DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/22/2026 has been entered.
Status of Application
The following is a Non-Final Office Action. In response to Examiner's communication on 04/07/2026, Applicant on 05/22/2026, amended Claims 1-2, 10-12, 18 and 20. Claims 1-20 are now pending in this application and have been rejected below.
Response to Amendment
Applicants’ amendments are insufficient to overcome the 35 USC 101 rejections set forth in the previous action. Accordingly, the rejections have been updated to address the amendments and maintained below.
Applicant’s amendments have been found to overcome the rejections under 35 USC 112(b) and 35 USC 103 set forth in the previous action. Therefore, these rejections are withdrawn.
Response to Arguments – 35 USC § 112(b)
Applicant's arguments with respect to the 35 USC 112(b) rejections have been fully considered and are found to be persuasive..
Applicant argues that the meaning of the “.clip()” function as defined in amended Claims 10 and 20 would now be apparent to one of ordinary skill in the art. In light of the newly added limitations that concretely specify the function’s implementation, Examiner agrees. Accordingly, the rejections under 35 USC 112(b) have been withdrawn.
Response to Arguments – 35 USC § 103
Applicant’s arguments have been fully considered and have been found to be persuasive. In particular, the prior art of record fails to teach, “constructing a pooled training dataset from interaction-based features of a plurality of similar accounts sharing a common vertical with the account, without direct data sharing of personally identifiable information across the plurality of similar accounts”. Accordingly, rejections under 35 USC 103 have been withdrawn.
Response to Arguments – 35 USC § 101
Applicant's arguments with respect to the 35 USC 101 rejections have been fully considered but they are not persuasive.
Applicant firstly argues that the limitations recite a concrete technical operation, in the form of pooled training dataset creation, insufficient training data detection, and training of machine learning models, and therefore do not recite abstract ideas, meeting eligibility requirements under Step 2A Prong One. Examiner respectfully disagrees. Firstly, Examiner notes that the creation of a pooled dataset and detecting insufficient training data is expressly performable in the human mind. “determining…if historical interaction data of the account fails to satisfy a sufficiency threshold” by mentally observing historical interaction data and performing mental judgments to assess its relative quantity to determine if a threshold is exceeded. The creation of a “pooled training dataset from interaction-based features” is possible by mentally observing additional data and performing judgments to screen for saliency, and organizing through pen and paper. Examiner notes that Applicant’s characterization of the invention as performing: “ingest interaction-based features from thousands of similar customer accounts” that would render the operations unable to be practically performed in the human mind is missing from the claims; there is no assertion as to a magnitude of data or complexity of judgment that a human could not facilitate. Further, even if the data were vast, accelerating a process of analyzing data "when the increased speed comes solely from the capabilities of a general-purpose computer" is not an improvement in computers. MPEP 2106.05(a). “'Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer' does not integrate a judicial exception into a practical application or provide an inventive concept." MPEP 2106.05(f). Judgments of threshold and formation of datasets amount to relatively mental assessments and therefore mental processes.
Regarding Applicant’s assertion that the deployment of machine learning model(s) trained “using xgBoost and balanced random forest techniques” does not recite a mental process, Examiner wholly agrees. However, it is essential to note that the performance of abstract ideas is not implicitly tied to the operation of machine learning models. The operations of determining data sufficiency and pooling with more datapoints if needed is itself an abstract idea, pursuant to the logic as outlined above. Deriving probabilistic scores is further an abstract idea, mentally performable in the human mind, by human judgments in tandem with pen and paper to enact requisite calculations. Without such an implicit connection between the recited abstract ideas and the machine learning model(s), it is appropriate to analyze said models as additional elements under Step 2A Prong II and Step 2B of the Alice/Mayo Subject Matter Eligibility Test, and Examiner respectfully points to the discussion of such below.
