DETAILED ACTION
Status of Claim
The following is a Final Office Action in response to applicant’s amendments received on 03/19/2026.
Claims 1-6, 9, 11, 16, 19, and 20 are amended. Claims 1-20 are considered in this Office Action. Claims 1-20 are currently pending.
Response to Arguments
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
Response to claim objections arguments: Applicant’s amendments to the claims to address punctuation issue are acknowledged. Applicant’s amendments overcome claims objection. Claim objections are withdrawn.
Response to § 101 arguments: Applicant's arguments and amendments with respect to the 35 U.S.C. §101 rejection to claims have been considered, however they are primarily raised in light of applicant's amendments. An updated the 35 U.S.C. §101 rejection will address applicant's amendment.
Response to § 103 arguments: Applicant's arguments and amendments with respect to the 35 U.S.C. §103 rejection to claims have been considered, however they are primarily raised in light of applicant's amendments. An updated the 35 U.S.C. §103 rejection will address applicant's amendment.
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”.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-10), the system (claim 11-15), and the computer program product (claims 16-20), are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea of providing decision support for users based on user expertise by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into “mental processes”. 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. The claims further fall into “certain method of organizing human activities”. (See MPEP 2106.04(a)(2)). 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 11, are: A system, comprising: a processor; and a memory, wherein the memory includes a computer program product configured to perform expertise and evidence based decision making, the operations comprising: differentiating, by a processor, between an average user and an expert user; monitoring, by the processor, command sequences entered by the expert user for a given task. identifying, by the processor, a command entered by the expert user that diverges from a baseline command sequence model for the given task; prompting, by the processor, the expert user for input of a confidence level for the diverging command and supporting evidence used by the expert user in entering the diverging command; and recalibrating, by the processor, the baseline command sequence model for the given task based upon the expert user entered confidence level and supporting evidence, wherein the recalibrating comprises updating and embedding expert user knowledge into the recalibrated baseline command sequence model using feedback data from the expert user, and applying, by processor, the recalibrated baseline command sequence model for the given task to a knowledge based application. Claims 1and 16 recite substantially the same limitation as claim 11 and therefore subject to the same rationale.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: system, a processor, and a memory, wherein the memory includes a computer program product configured to functions, prompting the expert user for input(extra-solution activity), applying, by processor, the recalibrated baseline command sequence model for the given task to a knowledge based application (recited at high level of generality and amounts to both “apply it” and extra-solution activity), and computer program product for expertise and evidence based decision making, the computer program product, and a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation. 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 paragraphs [0014]-[0016] describe high level general purpose computer) 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. In regard to “prompting user for input”, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) and a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011). The "baseline command sequence model" merely represents computer/ processor environment automatically executing predefined models per changes in the input data/parameters, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and mercy limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)). Furthermore, updating and embedding expert user knowledge into the recalibrated model as described in the claimed invention docs not necessarily alter the technology such as the computing device implementing these models or algorithms; and a possible improvement to the accuracy of resulting optimized model merely represent business model improvement rather than a technological one.
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: system, a processor, and a memory, wherein the memory includes a computer program product configured to functions, prompting the expert user for input, applying, by processor, the recalibrated baseline command sequence model for the given task to a knowledge based application (recited at high level of generality and amounts to both “apply it” and extra-solution activity), and computer program product for expertise and evidence based decision making, the computer program product, and a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation. 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 (paragraphs [0014]-[0016]) 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. The "baseline command sequence model" merely represents computer/ processor environment automatically executing predefined models per changes in the input data/parameters, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and mercy limiting the use of an abstract idea to a particular field or technological environment do not eliminate existence of an abstract idea, do not provide practical application for an abstract idea and do not provide significantly more to an abstract idea MPEP 2106.05(f) &(h)). Furthermore, updating and embedding expert user knowledge into the recalibrated model as described in the claimed invention docs not necessarily alter the technology such as the computing device implementing these models or algorithms; and a possible improvement to the accuracy of resulting optimized model merely represent business model improvement rather than a technological one.
