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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 5/1/2025, Applicant responded on 8/1/2025. Amended claims 1, 9, 17.
Claims 1-20 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1, 9, 17 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 9, 17 are not sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…At least the step of performing an action automatically, however, cannot be performed in the human mind or on pen and paper.… The Office Action also states that the above-recited steps are directed to certain methods of organizing human activity because the claims cover a human supervisor managing a contact center agent. The Office Action further states that aggregating the impact scores for the plurality of behavioral factors and determining an average of the impact scores to provide an overall impact score for the supervisor action, and performing an action automatically based on the overall impact score to improve contact center performance are mathematical concepts. At least performing an action automatically, however, is not a mathematical concept.…the claims as a whole are directed to utilizing supervisor impact scores to implement automated actions to improve contact center performance, such as targeted employee training, improved employee feedback, improved employee training, improved employee hours scheduling, and enhanced employee motivational efforts…The claims improve the field of contact center performance. The improvements include targeted employee training, improved employee feedback, improved employee training, improved employee hours scheduling, and enhanced employee motivation efforts (Specification at Paragraph [0002]). Measuring the impact of supervisor actions is important to help improve supervisor skills, manage supervisor staffing and scheduling, and compare and incentivize supervisors… The claims improve the field of contact center performance. The improvements include targeted employee training, improved employee feedback, improved employee training, improved employee hours scheduling, and enhanced employee motivation efforts (Specification at Paragraph [0002]). Measuring the impact of supervisor actions is important to help improve supervisor skills, manage supervisor staffing and scheduling, and compare and incentivize supervisors. Further, supervisor impact scores may indicate coaching/staffing needs of the contact center in terms of both agents and supervisors. Currently, a supervisor is allowed to monitor, join, or coach an agent during a live interaction, but there is no sufficient way to measure and track supervisor impact…. performing an automatic action is not extra-solution activity and is not data output. The reason for calculating the impact score is in order to perform actions that will improve contact center performance…. the claims integrate the alleged abstract idea into a practical application, and thus the claims are not directed to an abstract idea… , this combination of features is not well-understood, routine, or conventional activity in the field of contact center performance…” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, the claims, by Applicant’s own admission, are directed to, …the claims as a whole are directed to utilizing supervisor impact scores to implement automated actions to improve contact center performance, such as targeted employee training, improved employee feedback, improved employee training, improved employee hours scheduling, and enhanced employee motivational efforts…improvements include targeted employee training, improved employee feedback, improved employee training, improved employee hours scheduling, and enhanced employee motivation efforts…to help improve supervisor skills, manage supervisor staffing and scheduling, and compare and incentivize supervisors…., which is a problem directed to a mental process (i.e. human managing and evaluating human supervisors, human supervisor observing and evaluating human agent performance, human supervisor supervising human agent, human supervisor managing human agents’ behaviors using math, human automatically performing an action to improve contact center from managing human supervisor and human agent behaviors, instructing human to perform action to improve human contact center performance based on doing math), organizing human activities (i.e. human managing and evaluating human supervisors, human supervisor observing and evaluating human agent performance, human managing and evaluating human supervisors using math, human supervisor supervising human agents, human supervisor managing human agents’ behaviors using math, human automatically performing an action to improve contact center from managing human supervisor and human agent behaviors, instructing human to perform action to improve human contact center performance based on doing math) and mathematical concepts (i.e. human managing and evaluating human supervisors using math, human supervisor using math to supervise human agents’ behaviors), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. The alleged solutions are solutions directed to solving abstract ideas, which are still abstract ideas. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 or amount to significantly more under Step 2B. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered. However, Applicant’s remarks are moot in light of new grounds of rejections necessitated by 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 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 9, 17) recite,
identifying an interaction where a contact center supervisor performed a supervisor action, where the supervisor supervised a contact center agent;
identifying a supervisor intervention point in the interaction, wherein the supervisor action occurs during the supervisor intervention point;
determining an impact score for each of a plurality of behavioral factors of the agent in the interaction, wherein each behavioral factor from the plurality of behavioral factors reflects agent behavior in the interaction;
aggregating the impact scores for the plurality of behavioral factors in the interaction and determining an average of the impact scores to provide an overall impact score for the supervisor action in the interaction; and
performing an action automatically based on the overall impact score to improve contact center performance.
Analyzing under Step 2A, Prong 1:
The limitations regarding, …identifying an interaction where a contact center supervisor performed a supervisor action, where the supervisor supervised a contact center agent; identifying a supervisor intervention point in the interaction, wherein the supervisor action occurs during the supervisor intervention point; determining an impact score for each of a plurality of behavioral factors of the agent in the interaction, wherein each behavioral factor from the plurality of behavioral factors reflects agent behavior in the interaction; aggregating the impact scores for the plurality of behavioral factors in the interaction and determining an average of the impact scores to provide an overall impact score for the supervisor action in the interaction; and performing an action automatically based on the overall impact score to improve contact center performance…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to, …identifying an interaction where a contact center supervisor performed a supervisor action, where the supervisor supervised a contact center agent; identifying a supervisor intervention point in the interaction, wherein the supervisor action occurs during the supervisor intervention point; determining an impact score for each of a plurality of behavioral factors of the agent in the interaction, wherein each behavioral factor from the plurality of behavioral factors reflects agent behavior in the interaction; aggregating the impact scores for the plurality of behavioral factors in the interaction and determining an average of the impact scores to provide an overall impact score for the supervisor action in the interaction; and performing an action automatically based on the overall impact score to improve contact center performance…; therefore, the claims are directed to a mental process.
Further, …identifying an interaction where a contact center supervisor performed a supervisor action, where the supervisor supervised a contact center agent; identifying a supervisor intervention point in the interaction, wherein the supervisor action occurs during the supervisor intervention point; determining an impact score for each of a plurality of behavioral factors of the agent in the interaction, wherein each behavioral factor from the plurality of behavioral factors reflects agent behavior in the interaction; aggregating the impact scores for the plurality of behavioral factors in the interaction and determining an average of the impact scores to provide an overall impact score for the supervisor action in the interaction; and performing an action automatically based on the overall impact score to improve contact center performance…, under the broadest reasonable interpretation, are managing human supervisor managing human contact center agent, therefore it is, managing personal behavior or relationships or interactions between people. Thus, the claims are directed to certain methods of organizing human activity.
