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 .
Response to Arguments
Applicant's arguments with respect to 35 U.S.C. 112(b) rejection of claims 7-8 have been considered and found persuasive due to Applicant cancelling the claims, and the rejection has been withdrawn.
Applicant's arguments with respect to 35 U.S.C. 101 Abstract Idea rejection in regards to claims 1-20 have been considered, however are not found to be persuasive due to the following reasons. Examiner respectfully disagrees with Applicant’s arguments because the claims recite a certain method of organizing human activity: collecting information about a customer, identifying the customer’s need, evaluating and ranking employees based on their past performance, and assigning an employee to server the customer. Applicant incorrectly limits the organizing human activity grouping to financial transactions and fundamental economic practices. The grouping also includes commercial interactions, business relations, and managing relationships or interactions between people. Assigning a contact center employee to a customer based on business performance criteria falls reasonably within those categories.
Applicant’s practical-application argument is also not persuasive because the asserted technical improvement is not meaningfully reflected in the claims. The specification may discuss reducing communications and conserving network resources, but the claim does not require fewer questions, fewer network messages, reduced bandwidth, reduced latency, reduced memory use, or any particular change to the contact center communication infrastructure. The claims instead requires models that predict employee performance scores, determine performance measures, rank employees, and assign one employee. These feature improve the quality of the business routing decision, not the operation of the computer or network.
Finally, the amendments do not clearly provide an inventive concept under step 2B. The processor, communication session, caller data, trained models, performance scoring, ranking and assignment are recited largely by their desired functions, without a specific unconventional technical arrangement for performing them.
Applicant's arguments with respect to 35 U.S.C. 103 rejection of claims 1, 13, 19 and 20 have been considered and found persuasive, and the rejection has been withdrawn. See detailed reason for allowance below.
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 1-7, 10-15 and 17-23 are rejected under 35 U.S.C. 101.
Claims 1, 13, 19 and 20 are directed to a non-statutory abstract idea without significantly more to transform it into a patent-eligible invention. The claims collect information about a caller, determines the caller’s reason for contacting the business, evaluates the past performance of available employees, ranks those employee to assist the caller. This is a method of managing a commercial relationship and organizing the interaction between the customer and service employees. Such business relations and management of interactions between people fall within the recognized certain methods of organizing human activity grouping of abstract ideas.
The additional machine learning limitations do not integrate the abstract idea into practical technological application. The claims state that a set of trained ML models, including one or more gradient boosted decision tree models, predicts performance scores and ranks agents, but it does not recite a new model structure, a specific training improvement, a new data structure, or a change to how the computer or communication network operates. The models are used only as tools to make the business decision of which employee should handle the caller. Applying known machine learning technology to a new business setting generally does not create eligibility when the claims do not explain how the ML technology itself is improved or how a claimed technical improvement is achieved.
The claims also do not add an inventive concept amounting to significantly more than the abstract idea. The processor, communication session, caller data, trained models, performance scores, ranking, and agent assignment are stated broadly according to the results they perform. Merely naming a GBDT model does not provide a meaningful technical limitation when the claims do not specify an unconventional configuration or operation of that model. Therefore, the claims use general computer and ML tools to automate the abstract process of evaluating employees and assigning one to a customer.
Dependent claims 2-7, 10-15, 17-18 and 21-23 further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known in communication management systems.
Claim 2: Receiving a standard voice call is a conventional, everyday communication method that does not add a technical invention to the underlying abstract idea.
Claim 3: Initiating a text chat is a generic, off-the-shelf communication method that does not transform the abstract idea into a patentable, technical invention.
Claim 4: Using a computer to read statements and predict what a caller wants is merely automating the basic human mental process of listening and understanding language.
Claim 5: Specifying a "transformer architecture" simply names a type of mathematical algorithm used to process data, which is considered an unpatentable abstract concept.