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. However, these elements have been fully considered, however they are directed to the use of generic computing elements however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of concepts of mental process, certain methods of organizing human activity, and mathematical concept, 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 (i.e., changing from AIA to pre-AIA ) 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 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 1-3, 9, 11-13, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over David Skiba (US 2014/0301540 A1, hereinafter “Skiba”) in view of Steven David Jones (US 2014/0315164 A1, hereinafter “Jones”) in view of Christopher Farnham (US 2010/0023469 A1, hereinafter “Farnham”) in view of Williams JR. (US 20150254555 A1, hereinafter “Williams”) in view of Singh (US 20210158146 A1, hereinafter “Singh”).
Claims 1/11/16
Skiba teaches:
A computer-implemented method for expertise and evidence-based decision-making ([0005] a method for analyzing an agent dialog in a contact center; [0141] The computer system 800 may additionally include a computer-readable storage media reader 825; a communications system 830 (e.g., a modem, a network card (wireless or wired), an infra-red communication device, etc.); and working memory 840, which may include RAM and ROM devices as described above. The computer system 800 may also include a processing acceleration unit 835, which can include a DSP, a special-purpose processor, and/or the like. [0142] The computer-readable storage media reader 825 can further be connected to a computer-readable storage medium) comprising:
Monitoring, by the processor, command sequences entered by the expert user for a given task(Skiba[0005] analyzing an agent dialog in a contact center, the method comprising: receiving, by a processor, a contact from a consumer; starting an active dialog with the consumer; comparing the active dialog with one or more of the norms or the successful past dialog; wherein the comparison to the norms can include determining if an order of steps in the active dialog has been successful. [0035] The system can monitor the interactions over the communication media between a customer, with down Internet service, and a cable provider's agent. When the system determines that a successful dialog consists of steps A, B, C, and D in that order, the system can monitor the active dialog for the order and similar content to past dialogs);
identifying, by the processor, a command entered by the expert user that diverges from a baseline command sequence model for the given task ([0035] The system can monitor the interactions over the communication media between a customer, with down Internet service, and a cable provider's agent. When the system determines that a successful dialog consists of steps A, B, C, and D in that order, the system can monitor the active dialog for the order and similar content to past dialogs. [0036] The typical steps for the dialog may be: [0037] Step A: Check a browser with Internet access; [0038] Step B: Check the power at the cable modem; [0039] Step C: Check the cables at the cable modem; [0040] Step D: Reset the cable modem. [0041] If the agent attempts to tell a customer to try step D, without first doing A, B, and C, then the agent is not following a successful dialog interaction. When the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor. Examiner notes: Skiba teaches triggering a step based on a condition being met);
prompting, by the processor, the expert user for input for the diverging command; recalibrating the baseline command sequence model for the given task([0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating)).
While Skiba teaches in [0005] analyzing an agent dialog in a contact center and [0126] discloses to determine the degree of deviation, the agent dialog analysis engine 312 may evaluate the current agent's skill and/or /experience level, the intent of the dialog, the quality of the model dialogs used in any comparison, etc. Some agents may be allowed more deviation than other agents. Skiba does not explicitly teach the following limitation, however analogous reference, in the field of operator skill analysis, Jones teaches:
differentiating, by a processor, between an average user and an expert user (Jones [0012] Control circuit 102 may determine the skill level of the operator by, for example, comparing the performance of the operator with desired performance, such as an expert's performance. [0020] Control circuit 102 may assign the performance of the operator to one of a plurality of categories or skill levels based on the comparison result. Control circuit 102 may rate the operator as a novice operator, a skilled operator, or an expert operator. Control circuit 102 may indicate the skill levels using numerical or alphabetical indicators, such as "Level 1," "Level 2," "Level 3," "Level A," "Level B," or "Level C." Control circuit 102 may store data representing any number of skill levels. Examiner Notes: Jones differentiate user’s expertise by rating the operator as a novice operator, a skilled operator, or an expert operator).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba with Jones to include differentiating between an average user and an expert user as part of the evaluation process of the current agent's skill and/or /experience level, because in doing so will allow the system to provide assist the operator to improve the performance of the operator based on skill level and deviation level ([0005] of Jones).
