DETAILED ACTION
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 Office Action is responsive to Applicant's amendment filed on 22 December 2025. Applicant’s amendment on 22 December 2025 amended Claims 1, 8, and 15. Currently Claims 1-3, 5-10, 12-17, and 19-23 are pending and have been examined. Claims 4, 11, and 18 were previously canceled. The Examiner notes that the 101 rejections have been maintained.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 29 December 2025 has been entered.
Response to Arguments
Applicant's arguments filed 22 December 2025 have been fully considered but they are not persuasive.
The Applicant argues on pages 12-13 that “The Office Action alleges that the claims represent abstract concepts of work distribution among agents based on load and performance metrics, which could be performed in the human mind but for the recitation of generic computer components. Office Action, page 7. However, this is incorrect. Claim 1 recites a method that includes: obtaining, using at least one processing device of an electronic device, one or more properties of one or more interactions associated with an agent”
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that The argument that the claims do not represent abstract concepts because they recite "using at least one processing device" and a "machine learning network" is not persuasive. Under Step 2A Prong One of the subject matter eligibility analyses, the question is whether the claim recites a judicial exception, not whether the claim requires a computer to perform it. The mere nominal recitation of generic computer components does not take claim limitations out of the mental processes grouping or prevent the claim from reciting abstract ideas in the mathematical concepts or methods of organizing human activity groupings. See MPEP 2106.04(a)(2), subsection III; CyberSource Corp. v. Retail Decisions, Inc.
The claim still recites the abstract ideas identified in the rejection. The steps of determining an amount of load based on interaction properties, tracking changes in properties or agent behavior, analyzing effects on agent performance, identifying new interactions based on performance data, and determining load scores are mathematical calculations and methods of organizing human activity, regardless of whether they are performed "using at least one processing device." These steps describe (1) mathematical operations for calculating load values and scores from input data, (2) managing how human agents handle customer service work by tracking workload and assigning tasks based on capacity, and (3) observations and evaluations (tracking behavior changes, analyzing performance effects) that describe the type of thinking process a supervisor would use. The recitation of a processing device performing these steps does not change the nature of what is being claimed it simply specifies that a computer is performing the abstract idea rather than a human.
Similarly, the recitation of a "machine learning network" that determines complexity, tracks its own performance, changes interaction loads, and expands its knowledge base does not transform the claim into something other than an abstract idea at Step 2A Prong One. The claim recites this machine learning network in purely functional terms without any specific technical implementation details. The analysis of whether these computer components integrate the abstract idea into a practical application or provide an inventive concept occurs in Step 2A Prong Two and Step 2B, not in Step 2A Prong One. At Prong One, the question is simply whether the claim recites a judicial exception, and as explained in the rejection, the claim recites mathematical concepts (calculating loads and scores), methods of organizing human activity (managing agent task assignments), and mental processes (tracking and analyzing agent behavior and performance). The processing device and machine learning network recitations do not negate the presence of these abstract ideas in the claim, the rejection is therefore maintained.
The Applicant argues on pages 13-14 that “In the "Reminders on Evaluating subject matter eligibility of claims under 35 U.S.C. 101" ("Reminders"), the Office recently reiterated that one must distinguish claims reciting a judicial exception from claims merely involving a judicial exception. Claim 1 recites a machine learning network trained to handle at least one type of interaction on behalf of an agent or an agent set, tracking a quality of performance of the machine learning network, changing an amount of interaction load for the machine learning network and an amount of attention needed by the agent based on the tracked quality of performance, and expanding a knowledge base and capabilities of the machine learning network to handle one or more additional types of interactions using data collected over time including the tracked quality of performance (where expanding the knowledge base and the capabilities of the machine learning network includes determining to leave the new interaction for the agent to handle based on the complexity). This cannot be performed mentally. For example, a human being is unable to mentally expand a knowledge base and capabilities of a machine learning network to handle one or more additional types of interactions using data collected over time including a tracked quality of performance. Rather, that expansion represents an improvement to the machine learning network itself. Moreover, Claim 1 on its face does not recite any mathematical operations. In addition, operations such as expanding a knowledge base and capabilities of a machine learning network do not represent methods of organizing human activity. As a result, even if Claim 1 might involve a judicial exception, Claim 1 is not directed to a judicial exception”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that the argument misapplies the distinction between claims that recite a judicial exception versus claims that merely involve one. While the August 4, 2025 Memorandum correctly notes this important distinction, the present claims do in fact recite that is, set forth or describe judicial exceptions, not merely involve them.
The comparison to Examples 39 and 47 is not apt. In Example 39, the limitation "training the neural network in a first stage using the first training set" uses generic functional language that does not describe or set forth what mathematical operations occur during training. In contrast, Claim 1 here explicitly describes multiple mathematical operations: "determining an amount of load for the agent generated by the one or more interactions based on the one or more properties," "determining one or more load scores for the agent based on the one or more properties of the one or more interactions," and "analyzing an effect on agent performance for the agent based on (i) the amount of load for the agent and (ii) the one or more changes." These limitations affirmatively describe calculating numerical values (load amounts and scores) from input properties and analyzing mathematical relationships between variables (load, changes, and performance effects). The claim sets forth these mathematical operations using words that describe what calculations are performed, even though it does not recite specific algorithms or equations by name. This is sufficient to recite a mathematical concept under MPEP 2106.04(a)(2), subsection I. The distinction drawn in the Reminders is between limitations that merely use computers (which might involve math internally) versus limitations that actually describe mathematical operations being performed Claim 1's limitations fall into the latter category.
Regarding mental processes, the argument focuses only on the machine learning-specific operations while ignoring other claim limitations. The August 4, 2025 Memorandum correctly cautions against expanding the mental process grouping beyond what can be practically performed in the human mind. However, multiple limitations in Claim 1 describe observations and evaluations that can be practically performed mentally by a supervisor: tracking one or more changes in properties or behavior of the agent (a supervisor can mentally observe and track how an agent's performance changes), analyzing an effect on agent performance based on the amount of load and the changes (a supervisor can mentally evaluate how workload impacts an agent's performance through observation), identifying a new interaction based on analyzing agent performance data (a supervisor can mentally decide which interaction to assign next based on their knowledge of how agents are performing), and determining a complexity of the new interaction (a supervisor can mentally assess how difficult a customer issue will be to resolve). These mental processes remain abstract ideas even though other portions of the claim may involve machine learning operations that cannot be performed mentally. A claim can recite multiple types of abstract ideas, and the presence of some limitations that cannot be performed mentally does not negate the abstract nature of other limitations that can be. See MPEP 2106.04, subsection II.B (multiple judicial exceptions in the same claim are treated together as a single abstract idea).
