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 .
This communication is in response to application 18/830,905 filed 09/11/2024. Claims 1-20 are pending and hereby entered. No claims are allowed.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“dynamic routing profile component, configured to” in claim 1
“network routing management component, configured to” in claim 1
“agent console component, configured to” in claim 2
“feedback component configured to” in claim 8
Because this/these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “dynamic routing profile component”, “network routing management component”, “agent console component”, and “feedback component” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The specification does not disclose the necessary structure or algorithms to perform said functions. Although paragraph 65 of the specification discloses structure being a communication device and/or a computing device that includes a server or virtual server executed on computing hardware, it does not describe the necessary algorithms to perform recited functions. See MPEP 2181(II)(B), “Accordingly, a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph is appropriate if the specification discloses no corresponding algorithm associated with a computer or microprocessor… In addition, merely referencing a specialized computer (e.g., a "bank computer"), some undefined component of a computer system (e.g., "access control manager"), "logic," "code," or elements that are essentially a black box designed to perform the recited function, will not be sufficient because there must be some explanation of how the computer or the computer component performs the claimed function”. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) with no practical application and without significantly more.
The claimed invention is directed to an abstract idea in that the instant application is directed to certain methods of organizing human activity (See MPEP 2106.04(a)(2)(II)). The independent claims (1, 13, and 17) recite a method and systems to control interactions and route data to agents based on rules or instructions. These claim elements are being interpreted as managing personal behavior or relationships or interactions between people (including social activities, teaching, or following rules or instructions). Routing information by using attributes and rules recite an abstract idea consistent with the “certain methods of organizing human activity” grouping set forth in the MPEP 2106.04(a)(2)(II).
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites an “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea. The instant application is directed towards a method and systems to implement the identified abstract idea of certain methods of organizing human activity (i.e. routing information using rules or instructions) in a general computer environment. The claims do not include additional elements that integrate the judicial exception into practical application or amount to significantly more than the judicial exception. The independent claims recite the additional elements “a device”, “one or more memories”, “one or more processors”, and “user interface”. These claim elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a general computer environment. The machines merely act as a modality to implement the abstract idea and are not indicative of integration into a practical application (i.e., the additional elements are simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two analysis, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
In regards to the dependent claims
Claim 2 recites the additional element “user interface” However, this falls under the same analysis present in Step 2A Prong 2, as the user interface system merely acts as a modality to implement the abstract idea, see MPEP 2106.05(f)
Claim 7 recites the additional element “a machine learning model”. However, machine learning to perform the abstract idea is not indicative of practical application or significantly more. The machine learning model merely acts as a modality to implement the abstract idea, see MPEP 2106.05(f)
Claims 3-6, 8-12, 14-16, and 18-20 are directed to further embellishments of the identified abstract idea and introduce no new abstract ideas or additional elements that would otherwise impact analysis under 35 USC 101
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 7-9, 11-12, and 17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bhattacharjee (US 20250039303 A1).
Regarding Claim 1, Bhattacharjee teaches:
A system for task management and routing for a contact center network, the system comprising: a dynamic routing profile component, configured to: obtain one or more routing rules associated with one or more routing profiles, wherein the one or more routing profiles are associated with respective agents; [see at least Bhattacharjee (Para 0052) “Rules module 725 may be included within, or reside on a remote computer system coupled to, contact center 720. The rules module includes a list of preconfigured rules 740 for routing communications. The rules may indicate one or more conditions (e.g., business hours for holiday lists, emergency closures, overflows, outages, high call volume periods, etc.) and a corresponding static list of specified agents for routing of the communication.”]
determine, based on dynamic network activity of the contact center network and the one or more routing rules, routing profile information for respective routing profiles of the one or more routing profiles; and provide, to a network routing management component, the routing profile information; [see at least Bhattacharjee: (Para 0053) “Rules module 725 receives the API request, and determines an applicable rule 740 based on occurrence of conditions of a rule. When a rule 740 is identified (e.g., conditions of the rule are satisfied), the static list of specified agents for the conditions is returned. For example, when a rule is identified, a list of the agents specified in the rule is returned to routing module 124. The agents may be ordered or prioritized in the list. Routing module 124 routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”]
the network routing management component, configured to: obtain an indication of one or more network conditions associated with the contact center network; [see at least Bhattacharjee: (Para 0053) “ When a rule 740 is identified (e.g., conditions of the rule are satisfied), the static list of specified agents for the conditions is returned”]
obtain an indication of a task associated with the contact center network, wherein the task is associated with a task type and one or more task attributes; [see at least Bhattacharjee: (Para 0019) “The contact center routes user communications to agent devices 140 of contact center agents 145 that may provide various support to the users 105 with respect to any products or services (e.g., process requests, provide information, assist with use of a product or service, billing issues, etc.)”, (Para 0021) “Contact center 120 matches users 105 to contact center agents 145 to handle user requests or communications. The contact center predicts a reason for a call by a user 105 (e.g., using source attributes, such as customer/user attributes, historical data of customer/user, data collection at a self-service unit, etc.)”]