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference, in the field of decision-guided workflow analysis, Farnham teaches:
prompting, by a processor, the expert user for input of a confidence level for the diverging command and for supporting evidence used by the expert user in entering the diverging command ([0010] The participants then add confidence level in the various outcomes by providing statements, evidence and support to or detraction from the outcome possibilities);
recalibrating, by the processor, the baseline command sequence model for the given task based upon the confidence level and supporting evidence entered by expert user ([0021] All of these confidence levels are calculated and updated to create a probabilistic set of relationships that extend between the various possible solution sets. [0022] participant weighting and bias can serve to skew the model).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba and Jones with Farnham to include prompting the expert user for input of a confidence level for the diverging command and for supporting evidence used by the expert user in entering the diverging command and recalibrating the baseline command sequence model for the given task based upon the confidence level and supporting evidence entered by expert user, because in doing so will allow the system to determine whether the detected deviation should be accept and to what level based on operator’s expertise parameters which provides improvement to decision accuracy ([0009] of Farnham).
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference, in the field of decision-guided workflow analysis, Williams teaches:
wherein the recalibrating comprises updating and embedding expert user knowledge into the recalibrated baseline command sequence model using feedback data from the expert user ([00176] Whenever a user, such as a Domain Expert, adjusts the predicted output of the DLNN, that data element may be submitted to the training process of the Fast-Learning Model, quickly modifying and improving future output of the Fast-Learning Model. Subsequent runtime scoring of the Fast-Learning Model may have a higher accuracy and confidence (compared to the DLNN) for data similar to the type that have been submitted through Fast Learning Model training process. [0177] it may be necessary to re-train the DLNN, in order to incorporate the adjustments made by the expert).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba, Jones, and Farnham with Williams to include the recalibrating comprises updating and embedding expert user knowledge into the recalibrated baseline command sequence model using feedback data from the expert user, because in doing so will improve future outcomes by incrementally refining model based on expert input ([0010] and [00176] of Williams).
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference, in the field of decision-guided workflow analysis, Singh teaches:
and applying, by processor, the recalibrated baseline command sequence model for the given task to a knowledge-based application ([0009] Based on the optimal path of actions, the dynamic sequencing platform may identify and cause the next optimal action to be performed (e.g., via the service agent, the consumer, and/or an automated feature). [0031] the dynamic sequencing platform may directly control the network device (e.g., using a network command) to perform the optimal action, and monitor a response to the optimal action provided by the network device, where he dynamic sequencing platform may receive feedback data (e.g., a performance metric, an alarm, a notification, a response, and/or the like) relating to an efficacy of the optimal action in resolving the unresolved issue, and update the historical data based on the feedback data. In some examples, the dynamic sequencing platform may transmit the feedback data and/or the real-time data to the network storage device, and update the historical data with the feedback data and/or the real-time data. In some cases, the dynamic sequencing platform may train the machine learning model, the graph analytics model, and/or the long short-term memory model based on the updated historical data (e.g., historical data that has been updated with the feedback data and/or the real-time data). [0055] Device 300 may perform one or more processes described herein. Device 300 may perform these processes based on processor 320 executing software instructions).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba, Jones, Farnham, Williams with Singh to include applying, by processor, the recalibrated baseline command sequence model for the given task to a knowledge-based application, because in doing so will enable issues to be resolved in a consistent, efficient, and effective manner. ([0034] of Singh).
Claim 2
Skiba further teaches:
The method of claim 1, further comprising: adding a decision point for the diverging command entered by the expert user into the baseline command sequence model for the given task([0013] determining if a new sequence of steps is successful; and determining if a dialog variation is successful. [0106] An agent dialog response engine 320 can receive agent analysis 360 from the analysis tools component 216. The agent dialog response engine 320 may then provide an output, such as a response to the deviating dialog, shown in response 364. This response 364 may be some indication to involve a determination that a new dialog or some other change is required. [0109] A new dialog module 352 can determine that the interaction has deviated in a way that a new dialog may need to be created. [0042] and [0133] For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog).
Claim 3
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference, Farnham teaches:
The method of claim 1, wherein recalibrating the baseline command sequence model for the given task further comprises calculating a recommendation value by calculating a weighted confidence level and a weighted supporting evidence value and factoring the expert confidence level and factoring the supporting evidence value of commands of the baseline command sequence model ([0022] The participants then add confidence level in the various outcomes by providing statements, evidence and support to or detraction from the outcome possibilities. All of these confidence levels are calculated and updated to create a probabilistic set of relationships that extend between the various possible solution sets. [0022] participant weighting and bias can serve to skew the model. [0024] the system allows a biasing factor to be assigned to the participant that may reduce the impact of their input outside their areas of particular expertise while also allowing a weighting factor to be assigned to those categories in which their particular knowledge is of a high value. For example, their beliefs may be reduced in weighting by 20%, thereby allowing their input to impact the decision process by 80%).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba and Jones with Farnham to include recalibrating the baseline command sequence model for the given task further comprises calculating a weighted confidence level and a weighted supporting evidence value by and factoring the expert confidence level and factoring the supporting evidence value as part of the recalibration of baseline as taught in Skiba, because in doing so will allow the system to determine whether the detected deviation should be accept and to what level based on operator’s expertise parameters which provides improvement to decision accuracy ([0009] of Farnham).