Claim 6: Claiming a specific number of mathematical parameters (100 to 500 million) is just describing the size of a mathematical equation, not a physical or technical improvement to a computer.
Claim 7: Looking up a caller's history and using a "gradient boosted decision tree" is just routine data collection combined with a standard mathematical classification method.
Claim 10: Using common metrics like "customer satisfaction" or "average handling time" relies purely on conventional business administration and human resources concepts.
Claim 11: Deciding how much mathematical "weight" to give certain performance scores is a subjective human mental process and a basic math calculation.
Claim 12: Calculating these performance scores ahead of time is just a routine data processing step that organizes information but provides no new technical solution.
Claim 14: "Bayesian shrinkage" is a well-known statistical formula, and purely mathematical formulas cannot be patented.
Claim 15: Using a standard Interactive Voice Response (IVR) system or a smartphone app simply applies the abstract idea to generic, already-existing computer hardware.
Claim 17: Calculating multiple performance scores and combining them into one final score is a basic mathematical data aggregation step.
Claim 18: Using an "ensemble" model to process the scores is just stacking multiple mathematical equations on top of each other, which remains an abstract mathematical concept rather than a technological invention.
Claim 21: does not improve communication technology.
Claim 22: does not provide a specific technical improvement.
Claim 23: does not improve the ML model or computer itself.
Allowable Subject Matter
Claims 1-7, 10-15 and 17-23 are allowed if the Applicant can overcome the 101 Abstract Idea.
The following is a statement of reasons for the indication of allowable subject matter: Rubens (US 2023/0117113) in view of Ma et al. (US 2020/0099790):
Rubens teaches a processor based contact system that receives service request data from a caller or chat user, initiates a chatbot or conversational AI interaction, analyzes the service request data with a first machine learning model to determine the user’s intent, and uses a second trained machine learning model to classify the intent and adjust the request’s service level or routing priority. The system then assigns and notifies a particular live agent by matching the intent, urgency, customer profile classification, and relevant skill requirements with agents having corresponding skills and proficiency levels, while also considering agent availability and predicted waiting time, thus, Rubens routes the interaction to a specifically skilled agent rather than blindly transferring it to a general agent queue.
Ma teaches using DNNs to analyze a caller’s current query and prior conversation, predict the caller’s task category and NPS, and select a preferred live agent. Ma constructs an expertise matrix containing each agent’s historical NPS for different categories, calculates a predicted overall NPS for each agent and selects the agent with the highest score.
The difference between the prior art and the claimed inventio is that Rubens nor Ma explicitly teach the agent performance model comprises a set of trained machine learning(ML) models, wherein each trained ML model in the set is trained to predict, for each of the plurality of agents and with respect to communication sessions with callers having the same intent as the intent of the caller, a performance score for a corresponding performance indicator, identifying the agent to assign to the caller using the identified intent of the caller and the APM comprises using the set of trained ML models to determine a measure of performance for each of the at least some of the plurality of agents to obtain a set of measures of performance, and ranking the at least some of the plurality of agents using the plurality of measures of performance, and the set of trained ML models comprises a gradient boosted decision tree model and/or each of the ML models in the set of trained ML models is a gradient boosted decision tree model.
Therefore, it would not have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Rubens and Ma to include the agent performance model comprises a set of trained machine learning(ML) models, wherein each trained ML model in the set is trained to predict, for each of the plurality of agents and with respect to communication sessions with callers having the same intent as the intent of the caller, a performance score for a corresponding performance indicator, identifying the agent to assign to the caller using the identified intent of the caller and the APM comprises using the set of trained ML models to determine a measure of performance for each of the at least some of the plurality of agents to obtain a set of measures of performance, and ranking the at least some of the plurality of agents using the plurality of measures of performance, and the set of trained ML models comprises a gradient boosted decision tree model and/or each of the ML models in the set of trained ML models is a gradient boosted decision tree model.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659
Prosecution Insights