated based on the user's performance as tracked and monitored in step 6.02. In step 6.06, it is determined whether or not an improvement in this user's performance is sought; this might be by checking the score against a preset threshold (i.e., comparing the third score and the expected score), by reporting the score or data and eliciting input from the user or the user's manager, or other manner of determining this. If no improvement is sought, then the process might be over at step 6.08. Otherwise, in step 6.10, it is determined whether to adjust what cues the user is receiving, such as alarms or promptings).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan with Morrissey to include inputting, into the performance model, the third time period performance and the third score and wherein the comparison of the third time period performance of the employee based on the third score and the expected score comprises making a determination whether the employee is on target for achieving the target performance goal, is exceeding expectations for achieving the target performance goal, or is not on target for achieving the target performance goal, because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan (Morrissey [0001]).
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses and Morrissey teaches [0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop, Dandan and Morrissey do not explicitly teach the following, however analogous reference Akatsuka teaches:
comparing […] the expected score of the employee at an end time of the third time period based on (historical performance scores) ([0088] The history creation unit 116 collects the performance indexes calculated for a worker in each of the basic operations to create a history of performance indexes for the worker in each of the basic operations. The index calculating unit 112 may predict the future performance index for a worker in each of the basic operations from past performance indexes represented in the history created);
comparing the third score and the expected score ([0088] The comparator unit 113 may compare the performance index needed for each of the basic operations (i.e., the required performance index) in each of the work steps and a future performance index predicted for the worker in each of the basic operations).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan and Morrissey with Akatsuka to include computing […] an expected score of the employee at an end time of the third time period based on (historical performance scores) and computing the third score and the expected score, because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan to appropriately determine and assign a work step to a worker([0007]).
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses and Morrissey teaches [0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop. Akatsuka teaches in [0088] The history creation unit 116 collects the performance indexes calculated for a worker in each of the basic operations to create a history of performance indexes for the worker in each of the basic operations. The index calculating unit 112 may predict the future performance index for a worker in each of the basic operations from past performance indexes represented in the history created. The comparator unit 113 may compare the performance index needed for each of the basic operations (i.e., the required performance index) in each of the work steps and a future performance index predicted for the worker in each of the basic operations. Dandan, Morrissey, and Akatsuka do not explicitly teach the following, however analogous reference Delo teaches:
comparing, using the performance model, the third time period performance of the employee based [on the third score and the expected score] ([0046] The importance of certain KPIs to a strand can be examined as follows. A variance model is outlined to determine the importance of certain KPIs to a strand. While automated, a user may still be allowed to select the KPIs they want to generate a strand with and the strand will be generated from those KPIs considering the normalized variance of each KPI (either over past data, or data from a data-lake));
modifying, using the performance model, the training plan for the employee based on the comparison of the third time period performance of the employee based on (two variables) ([0056] FIG. 5 illustrates the plurality of metrics and plurality of agents from FIG. 3 with performance determinations and which metrics the agent should focus on improving. Agent 1 is determined to focus on Metric 2, Agent 2 is determined to focus on Metric 7, and so forth. In an embodiment, the metrics can be ranked from 1-7, providing more flexibility on what to improve and when. In another embodiment, the user of the system is able to exercise personal preference. For example, the user may not want to spend time improving Metric 1, and the next best metric for an agent can be reselected (such as for Agent 5, Metric 4 from Metric 1, in FIG. 5). [0057] the variance analysis can be used to determine the effectiveness of learning items based on past data. Gathering data on users who have undertaken the learning items, and those who have not (or even the same data but before the learning item was taken), can show how well each learning item performs and the correct one can be chosen based on the variance currently in the dataset. This can be done through examining the comparison of the variance over the dataset, the mean, and the “tailed-ness” of the set);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan, Morrissey, and Akatsuka with Delo to include comparing, using the performance model, the third time period performance of the employee based [on the third score and the expected score taught by Akatsuka] and modifying, using the performance model, the training plan for the employee based on the comparison of the third time period performance of the employee based on [two variables taught by Akatsuka], because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan[0002].