Additionally, …determining an impact score for each of a plurality of behavioral factors of the agent in the interaction, wherein each behavioral factor from the plurality of behavioral factors reflects agent behavior in the interaction; aggregating the impact scores for the plurality of behavioral factors in the interaction and determining an average of the impact scores to provide an overall impact score for the supervisor action in the interaction; and performing an action automatically based on the overall impact score to improve contact center performance…, are mathematical concepts.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activity, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 9, 17: A system adapted to measure impact of supervisor actions comprising: at least one processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the at least one processor, A non-transitory computer-readable medium having stored thereon computer-readable instructions executable by at least one processor to
Claim 8, 16, 20: workforce management application
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “identifying …”,” aggregating…”, “performing an action …”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “identifying…”, “aggregating…”, data output – “performing an action …”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0019] FIG. 1 illustrates a block diagram of an example environment 100 according to some embodiments of the present disclosure. As shown, environment 100 may include or implement a plurality of devices, servers, and/or software components that operate to perform various methodologies in accordance with the described embodiments. Exemplary devices and servers may include device, stand-alone, and enterprise-class servers, operating an operating system (OS) such as a MICROSOFT® OS, a UNIX® OS, a LINUX® OS, or another suitable device and/or server-based OS. It will be appreciated that the devices and/or servers illustrated in FIG. 1 may be deployed in other ways and that the operations performed, and/or the services provided, by such devices and/or servers may be combined or separated for a given embodiment and may be performed by a greater number or fewer number of devices and/or servers. For example, machine learning (ML), neural network (NN), and other artificial intelligence (AI) architectures have been developed to improve predictive analysis and classifications by systems in a manner similar to human decision-making, which increases efficiency and speed in performing predictive analysis of transaction data sets. One or more devices and/or servers may be operated and/or maintained by the same or different entities.
[0050] FIG. 2 is an exemplary flowchart 200 for measuring the impact of supervisor actions. Note that one or more steps, processes, and methods described herein of flowchart 200 may be omitted, performed in a different sequence, or combined as desired or appropriate based on the guidance provided herein. Flowchart 200 of FIG. 2 includes operations for determining a supervisor impact score, as discussed in reference to FIG. 1. One or more of steps 202-210 of flowchart 200 may be implemented, at least in part, in the form of executable code stored on non-transitory, tangible, machine-readable media that when run by one or more processors may cause the one or more processors to perform one or more of steps 202-210. In some embodiments, flowchart 200 can be performed by one or more computing devices discussed in environment 100 of FIG. 1.
[0054] Referring now to FIG. 3, shown is an exemplary flowchart 300 for identifying interactions that should be sampled for an impact score. Note that one or more steps, processes, and methods described herein of flowchart 300 may be omitted, performed in a different sequence, or combined as desired or appropriate based on the guidance provided herein. Flowchart 300 of FIG. 3 includes operations for determining a supervisor impact score, as discussed in reference to FIG. 1. One or more of steps 302-308 of flowchart 300 may be implemented, at least in part, in the form of executable code stored on non-transitory, tangible, machine-readable media that when run by one or more processors may cause the one or more processors to perform one or more of steps 302-308. In some embodiments, flowchart 300 can be performed by one or more computing devices discussed in environment 100 of FIG. 1.
[0094] Where applicable, various embodiments provided by the present disclosure may be implemented using hardware, software, or combinations of hardware and software. Also, where applicable, the various hardware components and/or software components set forth herein may be combined into composite components including software, hardware, and/or both without departing from the spirit of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein may be separated into sub-components including software, hardware, or both without departing from the scope of the present disclosure. In addition, where applicable, it is contemplated that software components may be implemented as hardware components and vice-versa.
[0095] Software, in accordance with the present disclosure, such as program code and/or data, may be stored on one or more computer readable mediums. It is also contemplated that software identified herein may be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein may be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein.
[0096] Although illustrative embodiments have been shown and described, a wide range of modifications, changes and substitutions are contemplated in the foregoing disclosure and in some instances, some features of the embodiments may be employed without a corresponding use of other features. One of ordinary skill in the art would recognize many variations, alternatives, and modifications of the foregoing disclosure. Thus, the scope of the present application should be limited only by the following claims, and it is appropriate that the claims be construed broadly and in a manner consistent with the spirit and full scope of the embodiments disclosed herein.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
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 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable by US Patent Publication to US20140140497A1 to Ripa et al., (hereinafter referred to as “Ripa”) in view of US Patent Publication to US20130142322A1 to Grasso et al., (hereinafter referred to as “Grasso”) in view of US Patent Publication to US20210295957A1 to Quan et al., (hereinafter referred to as “Quan”)
As per Claim 1, Ripa teaches: (Currently Amended) A system adapted to measure impact of supervisor actions comprising: at least one processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the at least one processor, to perform operations which comprise: ([0167]-[0171])
identifying an interaction where a contact center supervisor performed a supervisor action, where the supervisor supervised a contact center agent; (in at least [0114] when the alert frequency threshold is equaled or exceeded, a visual or audible alert may be sent to a supervisor station 118, and the alert may be displayed on the supervisor display screen. In some embodiments, an alert may be displayed when the alert frequency threshold is exceeded but not when the alert frequency threshold is equaled. In some embodiments, a green emotion light, a yellow emotion light, an amber emotion light, and a red emotion light may be displayed on the agent display screen along with the alert to show the light sequence (i.e., corresponding gradations of color) up to the highest frequency threshold that is met or exceeded. In some embodiments, the equaling or exceeding of the alert frequency threshold may cause a preemptive action (e.g., a call to a supervisor/compliance officer, or an opportunity for a supervisor to listen in on the call, coach the agent, or barge in on the call) to be initiated. In some embodiments, the equaling or exceeding of the alert frequency threshold may result in the incrementing of an emotion event counter.)
identifying a supervisor intervention point in the interaction, wherein the supervisor action occurs during the supervisor intervention point; (in at least [0114] when the alert frequency threshold is equaled or exceeded, a visual or audible alert may be sent to a supervisor station 118, and the alert may be displayed on the supervisor display screen. In some embodiments, an alert may be displayed when the alert frequency threshold is exceeded but not when the alert frequency threshold is equaled. In some embodiments, a green emotion light, a yellow emotion light, an amber emotion light, and a red emotion light may be displayed on the agent display screen along with the alert to show the light sequence (i.e., corresponding gradations of color) up to the highest frequency threshold that is met or exceeded. In some embodiments, the equaling or exceeding of the alert frequency threshold may cause a preemptive action (e.g., a call to a supervisor/compliance officer, or an opportunity for a supervisor to listen in on the call, coach the agent, or barge in on the call) to be initiated. In some embodiments, the equaling or exceeding of the alert frequency threshold may result in the incrementing of an emotion event counter. [0141] Circuitry on one or more servers 112 may be conducting emotion analysis of the conversation and generating emotion level meter 650 of FIG. 6B, which is substantially similar to emotion meter 600 of FIG. 6A, for display on the agent's display screen. If the phrase is not detected within a certain timeframe, a word/phrase alert may be sent to a supervisor station, and the supervisor may send an instruction to the agent to read the “mini-Miranda” phrase to the customer. Such an instruction may appear along the top of meter 650 as an alert 652. If the call proceeds and the “mini-Miranda” phrase is still not detected, a visual and audible alert may be activated at a supervisor station 118 so that a supervisor can intervene. [0144] emotion alerts may be displayed in columns 712 and 714 when a calculated agent or customer emotion frequency is equal to or exceeds an alert frequency threshold. Column 724 of real-time report 700 may display word/phrase detector alerts, for example an alert that a certain agent has not yet read the “mini-Miranda” rights to the customer with whom the agent is currently speaking. When alerted, a supervisor can coach a particular agent by speaking only to the agent, “barge in” on the conversation between the agent and the customer (i.e., speak to both the agent and the customer), or merely monitor (i.e., listen in on) the conversation between the agent and the customer.)