Claim 12/17Skiba further teaches:
The system of claim 11, wherein performing expertise and evidence-based decision making further comprising: using the recalibrated baseline command sequence model and updating the baseline command sequence model for the given task([0132] Based on the degree of deviation, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning. Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. [0133] For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog).
Claim 13/18
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference Farnham teaches:
The system of claim 11, wherein performing expertise and evidence-based decision making further comprising: selectively updating the baseline command sequence model for the given task based upon identifying a weighted confidence level and identifying a weighted supporting evidence([0022] The participants then add confidence level in the various outcomes by providing statements, evidence and support to or detraction from the outcome possibilities. All of these confidence levels are calculated and updated to create a probabilistic set of relationships that extend between the various possible solution sets. [0022] participant weighting and bias can serve to skew the model. [0024] the system allows a biasing factor to be assigned to the participant that may reduce the impact of their input outside their areas of particular expertise while also allowing a weighting factor to be assigned to those categories in which their particular knowledge is of a high value. For example, their beliefs may be reduced in weighting by 20%, thereby allowing their input to impact the decision process by 80%).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba and Jones with Farnham to include selectively updating the baseline command sequence model for the given task based upon identifying a weighted confidence level and identifying a weighted supporting evidence as part of the recalibration and the updating of baseline as taught in Skiba, because in doing so will allow the system to determine whether the detected deviation should be accept and to what level based on operator’s expertise parameters which provides improvement to decision accuracy ([0009] of Farnham).
Claim 9
Skiba teaches
The method of claim 1, further comprises identifying most common tasks, and generating baseline command sequence models for the identified most common tasks ([0007] receiving dialog choices from a supervisor; retrieving past dialogs associated with the choices; mapping dialogs for norms; establishing the norms for each dialog; providing the norms for analysis; and providing successful dialogs for analysis. [0025] An active agent dialog may then be compared to previous dialogs based on procedures, scripts, and word similarities. By reviewing and analyzing multiple successful dialogs, a supervisor can quantify what a good answer to the dialog might contain. [0099] the supervisor can select past dialogs past that have been successful for agents with respect to a particular problem. In other instances, the dialog analysis module 300 may automatically identify different dialogs within the database 218 that have been successful. For example, an instance of a dialog that has been successful may have an identifier which allows the dialog analysis module 300 to identify the successful dialogs and extract those from the database 218. These successful dialogs may be then matched for norms. Thus, the dialog analysis module 300 can determine a set of information to which indicates a dialog has been successful and then can store that information in norms database 308. The norms information and any successful dialogs may then be passed to the agent dialog analysis engine 312, wherein [0035] When the system determines that a successful dialog consists of steps A, B, C, and D in that order, the system can monitor the active dialog for the order and similar content to past dialogs. Further see paragraphs [0131]-[0133]).
Claims 4-7, 10, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Skiba in view of Jones in view of Farnham in view of Williams in view of Singh, as applied in claims 1, 11, and 16, and further in view of Roland Hochmuth (US Patent No. 6,046,741, hereinafter “Hochmuth”).