Claim 3/10/17
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses, Dandan does not explicitly teach the following, however analogous reference Morrisey:
The method of claim 2, which further comprises: and modifying, using the performance model, the training plan based on the calculated effectiveness of the training plan([0074] in step 7.12 it is determined whether to revisit or adjust those existing cues to something else that might be more effective; if so, in step 7.14 the cue(s) are revisited. Regardless of whether previous cues are revisited, additional or further cues might be suggested in step 7.16).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan with Morrissey to include and modifying, using the performance model, the training plan based on the calculated effectiveness of the training plan, because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan[0001].
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses and Morrissey teaches [0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop. Akatsuka teaches in [0088] The history creation unit 116 collects the performance indexes calculated for a worker in each of the basic operations to create a history of performance indexes for the worker in each of the basic operations. The index calculating unit 112 may predict the future performance index for a worker in each of the basic operations from past performance indexes represented in the history created. The comparator unit 113 may compare the performance index needed for each of the basic operations (i.e., the required performance index) in each of the work steps and a future performance index predicted for the worker in each of the basic operations. Dandan, Morrissey, and Akatsuka do not explicitly teach the following, however analogous reference Delo teaches:
comparing, using the performance model, least one additional time period performance of the employee, over at least one additional time period ([004]determining a variance of each desired metric using a variance formula and past data for the desired metric; normalizing the determined variances against other metrics associated with the agent and determine the importance of each metric; comparing the resulting distances for the agent with those of other agents in the contact center, while [0070] skillset scores or strands for a service provider or group of service providers can be tracked during time periods to provide insight into changes in service provider performance over time);
computing, using the performance model, a performance score of the training plan based on the comparison of the third time period performance of the employee and the at least one additional comparison of the at least one additional time period performance of the employee to calculate the effectiveness of the training plan([0004] generating the strand through the skills management platform; determining distance from a mean for each desired metric for the agent, wherein distances not meeting a threshold are selected for improvement for the agent; comparing the resulting distances for the agent with those of other agents in the contact center; and generating the improvement profile, wherein the other agents are ranked with suggestions provided on improvement metrics for each agent through a user interface associated with the skills management platform. [0057]the variance analysis can be used to determine the effectiveness of learning items based on past data. Gathering data on users who have undertaken the learning items, and those who have not (or even the same data but before the learning item was taken), can show how well each learning item performs and the correct one can be chosen based on the variance currently in the dataset. This can be done through examining the comparison of the variance over the dataset, the mean, and the “tailed-ness” of the set, while [0070] skillset scores or strands for a service provider or group of service providers can be tracked during time periods to provide insight into changes in service provider performance over time).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan, Morrissey, and Akatsuka with Delo to include comparing, using the performance model, least one additional time period performance of the employee, over at least one additional time period and computing, using the performance model, a performance score of the training plan based on the comparison of the third time period performance of the employee and the at least one additional comparison of the at least one additional time period performance of the employee to calculate the effectiveness of the training plan, because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan[0002].
Claim 6/13/20
Dandan teaches:
The method of claim 5, which further comprises: capturing, over a third time period, a third time period performance of an employee; assigning a third score to the third time period performance([0043] The user 102(1) may then access the enterprise system 106 at a third time after the first performance evaluation and the second performance evaluation have been submitted, such as partially through a third quarter, and request an up to date performance score);
using neural network model([0014] The machine-learned model may include one or more supervised models (e.g., classification, regression, similarity, or other type of model), unsupervised models (e.g., clustering, neural network, or other type of model), and/or semi-supervised models configured to determine performance over time).
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses, Dandan does not explicitly teach the following, however analogous reference Morrissey teaches:
inputting, into the performance model, the third time period performance and the third score ([0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop);
and updating, through backpropagation, the [model] based on [new data] ([0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan with Morrissey to include inputting, into the performance model, the third time period performance and the third score and updating, through backpropagation, the [model] based on [new data], because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan[0001].