determining an impact score for each of a plurality of behavioral factors of the agent in the interaction, wherein each behavioral factor from the plurality of behavioral factors reflects agent behavior in the interaction; (in at least [0115] In window 318, three of the five calculated emotion scores (i.e., 19, 15, and 21) are equal to or higher than the emotion score threshold value of 15. Therefore, the frequency at which scores in window 318 equal or exceed the emotion score threshold value of 15 is 3 out of 5, or 60%. In some embodiments, an emotion score may count in the frequency calculation if the score exceeds, but not if the score equals, the emotion score threshold value. Because the calculated frequency of 60% exceeds the yellow and amber frequency thresholds and equals the red frequency threshold, but is lower than the alert frequency threshold, the alert generated for the previous window 316 disappears and a red emotion light is displayed on the agent display screen for window 318. In some embodiments, the red emotion light may be displayed when the red frequency threshold is exceeded but not when the red frequency threshold is equaled. In some embodiments, a green emotion light, a yellow emotion light, and an amber emotion light may be displayed adjacent to the red emotion light on the agent display screen to show the light sequence (i.e., corresponding gradations of color) up to the highest frequency threshold that is met or exceeded. Similar calculations are performed for windows 320, 322, and 324, whose deepest gradations of emotion light color are amber, red, and amber, respectively. [0137] Meter 600 may be displayed on an agent's display screen (e.g., agent desktop at an agent station) during a conversation between the agent and a customer. The emotion lights of meter 600 may be lit based on the emotion scores and frequencies calculated by an emotion analysis engine, and in accordance with the expression property values of a specified emotion event profile, such as the emotion event profile corresponding to data structure 230 of FIG. 2B or the emotion event profile corresponding to data structure 260 of FIG. 2C. The determination of which emotion lights of meter 600 should be lit may be performed in real-time using an approach similar to the one illustrated in and discussed above with respect to FIG. 3. Thus, the display of lights on meter 600 may change in real-time in accordance with the most current calculated frequencies. [0148] The emotion scores represented in FIG. 8 are scores for the emotion of anger, and the score levels correspond to the heights of vertical bars. In some embodiments, the emotion scores represented in FIG. 8 may be calculated by emotion analysis engine module 156 and/or an instance of analysis service 186. Representations of emotion scores for other emotions, such as hesitation and uncertainty (which may correspond to other emotion event profiles created by and/or stored in, for example, expression builder module 170), may be displayed instead of or in addition to the anger emotion scores in schematic summary 800, and may appear as vertical bars having different colors or patterns.)
aggregating the impact scores for the plurality of behavioral factors in the interaction and determining an average of the impact scores to provide an overall impact score … the supervisor action …; and (in at least [0062] Expression builder module 170 allows emotion analysis, word/phrase detection, and targeted data detection to be combined. Expression builder module 170 enables more precise identification, action, and reporting of compliancy events by developing a context for detected emotions, words/phrases, and targeted data, reducing the number of false-positives and false-negatives that occur with phonetic analysis, and thus reducing the number of unnecessary alerts that are sent to agent and supervisor stations. By combining real-time emotion, word/phrase, and targeted data analysis of audio data from agents and customers, a more coordinated and accurate determination of call compliancy is possible. [0085] the agent side and customer side of all calls may be scored, and a running event average may be maintained for the agent side and customer side of all calls. The event averages may be used to evaluate agent performance. [0143] Agent emotion status column 712 and customer emotion status column 714 may include colored circles corresponding to the highest frequency threshold that is met or exceeded by the agent emotion frequency and customer emotion frequency, respectively, calculated in real-time for each conversation represented by a row in real-time report 700. The colors of the circles in columns 712 and 714 may change in real-time as agent and customer emotion levels change. For example, the color of a circle corresponding to a particular agent in column 712 may be the same color as the right-most emotion light (i.e., corresponding to the highest frequency threshold that is equaled or exceeded by the calculated frequency based on emotion scores) that is lit on the agent half of an emotion level meter, such as emotion level meter 600 of FIG. 6A, displayed on the agent's display screen. Similarly, the color of an adjacent circle in column 714 may be the same color as the right-most emotion light that is lit on the customer half of the emotion level meter displayed on the agent's display screen. Columns 716 and 718 of real-time report 700 show the number of times an agent emotion or customer emotion, respectively, is detected above a certain level. Columns 720 and 722 show the number of times an agent word/phrase event or customer word/phrase event, respectively, is generated. Word/phrase events may be defined by word/phrase event profiles, such as those corresponding to data structures 500 and 530. [0153] FIG. 9 shows a report 900 that may be generated based on emotion and word/phrase information compiled over time for several agents. Report 900 includes several columns for displaying agent IDs 902, last names 904, and first names 906. Report 900 also includes several columns for displaying an agent's emotion score averages, including a column for an overall average 908, a column for an average over the last 10 days 910, and a column for an average over the last 30 days 912. In addition, report 900 includes several columns for displaying an agent's average number of word/phrase events per call, including a column for an overall average 914, a column for an average over the last 10 days 916, and a column for an average over the last 30 days 918. In some embodiments, report 900 may include columns for displaying an agent's average number of emotion events per call and average number of data events per call. Rows 920, 922, 924, 926, and 928 of report 900 each correspond to one of a plurality of call center agents, for example agents assigned to a particular zone, and row 930 may correspond to mean values of the specified averages across the call center.)
performing an action automatically based on the overall impact score to improve contact center performance. (in at least [0144] emotion alerts may be displayed in columns 712 and 714 when a calculated agent or customer emotion frequency is equal to or exceeds an alert frequency threshold. Column 724 of real-time report 700 may display word/phrase detector alerts, for example an alert that a certain agent has not yet read the “mini-Miranda” rights to the customer with whom the agent is currently speaking. When alerted, a supervisor can coach a particular agent by speaking only to the agent, “barge in” on the conversation between the agent and the customer (i.e., speak to both the agent and the customer), or merely monitor (i.e., listen in on) the conversation between the agent and the customer. [0150] schematic summary 800 may include event tags that represent emotion events (also referred to herein as “emotion event tags”). Emotion event tags may have different geometric shapes and/or colors than word/phrase event tags. In some embodiments, emotion event tags may have various colors that correspond to gradations of color representing various frequency thresholds, as discussed above with respect to FIG. 3. It should be understood that geometric shapes other than those illustrated in FIG. 8 may be used for event tags, and that various shades of color may be used for event tags to represent emotion events and/or word/phrase events corresponding to different event profiles. In some embodiments, a user may toggle on/off the display of different types of event tags and representations of some or all emotions determined during the call. [0152] Through the use of a built-in mini-digital voice recording system in combination with stored event tags, a supervisor at supervisor station 118 can replay the audio and display detailed scores for each emotional analysis, as well as obtain precise insight into causality for call compliance. A pre-built detailed drilldown process may allow easy identification of best and worst agent performers. By drilling down to the lowest level of detail, each call's events (emotion and word/phrase) can be reviewed, which may aid in agent training, counseling, and recognition. [0153] FIG. 9 shows a report 900 that may be generated based on emotion and word/phrase information compiled over time for several agents. Report 900 includes several columns for displaying agent IDs 902, last names 904, and first names 906. Report 900 also includes several columns for displaying an agent's emotion score averages, including a column for an overall average 908, a column for an average over the last 10 days 910, and a column for an average over the last 30 days 912. In addition, report 900 includes several columns for displaying an agent's average number of word/phrase events per call, including a column for an overall average 914, a column for an average over the last 10 days 916, and a column for an average over the last 30 days 918. In some embodiments, report 900 may include columns for displaying an agent's average number of emotion events per call and average number of data events per call. Rows 920, 922, 924, 926, and 928 of report 900 each correspond to one of a plurality of call center agents, for example agents assigned to a particular zone, and row 930 may correspond to mean values of the specified averages across the call center.)