Finally, even if Applicant’s allegation that the aforementioned processes were unable to be performed in the human mind were true, creating a pooled data set in light of meeting a threshold, analyzing customer data to inform sales activities, are Commercial or Legal Interactions by virtue of being marketing or sales activities or behaviors. Applicant’s arguments with respect to this point, see the bottom of Page 12 of Amendment Submitted/Entered with Filing of RCE filed 05/22/2026, or corresponding Page 3 of Remarks, address the characterization of the invention as a “targeted machine-learning training pipeline”. As this argument amounts to an assertion of eligibility under Step 2A Prong II or Step 2B by the logic outlined above, Examiner respectfully notes the discussion below.
Applicant subsequently argues that additional elements, such as the aforementioned pooled training dataset creation, insufficient training data detection, and training of machine learning models serve to integrate alleged recited abstract ideas into a practical application, citing USPTO SME Examples 47 and 48 and Ex Parte Desjardins by virtue of the specific technical means of deploying machine learning models. Examiner respectfully disagrees. It is important to note that per the language of Applicant’s claims, this training process is not actually recited, but rather “combining two or more machine learning (ML) scores from two or more trained ML models…wherein the ML models are trained…”. The usage of machine learning scores effectively amounts to a black box tool that is invoked in tandem with an abstract idea, whereby the additional elements in the claim, considered as a whole, merely serve as a link to a particular technological environment or field of use by acting as generic computing components. This impacts Applicant’s characterization of the invention as “a specific technical way of training and using machine-learning models for that purpose.” As outlined above, limitations pertaining to the mechanics of dataset curation and score collation expressly recite abstract ideas, and therefore it cannot be said that the specific technical way of “using” the broadly recited machine-learning models integrate recited abstract ideas into a practical application when additional elements are generically deployed to effectuate mentally performable abstract ideas and commercial interactions.
Applicant finally argues that additional elements considered as an ordered combination amount to significantly more than any alleged abstract ideas, by virtue of the lack of well-understood, routine, or conventional activity in the field. Examiner notes that similar issues with respect to Applicant’s deployment of machine learning models persist as outlined above; with the mere mention of the deployment of machine learning models that have been “trained using xgBoost and balanced random forest techniques”, it is a mischaracterization of the invention to assert that the additional elements in Applicant’s claims amount to anything more than a general link of the abstract ideas recited to a particular technological environment or field of use.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
The claims are directed to an apparatus and method. Therefore, the claims are directed to at least one of the four statutory categories.
101 Analysis – Step 2A
Regarding Prong 1 of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether they recite subject matter that is directed to a judicial expectation, namely a law of nature, a natural phenomenon, or one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 1 includes limitations that recite an abstract idea and will henceforth be used as a representative claim for the 101 rejection until otherwise noted. Claim 1 recites:
A lead pooling and ranking system, comprising: at least one processor; and memory comprising a set of instructions, wherein the set of instructions is configured to cause the at least one processor to execute implementing a pooling technique enhancing and optimizing lead scoring machine learning techniques for a set of leads; wherein implementing the pooling technique comprises: determining, for an account in a customer relationship management (CRM) system associated with the set of leads, if historical interaction data of the account fails to satisfy a sufficiency threshold for training a lead scoring machine learning model for the account; and in response to determining if the historical interaction data fails to satisfy the sufficiency threshold, constructing a pooled training dataset from interaction-based features of a plurality of similar accounts sharing a common vertical with the account, without direct data sharing of personally identifiable information across the plurality of similar accounts; generating a rule-based lead score for each configured rule; combining two or more machine learning (ML) scores from two or more trained ML models and one or more rule-based scores to create a unitary score for each corresponding lead in the set of leads, wherein the ML models are trained on the pooled training dataset using xgBoost and balanced random forest techniques and generating a rank and rating for each lead in the set of leads.
The examiner submits that the foregoing bolded limitation(s) constitute an abstract idea because under its broadest reasonable interpretation, the claim recites mental processes that could be
performed by a human with a pen and paper, per the MPEP, merely
adapting them into the context of a technological environment with computing parts does not preclude them from being abstract. Further, the aggregation of certain scores amounts to mathematical relationships. The claim further recites Certain Methods of Organizing Human Activity, namely Commercial or Legal Interactions, as the scope of the claim pertains to organizing information to enhance the performance of sales.