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses and Morrissey teaches [0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop, Dandan and Morrissey do not explicitly teach the following, however analogous reference Akatsuka teaches:
comparing […] an expected score of the employee at an end time of the third time period based on (historical performance scores) ([0088] The history creation unit 116 collects the performance indexes calculated for a worker in each of the basic operations to create a history of performance indexes for the worker in each of the basic operations. The index calculating unit 112 may predict the future performance index for a worker in each of the basic operations from past performance indexes represented in the history created);
the third score and the expected score ([0088] The comparator unit 113 may compare the performance index needed for each of the basic operations (i.e., the required performance index) in each of the work steps and a future performance index predicted for the worker in each of the basic operations.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan and Morrissey with Akatsuka to include computing […] an expected score of the employee at an end time of the third time period based on (historical performance scores) and the expected score, because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan[0007].
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses and Morrissey teaches [0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop. Akatsuka teaches in [0088] The history creation unit 116 collects the performance indexes calculated for a worker in each of the basic operations to create a history of performance indexes for the worker in each of the basic operations. The index calculating unit 112 may predict the future performance index for a worker in each of the basic operations from past performance indexes represented in the history created. The comparator unit 113 may compare the performance index needed for each of the basic operations (i.e., the required performance index) in each of the work steps and a future performance index predicted for the worker in each of the basic operations. Dandan, Morrissey, and Akatsuka do not explicitly teach the following, however analogous reference Delo teaches:
comparing, […],the third time period performance of the employee based [on the third score and the expected score]( [0046] The importance of certain KPIs to a strand can be examined as follows. A variance model is outlined to determine the importance of certain KPIs to a strand. While automated, a user may still be allowed to select the KPIs they want to generate a strand with and the strand will be generated from those KPIs considering the normalized variance of each KPI (either over past data, or data from a data-lake) [0057] the variance analysis can be used to determine the effectiveness of learning items based on past data. Gathering data on users who have undertaken the learning items, and those who have not (or even the same data but before the learning item was taken), can show how well each learning item performs and the correct one can be chosen based on the variance currently in the dataset. This can be done through examining the comparison of the variance over the dataset, the mean, and the “tailed-ness” of the set)).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan, Morrissey, and Akatsuka with Delo to comparing, […], the third time period performance of the employee based [on the third score and the expected score], because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan [0002].
Claim 7/14
Dandan teaches:
The method of claim 6, which further comprises: receiving, as input, the performance at the time associated with the target performance goal of the employee ([0039] the enterprise system 106 may receive one or more goals for the employee, and metrics associated with the goal(s) over time. The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation);
assigning an outcome score to the performance at the time associated with the target performance goal ([0039] the enterprise system 106 may receive one or more goals for the employee, and metrics associated with the goal(s) over time. The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation [0043] and fig. 2 the user 102(1) may provide the first performance evaluation and the second performance evaluation for an employee according to a schedule, such as at the culmination of a first quarter and a second quarter of the enterprise, respectively);
Using neural network model ([0014] The machine-learned model may include one or more supervised models (e.g., classification, regression, similarity, or other type of model), unsupervised models (e.g., clustering, neural network, or other type of model), and/or semi-supervised models configured to determine performance over time).
the target performance goal and the target performance goal([0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses).
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses, Dandan does not explicitly teach the following, however analogous reference Morrissey teaches:
inputting, into the performance model, the third time period performance and the third score ([0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop);
and updating, through backpropagation, the [model] based on [new data] ([0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan with Morrissey to include a inputting, into the performance model, the third time period performance and the third score and updating, through backpropagation, the [model] based on [new data], because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan.