Although implied, Ripa does not expressly disclose the following limitations, which however, are taught by Grasso,
… an overall impact score for the supervisor action … (in at least [0098] 6. Challenges: In the exemplary system, challenges that can be issued include wagers for the agents, which may risk a portion of their virtual currency, on improvements of a given KPI or on their overall performance. Two options are considered. In one, wagers are controlled by supervisors. In another option, they are automatically issued by the system. In the first case, a supervisor can use the system to suggest to the agent that they bet on improvements in one or more of the displayed KPIs that are not satisfactory or when the agent is close to changing level, i.e., moving to a level up or down. The agent can accept the wager or ignore it. In the second case, the system makes the suggestions to the agents for wagers on improvements of KPIs that could be made according to the given thresholds that should be respected. For example, for the calls per hour the system provides an estimation of how this can be improved on the basis of the scheduling of the work and the time interval in which the parameter is evaluated, e.g., one week or one month, and allows the agent to wager a predetermined or variable (between minimum and maximum) amount of points according to a given scale. If the agent ignores or delays accepting the wager, the system can periodically re-submit the suggestion to the agent. If the agent rejects the wager, the system may simply discard it or propose a new wager. Satisfying an accepted wager will, in turn, result in an amount of credits being granted to the agent, whereas not satisfying it may remove a predetermined amount of credits. [0100] the same game mechanics used for the individual agents can also be introduced for teams of agents (e.g., for a supervisor's team). In this case, the corresponding values are averaged over all team members. This represents the overall team level and allows situating the team performance with respect to the other teams. It may also identify and visualize global strengths/weaknesses in the team and thus allows the supervisor to address them with appropriate actions, e.g., through training, challenges, or exceptional awards to increase performance on particular critical performance metrics. The system can detect positive and negative trends and alert the supervisor accordingly. [0105] A team profile may be generated and stored which aggregates the KPIs and other characteristics (badges, levels, credits, challenges accepted, etc.) of the members of the team. A call center profile may also be generated and stored which aggregates KPIs and other characteristics for the call center as a whole. The call center profile may also identify the strongest and weakest teams, according to one or more of the characteristics. The system may trigger or propose appropriate actions for call center level improvements including, for example, automatic detection of uneven distribution of skills across teams (if some teams lack particular skills whereas other teams have plenty the teams might be reorganized to redistribute skills); automatic detection of overall lacking skills (if there is some lack of particular skills, corresponding training can be proposed and organized); automatic detection whenever the queue reaches a critical threshold (this may trigger challenges motivating agents to shorten the call handle time in order to keep the queue manageable. If the goal is reached, the agents participating can be awarded additional credits). [0116] 3. A detailed, comparative visualization of an individual, selectable KPI for an agent, e.g., versus the team or the overall center average values with customizable time intervals (e.g., day, week, or month). This provides to the agent (or supervisor), at a glance, information about whether the average values for the displayed KPI for that agent, the team, and call center as a whole fall within or without the required threshold at all points in time for the selected time interval and provides information about the corresponding performance levels. For example, the AHT trend for a supervisor's team is shown at 120 in FIG. 6 and the selected time period at 122. Maximum and minimum team threshold values for the AHT KPI are show at 124 and 125. As will be appreciated, the KPI view may be configured to show more than one trend, such as the agent's performance on this metric vs. the team as a whole.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Ripa, as taught by Grasso above, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Ripa with the motivation of, …motivation of performance improvement and finds particular application in connection with a system and method for raising performance among agents working in a common environment, such as a call center.… visualizing performance information which can lead to improvements in performance in a workplace and an increase in worker job satisfaction…. provide integrated information on current work status and trends and indications on how individual and collective performance and quality of work experience could be improved…. award corresponding credits to the tutoring agent for helping the tutored, weaker one to improve. In some cases, the reward to the weaker agent is not through a wager but simply by being rewarded directly because he improved his topic expertise…. The system may trigger or propose appropriate actions for call center level improvements including, for example, automatic detection of uneven distribution of skills across teams (if some teams lack particular skills whereas other teams have plenty the teams might be reorganized to redistribute skills); automatic detection of overall lacking skills (if there is some lack of particular skills, corresponding training can be proposed and organized); automatic detection whenever the queue reaches a critical threshold (this may trigger challenges motivating agents to shorten the call handle time in order to keep the queue manageable. If the goal is reached, the agents participating can be awarded additional credits)… justify the need for agents to push their performance beyond their minimum requirements, even when and where there is room for improvement at the individual agent level that would in turn improve the aggregate performance of the call center as a whole…. the agents and supervisors may be able to compare their performance to that of their team and the call center as a whole and to understand better the relationship between their performance and the organizational goals of the call center. Supervisors are able to identify easily those agents and teams which have the best margins of improvement on strategically relevant metrics at any point in time and challenge or encourage them to improve…, as recited in Grasso.
