DETAILED ACTION
This Office Action is in response to Applicant's application filed on 20 May 2025. Currently, claims 1-14 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-14 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (system and method). Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1 and 8 recite the abstract idea of dynamically calibrating a predictive lead scoring model without retraining a core classification model by ingesting lead data for a plurality of leads and generating, by a binary classifier, an initial probability score for each lead and clustering leads into a plurality of performance-based segments according to the initial probability scores and one or more additional lead attributes and mapping each segment to a grade category based on an aggregate performance level of the leads assigned to that segment and receiving real-time outcome data indicating whether each of the plurality of leads converted to a defined outcome and adjusting the mapping from the performance-based segments to the grade categories based at least in part on the outcome data and applying the adjusted mapping to newly ingested leads so as to assign a grade category without retraining the binary classifier. The claims are directed to assigning a score to a lead and segmenting the leads based on the scores and mapping to grades. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain methods of organizing human activity such as certain methods of organizing human activity including commercial interactions including marketing or sales activities or behaviors. The claims are a type of organizing human activity because the claims show leads which are based on human activity of conversions and those leads/human activity is organized by being scored and graded. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as a computer-implemented method, calibrating in real time, receiving in real time, a system, receiving data into a standardized schema, one or more processors communicatively coupled to a memory storing instructions that, when executed by the one or more processors, cause the system to perform steps are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as a computer-implemented method, calibrating in real time, receiving in real time, a system, receiving data into a standardized schema, one or more processors communicatively coupled to a memory storing instructions that, when executed by the one or more processors, cause the system to perform steps (as evidenced by para [0018], [0024], [0035]-[0040] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-3, 5, 7, 10-11, 14 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing wherein the clustering module is configured to apply a k-means clustering algorithm to group leads according to one or more attributes selected from the group consisting of: geographic location, source provider, demographic fields, and behavioral interaction data and wherein the mapping module is further configured to designate a highest-performing segment as “A-grade” and lower-performing segments as consecutively lower grades, such that the grade categories maintain a relative ranking of leads over time and an automated feature engineering pipeline configured to generate derived features from raw lead attributes, and to provide said derived features as inputs to the binary classifier and a rules engine configured to filter or prioritize leads based on the grade category, wherein filtering rules include selectively routing leads to specific user groups or excluding leads below a predetermined grade threshold and wherein adjusting the mapping from the performance-based segments to the grade categories comprises automatically revising thresholds that define each grade category based on a relative ranking of segments conversion rates and extracting one or more derived features from raw lead data prior to generating the initial probability scores, the derived features including engagement metrics and historical lead interactions and monitoring a performance metric of the binary classifier over time and triggering an alert condition to retrain the binary classifier when the performance metric falls below a predefined threshold. Dependent claims 4, 6, 9, 12-13 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as wherein the binary classifier comprises a machine learning model selected from the group comprising: a logistic regression model; a decision tree or ensemble of decision trees; a gradient boosting machine; and a neural network and wherein the feedback module is communicatively coupled to a client relationship management (CRM) platform configured to automatically receive positive or negative conversion events for each lead without manual data entry and executing a k-means clustering process to group the leads into performance-based segments and recalculating cluster centroids in response to the real-time outcome data and providing a user interface for configuring lead filtering or prioritization rules based on the assigned grade categories, wherein said rules specify inclusion or exclusion of leads from sales outreach and receiving real-time outcome data includes automatically ingesting lead disposition events from a client relationship management (CRM) system. (as evidenced by para [0018], [0024], [0035]-[0040] of applicant’s own specification) are well understood, routine and conventional in the field.
Allowable Subject Matter
Claims 1-14 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Murphy et al. (US 2022/0207456 A1), a method for a lead grading platform can include, receiving a lead from a lead vendor, wherein the lead comprises a plurality of lead attribute values, converting the plurality of lead attribute values to a plurality of benchmark values, assigning the lead to a pre-determined cluster and a pre-determined sub-cluster based on the plurality of benchmark values, determining a grade for the lead based on the pre-determined cluster and the pre-determined sub-cluster, and responding to the grade being greater than a threshold grade by delivering the lead to a lead consumer
Santharam et al. (EP 4220523 A1), a user data lifecycle management system that can collect user data from multiple sources including the user, the organization implementing the lifecycle management system, and third parties and where the user data is used to create a user profile with a global user identifier and the user profile is scored based on the attributes within the user profile and the user data lifecycle management system can route the user profile to a destination source based on the score of the user profile
LeadScorz "The LeadScorz® Grading Engine™ Superior Lead Scoring Technology" (2023), a commercial product that optimizes the value of the consumer leads via a grading engine technology that grades leads in real time and filters all third-party leads before companies pay for them
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUJAY KONERU whose telephone number is 571-270-3409. The examiner can normally be reached on Monday-Friday, 9 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached on 571- 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