Claim 4
While Skiba teaches in [0005] analyzing an agent dialog in a contact center and [0126] discloses to determine the degree of deviation, the agent dialog analysis engine 312 may evaluate the current agent's skill and/or /experience level, the intent of the dialog, the quality of the model dialogs used in any comparison, etc. Some agents may be allowed more deviation than other agents. Skiba does not explicitly teach the following limitation, however analogous reference Jones teaches:
The method of claim 3, wherein calculating the recommendation value further comprises identifying and factoring an expertise level of the expert user for the given task([0020] For example, control circuit 102 may rate or categorize the performance of the operator based on the deviation of the operational status of the machine from the desired operational status. Control circuit 102 may rate the operator as a novice operator, a skilled operator, or an expert operator. Control circuit 102 may indicate the skill levels using numerical or alphabetical indicators, such as "Level 1," "Level 2," "Level 3," "Level A," "Level B," or "Level C." Control circuit 102 may store data representing any number of skill levels. [0031] control circuit 102 may determine the skill level of the operator through, for example, explicit user inputs).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba with Jones to include calculating a recommendation value further comprises identifying and factoring an expertise level of the expert user for the given task as part of the parameter used in determining the recalibration criteria, because in doing so will allow the system to provide assist the operator to improve the performance of the operator based on skill level and deviation level ([0005] of Jones).
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference, in the field of decision-guided workflow analysis, Hochmuth teaches:
and identifying and factoring a frequency value of commands of the baseline command sequence model (Col. 3 lines 61-67 in a step 604 the command log is examined for repeating sequences of commands and parameters, or patterns of commands and parameters. This may be done by successively taking each command and it's parameters in the command log and comparing it with every other command and it's parameters in the command log to see how many occurrences of that command and the same parameters has occurred).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba, Jones, and Farnham with Hochmuth to include identifying and factoring a frequency value of commands of the baseline command sequence model as part of the recalibration of baseline criteria as taught in Skiba, because in doing so will allow the system to determine if the deviation is routine based on how frequency which yield to a predictable improvement in the workflow analysis (Col. 3 lines 61-67 of Hochmuth).
Claim 5
While Skiba teaches in [0005] analyzing an agent dialog in a contact center and [0126] discloses to determine the degree of deviation, the agent dialog analysis engine 312 may evaluate the current agent's skill and/or /experience level, the intent of the dialog, the quality of the model dialogs used in any comparison, etc. Some agents may be allowed more deviation than other agents. Skiba does not explicitly teach the following limitation, however analogous reference Jones teaches:
The method of claim 4, wherein identifying and factoring the expertise level of the expert user for the given task further comprises identifying and validating a user entered expertise level([0031]control circuit 102 may determine the skill level of the operator through, for example, explicit user inputs or third-party information provided by, for example, an employer of the operator, a professional association to which the operator belongs, a manufacturer, a dealer, or an owner of machine 119. [0032] control circuit 102 may adjust the skill level associated with the operator during the operation of the machine according to changes in the performance of the operator. For example, when the operator improves his/her performance, control circuit 102 may detect a reduction in the difference between the desired performance and the performance of the operator or the errors made by the operator, control circuit 102 may adjust the skill level of the operator to a more experienced skill level).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba with Jones to include identifying and factoring an expertise level of the expert user for the given task further comprises identifying and validating a user entered expertise level as part of the recalibration process taught in Skiba, because in doing so will allow the system to provide assist the operator to improve the performance of the operator based on skill level and deviation level ([0005] of Jones).
Claim 6
While Skiba teaches in [0005] analyzing an agent dialog in a contact center and [0126] discloses to determine the degree of deviation, the agent dialog analysis engine 312 may evaluate the current agent's skill and/or /experience level, the intent of the dialog, the quality of the model dialogs used in any comparison, etc. Some agents may be allowed more deviation than other agents. Skiba does not explicitly teach the following limitation, however analogous reference Jones further teaches:
The method of claim 4, wherein identifying and factoring the expertise level of the expert user for the given task comprises using a user profile to identify the expertise level of the expert user ([0039] Computer system 124 may store data indicating a profile of the operator and a track record of the operator in operating machine 119. [0031] Each skill level represents the operator's familiarity with the machine or the performance of the operator in operating machine 119. Control circuit 102 may determine the skill level of the operator by, for example, comparing the performance of the operator with desired performance, such as an expert's performance).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba with Jones to include identifying and factoring an expertise level of the expert user for the given task comprises using a user profile to identify the expertise level of the expert user as part of the recalibration process taught in Skiba, because in doing so will allow the system to provide assist the operator to improve the performance of the operator based on skill level and deviation level ([0005] of Jones).