assign, for the task, a position in a network queue, wherein the position is based on one or more rules and the one or more network conditions, and wherein the network queue includes tasks associated with multiple task types; [see at least Bhattacharjee: (Para 0029) “Routing module 124 of contact center 120 receives the list of agents determined by the corresponding machine learning model from machine learning inference service 132 and routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”, (Para 0019) “The contact center routes user communications to agent devices 140 of contact center agents 145 that may provide various support to the users 105 with respect to any products or services (e.g., process requests, provide information, assist with use of a product or service, billing issues, etc.)”]
select, for the task, an agent channel that is associated with an agent of the contact center network based on the routing profile information, the task type, and the one or more task attributes; and transfer, from the network queue, the task to the agent channel.
[see at least Bhattacharjee: (Para 0021) “Contact center 120 matches users 105 to contact center agents 145 to handle user requests or communications. The contact center predicts a reason for a call by a user 105 (e.g., using source attributes, such as customer/user attributes, historical data of customer/user, data collection at a self-service unit, etc.), selects a best agent 145 (e.g., based on sink attributes) to handle the call, and routes the call to the selected agent. A best agent may be determined based on a most skilled agent, optimization of metrics (e.g., agent efficacy, contact efficacy or handle times), and/or optimization of customer experience metrics or outcomes (e.g., customer satisfaction score (CSAT) indicating customer satisfaction and based on a customer provided rating, net promoter score (NPS) indicating customer loyalty and based on a customer provided rating, customer effort score (CES) indicating effort by a customer to interact with an organization and indicated by a customer rating, post-call sentiment, call reasons, etc.). ”]
Regarding Claim 2, Bhattacharjee teaches the limitations of claim 1. Bhattacharjee further teaches:
The system of claim 1, further comprising an agent console component, configured to: provide, from the agent channel, information indicative of the task for display via a user interface system. [see at least Bhattacharjee: (Para 0019) “The contact center routes user communications to agent devices 140 of contact center agents 145 that may provide various support to the users 105 with respect to any products or services (e.g., process requests, provide information, assist with use of a product or service, billing issues, etc.). User devices 110 and agent device 140 can take on a variety of forms, including a smartphone, tablet, laptop computer, desktop computer, video conference endpoint, corresponding accessories (e.g., headset, etc.), and the like”, (Para 0092) “The present embodiments may employ any number of any type of user interface (e.g., graphical user interface (GUI), command-line, prompt, etc.) for obtaining or providing information”]
Regarding Claim 7, Bhattacharjee teaches the limitations of claim 1. Bhattacharjee further teaches:
The system of claim 1, wherein the task is a machine learning feedback task for an evaluation of a machine learning output from a machine learning model, and wherein the machine learning output is associated with a contact center task. [see at least Bhattacharjee: (Para 0022) “A feedback mechanism may be used to obtain information for an outcome of an interaction from customers, agents, and/or supervisors. Continuous learning may be performed at a tenant level to improve performance over a period of time”, (Para 0029) “A feedback mechanism may be used to obtain information for an outcome of an interaction from customers, agents, and/or supervisors. Continuous learning may be performed at a tenant level to improve performance over a period of time.”]
Regarding Claim 8, Bhattacharjee teaches the limitations of claim 7. Bhattacharjee further teaches:
The system of claim 7, further comprising a feedback component configured to: obtain, via the agent channel, an indication of the evaluation of the machine learning output, wherein the evaluation is input by the agent; and provide the evaluation to cause the machine learning model to be trained based on the evaluation. [see at least Bhattacharjee: (Para 0034) “The feedback may include agent, supervisor, and customer feedback”, (Para 0043) “A series of different types of machine learning models may be generated and trained by continuous learning module 134 for each objective 430 based on historical data (e.g., from post contact/interaction data, customer experience data sources, customer relationship management (CRM) systems, etc.). Further, various feedback may be provided for training (e.g., human feedback, feedback from contact metrics and experience sources, etc.). Performance of the different types of machine learning models and a currently used machine learning model are measured. A best machine learning model for each objective is selected based on testing results (e.g., greatest accuracy, etc.) and stored in database”, (Para 0089) “ For example, feedback from user and agent interactions may be used to update or train the machine learning models with new or different training data (e.g., derived from attributes of the interactions, etc.) and/or dynamically adjust (e.g., expand, modify, etc.) a feature set of the machine learning models to improve accuracy. Thus, the machine learning models may continuously evolve (or be trained) to learn characteristics of users, agents, and corresponding interactions to improve routing (e.g., identify a best destination/agent with lowest wait or idle time, etc.)”]