Although implied, Ripa in view of Grasso does not expressly disclose the following limitations, which however, are taught by Quan,
…supervisor action in the interaction… (in at least [0015] a coach-participant interface including a coach dashboard (e.g., as shown in FIG. 4) [0149] S270 can optionally be used in determining an optimal matching between a coach and a participant. In some variations, for instance, when a coach is being selected for a participant, a coach quality (e.g., overall coach quality metric, identified coach strength, etc.) and/or intermediate metrics/parameters (e.g., a medical parameter, demographic information, OKD, coach's prior performance metrics, etc.) can be used to optimally match the participant with a coach. [0151] method 200 (e.g., as shown in FIG. 2, as shown in FIG. 3, etc.), the method 200 includes: determining a set of OKDs associated with success of the participant within the coaching platform, wherein the set of OKDs includes an adjusted health outcome metric (e.g., adjusted weight loss metric, adjusted medical parameter metric, etc.) determined through the development and training of a set of models, wherein the set of models determines values for the OKDs based at least in part on participant information (e.g., demographic information); collecting information from each of a set of participants in a coach's participant cohort, wherein the information includes at least participant demographic information and optionally participant sensor information (e.g., weight from a scale); at a computing system (e.g., a remote computing system) determining values of the OKDs for each participant based on the participant information; at the computing system, determining a set of one or more coach quality metrics for the coach based on the OKDs; optionally transmitting the coach quality metrics to a coach supervisor associated with the coach; and optionally producing an output based on the coach quality metric wherein the outputs can be any or all of: determined automatically at the computing system, determined by the coach supervisor based on the coach quality metric, and/or otherwise determined.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Ripa in view of Grasso, as taught by Quan above, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Ripa in view of Grasso with the motivation of, … confers the benefit of learning from the best coaches, which can provide information and/or insights with which to improve the overall coaching program… improve an efficiency of a set of coaches (e.g., through prioritization of coach tasks… establish a widely applicable, quantifiable metric (e.g., as described below) of participant progress; enable a comparison between coaches; enable an optimal matching between a participant and a coach; reward coaches for high quality performance; assist coaches in improving the quality of their coaching (e.g., through actionable insights into the causes of low and high coach quality)… triggering one or more actions commensurate with coach quality (e.g., rewarding a high quality coach, highlighting an area of improvement to a low quality coach, incentivizing a coach, etc.), maintaining a high quality coach cohort, learning from high performing coaches, directing a coach manager's attention to a subset of struggling or low-performing coaches, updating one or more models…, as recited in Quan.
As per Claim 2, Ripa teaches: (Original) The system of claim 1, wherein determining an impact score for each of the plurality of behavioral factors comprises:
calculating a first score for a plurality of behavioral factors of the agent before the supervisor intervention point, and (in at least [0153] FIG. 9 shows a report 900 that may be generated based on emotion and word/phrase information compiled over time for several agents. Report 900 includes several columns for displaying agent IDs 902, last names 904, and first names 906. Report 900 also includes several columns for displaying an agent's emotion score averages, including a column for an overall average 908, a column for an average over the last 10 days 910, and a column for an average over the last 30 days 912. In addition, report 900 includes several columns for displaying an agent's average number of word/phrase events per call, including a column for an overall average 914, a column for an average over the last 10 days 916, and a column for an average over the last 30 days 918. In some embodiments, report 900 may include columns for displaying an agent's average number of emotion events per call and average number of data events per call. Rows 920, 922, 924, 926, and 928 of report 900 each correspond to one of a plurality of call center agents, for example agents assigned to a particular zone, and row 930 may correspond to mean values of the specified averages across the call center.)
calculating a second score for the plurality of behavioral factors of the agent for the entire interaction, wherein the impact score comprises …. (in at least [0153] FIG. 9 shows a report 900 that may be generated based on emotion and word/phrase information compiled over time for several agents. Report 900 includes several columns for displaying agent IDs 902, last names 904, and first names 906. Report 900 also includes several columns for displaying an agent's emotion score averages, including a column for an overall average 908, a column for an average over the last 10 days 910, and a column for an average over the last 30 days 912. In addition, report 900 includes several columns for displaying an agent's average number of word/phrase events per call, including a column for an overall average 914, a column for an average over the last 10 days 916, and a column for an average over the last 30 days 918. In some embodiments, report 900 may include columns for displaying an agent's average number of emotion events per call and average number of data events per call. Rows 920, 922, 924, 926, and 928 of report 900 each correspond to one of a plurality of call center agents, for example agents assigned to a particular zone, and row 930 may correspond to mean values of the specified averages across the call center.)
Although implied, Ripa in view of Grasso does not expressly disclose the following limitations, which however, are taught by Quan,
…impact score comprises a difference between (1) the calculated first score and (2) the calculated second score… (in at least [0035] a coach dashboard (equivalently referred to herein as a coach interface), which functions to provide information associated with a set of one or more participants (e.g., through a participant profile as shown in FIG. 5) to the coach or coaches associated with the set of participants. The dashboard preferably additionally functions to provide a set of tools for the coach (e.g., recommended conversation topics, stored conversation topics, etc.) and to receive inputs from the coach, but can additionally or alternatively perform any other functions. [0057] baseline ability based on current weight/fitness level, etc.) to lose weight (e.g., in light of a medical condition of the participant, in light of the patient's age, in light of the patient's lifestyle, in light of the patient's sex, in light of the patient's race, etc.). Additionally or alternatively, any other suitable parameter associated with weight loss and/or body composition change (e.g., muscle gain, fat loss, etc.) can form an OKD (e.g., through a set of normalization processes, based on normalization factors described above, based on different normalization factors as described above, etc.), such as an any or all of: an adjusted body fat percentage loss, an adjusted body muscle percentage gain, an adjusted decrease in one or more body measurements,)
The reason and rationale to combine Ripa, Grasso, Quan is the same as recited above.
As per Claim 3, Ripa teaches: (Original) The system of claim 1, wherein identifying an interaction where a contact center supervisor performed a supervisor action comprises:
receiving, from an organizational customer of the contact center, one or more of a threshold … for a voice interaction, a threshold total number of messages for a digital interaction, or a threshold total number of words for a voice or a digital interaction; and (in at least [0041] Analysis engines may generate visual displays based on emotion levels, calculate statistics, and generate alerts and reports, as discussed below with respect to FIGS. 3 and 6-10. Analysis engines may include an emotion analysis engine, which includes circuitry for analyzing emotion levels of call center agents and customers in real-time. The emotion analysis may take into account a person's voice inflection, tone, and volume. An emotion analysis engine may access a database of emotion base expressions for analyzing agent and customer emotion levels, as discussed further with respect to FIGS. 2A-3. [0059] Keyword lists module 166 may use word dictionaries stored in word dictionaries module 162 to create various keyword lists, which may be used by word/phrase detection engine module 158. A keyword list represents a list of target words to be identified during word/phrase analysis. Word/Phrase detection engine module 158 may use multiple keyword lists while analyzing various conversations on different customer call channels. A keyword list may be a random list of selected words from a specified dictionary, an ordered list of words (i.e., phrase) from a specified dictionary, a full referenced dictionary, or any combination of the above. An agent's supervisor or a call center administrator (i.e. organizational customer) with the appropriate privileges can use keyword lists module 166 to define one or more keyword lists to include words from one or more stored dictionaries, display stored keyword lists (e.g., on a supervisor's/administrator's display screen), and modify keyword lists as