The argument that expanding the knowledge base and capabilities of the machine learning network represents an improvement to the machine learning network itself misconstrues what constitutes a technological improvement under Step 2A Prong Two. Under the 2024 AI-SME Guidance, "an improvement in the judicial exception itself is not an improvement in the technology." The Federal Registry guidance accompanying the 2024 update explains that courts distinguish "between a claim that reflects an improvement to a computer or other technology described in the specification (which is eligible) and a claim in which the additional elements amount to no more than...instructions to implement a judicial exception on a computer." Here, expanding the machine learning network's knowledge base to handle more types of customer service interactions improves the ML network's ability to perform the abstract task of handling customer interactions this is an improvement in the application of the abstract idea (better customer service automation), not an improvement to machine learning technology itself. The specification confirms this, describing the ML/AI layer as handling "routine aspects of the interactions" on behalf of agents and being "trained to expand its knowledge base and capabilities to handle more types of interactions" using collected data (Spec. [0075]-[0076]). This describes improving the system's ability to automate more customer service tasks, not improving how machine learning networks function from a technical standpoint. There is no disclosure of any novel training algorithm, network architecture, data processing technique, or other technical innovation in machine learning only the use of machine learning to increasingly automate the business function of handling customer interactions.
Finally, the claim does recite mathematical operations on its face. The limitations explicitly use mathematical terminology: "determining an amount of load...based on the one or more properties," "determining one or more load scores...based on the one or more properties," and "analyzing an effect...based on (i) the amount of load and (ii) the one or more changes" all describe mathematical determinations where numerical values are calculated from input data. The claim language describes these mathematical operations using words, which is sufficient under Step 2A Prong One. As explained in MPEP 2106.04(a)(2), mathematical concepts include "mathematical calculations," and these limitations describe calculating load amounts and scores through mathematical operations on interaction properties.
For these reasons, the claims recite not merely involve judicial exceptions consisting of mathematical concepts, certain methods of organizing human activity, and mental processes. The analysis properly proceeds to Step 2A Prong Two to determine whether these recited exceptions are integrated into a practical application. The rejection is therefore maintained.
The Applicant argues on pages 14-15 that “According to MPEP 2106.04(a)(2)(III), the "mental process" abstract idea grouping is defined as "concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions." According to MPEP 2106.03(II), a claim as a whole must fall within a statutory category. As noted above, Claim 1 as a whole does not recite a mental process.
Moreover, the Reminders notes that one consideration under Prong Two is "whether the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished, or the claim covers a particular solution to a problem or a particular way to achieve a desired outcome." Claim 1 above does not merely recite work distribution among agents. Rather, among other things, Claim 1 recites a concrete solution that involves an iterative system optimization including expansion of a knowledge base and capabilities of a machine learning network to handle one or more additional types of interactions. This is a technological mechanism that improves computer functionality, such as via iterative knowledge expansion, and solves a technical problem of load balancing based on the expanded knowledge base”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that it appears that the argument misunderstands the structure of the Step 2A analysis and the distinction between an idea of a solution versus a particular solution. Regarding the mental process grouping, it is correct that a claim as a whole must fall within a statutory category under Step 1, but this is a separate inquiry from whether the claim recites a judicial exception under Step 2A Prong One. At Step 2A Prong One, individual limitations are evaluated to determine whether they recite abstract ideas, and a claim can recite multiple types of abstract ideas including mental processes, mathematical concepts, and methods of organizing human activity simultaneously. As explained in MPEP 2106.04, subsection II.B, when claims recite multiple judicial exceptions, "these abstract ideas are considered together as a single abstract idea." The fact that some limitations in Claim 1 involve machine learning operations that cannot be performed mentally does not negate those other limitations (tracking changes in agent behavior, analyzing effects on performance, identifying which interaction to assign) describe mental processes that a human supervisor could perform through observation and judgment. The claim as a whole is then evaluated at Step 2A Prong Two to determine whether it integrates these recited exceptions into a practical application.
The argument that Claim 1 recites "a concrete solution that involves an iterative system optimization including expansion of a knowledge base and capabilities of a machine learning network" fundamentally misapplies the "idea of solution versus particular solution" consideration from the August 4, 2025 Memorandum. The Memorandum explains that examiners should consider "whether the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished, or the claim covers a particular solution to a problem or a particular way to achieve a desired outcome." The critical question is whether the claim provides technical details about how the solution is accomplished. Here, Claim 1 recites what is to be achieved determining complexity, tracking performance, changing loads, expanding knowledge base but provides no technical details about how any of these operations are accomplished. The claim does not specify what algorithm determines complexity, what metrics or methods track machine learning performance, what technical mechanism changes the interaction loads, or what training methodology expands the knowledge base. These are high-level functional results without any recitation of the particular technical means by which they are achieved. This is precisely what the Memorandum identifies as "the idea of a solution" rather than a particular technical implementation.
The specification confirms this characterization. Paragraphs [0075]-[0076] describe the machine learning network generically as using "ML or AI algorithms, routines, or networks (such as one or more large language models, neural networks, and the like)" that can "track the quality of its own performance" and "be trained to expand its knowledge base and capabilities to handle more types of interactions" using "data collected over time." This generic description referring broadly to ML/AI without any specific technical details indicates that the claim covers any way of achieving these results, not a particular technical solution. Under the guidance in MPEP 2106.05(f), this type of high-level functional claiming amounts to mere instructions to apply the exception (use machine learning to automate customer service work distribution) without reciting the particulars of how it is done.
Regarding the assertion that this represents "a technological mechanism that improves computer functionality, such as via iterative knowledge expansion," this argument confuses improving the performance of the abstract idea with improving technology. Under the 2024 AI-SME Guidance, "an improvement in the judicial exception itself is not an improvement in the technology." The Federal Registry explanation states: "A key point of distinction to be made for AI inventions is between a claim that reflects an improvement to a computer or other technology described in the specification (which is eligible) and a claim in which the additional elements amount to no more than...instructions to implement a judicial exception on a computer." The specification here describes improving the system's ability to handle more types of customer service interactions by training the machine learning network to automate more tasks over time. This is an improvement in how well the system performs the abstract business function (distributing customer service work), not an improvement to machine learning technology or computer functionality. There is no disclosure of any novel machine learning architecture, training algorithm, knowledge representation method, or other technical innovation only the application of generic machine learning to increasingly automate the business task. See Trading Techs. Int'l, Inc. v. IBG LLC, (system that gives traders more information improves the business process but not computer technology).