Regarding Claim 9, Bhattacharjee teaches the limitations of claim 1. Bhattacharjee further teaches:
The system of claim 1, wherein the dynamic routing profile component is further configured to: obtain agent preference information associated with one or more agents including the agent; and wherein the routing profile information is based on the agent preference information. [see at least Bhattacharjee: (Para 0014) “Some example factors, such as agent skill, historical interaction data, time of day, customer emotions during a call, agent availability metrics”, (Para 0071) “Routing module 124 receives the list of agents (determined from rules at operation 815 or machine learning at operation 825), and routes the communication from queue 128 to an agent device 140 of an agent 145 at operation 830 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”]
Regarding Claim 11, Bhattacharjee teaches the limitations of claim 1. Bhattacharjee further teaches:
The system of claim 1, wherein the dynamic routing profile component is further configured to: obtain result information indicative of a result of the task as performed by the agent; [see at least Bhattacharjee: (Para 0016) “Skill level parameters of an agent are altered based on success criteria of a resulting match. Data (e.g., attributes/features and routing results) is collected over a period of time and used to train a machine learning model to provide agent selection based on input and output parameters”]
update, based on the result information, the routing profile information to updated routing profile information; and provide, to the network routing management component, the updated routing profile information.[see at least Bhattacharjee: (Para 0016) “This removes a restriction of a static set of features/configurations being used as input for routing. Further, continuous learning enables the machine learning model to dynamically learn newer mappings between customers and agents and remain updated. This is used to constantly/continuously change input parameters for the agents based on an outcome of an agent selection”, (Para 0017) “An example embodiment uses supervised machine learning. Data collected over time on completed interactions is used to train a machine learning model based on outcome variables, which removes the need for a static set of input features. Continuous learning enables the machine learning model to dynamically learn new mappings and remain updated, where these updates are also propagated to entities used in the initial machine learning model (by updating configurations)”]
Regarding Claim 12, Bhattacharjee teaches the limitations of claim 1. Bhattacharjee further teaches:
The system of claim 1, wherein the dynamic routing profile component, to determine the routing profile information, is configured to: provide, to a machine learning model, the dynamic network activity, the one or more routing rules, and agent preference information; and [see at least Bhattacharjee: (Para 0016) “Data (e.g., attributes/features and routing results) is collected over a period of time and used to train a machine learning model to provide agent selection based on input and output parameters. This removes a restriction of a static set of features/configurations being used as input for routing. Further, continuous learning enables the machine learning model to dynamically learn newer mappings between customers and agents and remain updated. This is used to constantly/continuously change input parameters for the agents based on an outcome of an agent selection”, (Para 0053) “Rules module 725 receives the API request, and determines an applicable rule 740 based on occurrence of conditions of a rule. When a rule 740 is identified (e.g., conditions of the rule are satisfied), the static list of specified agents for the conditions is returned”, (Para 0071) “Routing module 124 receives the list of agents (determined from rules at operation 815 or machine learning at operation 825), and routes the communication from queue 128 to an agent device 140 of an agent 145 at operation 830 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”]
obtain, via an output of the machine learning model, the routing profile information [see at least Bhattacharjee: (Para 0054) “ In other words, a machine learning model selects an agent that produces an optimal score for a corresponding objective from an interaction between the selected agent and user (e.g., an optimal CSAT, NPS, CES, match to agent skill, match to the call reason, etc.)”]
Regarding Claim 17, Bhattacharjee teaches:
A system for task management and routing, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: obtain one or more routing rules associated with one or more routing profiles, wherein the one or more routing profiles are associated with respective agents; [see at least Bhattacharjee: (Para 0081), (Para 0052) “Rules module 725 may be included within, or reside on a remote computer system coupled to, contact center 720. The rules module includes a list of preconfigured rules 740 for routing communications. The rules may indicate one or more conditions (e.g., business hours for holiday lists, emergency closures, overflows, outages, high call volume periods, etc.) and a corresponding static list of specified agents for routing of the communication.”]