While Dandan teaches [0039] The enterprise system 106 may receive the goal as part of the first performance evaluation, and may receive a status that corresponds to completion of the goal as part of the second performance evaluation. [0040] In an illustrative example, the employee (and/or another person with access to the enterprise system 106) may set a sales goal for the employee of $100,000 (target performance threshold) for a quarter, and the enterprise system 106 may monitor the amount in sales for the employee as the quarter progresses and Morrissey teaches [0078] human performance model 1100 (“the AI model 1100”), receiving input regarding employee performance and being utilized to generate predictions and perform operations relevant to the employee's performance. a Feedback loop monitoring score progress 1120 may be initiated and maintained to continuously update the calculated score with receipt of new data (i.e., the third time period performance and the third score). All of this supports an ability to advise the user on the score output 1122. In a second case 1128 where the user's score is under a desired value (the target employee performance threshold), a second action 1130 may be enacted of suggesting methods of improving the user's score, and/or a third action 1132 may be taken of continuing to monitor the input loop. Akatsuka teaches in [0088] The history creation unit 116 collects the performance indexes calculated for a worker in each of the basic operations to create a history of performance indexes for the worker in each of the basic operations. The index calculating unit 112 may predict the future performance index for a worker in each of the basic operations from past performance indexes represented in the history created. The comparator unit 113 may compare the performance index needed for each of the basic operations (i.e., the required performance index) in each of the work steps and a future performance index predicted for the worker in each of the basic operations. Dandan, Morrissey, and Akatsuka do not explicitly teach the following, however analogous reference Delo teaches:
comparing the third time period performance of the employee based on the performance at the time associated with [data] ([0046] The importance of certain KPIs to a strand can be examined as follows. A variance model is outlined to determine the importance of certain KPIs to a strand. While automated, a user may still be allowed to select the KPIs they want to generate a strand with and the strand will be generated from those KPIs considering the normalized variance of each KPI (either over past data, or data from a data-lake) [0057] the variance analysis can be used to determine the effectiveness of learning items based on past data. Gathering data on users who have undertaken the learning items, and those who have not (or even the same data but before the learning item was taken), can show how well each learning item performs and the correct one can be chosen based on the variance currently in the dataset. This can be done through examining the comparison of the variance over the dataset, the mean, and the “tailed-ness” of the set)).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan, Morrissey, and Akatsuka with Delo to include comparing the third time period performance of the employee based on the performance at the time associated with [data], because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan[0002].
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dandan in view of Morrissey, as applied in claim 1, 8, and 15, and further in view of Keigo Tsutaki(US 20200211517 A1, hereinafter “Tsutaki”).
Claim 4/11/18
While Dandan teaches [0025] an enterprise utilizing the enterprise system 106 may conduct performance evaluations of employees on a regular schedule, such as once a month, once a quarter, biannually (e.g., one review each half-year), once a year, and so forth. [0031] the user 102(1) may provide the second performance evaluation independent of a schedule but at a different time than the first performance evaluation, such as in response to an event (e.g., the employee receiving the performance evaluation completing a difficult project, the employee receiving the performance evaluation failing to meet a deadline, etc.). Dandan and Morrissey do not explicitly teach the following, however analogous reference in the field of performance evaluation Tsutaki teaches:
The method of claim 1, wherein each subsequent time period is twice as long as a preceding time period of at least the first time period and the second time period (he adaptive filter unit 1023A is a unit evaluating the period T1 in the first embodiment and the adaptive filter unit 1023B is a unit evaluating the period T2 which has a double length of the period T1).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Dandan and Morrissey with Tsutaki to each subsequent time period is twice as long as a preceding time period of at least the first time period and the second time period, because it will provide improvement profile based on performance data of an employee and generate an efficient improvement plan by evaluating long-term goals.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20220253786 A1
System And Method for Providing Attributive Factors, Predictions, and Prescriptive Measures for Employee Performance
Epstein Koch; Ron Peretz et al.
US 20180315001 A1
Agent Performance Feedback
Garner; Brandon
US 20090164311 A1
Human Resource Management System
Deyo; Roderic C.
US 20120035987 A1
Performance Management System
Anand; Ritu et al.
US 20150095120 A1
Objective Metrics Measuring Value of Employees
Gibson; Ray A. et al.
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 REHAM K ABOUZAHRA whose telephone number is (571)272-0419. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/REHAM K ABOUZAHRA/ Examiner, Art Unit 3625
/TIMOTHY PADOT/ Primary Examiner, Art Unit 3625
Prosecution Insights