Accordingly, the claim recites at least one abstract idea.
Claim 11 recites at least one abstract idea by virtue of reciting substantially similar limitations.
Claims 5-6, 8-10, 15-16, 18-20 further recite Mathematical Concepts, namely a Mathematical Calculation. The relationships recited in said claims reflect a mathematical formula that amounts to computing a weighted sum as well as the mechanics for doing so.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into practical application. As noted in the MPEP, it must be determined whether any additional elements in the claim beyond the judicial exception integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements, such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A lead pooling and ranking system, comprising: at least one processor; and memory comprising a set of instructions, wherein the set of instructions is configured to cause the at least one processor to execute implementing a pooling technique enhancing and optimizing lead scoring machine learning techniques for a set of leads; wherein implementing the pooling technique comprises: determining, for an account in a customer relationship management (CRM) system associated with the set of leads, if historical interaction data of the account fails to satisfy a sufficiency threshold for training a lead scoring machine learning model for the account; and in response to determining if the historical interaction data fails to satisfy the sufficiency threshold, constructing a pooled training dataset from interaction-based features of a plurality of similar accounts sharing a common vertical with the account, without direct data sharing of personally identifiable information across the plurality of similar accounts; generating a rule-based lead score for each configured rule; combining two or more machine learning (ML) scores from two or more trained ML models and one or more rule-based scores to create a unitary score for each corresponding lead in the set of leads, wherein the ML models are trained on the pooled training dataset using xgBoost and balanced random forest techniques and generating a rank and rating for each lead in the set of leads.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
As it pertains to Claim 1, the additional elements in the claims include “A lead pooling and ranking system, comprising: at least one processor; and memory comprising a set of instructions, wherein the set of instructions is configured to cause the at least one processor to execute”, “machine learning”, “two or more machine learning (ML) scores”, and “wherein the ML models are trained using xgBoost and balanced random forest techniques”.
When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements are generic computing components that are merely used as a tool to perform the recited abstract idea and/or do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use under Step 2A Prong Two.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05).
Accordingly, the additional limitation(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing an abstract idea.
Claim 11 does not serve to integrate recited abstract ideas into a practical application by analogous reasoning.
Claims 3, 13 recite “a single artificial intelligence (AI) score”, “a fit model, an engagement model, and a semantic model”.
Claims 9, 19 recite “static, interest, semantic models”.
These limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing an abstract idea.
Claims 2, 4-8, 10, 12, 14-18, 20 do not recite additional limitations beyond those found in claims from which they depend and therefore do not integrate the recited abstract ideas into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the MPEP, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to generic computing components that are merely used
as a tool to perform the recited abstract idea and/or do no more than
generally link the use of the recited abstract idea to a particular
technological environment or field of use. Further, looking at the additional
elements as an ordered combination adds nothing that is not already
present when considering the additional elements individually.
Claim 11 does not serve to integrate recited abstract ideas into a practical application or amount to significantly more by analogous reasoning.
Claims 3, 13 recite “a single artificial intelligence (AI) score”, “a fit model, an engagement model, and a semantic model”.
Claims 9, 19 recite “static, interest, semantic models”.
These limitations do not integrate the abstract idea into a practical application or amount to significantly more because they do not impose any meaningful limits on practicing an abstract idea.
Claims 2, 4-8, 10, 12, 14-18, 20 do not recite additional limitations beyond those found in claims from which they depend and therefore do not integrate the recited abstract ideas into a practical application or amount to significantly more.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THEODORE L XIE whose telephone number is (571)272-7102. The examiner can normally be reached M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THEODORE XIE/ Examiner, Art Unit 3623
/WILLIAM S BROCKINGTON III/ Primary Examiner, Art Unit 3623
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