The characterization of "load balancing based on the expanded knowledge base" as solving "a technical problem" is also incorrect. Load balancing among human workers based on their capacity and workload is fundamentally a business management problem, not a technical problem. The specification describes the problem in business terms: "distribution of interactions among agents can be different for various remote customer support environments" and agents have "different capacities at which the agent can perform different tasks" (Spec. [0030]-[0032]). The solution is similarly framed as a business optimization: "strategically distribute the interactions among the different agents" to "maintain maximum overall performance" and enable "load balancing" (Spec. [0033]-[0034], [0088]). This describes optimizing a business process how to assign customer service work to maximize agent performance not solving a technical problem in computer science or machine learning. The fact that the business optimization uses machine learning as a tool does not transform the business problem into a technical problem. See SAP Am., Inc. v. InvestPic, LLC, (using a technical tool to solve a business problem does not make the problem technical).
For these reasons, Claim 1 recites the idea of a solution (use machine learning to help distribute customer service work) without the details of a particular technical implementation, and the claimed iterative expansion of machine learning capabilities represents an improvement to the abstract business process rather than an improvement to computer or machine learning technology. The claims remain directed to abstract ideas that are not integrated into a practical application. The rejection is therefore maintained.
The Applicant argues on pages 15-16 that “Further, the Applicant's disclosure provides various technological improvements, and the claims are directed to a practical application and significantly more than any alleged abstract idea. For example, as recited in Claim 1, the iterative system optimization operation includes tracking a quality of performance of a machine learning network, changing an interaction load for the machine learning network (and an amount of attention needed by an agent) based on the tracked quality of performance, and expanding the knowledge base and capabilities of the machine learning network using data collected over time including the tracked quality of performance. Among other things, this can be done to adaptively increase interaction types that the machine learning network is able to handle. See also Specification, [0075]. This is a technical process that involves iterative system optimization by analyzing the machine learning network's quality of performance and expanding its knowledge base and capabilities to adaptively handle additional interactions types. This process represents a technical improvement to machine learning functionality that is distinct from its use in workload management.
These features can enhance a machine learning network's robustness and adaptability as a computational system. For example, tracking the machine learning network's performance quality enables the machine learning network to self-monitor. This is akin to the continual learning mechanisms found patent eligible in Ex Parte Desjardins, where parameter importance measures preserved prior-task performance, reducing system complexity and storage needs. Ex Parte Desjardins, Appeal No. 2024-000567 (September 26, 2025, ARP Decision) (precedential); see also Ex Parte Desjardin incorporated into MPEP 2106.04(d), 2106.05(a), and 2106.05(f).
These features improve the functionality of the machine learning network itself under Enfish, LLC v. Microsoft Corp., as they improve the machine learning network's processes for sustained performance quality and expansion of its own knowledge base, separate from managerial tasks. Such features specifically provide technical improvement to the functioning of a machine learning network and are patent eligible under Enfish and Ex Parte Desjardins. For at least these reasons, Claim 1 and its dependent claims recite patent-eligible subject matter. For similar reasons, Claims 8 and 15 and their respective dependent claims recite patent-eligible subject matter. The Applicant therefore respectfully requests that the 101 rejection be withdrawn”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that because the claims themselves do not reflect the asserted technological improvements with sufficient specificity. Specification Details Must Be Reflected in the Claims: While Applicant cites paragraph [0075] of the specification and argues the disclosure provides technological improvements, the critical requirement under MPEP 2106.04(d)(1) and 2106.05(a) is that "the claim must include the components or steps of the invention that provide the improvement described in the specification." Intellectual Ventures I LLC v. Symantec Corp., (finding claims ineligible where the specification described improvements but "the claims themselves did not have any limitations that addressed these issues"). The claim itself need not explicitly recite the improvement (e.g., "thereby reducing catastrophic forgetting"), but it must recite the technical components or mechanisms that achieve the improvement, not merely the functional outcome.
Here, the claim recites that the system performs "tracking a quality of performance of the machine learning network," "changing, based on the tracked quality of performance, an amount of interaction load," and "expanding a knowledge base and capabilities of the machine learning network to handle one or more additional types of interactions using data collected over time." These limitations describe what the system accomplishes (the outcomes: expanded knowledge base, tracked quality, changed load) but do not recite how these outcomes are technically achieved. The claim provides no details regarding:
The specific mechanism or algorithm for tracking ML network performance quality
The technical structure or parameters used to represent "knowledge base and capabilities"
The particular method of expansion (e.g., specific network architecture modifications, parameter adjustment techniques, data structures)
How "data collected over time" is processed and integrated to expand capabilities
Technical criteria for determining when and how to expand the knowledge base
Without such technical details reflected in the claim language, the claim recites only the idea of a solution (expanding ML capabilities through performance tracking) rather than a particular technical solution to a technical problem.
Distinguishing Ex Parte Desjardins:
Applicant's citation to Ex Parte Desjardins is inapposite because that case involved claims with significantly more technical specificity than the instant claims. As noted in the USPTO memorandum incorporating Desjardins into the MPEP (August 4, 2025), the Desjardins claims "included data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values." The Desjardins specification "identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of 'catastrophic forgetting,'" and critically, "the claims reflected the improvement identified in the specification" through specific structural limitations.
The enumerated improvements in Desjardins effective learning of new tasks while protecting knowledge concerning previously accomplished tasks, reducing storage capacity usage, and enabling reduced system complexity were tied to specific claim limitations regarding data structures and parameter adjustments. The PTAB found these improvements were "tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation."
In contrast, the instant claims lack comparable structural or algorithmic specificity. The claim recites only functional language about "expanding knowledge base and capabilities" without reciting the data structures, parameter adjustments, or technical mechanisms that would demonstrate how this expansion improves ML network operation. The claim does not recite specific technical features that address a defined technical problem in ML technology (such as catastrophic forgetting in continual learning). Instead, it recites generic ML operations (training, tracking performance, expanding capabilities) applied to a business problem (workforce task assignment and load balancing).
Self-Monitoring Does Not Establish Technological Improvement: Applicant argues that "tracking the machine learning network's performance quality enables the machine learning network to self-monitor" and this is "akin to the continual learning mechanisms found patent eligible in Ex Parte Desjardins." This argument fails for two reasons. First, the claim does not recite "self-monitoring" it recites that the system "tracks quality of performance," which could be accomplished through external monitoring without any self-monitoring capability. Second, even if the claim implicitly requires self-monitoring, this alone does not establish a technological improvement without technical details regarding how self-monitoring is implemented and how it improves ML network operation beyond conventional performance tracking.
Business Improvement vs. Technological Improvement: Applicant's argument conflates improvements to the business application (adaptively handling more types of customer service interactions) with improvements to ML technology itself. The asserted improvements "enhance a machine learning network's robustness and adaptability as a computational system" describe business benefits of applying ML to workforce management, not technological improvements to how ML networks operate. As explained in MPEP 2106.05(a)(II), "an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology." See also Trading Technologies Int'l v. IBG, (user interface that "simply provided a trader with more information to facilitate market trades...improved the business process of market trading but did not improve computers or technology").