needed. Defined keyword lists may be passed as arguments to word/phrase detection engine module 158. Keyword lists module 166 may receive requests to modify defined keyword lists, for example by deleting some words and adding others. [0067] when a user selects an emotion type in an expression build area, the user may also input a threshold value for an emotion score for the selected emotion type to include in the emotion base expression. For example, a user may select emotion type “anger” and input a threshold value of 20, and “(anger >20)” may be added to the expression. If a threshold is not specified, a default threshold may be used, although the threshold value may not appear in the expression. [0068] a word/phrase base expression, which is a named binary expression having one or more words (e.g., “hate”, “lawyer”) and/or phrases (e.g., “date of birth”, “talk to a manager”). A word/phrase base expression is built by combining words from one or more word dictionaries. In some embodiments, there may be an inferred OR between each word in a word/phrase base expression. Different word/phrase base expressions may be created for an agent side and a customer side of a call. A user may specify values for various expression properties (e.g., start/stop time, event type) for a word/phrase base expression, as discussed further with respect to FIGS. 4A-B and 5A-B. [0078] Profile properties include scoring, timers, nesting, exceptions, and actions. A user-defined scoring system may be set up to allow base/composite expressions to be scored so that each event can have a weighted score. A user may specify start and end timers for each base/composite expression in a profile to control when expressions are evaluated during a call. [0074] use of an emotion event profile involves examining the frequency of occurrence of threshold violations over a specified window (see discussion of FIG. 3), use of a composite event profile may involve using the historical window of an emotion event profile to determine when an emotion event should be generated, as well as concurrently checking whether any words and/or phrases defined in a word/phrase event profile were detected during the historical window. An exemplary expression for a composite event profile according to an embodiment of the disclosure is: (Base Expression Name 1 AND Base Expression Name 2),where Base Expression Name 1 could be an emotion base expression and Base Expression Name 2 could be a word/phrase base expression. If Base Expression Name 1 and Base Expression Name 2 both evaluate to true, an alert/event may be generated for the composite expression. [0100] The expression property value in window size field 202 designates a buffer size passed to an alert processor every second, or some other appropriate time interval. The buffer size may determine a number of consecutive emotion scores to compare to a threshold value for alert processing (e.g., performed by alert processor module 172) and/or driving state lights (e.g., in an emotion level meter) on an agent and/or supervisor desktop. The expression property value in emotion score threshold value field 204 designates an emotion score limit; agent or customer emotion scores that equal or exceed this limit too often will cause the alert processor to generate alerts. [0150] schematic summary 800 may include event tags that represent emotion events (also referred to herein as “emotion event tags”). Emotion event tags may have different geometric shapes and/or colors than word/phrase event tags. In some embodiments, emotion event tags may have various colors that correspond to gradations of color representing various frequency thresholds, as discussed above with respect to FIG. 3. It should be understood that geometric shapes other than those illustrated in FIG. 8 may be used for event tags, and that various shades of color may be used for event tags to represent emotion events and/or word/phrase events corresponding to different event profiles.)
determining that a total talk time of … in the interaction exceeds the threshold … ; or determining a number of messages the supervisor sent in the interaction exceeds the threshold total number of messages; or determining a total number of words used by … in the interaction exceeds the threshold total number of words. (in at least [0041] Analysis engines may generate visual displays based on emotion levels, calculate statistics, and generate alerts and reports, as discussed below with respect to FIGS. 3 and 6-10. Analysis engines may include an emotion analysis engine, which includes circuitry for analyzing emotion levels of call center agents and customers in real-time. The emotion analysis may take into account a person's voice inflection, tone, and volume. An emotion analysis engine may access a database of emotion base expressions for analyzing agent and customer emotion levels, as discussed further with respect to FIGS. 2A-3. [0059] Keyword lists module 166 may use word dictionaries stored in word dictionaries module 162 to create various keyword lists, which may be used by word/phrase detection engine module 158. A keyword list represents a list of target words to be identified during word/phrase analysis. Word/Phrase detection engine module 158 may use multiple keyword lists while analyzing various conversations on different customer call channels. A keyword list may be a random list of selected words from a specified dictionary, an ordered list of words (i.e., phrase) from a specified dictionary, a full referenced dictionary, or any combination of the above. An agent's supervisor or a call center administrator (i.e. organizational customer) with the appropriate privileges can use keyword lists module 166 to define one or more keyword lists to include words from one or more stored dictionaries, display stored keyword lists (e.g., on a supervisor's/administrator's display screen), and modify keyword lists as needed. Defined keyword lists may be passed as arguments to word/phrase detection engine module 158. Keyword lists module 166 may receive requests to modify defined keyword lists, for example by deleting some words and adding others. [0067] when a user selects an emotion type in an expression build area, the user may also input a threshold value for an emotion score for the selected emotion type to include in the emotion base expression. For example, a user may select emotion type “anger” and input a threshold value of 20, and “(anger >20)” may be added to the expression. If a threshold is not specified, a default threshold may be used, although the threshold value may not appear in the expression. [0068] a word/phrase base expression, which is a named binary expression having one or more words (e.g., “hate”, “lawyer”) and/or phrases (e.g., “date of birth”, “talk to a manager”). A word/phrase base expression is built by combining words from one or more word dictionaries. In some embodiments, there may be an inferred OR between each word in a word/phrase base expression. Different word/phrase base expressions may be created for an agent side and a customer side of a call. A user may specify values for various expression properties (e.g., start/stop time, event type) for a word/phrase base expression, as discussed further with respect to FIGS. 4A-B and 5A-B. [0078] Profile properties include scoring, timers, nesting, exceptions, and actions. A user-defined scoring system may be set up to allow base/composite expressions to be scored so that each event can have a weighted score. A user may specify start and end timers for each base/composite expression in a profile to control when expressions are evaluated during a call. [0074] use of an emotion event profile involves examining the frequency of occurrence of threshold violations over a specified window (see discussion of FIG. 3), use of a composite event profile may involve using the historical window of an emotion event profile to determine when an emotion event should be generated, as well as concurrently checking whether any words and/or phrases defined in a word/phrase event profile were detected during the historical window. An exemplary expression for a composite event profile according to an embodiment of the disclosure is: (Base Expression Name 1 AND Base Expression Name 2),where Base Expression Name 1 could be an emotion base expression and Base Expression Name 2 could be a word/phrase base expression. If Base Expression Name 1 and Base Expression Name 2 both evaluate to true, an alert/event may be generated for the composite expression. [0100] The expression property value in window size field 202 designates a buffer size passed to an alert processor every second, or some other appropriate time interval. The buffer size may determine a number of consecutive emotion scores to compare to a threshold value for alert processing (e.g., performed by alert processor module 172) and/or driving state lights (e.g., in an emotion level meter) on an agent and/or supervisor desktop. The expression property value in emotion score threshold value field 204 designates an emotion score limit; agent or customer emotion scores that equal or exceed this limit too often will cause the alert processor to generate alerts. [0150] schematic summary 800 may include event tags that represent emotion events (also referred to herein as “emotion event tags”). Emotion event tags may have different geometric shapes and/or colors than word/phrase event tags. In some embodiments, emotion event tags may have various colors that correspond to gradations of color representing various frequency thresholds, as discussed above with respect to FIG. 3. It should be understood that geometric shapes other than those illustrated in FIG. 8 may be used for event tags, and that various shades of color may be used for event tags to represent emotion events and/or word/phrase events corresponding to different event profiles.)