Claim 7
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference Farnham teaches:
The method of claim 4, wherein recalibrating the baseline command sequence model further comprises identifying a record value based upon a weighted value for said expert confidence, said supporting evidence, said expertise level, ([0009] the various participants have the opportunity to make adjustments to the weighting and credibility of the evidence and participants in the decision-making process in order to arrive at what may be perceived as a more objective outcome. [0010] The participants then add confidence level in the various outcomes by providing statements, evidence and support to or detraction from the outcome possibilities. All of these confidence levels are calculated and updated to create a probabilistic set of relationships that extend between the various possible solution sets. [0024] allows a biasing factor to be assigned to the participant that may reduce the impact of their input outside their areas of particular expertise while also allowing a weighting factor to be assigned to those categories in which their particular knowledge is of a high value. For example, their beliefs may be reduced in weighting by 20%, thereby allowing their input to impact the decision process by 80%)
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Farnham teaches [0009] the various participants have the opportunity to make adjustments to the weighting and credibility of the evidence and participants in the decision-making process in order to arrive at what may be perceived as a more objective outcome. [0010] The participants then add confidence level in the various outcomes by providing statements, evidence and support to or detraction from the outcome possibilities. All of these confidence levels are calculated and updated to create a probabilistic set of relationships that extend between the various possible solution sets. [0024] allows a biasing factor to be assigned to the participant that may reduce the impact of their input outside their areas of particular expertise while also allowing a weighting factor to be assigned to those categories in which their particular knowledge is of a high value. For example, their beliefs may be reduced in weighting by 20%, thereby allowing their input to impact the decision process by 80%. Skiba and Farnham do not explicitly teach the following, however analogous reference Hochmuth teaches:
and said command sequence frequency (Col. 3 lines 61-67 in a step 604 the command log is examined for repeating sequences of commands and parameters, or patterns of commands and parameters. This may be done by successively taking each command and it's parameters in the command log and comparing it with every other command and it's parameters in the command log to see how many occurrences of that command and the same parameters has occurred).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba, Jones, and Farnham with Hochmuth to include said command sequence frequency as part of the recalibration of baseline criteria as taught in Skiba, because in doing so will allow the system to determine if the deviation is routine based on how frequency which yield to a predictable improvement in the workflow analysis (Col. 3 lines 61-67 of Hochmuth).
Claim 10
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Farnham teaches [0009] the various participants have the opportunity to make adjustments to the weighting and credibility of the evidence and participants in the decision-making process in order to arrive at what may be perceived as a more objective outcome. [0010] The participants then add confidence level in the various outcomes by providing statements, evidence and support to or detraction from the outcome possibilities. All of these confidence levels are calculated and updated to create a probabilistic set of relationships that extend between the various possible solution sets. [0024] allows a biasing factor to be assigned to the participant that may reduce the impact of their input outside their areas of particular expertise while also allowing a weighting factor to be assigned to those categories in which their particular knowledge is of a high value. For example, their beliefs may be reduced in weighting by 20%, thereby allowing their input to impact the decision process by 80%. Skiba and Farnham do not explicitly teach the following, however analogous reference Hochmuth teaches:
The method of claim 9, measuring commands entered over time and storing command frequency for all users (Col. 3 lines 61-67 in a step 604 the command log is examined for repeating sequences of commands and parameters, or patterns of commands and parameters. This may be done by successively taking each command and it's parameters in the command log and comparing it with every other command and it's parameters in the command log to see how many occurrences of that command and the same parameters has occurred. If they have, a repeating command sequence has probably been detected. The threshold number of times a command, or pattern of commands, must occur before it is considered a pattern may be initially set to a default value. Col. 3 Lines 43-53 In a step 504 the text form of each command, and it's parameters, are stored in a log along with a time stamp indicating when the command was executed).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba, Jones, and Farnham with Hochmuth to include measuring commands entered over time and storing command frequency for all users as part of the recalibration of baseline criteria as taught in Skiba, because in doing so will allow the system to determine if the deviation is routine based on how frequency which yield to a predictable improvement in the workflow analysis (Col. 3 lines 61-67 of Hochmuth).