Enfish Is Distinguishable: Enfish is distinguishable because the claims there recited a specific data structure a self-referential table with defined technical characteristics and the specification explained how this particular structure provided measurable technical benefits (faster search times, increased flexibility, smaller memory requirements). Enfish, LLC v. Microsoft Corp.,. The instant claims recite no such specific structures or mechanisms, only functional outcomes.
Conclusion:
The claims do not integrate the recited judicial exceptions into a practical application at Step 2A Prong Two because they do not reflect specific technological improvements to ML network functionality. The claims recite only generic ML operations (tracking performance, expanding knowledge base) applied to workforce management without technical details demonstrating unconventional implementation or how ML network technology itself is improved. To overcome this rejection, Applicant should amend the claims to incorporate specific technical limitations from the specification that demonstrate a particular technical solution, such as specific data structures, algorithms, parameter adjustment mechanisms, or other technical features that show how the ML network operates in an improved manner, consistent with the level of specificity present in Ex Parte Desjardins. The rejection is therefore maintained.
The remaining Applicant's arguments filed 22 December 2025 have been fully considered but they are moot in view of new grounds of rejection as necessitated by 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 1-3, 5-10, 12-17, and 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-3, 5-10, 12-17, and 19-23 as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) 1-3, 5-10, 12-17, and 19-23 is/are directed to the abstract idea of distributing customer support between users based on contact load.
Step 1:
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes, claims 1-7 and 21-23 are directed to a process/method, claim 15-19 is directed to a non-transitory machine-readable medium / manufacture, and claim 8-14 is directed to a system / machine. Therefore, the claims fall within the statutory categories of invention.
Step 2A Prong One:
The claims recite an abstract idea. Specifically, the independent claims 1, 8, and 15 recite the following limitations:
"obtaining...one or more properties of one or more interactions associated with an agent"
"determining...an amount of load for the agent generated by the one or more interactions based on the one or more properties of the one or more interactions"
"tracking...one or more changes in the one or more properties or a behavior of the agent"
"analyzing...an effect on agent performance for the agent based on (i) the amount of load for the agent and (ii) the one or more changes in the one or more properties or the behavior of the agent"
"identifying...a new interaction based on analyzing agent performance data for an agent set including the agent"
"determining, using a machine learning network, a complexity of the new interaction"
"tracking a quality of performance of the machine learning network"
"changing...an amount of interaction load for the machine learning network and an amount of attention needed by the agent"
"expanding a knowledge base and capabilities of the machine learning network to handle one or more additional types of interactions using data collected over time"
"determining one or more load scores for the agent based on the one or more properties of the one or more interactions, each load score associated with a measure of computing power and agent attention needed for the associated interaction"
Abstract Idea Grouping Analysis
Mathematical Concept:
These limitations recite mathematical calculations. Specifically, the limitations "determining an amount of load for the agent generated by the one or more interactions based on the one or more properties" and "determining one or more load scores for the agent based on the one or more properties of the one or more interactions" recite calculating numerical values (load amounts and scores) from input data through mathematical operations. The limitation "analyzing...an effect on agent performance...based on (i) the amount of load for the agent and (ii) the one or more changes" recites analyzing mathematical relationships between variables (load, property changes, behavior changes, and performance effects). The limitation "determining...a complexity of the new interaction" recites computing a complexity value through mathematical calculation. These are mathematical calculations and mathematical relationships that fall within the mathematical concepts grouping. See MPEP 2106.04(a)(2), subsection I.
Certain Methods of Organizing Human Activity:
These limitations fall within the "managing personal behavior or relationships or interactions between people" and "fundamental economic principles or practices" sub-groupings of certain methods of organizing human activity. Specifically, the claims recite managing how human customer service agents handle customer interactions by tracking agent workload, analyzing agent performance, determining agent capacity, and assigning new customer interactions to agents based on their workload and performance. These activities constitute managing human work activities and interpersonal interactions between agents and customers. The specification confirms this, describing "distribution of interactions between users based on load" and optimizing "how different agents are able to handle different interactions" to "maintain maximum overall performance of the agents" (Spec. Abstract, [0066]). See MPEP 2106.04(a)(2), subsection II; Versata Dev. Group, Inc. v. SAP Am., Inc.
Mental Process:
These limitations, under their broadest reasonable interpretation, cover performance of the limitations in the human mind. Specifically, the step of "tracking...one or more changes in the one or more properties or a behavior of the agent" encompasses mental observation where a supervisor could observe and mentally track changes in how an agent performs; the step of "analyzing...an effect on agent performance" encompasses a mental evaluation where a supervisor could mentally analyze how workload affects performance through observation; the step of "identifying...a new interaction based on analyzing agent performance data" encompasses a mental judgment where a supervisor could mentally identify which interaction to assign next based on their knowledge of agent performance; the step of "determining...a complexity of the new interaction" encompasses a mental assessment where a supervisor could mentally evaluate how complex a customer interaction will be. The mere nominal recitation of "at least one processing device" and "machine learning network" does not take the claim limitations out of the mental processes grouping. See MPEP 2106.04(a)(2), subsection III; CyberSource Corp. v. Retail Decisions, Inc.
Because the claims recite limitations falling within multiple abstract idea groupings, these limitations are considered together as a single abstract idea. See MPEP 2106.04, subsection II.B. Accordingly, the claims recite judicial exceptions.
Step 2A Prong Two:
Identification of Additional Elements
The claims recite the following additional elements beyond the identified abstract idea:
"using at least one processing device of an electronic device" (claim 1)
"at least one processing device" (claim 8) / "at least one processor" (claim 15)
"electronic device" (claim 8)
"non-transitory machine-readable medium containing instructions" (claim 15)
"a machine learning network trained to handle at least one type of interaction on behalf of the agent or the agent set"
"iteratively performing...a system optimization operation" (functional language describing what is performed)
Analysis of Additional Elements
Improvement to Technology or Technical Field (MPEP 2106.05(a)):
The claims do not recite an improvement to the functioning of a computer or to any other technology or technical field. The specification describes the invention as addressing "distribution of interactions between users based on load" and improving "system performance" in managing agent workload, and describes the problem and solution in business terms: "distribution of interactions among agents can be different for various remote customer support environments" with a solution of "strategically distribute the interactions among the different agents" to "maintain maximum overall performance" (Spec. [0030]-[0034], [0088]). This describes using computers as tools to automate the business practice of assigning customer service work more efficiently, rather than improving computer functionality itself. The specification's description of system components (FIG. 1 showing standard electronic device elements; FIGS. 2-9 showing conventional operations like "operation 220," "operation 225," "operation 240") reveals only conventional computing elements performing conventional functions: obtaining data, performing calculations, tracking information, analyzing data, and distributing tasks.