determine, based on dynamic network activity of a contact center network and the one or more routing rules, routing profile information for respective routing profiles of the one or more routing profiles; and [see at least Bhattacharjee: (Para 0053) “Rules module 725 receives the API request, and determines an applicable rule 740 based on occurrence of conditions of a rule. When a rule 740 is identified (e.g., conditions of the rule are satisfied), the static list of specified agents for the conditions is returned. For example, when a rule is identified, a list of the agents specified in the rule is returned to routing module 124. The agents may be ordered or prioritized in the list. Routing module 124 routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”]
obtain an indication of a task associated with the contact center network, wherein the task is associated with a task type and one or more task attributes; [see at least Bhattacharjee: (Para 0019) “The contact center routes user communications to agent devices 140 of contact center agents 145 that may provide various support to the users 105 with respect to any products or services (e.g., process requests, provide information, assist with use of a product or service, billing issues, etc.)”, (Para 0021) “Contact center 120 matches users 105 to contact center agents 145 to handle user requests or communications. The contact center predicts a reason for a call by a user 105 (e.g., using source attributes, such as customer/user attributes, historical data of customer/user, data collection at a self-service unit, etc.)”]
assign, for the task, a position in a network queue, wherein the position is based on one or more rules and one or more network conditions, and wherein the network queue includes tasks associated with multiple task types; [see at least Bhattacharjee: (Para 0029) “Routing module 124 of contact center 120 receives the list of agents determined by the corresponding machine learning model from machine learning inference service 132 and routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”, (Para 0019) “The contact center routes user communications to agent devices 140 of contact center agents 145 that may provide various support to the users 105 with respect to any products or services (e.g., process requests, provide information, assist with use of a product or service, billing issues, etc.)”]
select, for the task, an agent channel that is associated with an agent of the contact center network based on the routing profile information, the task type, and the one or more task attributes; transfer, from the network queue, the task to the agent channel; and [see at least Bhattacharjee: (Para 0021) “Contact center 120 matches users 105 to contact center agents 145 to handle user requests or communications. The contact center predicts a reason for a call by a user 105 (e.g., using source attributes, such as customer/user attributes, historical data of customer/user, data collection at a self-service unit, etc.), selects a best agent 145 (e.g., based on sink attributes) to handle the call, and routes the call to the selected agent. A best agent may be determined based on a most skilled agent, optimization of metrics (e.g., agent efficacy, contact efficacy or handle times), and/or optimization of customer experience metrics or outcomes (e.g., customer satisfaction score (CSAT) indicating customer satisfaction and based on a customer provided rating, net promoter score (NPS) indicating customer loyalty and based on a customer provided rating, customer effort score (CES) indicating effort by a customer to interact with an organization and indicated by a customer rating, post-call sentiment, call reasons, etc.). ”]
provide, from the agent channel, information indicative of the task for display via a user interface system. [see at least Bhattacharjee: (Para 0019) “The contact center routes user communications to agent devices 140 of contact center agents 145 that may provide various support to the users 105 with respect to any products or services (e.g., process requests, provide information, assist with use of a product or service, billing issues, etc.). User devices 110 and agent device 140 can take on a variety of forms, including a smartphone, tablet, laptop computer, desktop computer, video conference endpoint, corresponding accessories (e.g., headset, etc.), and the like”, (Para 0092) “The present embodiments may employ any number of any type of user interface (e.g., graphical user interface (GUI), command-line, prompt, etc.) for obtaining or providing information”]
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.
Claims 6, 10, 13, 15-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharjee (US 20250039303 A1) in view of Lewis (US 11115536 B1)
Regarding Claim 6, Bhattacharjee teaches the limitations of claim 1. Bhattacharjee further teaches:
The system of claim 1, wherein the one or more rules are prioritization rules for prioritizing tasks in the network queue, and [see at least Bhattacharjee: (Para 0029) “Routing module 124 of contact center 120 receives the list of agents determined by the corresponding machine learning model from machine learning inference service 132 and routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”]
While Bhattacharjee teaches rules, it does not explicitly teach rules being associated with intra task types. However, Lewis teaches:
wherein the one or more rules include one or more intra-task-type rules associated with the task type and one or more inter-task-type rules associated with the multiple task types. [see at least Lewis: (Column 5, Lines 50-51) “Attributes identify a call routing requirement such as language, location, or agent expertise”, (Column 6, Lines 3-7) “A “term” is a threshold value against which an agent's attribute is compared. For example, the term for a language attribute might be “English>6” where the agent's language proficiency in the English language must be greater than level 6 in order to meet the threshold indicated by the term”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine rule-based routing systems (Bhattacharjee) with inter and intra-task type rules (Lewis). One of ordinary skill in the art would have recognized that routing tasks to the proper agents would involve various prioritization and routing based on task type. Using inter and intra-task type prioritization in a routing system would have yielded the predictable result of a prioritized and accurate routing system to one of ordinary skill in the art.