Claim 14/19
While Skiba teaches in [0005] analyzing an agent dialog in a contact center and [0126] discloses to determine the degree of deviation, the agent dialog analysis engine 312 may evaluate the current agent's skill and/or /experience level, the intent of the dialog, the quality of the model dialogs used in any comparison, etc. Some agents may be allowed more deviation than other agents. Skiba does not explicitly teach the following limitation, however analogous reference Jones teaches:
The system of claim 13, wherein recalibrating the baseline command sequence model further comprises identifying and factoring an expertise level of the expert user for the given task [0020] For example, control circuit 102 may rate or categorize the performance of the operator based on the deviation of the operational status of the machine from the desired operational status. Control circuit 102 may rate the operator as a novice operator, a skilled operator, or an expert operator. Control circuit 102 may indicate the skill levels using numerical or alphabetical indicators, such as "Level 1," "Level 2," "Level 3," "Level A," "Level B," or "Level C." Control circuit 102 may store data representing any number of skill levels. [0031] control circuit 102 may determine the skill level of the operator through, for example, explicit user inputs).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba with Jones to include recalibrating the baseline command sequence model further comprises identifying and factoring an expertise level of the expert user for the given task as part of the recalibration process criteria taught in Skiba, because in doing so will allow the system to provide assist the operator to improve the performance of the operator based on skill level and deviation level ([0005] of Jones).
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference, Hochmuth teaches:
and identifying and factoring command frequency of the baseline command sequence model (Col. 3 lines 61-67 in a step 604 the command log is examined for repeating sequences of commands and parameters, or patterns of commands and parameters. This may be done by successively taking each command and it's parameters in the command log and comparing it with every other command and it's parameters in the command log to see how many occurrences of that command and the same parameters has occurred).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the teaching of Skiba, Jones, and Farnham with Hochmuth to include identifying and factoring command frequency of the baseline command sequence model as part of the recalibration of baseline criteria as taught in Skiba, because in doing so will allow the system to determine if the deviation is routine based on how frequency which yield to a predictable improvement in the workflow analysis (Col. 3 lines 61-67 of Hochmuth).
Claims 8, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Skiba in view of Jones in view of Farnham in view of Williams in view of Singh in view of Hochmuth, as applied in claims 4, 14, and 19, and further in view of Dimitri Kanevsky (US Patent No. 6,505,208 B1, hereinafter “Kanevsky”).
Claim 8
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Farnham teaches [0009] the various participants have the opportunity to make adjustments to the weighting and credibility of the evidence and participants in the decision-making process in order to arrive at what may be perceived as a more objective outcome. [0010] The participants then add confidence level in the various outcomes by providing statements, evidence and support to or detraction from the outcome possibilities. All of these confidence levels are calculated and updated to create a probabilistic set of relationships that extend between the various possible solution sets. [0024] allows a biasing factor to be assigned to the participant that may reduce the impact of their input outside their areas of particular expertise while also allowing a weighting factor to be assigned to those categories in which their particular knowledge is of a high value. For example, their beliefs may be reduced in weighting by 20%, thereby allowing their input to impact the decision process by 80%. Skiba and Farnham do not explicitly teach the following, however analogous reference, in the field of task sequence optimization, Kanevsky teaches:
The method of claim 7, wherein recalibrating the baseline command sequence model further comprises selectively updating the baseline command sequence model based upon the identified record value(column 4 lines 22-24 discloses all user action segments produced at any time are compared with the stored segment action. At step 852, if the new user segment action is determined to be more efficient then the stored ones, the new user segment action is stored in place of the old).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the teaching of Skiba, Jones, Farnham, and Hochmuth with Kanevsky to include selectively updating the baseline command sequence model based upon the identified record value as part of the recalibration of baseline criteria as taught in Skiba, because in doing so will allow the system to determine if the deviation is determined to be more efficient to update the baseline path with the efficient commands which yield to a predictable improvement in the workflow analysis (column 4 lines 22-24 of Kanevsky).
Claim 15/20
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference, Farnham teaches:
The system of claim 14, wherein recalibrating the baseline command sequence further comprises identifying a record value based upon a weighted value for said confidence level, said supporting evidence, said expertise level([0009] the various participants have the opportunity to make adjustments to the weighting and credibility of the evidence and participants in the decision making process in order to arrive at what may be perceived as a more objective outcome. [0010] The participants then add confidence level in the various outcomes by providing statements, evidence and support to or detraction from the outcome possibilities. All of these confidence levels are calculated and updated to create a probabilistic set of relationships that extend between the various possible solution sets. [0024] allows a biasing factor to be assigned to the participant that may reduce the impact of their input outside their areas of particular expertise while also allowing a weighting factor to be assigned to those categories in which their particular knowledge is of a high value. For example, their beliefs may be reduced in weighting by 20%, thereby allowing their input to impact the decision process by 80%).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Skiba and Jones with Farnham to include identifying a record value based upon a weighted value for said confidence level, said supporting evidence, said expertise level e as part of the recalibration and the updating of baseline as taught in Skiba, because in doing so will allow the system to determine whether the detected deviation should be accept and to what level based on operator’s expertise parameters which provides improvement to decision accuracy ([0009] of Farnham).