The "machine learning network" is recited only in functional terms as being "trained to handle at least one type of interaction" and capable of "determining...a complexity" and "expanding a knowledge base." The specification describes the ML/AI layer generically as using "ML or AI algorithms, routines, or networks (such as one or more large language models, neural networks, and the like)" to handle "routine aspects of the interactions" (Spec. [0075]-[0076]). This describes using machine learning as a tool to automate routine customer service tasks, not any improvement to machine learning technology itself. Under the 2024 AI-SME Guidance, an improvement in the judicial exception itself (improving task distribution methods) is not an improvement in technology. See MPEP 2106.05(a); Trading Techs. Int'l, Inc. v. IBG LLC; SAP Am., Inc. v. InvestPic, LLC)
Particular Machine (MPEP 2106.05(b)):
The claims do not recite use of a particular machine that imposes meaningful limits on the claim. The recited "electronic device," "processing device," "at least one processor," and "machine learning network" are generic computing components described at a high level of generality without structural particularity. The machine learning network is described only by its generic capabilities, not by any specific architecture or configuration. The specification describes standard electronic device components (processor, memory, display, communication interface) without requiring any particular implementation (Spec. [0038]-[0048], FIG. 1). See MPEP 2106.05(b); Elec. Power Grp., LLC v. Alstom S.A.
Mere Instructions to Apply the Exception (MPEP 2106.05(f)):
The additional elements amount to no more than mere instructions to implement the abstract idea on a computer. The claims recite generic computing components (processing device, electronic device, machine learning network, non-transitory medium) performing generic computing functions (obtaining data, performing calculations, tracking information, analyzing data, determining complexity). Under the August 4, 2025 Memorandum on evaluating subject matter eligibility, the claims “recite only the idea of a solution or outcome” without details of how the solution is technically accomplished. For example, the claims recite “determining, using a machine learning network, a complexity of the new interaction” but provide no technical details about how this determination is made what algorithm is used, what features are extracted, what network architecture processes the data, or what makes this approach technically superior. This is tantamount to adding the words “apply it” to the judicial exception. See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223 (2014); MPEP 2106.05(f).
Insignificant Extra-Solution Activity (MPEP 2106.05(g)):
The additional elements of "obtaining...one or more properties of one or more interactions" and "tracking...one or more changes in the one or more properties or a behavior of the agent" constitute insignificant extra-solution activity. The "obtaining" and "tracking" steps are mere data gathering that is necessary to provide inputs for the abstract idea calculations. These data gathering steps are incidental to the primary process of calculating load scores and assigning interactions, and do not impose any meaningful limits on practicing the abstract idea beyond requiring that data be collected. See MPEP 2106.05(g); OIP Techs., Inc. v. Amazon.com, Inc.
Considering the additional elements individually and in combination, the claim as a whole does not integrate the judicial exception into a practical application. The additional elements do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Step 2B:
As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components and insignificant extra-solution activity (data gathering). The same analysis applies in Step 2B mere instructions to apply an exception using generic computer components and data gathering cannot provide an inventive concept. See MPEP 2106.05(f) and (g).
Well-Understood, Routine, Conventional Activity Analysis
The additional elements, when considered individually and in combination, are well-understood, routine, and conventional activities in the field. Specifically:
Using a processing device to perform calculations - The courts have recognized that using a computer processor to perform routine calculations is well-understood, routine, conventional activity. See MPEP 2106.05(d)(II), citing Parker v. Flook, (post-solution activity of adjusting values); Bancorp Servs., LLC v. Sun Life Assur. Co. of Canada (U.S.), ("The computer required by some of Bancorp's claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims"); SAP Am., Inc. v. InvestPic, LLC,. The specification's description confirms conventional processing operations: "obtaining," "determining," "tracking," "analyzing," and "identifying" using generic electronic devices with conventional components (Spec. [0038]-[0048], FIG. 1).
Using machine learning networks - The specification itself provides evidence that machine learning networks are well-understood, routine, conventional. The specification describes using "ML or AI algorithms, routines, or networks (such as one or more large language models, neural networks, and the like)" (Spec. [0075]) without describing any particular innovations in these algorithms. This generic description indicates standard, commercially available machine learning technology. The specification describes the machine learning as performing conventional functions: processing customer interactions, handling routine conversations, determining complexity, and tracking performance (Spec. [0075]-[0076]). Under MPEP 2106.05(d), subsection III.A, a specification demonstrates the well-understood, routine, conventional nature of additional elements when it describes them "as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a)." Additionally, the courts have recognized using conventional tools to automate processes as well-understood, routine, conventional. See In re TLI Commc'ns LLC Patent Litig., (using a conventional digital camera in a conventional manner).
Obtaining and tracking data - The courts have recognized receiving or collecting data as well-understood, routine, conventional activity. See MPEP 2106.05(d)(II), citing OIP Techs., Inc. v. Amazon.com, Inc., (gathering and presenting information); Elec. Power Grp., LLC v. Alstom S.A., (gathering data).
Performing calculations and determining scores - The courts have recognized performing calculations as well-understood, routine, conventional activity. See MPEP 2106.05(d)(II), citing Parker v. Flook, (mathematical calculations); Bancorp Servs., (performing calculations).
Assigning tasks based on calculated values - Assigning tasks to workers based on availability and workload is a fundamental business practice. The courts have recognized that automating fundamental business practices with computers is well-understood, routine, conventional. See Alice Corp., (automating fundamental business practices); Credit Acceptance Corp. v. Westlake Servs., (conventional business practice implemented on computer).
Considering the additional elements individually and in combination, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Dependent Claims Analysis
The dependent claims do not add limitations that integrate the judicial exception into a practical application or provide an inventive concept:
Claims 2, 9, 16: These claims add steps of obtaining a new interaction from a queue, determining its load level, and selecting an agent based on load level with agents sorted by performance data. These limitations merely further specify the abstract idea of assigning work to available workers based on their capacity and current workload. Task queuing and worker selection based on availability and performance are fundamental business practices that constitute additional methods of organizing human activity and do not add any additional elements that would integrate the exception into a practical application or provide significantly more.
Claims 3, 10, 17: These claims specify that the properties of interactions include "a type of the interaction, data collected during the interaction, and one or more behaviors of a customer during the interaction." These limitations merely identify specific types of data inputs for the abstract idea calculations. Specifying particular data inputs constitutes additional insignificant extra-solution activity (data gathering) and does not integrate the abstract idea into a practical application or provide significantly more.
Claims 5, 12, 19: These claims add that the agent set includes at least one other agent and that performance data includes information for multiple agents. These limitations merely specify that the abstract idea is applied to a group of workers rather than a single worker, which does not change the nature of the abstract idea or add technological innovation.