Regarding Claim 10, Bhattacharjee teaches the limitations of claim 1. Bhattacharjee further teaches:
The system of claim 1, wherein the agent channel is associated with a routing profile of the one or more routing profiles, [see at least Bhattacharjee: (Para 0015) “An example embodiment pertains to contact center agent selection for omni-channel interactions based on a growing set of dynamic parameters from prior customer interactions”, (Para 0021) “Contact center 120 matches users 105 to contact center agents 145 to handle user requests or communications. The contact center predicts a reason for a call by a user 105 (e.g., using source attributes, such as customer/user attributes, historical data of customer/user, data collection at a self-service unit, etc.), selects a best agent 145 (e.g., based on sink attributes) to handle the call, and routes the call to the selected agent”]
While Bhattacharjee teaches a routing system with channels associated with routing profiles, it does not explicitly teach but Lewis does teach:
wherein the routing profile indicates a cognitive switching limit indicative of one or more permissible task types for the agent channel, and wherein the network routing management component, to select the agent channel, is configured to: select the agent channel based on the task type being included in the one or more permissible task types. [(Column 6, Lines 8-14) “An “expression” is a collection of one or more terms that are interrelated via Boolean logical operators such that, for example, requiring that an agent speak English above level 6, be located in Texas, and be proficient in sales (e.g., above level 4), then the expression for this collection of terms (i.e., combination of thresholds) would be “English>6 AND Dallas=TRUE AND Sales>4””, (Column 6, Lines 23-36) “Accordingly, each step may have unique attributes, a different pool of agents, a Wait Time, and/or a “Consider If” formula. In particular, a precision queue term compares an attribute against a value. For example, if you have an attribute for English (English) and assign it a value of >7, the term is “English>7”. Many precision queues may exist in the system, each precision queue comprising many queue steps, and each precision queue step comprising a plurality of precision queue terms. Additionally, an agent may be a member of a plurality of precision queues. In a precision queue system (PQS) having precision queues (PQs), the steps and scripts for said steps comprise the precision queue step rules (PQSRs) for that CRE”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine rule-based routing systems (Bhattacharjee) with cognitive switching limit with permissible task types to select agent channels. One of ordinary skill in the art would have recognized the benefits of combining a routing system with permissions to ensure that agents only handled tasks they are equipped for. The combination yields the predictable results of an organized routing system based on permissible task types.
Regarding Claim 13, Bhattacharjee teaches:
A method for task management and routing, comprising: obtaining, by a device, one or more routing profiles associated with respective agents of a contact center network; obtaining, by the device, a task associated with a task type and one or more task attributes; [see at least Bhattacharjee: (Para 0021) “Contact center 120 matches users 105 to contact center agents 145 to handle user requests or communications. The contact center predicts a reason for a call by a user 105 (e.g., using source attributes, such as customer/user attributes, historical data of customer/user, data collection at a self-service unit, etc.), selects a best agent 145 (e.g., based on sink attributes) to handle the call, and routes the call to the selected agent”]
determining, by the device in accordance with a rule set, an order of tasks that includes the task, [see at least Bhattacharjee: (Para 0024) “Referring to FIG. 2, contact center 120 includes a communication processing module 122, a routing module 124, an application programming interface (API) 126, and a communication queue 128 to hold communications for routing”, (Para 0029) “Routing module 124 of contact center 120 receives the list of agents determined by the corresponding machine learning model from machine learning inference service 132 and routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”]
determining, by the device in accordance with the order of tasks, an agent channel for the task based on the task type, one or more network conditions, and the one or more routing profiles, wherein the agent channel is associated with a routing profile, of the one or more routing profiles, based on current task load information of the routing profile and the one or more task attributes; [see at least Bhattacharjee: (Para 0029) “Routing module 124 of contact center 120 receives the list of agents determined by the corresponding machine learning model from machine learning inference service 132 and routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”]
routing, by the device, the task to the agent channel; and [see at least Bhattacharjee: (Para 0029) “Routing module 124 of contact center 120 receives the list of agents determined by the corresponding machine learning model from machine learning inference service 132 and routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)]
providing, by the device and based on routing the task to the agent channel, information associated with the task to a user interface system to enable an agent, associated with the routing profile, to perform the task. [see at least Bhattacharjee: (Para 0019) “The contact center routes user communications to agent devices 140 of contact center agents 145 that may provide various support to the users 105 with respect to any products or services (e.g., process requests, provide information, assist with use of a product or service, billing issues, etc.). User devices 110 and agent device 140 can take on a variety of forms, including a smartphone, tablet, laptop computer, desktop computer, video conference endpoint, corresponding accessories (e.g., headset, etc.), and the like”, (Para 0092) “The present embodiments may employ any number of any type of user interface (e.g., graphical user interface (GUI), command-line, prompt, etc.) for obtaining or providing information”]
While Bhattacharjee teaches a routing system, it does not explicitly teach but Lewis does teach:
wherein the rule set includes one or more intra-task-type rules associated with the task type and one or more inter-task-type rules associated with multiple task channels; [see at least Lewis: (Column 5, Lines 50-51) “Attributes identify a call routing requirement such as language, location, or agent expertise”, (Column 6, Lines 3-7) “A “term” is a threshold value against which an agent's attribute is compared. For example, the term for a language attribute might be “English>6” where the agent's language proficiency in the English language must be greater than level 6 in order to meet the threshold indicated by the term”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine rule-based routing systems (Bhattacharjee) with inter and intra-task type rules (Lewis). One of ordinary skill in the art would have recognized that routing tasks to the proper agents would involve various prioritization and routing based on task type. Using inter and intra-task type prioritization in a routing system would have yielded predictable of a prioritized and accurate routing system to one of ordinary skill in the art.