Although implied, Ripa does not expressly disclose the following limitations, which however, are taught by Grasso,
…receiving, from an organizational customer of the contact center, one or more of a threshold total talk time for a voice interaction… interaction exceeds the threshold total talk time … (in at least [0065] 1. Call center Service Level Agreement (SLA), i.e., the call center threshold KPIs, e.g., the minimum and maximum acceptable length for a call. The SLA may be agreed upon with a client (the outsourcing company for which the center is run). (i.e. organizational customer) [0082] The call center KPI thresholds can be more or less directly related to individual call center agent's KPI thresholds. For example, the constraints on the acceptable call length are translated into minimum and maximum threshold values for AHT. Other agent KPI thresholds can be related to parameters such as the agent's adherence to the schedule, the transfer rate, quality, etc. [0098] allows the agent to wager a predetermined or variable (between minimum and maximum) amount of points according to a given scale. If the agent ignores or delays accepting the wager, the system can periodically re-submit the suggestion to the agent. If the agent rejects the wager, the system may simply discard it or propose a new wager. Satisfying an accepted wager will, in turn, result in an amount of credits being granted to the agent, whereas not satisfying it may remove a predetermined amount of credits. [0123] The system helps Agent White by providing clear, at-a-glance and real-time information about his past, current and projected performance levels, and allows him to self-monitor more effectively. Over the rest of the shift, agent White can make the extra effort on those features of the phone calls that he can exercise some control over (e.g., he maximizes the use of his on-line knowledge base rather than asking a supervisor for help when he needs assistance and ensures that he does not lose time in idle chatter with the customers, while still remaining attentive and polite, as company policy demands). In this way, he manages to bring his average handle time back below the maximum threshold before the end of the shift, as illustrated at 136 in in FIG. 10.)
The reason and rationale to combine Ripa, Grasso is the same as recited above.
Although implied, Ripa in view of Grasso does not expressly disclose the following limitations, which however, are taught by Quan,
…of the supervisor…(in at least [0092] The coach inputs can further additionally or alternatively include information associated with the coach's activity in the platform, such as messaging information between the coach and his or her participants (and/or any other communications between the coach and his or her participants). This can include, for instance, any or all of: time spent logged in to the coach program, number of messages exchanged with one or more participants, length of messages exchanged with one or more participants, time between reaching out to a participant, frequency of interactions with a participant, the tone (e.g., encouraging, demeaning, active listening, etc.) of the coach's messages to one or more participants, the types of resources sent to the participants and whether or not they were relevant and/or useful, and/or any other suitable information.)
The reason and rationale to combine Ripa, Grasso, Quan is the same as recited above.
Note: Examiner is interpretating “organizational customer” to be internal facing customers of the organization.
As per Claim 4, Ripa teaches: (Original) The system of claim 1,
wherein the supervisor action comprises coaching a contact center agent, joining the interaction, or taking control of the interaction from the agent. (in at least [0144] emotion alerts may be displayed in columns 712 and 714 when a calculated agent or customer emotion frequency is equal to or exceeds an alert frequency threshold. Column 724 of real-time report 700 may display word/phrase detector alerts, for example an alert that a certain agent has not yet read the “mini-Miranda” rights to the customer with whom the agent is currently speaking. When alerted, a supervisor can coach a particular agent by speaking only to the agent, “barge in” on the conversation between the agent and the customer (i.e., speak to both the agent and the customer), or merely monitor (i.e., listen in on) the conversation between the agent and the customer.)
As per Claim 5, Ripa teaches: (Original) The system of claim 1, wherein the plurality of behavioral factors comprises two or more of:
demonstrating ownership, active listening, being empathetic, building rapport, setting expectations, effective questioning, promoting self-service, inappropriate actions, acknowledging loyalty, speech velocity, or interruptions. (in at least [0041] Analysis engines may generate visual displays based on emotion levels, calculate statistics, and generate alerts and reports, as discussed below with respect to FIGS. 3 and 6-10. Analysis engines may include an emotion analysis engine, which includes circuitry for analyzing emotion levels of call center agents and customers in real-time. The emotion analysis may take into account a person's voice inflection, tone, and volume. An emotion analysis engine may access a database of emotion base expressions for analyzing agent and customer emotion levels, as discussed further with respect to FIGS. 2A-3. [0066] One type of base expression is an emotion base expression, which is a named binary expression that includes emotion types (e.g., anger, uncertainty, stress, embarrassment, hesitation, excitement) and logical operators (e.g., AND, NOT, OR). Parentheses (i.e., ‘(’ and ‘)’) may be used to group emotion types and logical operators within an emotion base expression. An exemplary expression for an emotion base expression according to an embodiment of the disclosure is: (Anger AND Stress) OR (Talkover), in which the emotion types are anger, stress, and talkover (i.e. interruptions). Emotion analysis engine module 156 may generate emotion scores for each emotion type during evaluation of a conversation between an agent and a caller. For each emotion type, a score range may be specified and a smaller range of scores within the full score range may be designated as “normal” for the emotion. Different emotion base expressions may be created for an agent side and a customer side of a call. A user may specify values for various expression properties (e.g., window size, frequency thresholds) for an emotion base expression, as discussed further with respect to FIGS. 2A-C. [0141] If the call proceeds and the “mini-Miranda” phrase is still not detected, (i.e. inappropriate actions) a visual and audible alert may be activated at a supervisor station 118 so that a supervisor can intervene. In some embodiments, alert 652 and similar alerts, messages, and instructions sent from a supervisor station 118 may appear as a scrolling marquee, and may disappear once the “mini-Miranda” phrase is detected.)
As per Claim 6, Ripa teaches: (Original) The system of claim 1, which further comprises
normalizing the calculated impact scores for the plurality of behavioral factors. (in at least [0066] Emotion analysis engine module 156 may generate emotion scores for each emotion type during evaluation of a conversation between an agent and a caller. For each emotion type, a score range may be specified and a smaller range of scores within the full score range may be designated as “normal” for the emotion. Different emotion base expressions may be created for an agent side and a customer side of a call. A user may specify values for various expression properties (e.g., window size, frequency thresholds) for an emotion base expression, as discussed further with respect to FIGS. 2A-C. [0107] Data structure 260 may be stored in a database in a memory, for example on one or more of servers 112, and may include fields for a window size 262, an emotion score threshold value 264, a yellow frequency threshold 266, an amber frequency threshold 268, a red frequency threshold 270, and an alert frequency threshold 272. It should be understood that the order of these fields is not necessarily of any particular significance, and that other fields may be included in data structure 230 instead of or in addition to the data described herein, as necessary or helpful to the effectiveness or robustness of an emotion base expression and emotion analysis. The expression property values in the fields of data structure 260 are higher than those of data structure 230 because emotion levels for an easily excitable agent tend to be higher than average, and thus calculated emotion scores can be higher than normal before there is an actual cause for alarm.)