Claims 6, 13, 20: These claims add optimizing the agent set based on load scores and system performance requirements. This further defines the abstract idea of managing worker allocation to meet business requirements a fundamental economic practice that does not integrate the exception into a practical application.
Claims 7, 14: These claims specify that the agent is a support agent. This is a field of use limitation that merely narrows the application of the abstract idea to a specific industry and does not impose any meaningful technical constraint.
Claim 21: This claim adds distributing the new interaction to the agent or another agent based on the load scores of the agents. This explicitly recites the abstract idea of assigning work based on worker capacity and does not add additional elements beyond the abstract idea.
Claim 22: This claim adds pausing the new interaction until a suitable agent is available. Placing tasks in a queue when no worker is available is a conventional business practice that constitutes well-understood, routine, conventional activity and does not provide an inventive concept.
Claim 23: This claim adds creating a pre-sorted queue of agents based on capability information for each agent. Maintaining a sorted list of workers based on their skills and capabilities is a conventional management practice. Implementing this using computer data structures (a queue) is well-understood, routine, conventional activity.
For the foregoing reasons, claims 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, and 23 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-3, 5-10, 12-17, and 19-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hill et al. (U.S. Patent Publication 2006/0080107 A1) (hereafter Hill) in view of Waalkes et al. (U.S. Patent Publication 2008/0043987 A1) (hereafter Waalkes) in further view of McGann et al. (U.S. Patent 9,635,181 B1) (hereafter McGann).
Referring to Claim 1, Hill teaches a method comprising:
obtaining, using at least one processing device of an electronic device, one or more properties of one or more interactions associated with an agent (see; par. [0058] of Hill teaches collect interactions in a database, par. [0029] where the interactions are made up of monitored actions taken (i.e. properties), par. [0012] and Fig. 4 utilizing a workstation (i.e. electronic device)).
determining, using the at least one processing device, an amount of load for the agent generated by the one or more interactions based on the one or more properties of the one or more interactions (see; par. [0024]-[0025] of Hill teaches based on the collection of historical data of an agent (i.e. previous amount of load), evaluating the responses of the agent, par. [0027] the body of work, par. [0028] and understanding the responses (i.e. interactions and properties of the interaction)).
tracking, using the at least one processing device, one or more changes in the one or more properties or a behavior of the agent (see; par. [0243] of Hill teaches tracking the agent and adding new interactions as well as continually improve based on, par. [0029] actions taken for multiple calls).
analyzing, using the at least one processing device, an effect on agent performance for the agent based on (i) the amount of load for the agent and (ii) the one or more changes in the one or more properties or the behavior of the agent (see; par. [0010] of Hill teaches analyzing the body of information representing communications and their relationships, par. [0126] modifying behavior of the agent can alter behavior data with respect to future interactions, par. [0286] and a validation to performance and accuracy).
Hill does not explicitly disclose the following limitation, however,
Waalkes teaches identifying, using the at least one processing device, a new interaction based on analyzing agent performance data for an agent set including the agent, wherein the agent performance data for the agent set includes the effect on agent performance for the agent (see; par. [0048] of Waalkes teaches dynamic load balancing based on current load and allows for additional caller interaction, Abstract utilizing a load balance so all calls are addressed accordingly, par. [0040] where the agent is evaluated on the load balancing exchange based on objective measurements (i.e. performance)).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills fails to disclose identifying, using the at least one processing device, a new interaction for distribution to the agent based on analyzing agent performance data for an agent set including the agent, wherein the agent performance data for the agent set includes the effect on agent performance for the agent.
Waalkes discloses identifying, using the at least one processing device, a new interaction for distribution to the agent based on analyzing agent performance data for an agent set including the agent, wherein the agent performance data for the agent set includes the effect on agent performance for the agent.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Hill identifying, using the at least one processing device, a new interaction for distribution to the agent based on analyzing agent performance data for an agent set including the agent, wherein the agent performance data for the agent set includes the effect on agent performance for the agent as taught by Waalkes since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill and Waalkes teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Hill in view of Waalkes does not explicitly disclose the following limitations, however,
McGann teaches determining, using a machine learning network, a complexity of the new interaction, the machine learning network trained to handle at least one type of interaction on behalf of the agent or the agent set, (see; col. 4, lines (7-17) of McGann teaches machine learning improving routing of interactions, and col. 6, lines (26-42) using multiple additional types of interactions for the agents), and
wherein determining the amount of load for the agent comprises determining one or more load scores for the agent based on the one or more properties of the one or more interactions, each load score associated with a measure of computing power and agent attention needed for the associated interaction (see; col. 20, line (44) – col. 21, line (9) of McGann teaches routing optimization scheme where the routing utilize multiple scores including performance scores, col. 7, lines (47-65) including with the correct skill, col. 23, lines (10-26) based on multiple properties such as time),
determining to leave the new interaction for the agent to handle based on the complexity (see; col. 4, lines (7-17) of McGann teaches machine learning improving routing of interactions, and col. 6, lines (26-42) using multiple additional types of interactions for the agents).
Machine learning network (see; col. 4, lines (7-17) of McGann teaches machine learning improving routing of interactions).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, McGann teaches optimized routing of interactions to contact center agents based on machine learning and as it is comparable in certain respects to Hill and Waalkes and which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill and Waalkes discloses the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills fand Waalkes fails to disclose determining, using a machine learning network, a complexity of the new interaction, the machine learning network trained to handle at least one type of interaction on behalf of the agent or the agent set, wherein determining the amount of load for the agent comprises determining one or more load scores for the agent based on the one or more properties of the one or more interactions, each load score associated with a measure of computing power and agent attention needed for the associated interaction, determining to leave the new interaction for the agent to handle based on the complexity, and machine learning network.
McGann discloses determining, using a machine learning network, a complexity of the new interaction, the machine learning network trained to handle at least one type of interaction on behalf of the agent or the agent set, wherein determining the amount of load for the agent comprises determining one or more load scores for the agent based on the one or more properties of the one or more interactions, each load score associated with a measure of computing power and agent attention needed for the associated interaction, determining to leave the new interaction for the agent to handle based on the complexity, and machine learning network..