Regarding Claim 15, The combination of Bhattacharjee and Lewis teach the limitations of claim 13. Bhattacharjee further teaches:
The method of claim 13, wherein the rule set includes one or more prioritization rules for prioritizing tasks associated with the multiple task channels. [see at least Bhattacharjee: (Para 0029) “Routing module 124 of contact center 120 receives the list of agents determined by the corresponding machine learning model from machine learning inference service 132 and routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”, (Para 0015) “An example embodiment pertains to contact center agent selection for omni-channel interactions based on a growing set of dynamic parameters from prior customer interactions”]
Regarding Claim 16, The combination of Bhattacharjee and Lewis teach the limitations of claim 13. Bhattacharjee further teaches:
The method of claim 13, further comprising: obtaining result information indicative of a result of the task as performed by the agent; and [see at least Bhattacharjee: (Para 0016) “Skill level parameters of an agent are altered based on success criteria of a resulting match. Data (e.g., attributes/features and routing results) is collected over a period of time and used to train a machine learning model to provide agent selection based on input and output parameters”]
updating, based on the result information, the routing profile to updated routing profile. [see at least Bhattacharjee: (Para 0016) “This removes a restriction of a static set of features/configurations being used as input for routing. Further, continuous learning enables the machine learning model to dynamically learn newer mappings between customers and agents and remain updated. This is used to constantly/continuously change input parameters for the agents based on an outcome of an agent selection”, (Para 0017) “An example embodiment uses supervised machine learning. Data collected over time on completed interactions is used to train a machine learning model based on outcome variables, which removes the need for a static set of input features. Continuous learning enables the machine learning model to dynamically learn new mappings and remain updated, where these updates are also propagated to entities used in the initial machine learning model (by updating configurations)”]
Regarding Claim 20, Bhattacharjee further teaches:
The system of claim 17, wherein the one or more rules are prioritization rules for prioritizing tasks in the network queue, and [see at least Bhattacharjee: (Para 0029) “Routing module 124 of contact center 120 receives the list of agents determined by the corresponding machine learning model from machine learning inference service 132 and routes the communication from queue 128 to an agent device 140 of an agent 145 based on priority within the list and agent availability (e.g., an available agent with a highest priority in the list, etc.)”]
While Bhattacharjee teaches rules, it does not explicitly teach rules being associated with intra task types. However, Lewis teaches:
wherein the one or more rules include one or more intra-task-type rules associated with the task type and one or more inter-task-type rules associated with the multiple task types. [see at least Lewis: (Column 5, Lines 50-51) “Attributes identify a call routing requirement such as language, location, or agent expertise”, (Column 6, Lines 3-7) “A “term” is a threshold value against which an agent's attribute is compared. For example, the term for a language attribute might be “English>6” where the agent's language proficiency in the English language must be greater than level 6 in order to meet the threshold indicated by the term”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine rule-based routing systems (Bhattacharjee) with inter and intra-task type rules (Lewis). One of ordinary skill in the art would have recognized that routing tasks to the proper agents would involve various prioritization and routing based on task type. Using inter and intra-task type prioritization in a routing system would have yielded the predictable result of a prioritized and accurate routing system to one of ordinary skill in the art.
Claims 3-5, 14, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharjee (US 20250039303 A1) in view of Lewis (US 11115536 B1) in further view of Crawford (US 12388772 B1)
Regarding Claim 3, Bhattacharjee teaches all the limitations of claim 1. While Bhattacharjee teaches a routing system, it does not explicitly teach but Lewis does teach:
wherein the routing profile information indicates that a task load score associated with the routing profile satisfies a task load threshold, and [see at least Lewis: (Column 6, Lines 3-7) “A “term” is a threshold value against which an agent's attribute is compared. For example, the term for a language attribute might be “English>6” where the agent's language proficiency in the English language must be greater than level 6 in order to meet the threshold indicated by the term”]
wherein the network routing management component, to select the agent channel, is configured to: select the agent channel based on the task load score satisfying the task load threshold and [see at least Lewis: (Column 5, Lines 7-12) “A call routing engine (CRE)—also known as a call routing system (CRS) or an automatic call distributor (ACD)—is a call-processing tool that routes inbound calls to individual agents or queues based on one or more pre-established criteria”, (Column 6, Lines 8-14) “ An “expression” is a collection of one or more terms that are interrelated via Boolean logical operators such that, for example, requiring that an agent speak English above level 6, be located in Texas, and be proficient in sales (e.g., above level 4), then the expression for this collection of terms (i.e., combination of thresholds) would be “English>6 AND Dallas=TRUE AND Sales>4”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine routing systems (Bhattacharjee) with threshold scores (Lewis). One of ordinary skill in the art would have recognized that routing tasks to the proper agents would involve various prioritization and attribute considerations of tasks and agents. Using threshold scores in a routing system would have yielded the predictable result of a prioritized and accurate routing system to one of ordinary skill in the art.