As per Claim 7, Ripa teaches: (Original) The system of claim 1,
wherein performing an action automatically based on the overall impact score comprises: (in at least [0144] emotion alerts may be displayed in columns 712 and 714 when a calculated agent or customer emotion frequency is equal to or exceeds an alert frequency threshold. Column 724 of real-time report 700 may display word/phrase detector alerts, for example an alert that a certain agent has not yet read the “mini-Miranda” rights to the customer with whom the agent is currently speaking. When alerted, a supervisor can coach a particular agent by speaking only to the agent, “barge in” on the conversation between the agent and the customer (i.e., speak to both the agent and the customer), or merely monitor (i.e., listen in on) the conversation between the agent and the customer. [0150] schematic summary 800 may include event tags that represent emotion events (also referred to herein as “emotion event tags”). Emotion event tags may have different geometric shapes and/or colors than word/phrase event tags. In some embodiments, emotion event tags may have various colors that correspond to gradations of color representing various frequency thresholds, as discussed above with respect to FIG. 3. It should be understood that geometric shapes other than those illustrated in FIG. 8 may be used for event tags, and that various shades of color may be used for event tags to represent emotion events and/or word/phrase events corresponding to different event profiles. In some embodiments, a user may toggle on/off the display of different types of event tags and representations of some or all emotions determined during the call. [0152] Through the use of a built-in mini-digital voice recording system in combination with stored event tags, a supervisor at supervisor station 118 can replay the audio and display detailed scores for each emotional analysis, as well as obtain precise insight into causality for call compliance. A pre-built detailed drilldown process may allow easy identification of best and worst agent performers. By drilling down to the lowest level of detail, each call's events (emotion and word/phrase) can be reviewed, which may aid in agent training, counseling, and recognition. [0153] FIG. 9 shows a report 900 that may be generated based on emotion and word/phrase information compiled over time for several agents. Report 900 includes several columns for displaying agent IDs 902, last names 904, and first names 906. Report 900 also includes several columns for displaying an agent's emotion score averages, including a column for an overall average 908, a column for an average over the last 10 days 910, and a column for an average over the last 30 days 912. In addition, report 900 includes several columns for displaying an agent's average number of word/phrase events per call, including a column for an overall average 914, a column for an average over the last 10 days 916, and a column for an average over the last 30 days 918. In some embodiments, report 900 may include columns for displaying an agent's average number of emotion events per call and average number of data events per call. Rows 920, 922, 924, 926, and 928 of report 900 each correspond to one of a plurality of call center agents, for example agents assigned to a particular zone, and row 930 may correspond to mean values of the specified averages across the call center.)
Although implied, Ripa in view of Grasso does not expressly disclose the following limitations, which however, are taught by Quan,
automating performance evaluation of the supervisor; or determining performance-linked incentives for the supervisor. (in at least [0139] The method 200 can include producing an output based on the coach quality S270, which can function to perform any or all of: triggering one or more actions commensurate with coach quality (e.g., rewarding a high quality coach, highlighting an area of improvement to a low quality coach, incentivizing a coach, etc.), maintaining a high quality coach cohort, learning from high performing coaches, directing a coach manager's attention to a subset of struggling or low-performing coaches, updating one or more models, and/or performing any other suitable functions.)
The reason and rationale to combine Ripa, Grasso, Quan is the same as recited above.
As per Claim 8, Ripa teaches: (Original) The system of claim 1, wherein performing an action automatically based on the overall impact score comprises:
scheduling the supervisor in a workforce management application; or distributing the interaction to one or more of the following: an organizational client, the supervisor, or the agent for evaluation; or assigning coaching to the supervisor or an agent associated with the interaction. (in at least [0085] Abstract and custom profiles may be used (i.e., active) simultaneously. An active profile may be used in measuring the variance between analysis results and event notification specifications. Special cases of the measured variances may be identified as events and stored. The first event of an active profile in use during a call may trigger an alert, which may be used to generate a preemptive action (e.g., generating a phone call to a supervisor/compliancy officer, or generating actionable choices such as coaching the agent, barging in on the call, or simply monitoring the call). Continuous processing of an audio stream against active profiles may result in the incrementing of the number of events, which may be displayed in a real-time report (discussed further with respect to FIG. 7). The total number of events identified/generated during a call may be stored and used to assess the overall quality of the call in a post-call report (discussed further with respect to FIG. 9). In some embodiments, the agent side and customer side of all calls may be scored, and a running event average may be maintained for the agent side and customer side of all calls. The event averages may be used to evaluate agent performance. [0144] emotion alerts may be displayed in columns 712 and 714 when a calculated agent or customer emotion frequency is equal to or exceeds an alert frequency threshold. Column 724 of real-time report 700 may display word/phrase detector alerts, for example an alert that a certain agent has not yet read the “mini-Miranda” rights to the customer with whom the agent is currently speaking. When alerted, a supervisor can coach a particular agent by speaking only to the agent, “barge in” on the conversation between the agent and the customer (i.e., speak to both the agent and the customer), or merely monitor (i.e., listen in on) the conversation between the agent and the customer. [0150] schematic summary 800 may include event tags that represent emotion events (also referred to herein as “emotion event tags”). Emotion event tags may have different geometric shapes and/or colors than word/phrase event tags. In some embodiments, emotion event tags may have various colors that correspond to gradations of color representing various frequency thresholds, as discussed above with respect to FIG. 3. It should be understood that geometric shapes other than those illustrated in FIG. 8 may be used for event tags, and that various shades of color may be used for event tags to represent emotion events and/or word/phrase events corresponding to different event profiles. In some embodiments, a user may toggle on/off the display of different types of event tags and representations of some or all emotions determined during the call. [0152] Through the use of a built-in mini-digital voice recording system in combination with stored event tags, a supervisor at supervisor station 118 can replay the audio and display detailed scores for each emotional analysis, as well as obtain precise insight into causality for call compliance. A pre-built detailed drilldown process may allow easy identification of best and worst agent performers. By drilling down to the lowest level of detail, each call's events (emotion and word/phrase) can be reviewed, which may aid in agent training, counseling, and recognition. [0153] FIG. 9 shows a report 900 that may be generated based on emotion and word/phrase information compiled over time for several agents. Report 900 includes several columns for displaying agent IDs 902, last names 904, and first names 906. Report 900 also includes several columns for displaying an agent's emotion score averages, including a column for an overall average 908, a column for an average over the last 10 days 910, and a column for an average over the last 30 days 912. In addition, report 900 includes several columns for displaying an agent's average number of word/phrase events per call, including a column for an overall average 914, a column for an average over the last 10 days 916, and a column for an average over the last 30 days 918. In some embodiments, report 900 may include columns for displaying an agent's average number of emotion events per call and average number of data events per call. Rows 920, 922, 924, 926, and 928 of report 900 each correspond to one of a plurality of call center agents, for example agents assigned to a particular zone, and row 930 may correspond to mean values of the specified averages across the call center.)
As per Claim 9-16 for a method (see at least Ripa [0031]), substantially recite the subject matter of Claim 1-8 and are rejected based on the same reasoning and rationale.
As per Claim 17-20 for A non-transitory computer-readable medium (see at least Ripa [0167][0168]), substantially recite the subject matter of Claim 1-3, 7-8 and are rejected based on the same reasoning and rationale.
Conclusion
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 extension fee 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 date of this final action.
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/PO HAN LEE/Examiner, Art Unit 3623
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