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Hill and Waalkes determining, using a machine learning network, a complexity of the new interaction, the machine learning network trained to handle at least one type of interaction on behalf of the agent or the agent set, wherein determining the amount of load for the agent comprises determining one or more load scores for the agent based on the one or more properties of the one or more interactions, each load score associated with a measure of computing power and agent attention needed for the associated interaction, determining to leave the new interaction for the agent to handle based on the complexity, and machine learning network as taught by McGann since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill, Waalkes, and McGann teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Hill in view of Waalkes in further view McGann does not explicitly disclose the following limitations, however,
Bourne teaches iteratively performing, using the at least one processing device, a system optimization operation, wherein iteratively performing the system optimization operation comprises (see; col. 1, lines (34-45) of Bourne teaches the iteration of a process in optimizing operations at a contact center, col. 8, lines (20-38) where the system that provides the training an optimization of the operations includes automated training), and
tracking a quality of performance of the network (see; col. 3, lines (20-28) of Bourne teaches a quality monitoring score that provides analytics related to workforce performance), and
changing, based on the tracked quality of performance, an amount of interaction load for the network and an amount of attention needed by the agent (see; col. 3, lines (34-49) of Bourne teaches the integrated system of workforce monitoring and providing updated training in order to reduce risk, decrease handling time, and improve quality scores, col. 1, lines (34-45) while also iteratively updating plans (i.e. making training changes based on measured performance related to quality, and optimizing operations of the contact center), and
expanding a knowledge base and capabilities to handle one or more additional types of interactions using data collected over time including the tracked quality of performance, wherein expanding the knowledge base and the capabilities of the network comprises (see; col. 8, lines (4-38) of Bourne teaches a quality manager system that provides program stores interaction data as analytics of performance (i.e. quality performance) in a library of best practices (i.e. knowledge base) and over time the data is used to track and then schedule training from the stored data (i.e. knowledge base access) to increase performance and skill of the worker).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, McGann teaches optimized routing of interactions to contact center agents based on machine learning and as it is comparable in certain respects to Hill and Waalkes and which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Bourne teaches system and method for workforce optimization at a contact center and as it is comparable in certain respects to Hill, Waalkes, and McGann and which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill, Waalkes, and McGann discloses the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills fand Waalkes fails to disclose iteratively performing, using the at least one processing device, a system optimization operation, wherein iteratively performing the system optimization operation comprises, tracking a quality of performance of the network, changing, based on the tracked quality of performance, an amount of interaction load for the network and an amount of attention needed by the agent, and expanding a knowledge base and capabilities to handle one or more additional types of interactions using data collected over time including the tracked quality of performance, wherein expanding the knowledge base and the capabilities of the network comprises.
Bourne discloses iteratively performing, using the at least one processing device, a system optimization operation, wherein iteratively performing the system optimization operation comprises, tracking a quality of performance of the network, changing, based on the tracked quality of performance, an amount of interaction load for the network and an amount of attention needed by the agent, and expanding a knowledge base and capabilities to handle one or more additional types of interactions using data collected over time including the tracked quality of performance, wherein expanding the knowledge base and the capabilities of the network comprises.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Hill, Waalkes, and McGann iteratively performing, using the at least one processing device, a system optimization operation, wherein iteratively performing the system optimization operation comprises, tracking a quality of performance of the network, changing, based on the tracked quality of performance, an amount of interaction load for the network and an amount of attention needed by the agent, and expanding a knowledge base and capabilities to handle one or more additional types of interactions using data collected over time including the tracked quality of performance, wherein expanding the knowledge base and the capabilities of the network comprises as taught by Bourne since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill, Waalkes, McGann, and Bourne teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Referring to Claim 2, see discussion of claim 1 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the method above, Hill does not explicitly disclose a method having the limitations of, however,
Waalkes teaches obtaining the new interaction from a queue of interactions to assign (see; par. [0030] of Waalkes teaches providing new interaction data, par. [0010] where balancing loads are assigned to call agents using objective and subjective conditions pertinent to each call session as part of interaction (i.e. from a queue of incoming calls)), and
determining a load level of the new interaction (see; par. [0028] of Waalkes teaches based on load balancing and interaction data assign a new call to a user), and
selecting the agent from a group of available agents in the agent set based on the load level of the new interaction, the group of available agents being sorted based on the agent performance data (see; Abstract of Waalkes teaches selecting an agent from available agents based on determined load balancing metrics, par. [0028] where the load balancing takes into account interaction and current load on all agents, and par. [0008] a discretionary assessment of each responsible agent (i.e. performance)).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills fails to disclose obtaining the new interaction from a queue of interactions to assign, determining a load level of the new interaction, and selecting the agent from a group of available agents in the agent set based on the load level of the new interaction, the group of available agents being sorted based on the agent performance data.
Waalkes discloses obtaining the new interaction from a queue of interactions to assign, determining a load level of the new interaction, and selecting the agent from a group of available agents in the agent set based on the load level of the new interaction, the group of available agents being sorted based on the agent performance data.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Hill obtaining the new interaction from a queue of interactions to assign, determining a load level of the new interaction, and selecting the agent from a group of available agents in the agent set based on the load level of the new interaction, the group of available agents being sorted based on the agent performance data as taught by Waalkes since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill and Waalkes teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Referring to Claim 3, see discussion of claim 1 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the method above, Hill does not explicitly disclose a method having the limitations of, however,
Waalkes teaches the one or more properties of the one or more interactions include at least one of: a type of the interaction, data collected during the interaction, and one or more behaviors of a customer during the interaction (see; par. [0008] of Waalkes teaches the interaction data with respect to the customers is based on an analysis of objective and subjective conditions (i.e. properties) regarding the call).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills fails to disclose the one or more properties of the one or more interactions include at least one of: a type of the interaction, data collected during the interaction, and one or more behaviors of a customer during the interaction.
Waalkes discloses the one or more properties of the one or more interactions include at least one of: a type of the interaction, data collected during the interaction, and one or more behaviors of a customer during the interaction.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Hill the one or more properties of the one or more interactions include at least one of: a type of the interaction, data collected during the interaction, and one or more behaviors of a customer during the interaction as taught by Waalkes since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill and Waalkes teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Referring to Claim 5, see discussion of claim 1 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the method above, Hill does not explicitly disclose a method having the limitations of, however,
Waalkes teaches the agent set further includes at least one other agent (see; par. [0038] of Waalkes teaches multiple agents are utilized to handle calls as part of a group (i.e. set) where the assignment is handled by the load balancer) and
the agent performance data for the agent set includes information associated with agent performance for the at least one other agent (see; par. [0040] of Waalkes teaches agents are evaluated on the exchanges based on objective measurements (i.e. performance)).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills fails to disclose the agent set further includes at least one other agent, and the agent performance data for the agent set includes information associated with agent performance for the at least one other agent.
Waalkes discloses the agent set further includes at least one other agent, and the agent performance data for the agent set includes information associated with agent performance for the at least one other agent.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Hill the agent set further includes at least one other agent, and the agent performance data for the agent set includes information associated with agent performance for the at least one other agent as taught by Waalkes since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill and Waalkes teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Referring to Claim 6, see discussion of claim 5 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the method above, Hill does not explicitly disclose a method having the limitations of, however,
Waalkes teaches optimizing the agent set based on a load score for each agent and one or more system performance requirements (see; par. [0010] of Waalkes teaches automatic load balancing based on a load factor (i.e. score) determined from objective and subjective conditions, par. [0038] continued monitoring and evaluation of the load).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills fails to disclose the agent set further includes at least one other agent, and the agent performance data for the agent set includes information associated with agent performance for the at least one other agent.