However, the combination of Bhattacharjee and Lewis do not teach but Crawford does teach:
wherein the agent channel is associated with a routing profile of the one or more routing profiles, wherein the one or more task attributes indicate that a difficulty score for the task does not satisfy a difficulty threshold, [see at least Crawford: (Column 18, Lines 50-53) “In some instances, the processing circuit 114 is authorized to autonomously perform tasks that correspond with message parameters that include a difficulty score that is below a difficulty threshold”]
based on the difficulty score not satisfying the difficulty threshold. [see at least Crawford: (Column 18, Lines 50-53) “In some instances, the processing circuit 114 is authorized to autonomously perform tasks that correspond with message parameters that include a difficulty score that is below a difficulty threshold”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine score-based routing systems (Bhattacharjee and Lewis) with a difficulty scores (Crawford). One of ordinary skill in the art would have recognized that routing tasks to the proper agents would involve various prioritization and attribute considerations of tasks and agents. Including a difficulty score in a routing system would have yielded the predictable result of a preventing an unqualified agent from working on a difficult task.
Regarding Claim 4, the combination of Bhattacharjee, Lewis, and Crawford teach all the limitations of claim 3. Bhattacharjee further teaches:
The system of claim 3, wherein the task type is a work break. [The claim recites nonfunctional descriptive material that does not carry patentable weight in the claims]
Regarding Claim 5, the combination of Bhattacharjee, Lewis, and Crawford teach all the limitations of claim 3. Bhattacharjee further teaches:
The system of claim 3, wherein the task load score is based on one or more previous tasks performed by the agent as indicated by the dynamic network activity. [see at least Bhattacharjee: (Para 0021) “A best agent may be determined based on a most skilled agent, optimization of metrics (e.g., agent efficacy, contact efficacy or handle times), and/or optimization of customer experience metrics or outcomes (e.g., customer satisfaction score (CSAT) indicating customer satisfaction and based on a customer provided rating, net promoter score (NPS) indicating customer loyalty and based on a customer provided rating, customer effort score (CES) indicating effort by a customer to interact with an organization and indicated by a customer rating, post-call sentiment, call reasons, etc.). A result of the interaction between the user and selected agent (e.g., customer satisfaction, call metrics, etc.) is fed back for a next prediction of an agent for a user”]
Regarding Claim 14, the combination of Bhattacharjee and Lewis teach all the limitations of claim 13. While Bhattacharjee teaches a routing system, it does not explicitly teach but Lewis does teach:
wherein the routing profile indicates that a task load score associated with the routing profile satisfies a task load threshold, and [see at least Lewis: (Column 6, Lines 3-7) “A “term” is a threshold value against which an agent's attribute is compared. For example, the term for a language attribute might be “English>6” where the agent's language proficiency in the English language must be greater than level 6 in order to meet the threshold indicated by the term”]
wherein determining the agent channel comprises: selecting the agent channel for the task based on the task load score satisfying the task load threshold and [see at least Lewis: (Column 5, Lines 7-12) “A call routing engine (CRE)—also known as a call routing system (CRS) or an automatic call distributor (ACD)—is a call-processing tool that routes inbound calls to individual agents or queues based on one or more pre-established criteria”, (Column 6, Lines 8-14) “ An “expression” is a collection of one or more terms that are interrelated via Boolean logical operators such that, for example, requiring that an agent speak English above level 6, be located in Texas, and be proficient in sales (e.g., above level 4), then the expression for this collection of terms (i.e., combination of thresholds) would be “English>6 AND Dallas=TRUE AND Sales>4”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine routing systems (Bhattacharjee) with threshold scores (Lewis). One of ordinary skill in the art would have recognized that routing tasks to the proper agents would involve various prioritization and attribute considerations of tasks and agents. Using threshold scores in a routing system would have yielded the predictable result of a prioritized and accurate routing system to one of ordinary skill in the art.