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference, Hochmuth teaches:
and said command frequency(Col. 3 lines 61-67 in a step 604 the command log is examined for repeating sequences of commands and parameters, or patterns of commands and parameters. This may be done by successively taking each command and it's parameters in the command log and comparing it with every other command and it's parameters in the command log to see how many occurrences of that command and the same parameters has occurred),
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the teaching of Skiba, Jones, and Farnham with Hochmuth to include command frequency as part of the recalibration of baseline criteria as taught in Skiba, because in doing so will allow the system to determine if the deviation is routine based on how frequency which yield to a predictable improvement in the workflow analysis (Col. 3 lines 61-67 of Hochmuth).
While Skiba teaches triggering a step based on a condition being met as disclosed in [0035] when the agent asks the customer to perform Step D (out of sequence), the system may warn the agent and/or a supervisor and the ability of the system to learn sequential steps and acceptable variations on processes. [0132]-[0133] Based on the degree of deviation, or the seriousness of the problem, one of the modules 344 through 356 determines a resolution to the problem. This response then is sent in packet 364. The response can be a warning to the agent, a warning to the supervisor, a dialog readjustment, and/or a new dialog request. One of these responses may be enacted by the contact center server 116, in step 636. Thus, if the warning module is sent to an agent, the agent 228 may enact a resolution by reacting to the warning (prompt for input). Similarly, the dialog readjustment 344 may be performed by the agent to readjust the dialog. For example, if a deviation from past dialogs is successful, the deviating dialog may be used to create a new dialog for agents in the future. Thus, the reaction to a deviation can be to create a newly successful dialog by learning the parameters that made the dialog successful and incorporating those parameters into a new dialog(recalibrating). Skiba does not explicitly teach the following, however analogous reference, Kanevsky teaches:
and selectively updating the baseline command sequence model based upon the identified record value(column 4 lines 22-24 discloses all user action segments produced at any time are compared with the stored segment action. At step 852, if the new user segment action is determined to be more efficient then the stored ones, the new user segment action is stored in place of the old).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the teaching of Skiba, Jones, Farnham, and Hochmuth with Kanevsky to include selectively updating the baseline command sequence model based upon the identified record value as part of the recalibration of baseline criteria as taught in Skiba, because in doing so will allow the system to determine if the deviation is determined to be more efficient to update the baseline path with the efficient commands which yield to a predictable improvement in the workflow analysis (column 4 lines 22-24 of Kanevsky).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Samarth Aggarwal (US 20220019909 A1): Methods, systems, and computer storage media for providing command recommendations for an analysis-goal, using analytics system operations in an analytics system. In operation, an analytics client is configured to provide an analytics interface for receiving a selection of analysis-goal information that corresponds to an analysis-goal model. A goal engine selects an analysis-goal based on the analysis-goal information. A command engine is configured to use the analysis-goal and goal-driven models to predict probable commands for the analysis goal. The command engine also selects a next command recommendation from the probable commands.
Donald Charles Laing(US 20200089561 A1): Disclosed is a computer-implemented method of finding, troubleshooting and auto-remediating problems in storage environments. The method includes guiding a user, by a data processing system of an active storage environment, to select an applicable playbook of troubleshooting logic from among playbooks of different troubleshooting logic to address problem(s) with infrastructure device(s) of the active storage environment, asking the user, by the data processing system, questions from the applicable playbook to identify a possible resolution path for the problem(s), resulting in an identified resolution path, receiving, by the data processing system, answers to the questions from the user, obtaining, by the data processing system, cross-domain information regarding infrastructure device(s) potentially relevant to the problem(s), resulting in obtained cross-domain information, cognitively determining, by the data processing system, possible resolution(s) based on the answers and the obtained cross-domain information, and auto-remediating, by the data processing system, the problem(s).
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
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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