Waalkes discloses the agent set further includes at least one other agent, and the agent performance data for the agent set includes information associated with agent performance for the at least one other agent.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Hill the agent set further includes at least one other agent, and the agent performance data for the agent set includes information associated with agent performance for the at least one other agent as taught by Waalkes since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill and Waalkes teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Referring to Claim 8, Hill in view of Waalkes in further view of McGann in further view of Bourne teaches an electronic device. Claim 8 recites the same or similar limitations as those addressed above in claim 1, Claim 8 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exception.
at least one processing device (see; par. [0012] and Fig. 4 of Hill teaches utilizing a workstation (i.e. processing device)).
Referring to Claim 9, see discussion of claim 8 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the electronic device above Claim 9 recites the same or similar limitations as those addressed above in claim 2, Claim 9 is therefore rejected for the same or similar limitations as set forth above in claim 2.
Referring to Claim 10, see discussion of claim 8 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the electronic device above Claim 10 recites the same or similar limitations as those addressed above in claim 3, Claim 10 is therefore rejected for the same or similar limitations as set forth above in claim 3.
Referring to Claim 12, see discussion of claim 8 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the electronic device above Claim 12 recites the same or similar limitations as those addressed above in claim 5, Claim 12 is therefore rejected for the same or similar limitations as set forth above in claim 5.
Referring to Claim 13, see discussion of claim 12 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the electronic device above Claim 13 recites the same or similar limitations as those addressed above in claim 6, Claim 13 is therefore rejected for the same or similar limitations as set forth above in claim 6.
Referring to Claim 14, see discussion of claim 8 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the electronic device above Claim 14 recites the same or similar limitations as those addressed above in claim 7, Claim 14 is therefore rejected for the same or similar limitations as set forth above in claim 7.
Referring to Claim 15, Hill in view of Waalkes in further view of McGann in further view of Bourne teaches a non-transitory machine-readable medium. Claim 15 recites the same or similar limitations as those addressed above in claim 1, Claim 15 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 16, see discussion of claim 15 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the non-transitory machine-readable medium above Claim16 recites the same or similar limitations as those addressed above in claim 2, Claim 16 is therefore rejected for the same or similar limitations as set forth above in claim 2.
Referring to Claim 17, see discussion of claim 15 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the non-transitory machine-readable medium above Claim17 recites the same or similar limitations as those addressed above in claim 3, Claim 17 is therefore rejected for the same or similar limitations as set forth above in claim 3.
Referring to Claim 19, see discussion of claim 15 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the non-transitory machine-readable medium above Claim19 recites the same or similar limitations as those addressed above in claim 5, Claim 19 is therefore rejected for the same or similar limitations as set forth above in claim 5.
Referring to Claim 20, see discussion of claim 19 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the non-transitory machine-readable medium above Claim 20 recites the same or similar limitations as those addressed above in claim 6, Claim 20 is therefore rejected for the same or similar limitations as set forth above in claim 6.
Referring to Claim 21, see discussion of claim 1 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the method above, Hill in view of Waalkes does not explicitly disclose a method having the limitations of, however,
McGann teaches the agent set further includes at least one other agent (see; col. 7, line (66) – col. 8, line (8) of McGann teaches set of agents), and
distributing the new interaction to the agent or the at least one other agent based on the load scores of the agents (see; Figure 10 of McGann teaches based on calculate time discount, col. 20, line (44) – col. 21, line (9) the routing optimization scheme where the routing utilize multiple scores including performance scores, col. 7, lines (47-65) including with the correct skill, col. 23, lines (10-26) based on multiple properties such as time).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, McGann teaches optimized routing of interactions to contact center agents based on machine learning and as it is comparable in certain respects to Hill and Waalkes which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill and Waalkes disclose the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills and Waalkes fails to disclose the agent set further includes at least one other agent and distributing the new interaction to the agent or the at least one other agent based on the load scores of the agents.
Waalkes discloses the agent set further includes at least one other agent and distributing the new interaction to the agent or the at least one other agent based on the load scores of the agents.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Hill the agent set further includes at least one other agent and distributing the new interaction to the agent or the at least one other agent based on the load scores of the agents as taught by Waalkes since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill and Waalkes teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Referring to Claim 22, see discussion of claim 21 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the method above, Hill in view of Waalkes does not explicitly disclose a method having the limitations of, however,
McGann teaches distributing the new interaction comprises pausing the new interaction until a suitable agent is available (see; col. 5, lines (56-63) of McGann teaches interactions making determinations on routing based on skill).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, McGann teaches optimized routing of interactions to contact center agents based on machine learning and as it is comparable in certain respects to Hill and Waalkes which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill and Waalkes disclose the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills and Waalkes fails to disclose distributing the new interaction comprises pausing the new interaction until a suitable agent is available.
Waalkes discloses distributing the new interaction comprises pausing the new interaction until a suitable agent is available.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Hill distributing the new interaction comprises pausing the new interaction until a suitable agent is available as taught by Waalkes since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill and Waalkes teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Referring to Claim 23, see discussion of claim 21 above, while Hill in view of Waalkes in further view of McGann in further view of Bourne teaches the method above, Hill in view of Waalkes does not explicitly disclose a method having the limitations of, however,
McGann teaches creating a pre-sorted queue of agents based on capability information for each agent (see; col. 20, lines (56-67) of McGann teaches a queue taking performance score and qualifications).
The Examiner notes that Hill teaches similar to the instant application teaches management of conversations in a contact center. Specifically, Hill discloses the recognizing the contact center process and providing agents with useful information during a work distribution process it is therefore viewed as analogous art in the same field of endeavor. Additionally, Waalkes teaches balancing agent console load during automated call processing and as it is comparable in certain respects to Hill which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, McGann teaches optimized routing of interactions to contact center agents based on machine learning and as it is comparable in certain respects to Hill and Waalkes which management of conversations in a contact center as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Hill and Waalkes disclose the recognizing the contact center process and providing agents with useful information during a work distribution process. However, Hills and Waalkes fails to disclose creating a pre-sorted queue of agents based on capability information for each agent.
Waalkes discloses creating a pre-sorted queue of agents based on capability information for each agent.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Hill creating a pre-sorted queue of agents based on capability information for each agent as taught by Waalkes since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Hill and Waalkes teach the collecting and analysis of data in order to maximize the performance of call center individuals based on performance and load balancing and they do not contradict or diminish the other alone or when combined.
Conclusion
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/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625