However, the combination of Bhattacharjee and Lewis do not teach but Crawford does teach:
wherein the one or more task attributes indicate that a difficulty score for the task does not satisfy a difficulty threshold, [see at least Crawford: (Column 18, Lines 50-53) “In some instances, the processing circuit 114 is authorized to autonomously perform tasks that correspond with message parameters that include a difficulty score that is below a difficulty threshold”]
based on the difficulty score not satisfying the difficulty threshold.
[see at least Crawford: (Column 18, Lines 50-53) “In some instances, the processing circuit 114 is authorized to autonomously perform tasks that correspond with message parameters that include a difficulty score that is below a difficulty threshold”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine score-based routing systems (Bhattacharjee and Lewis) with a difficulty scores (Crawford). One of ordinary skill in the art would have recognized that routing tasks to the proper agents would involve various prioritization and attribute considerations of tasks and agents. Including a difficulty score in a routing system would have yielded the predictable result of a preventing an unqualified agent from working on a difficult task.
Regarding Claim 18, Bhattacharjee teaches all the limitations of claim 17. Bhattacharjee further teaches:
The system of claim 17, wherein the agent channel is associated with a routing profile of the one or more routing profiles, [see at least Bhattacharjee: (Para 0015) “An example embodiment pertains to contact center agent selection for omni-channel interactions based on a growing set of dynamic parameters from prior customer interactions”, (Para 0021) “Contact center 120 matches users 105 to contact center agents 145 to handle user requests or communications. The contact center predicts a reason for a call by a user 105 (e.g., using source attributes, such as customer/user attributes, historical data of customer/user, data collection at a self-service unit, etc.), selects a best agent 145 (e.g., based on sink attributes) to handle the call, and routes the call to the selected agent”]
While Bhattacharjee teaches a routing system, it does not explicitly teach but Lewis does teach:
wherein the routing profile information indicates that a task load score associated with the routing profile satisfies a task load threshold, and [see at least Lewis: (Column 6, Lines 3-7) “A “term” is a threshold value against which an agent's attribute is compared. For example, the term for a language attribute might be “English>6” where the agent's language proficiency in the English language must be greater than level 6 in order to meet the threshold indicated by the term”]
wherein the one or more processors, to select the agent channel, are configured to: select the agent channel based on the task load score satisfying the task load threshold and [see at least Lewis: (Column 5, Lines 7-12) “A call routing engine (CRE)—also known as a call routing system (CRS) or an automatic call distributor (ACD)—is a call-processing tool that routes inbound calls to individual agents or queues based on one or more pre-established criteria”, (Column 6, Lines 8-14) “ An “expression” is a collection of one or more terms that are interrelated via Boolean logical operators such that, for example, requiring that an agent speak English above level 6, be located in Texas, and be proficient in sales (e.g., above level 4), then the expression for this collection of terms (i.e., combination of thresholds) would be “English>6 AND Dallas=TRUE AND Sales>4”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine routing systems (Bhattacharjee) with threshold scores (Lewis). One of ordinary skill in the art would have recognized that routing tasks to the proper agents would involve various prioritization and attribute considerations of tasks and agents. Using threshold scores in a routing system would have yielded the predictable result of a prioritized and accurate routing system to one of ordinary skill in the art.
However, the combination of Bhattacharjee and Lewis do not teach but Crawford does teach:
wherein the one or more task attributes indicate that a difficulty score for the task does not satisfy a difficulty threshold, [see at least Crawford: (Column 18, Lines 50-53) “In some instances, the processing circuit 114 is authorized to autonomously perform tasks that correspond with message parameters that include a difficulty score that is below a difficulty threshold”]
based on the difficulty score not satisfying the difficulty threshold. [see at least Crawford: (Column 18, Lines 50-53) “In some instances, the processing circuit 114 is authorized to autonomously perform tasks that correspond with message parameters that include a difficulty score that is below a difficulty threshold”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine score-based routing systems (Bhattacharjee and Lewis) with a difficulty scores (Crawford). One of ordinary skill in the art would have recognized that routing tasks to the proper agents would involve various prioritization and attribute considerations of tasks and agents. Including a difficulty score in a routing system would have yielded the predictable result of a preventing an unqualified agent from working on a difficult task.
Regarding Claim 19, the combination of Bhattacharjee, Lewis, and Crawford teach all the limitations of claim 18. Bhattacharjee further teaches:
The system of claim 18, wherein the task type is a service-based task or a non-user-facing task for the contact center network. [The limitation recites nonfunctional descriptive material that does not carry patentable weight in the claims]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Benjamin Truong, whose telephone number is 703-756-5883. The examiner can normally be reached on Monday-Friday from 9 am to 5 pm (EST)
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/B.L.T./
Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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