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 . Claims 1-2, 4-5, 7-10, 12-13, 15-18, and 20-24 have been reviewed and are under consideration by this office 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/19/2026 has been entered.
Notice to Applicant
The following is a Non-Final Office action. Applicant amended claims, cancelled claims 6 and 14, added claim 24 and previously cancelled claims 3, 11, and 19. Claims 1-2, 4-5, 7-10, 12-13, 15-18, and 20-24 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The 103 Rejections was withdrawn in view of the amended claims limitations and arguments presented by the Applicant in the Final Office Action dated 06/10/2025.
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 contends that claims recite “automatic feature transformation” integrates the abstract idea into a practical application similar to Example 42. Applicant further asserts that similar to Example 42 the claims standardize formatting for downstream technical function.
Examiner respectfully disagrees. The claims as recited do not match the fact pattern of the cited example as the example requires converting non-standardized formats to standardized formats in real-time through use of hardware and software and further automatically generates a message while the present claims merely recites an automatic feature transformation (i.e. attributes (Specification, [39]) in to a feature type with no technical detail as to what transformation is taking place. The additional element of automatic feature transformation amount to apply it on a general purpose computing device (MPEP 2106.05(f)(1) “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. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016) and further amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Examiner recommends adding further technical detail of how the technology is leveraged to accomplish the resulting transformation.
Applicant further contends with respect to Example 42 that the claims transform disparate data sources with unequal dimensions and calibrates the segments such that they may be compared directly. Applicant further asserts that the claims address a technical problem similar to the example and integrate the abstract idea into a practical application.
Examiner respectfully disagrees. The claims as amended merely recites an automatic feature transformation (i.e. attributes (Specification, [39]) in to a feature type with no technical detail as to what transformation is taking place. Examiner recommends adding further technical detail of how the technology is leveraged to accomplish the resulting transformation.
Applicant further contends that the feature space may be exponentially larger for models and dimensions may be unequal causing a failure to identify segment key attributes. Applicant further asserts that the inputs undergo an automatic feature transformation providing the technical improvement of “model(ing) candidate segments using many different types…” and “in this way the, the data objects may be input into a particular cluster model…”
Examiner respectfully disagrees. The transformation is recited at a high level of generality and the cited improvement merely improves upon the abstract idea itself and not the technology nor technological environment as a whole.
Applicant further contends that the claims recite the use of a combinatorial model which provides a technical solution through several integrated technical mechanisms such as calculating divergence scores with complexity…, calibrates different model outputs to enable direct comparison, and further using a combinatorial ranking function identify features…
Examiner respectfully disagrees. The combinatorial model1 is recited at a high level of generality and does not inherently imply the use of machine learning/artificial intelligence as such the combinatorial model is interpreted in light of the specification and further under the broadest reasonable interpretation. As such, the combinatorial model and stated functions are abstract elements which are “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the limitations is further directed towards calculating divergences scores which is a concept capable of being performed in the human mind (i.e. via pen and paper) and further “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
Applicant contends that the amended claims provide an improvement to the technical field with amended claims reciting model types and generation of features. Applicant points to Desjardins asserting the claims describe improvements to computer components…. Allowing the system to reduce storage and complexity.
Examiner respectfully disagrees. The claims limitations discussed are additional elements (with the cluster of machine learning algorithms and various model types are recited at a high level of generality) which are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Further the claims are directed to generating respective features of data and further determining similarity using a computer to improve the performance of that determination—not the performance of a computer. (See MPEP 2106.05(a)(II)(i); A commonplace business method or mathematical algorithm being applied on a general purpose computer, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Examiner notes the additional elements are addressed above.
The 101 Rejection is updated and maintained 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, 2, 4-5, 7-10, 12-13, 15-18, and 20-24 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.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) 1, 2, 4-5, 7-10, 12-13, 15-18, and 20-24 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims 1, 9, and 17 recite a series of steps for generating candidate segments and calculating similarity scores:
Regarding Claims 1, 9, and 17; (additional elements bolded)
A method for data processing, comprising/ apparatus for data processing, comprising: a processor; memory coupled with the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to/ A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by a processor;
inputting, into a set of cluster models, a corpus of entity data associated with a set of features;
transforming, using an automatic feature transformation, the set of features into one or more feature types such that each cluster model of the set of cluster models is associated with a different feature type;
generating, using the set of cluster models and based at least in part on a seed segment, a corpus of entity data, a set of candidate segments, and transforming the set of features into one or more feature types, a set of candidate segments,
wherein a candidate segment of the set of candidate segments includes a plurality of entities associated with the corpus of entity data; and
wherein the set of cluster models includes at least two cluster models of different machine learning model types (recited at a high level of generality) that each generate a respective candidate segment of the set of candidate segments, wherein the different machine learning model types include at least one of a classification model, a locality-sensitive hashing model, or a user-to-user similarity model, and wherein each cluster model of the at least two cluster models is associated with a different feature type of the one or more feature types and processes a different respective subset of features of a set of features corresponding to each entity associated with the corpus of entity data;
generating, based at least in part on the different respective subsets of features associated with entities of each candidate segment and the set of features associated with entities of the seed segment, a set of candidate segment fingerprints and a seed segment fingerprint, a segment fingerprint indicative of a distribution of the entities within a segment based at least in part on similarities between features associated with the entities within the segment;
calculating a set of similarity scores between the seed segment and the set of candidate segments based at least in part on the seed segment fingerprint and the set of candidate segment fingerprints; and
calculating, using a combinatorial model (does not imply explicit the use of machine learning) and based at least in part on the set of similarity scores, a set of divergence scores between the seed segment and the candidate segments and a set of combinatorial scores for the entities of the set of candidate segments;
merging the candidate segments to obtain a total candidate segment by the combinatorial model calculating the set of divergence scores and the set of combinatorial scores;
identifying, based at least in part on obtaining the total candidate segment by the combinatorial model, a segment of lookalike entities corresponding to the seed segment.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards generating a set of candidate segments, generating a set of a set of candidate and seed segment fingerprints, calculating similarity scores, and identifying a segment of lookalike entities all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards determining segments for marketing sales, service, and other applications (See Specification, [at least 02, 23, 40]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least the additional elements bolded above. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) ((MPEP 2106.05(f)(1) “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. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016)) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)..
Regarding Claims 2, 4-5, 7-8, 10, 12-13, 15-16, 18, and 20-23, the claim further narrows the abstract idea or recite additional elements previously rejected in the independent claims.
Regarding Claim 24, the claim recites additional element of displaying, via a user interface of a user device, one or more of the visual representations. This element is performing are performing the steps would be no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) in Steps 2A-Prong Two and 2B.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
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/JEREMY L GUNN/Examiner, Art Unit 3624
1 Combinatorial model - A cluster model does not inherently imply combining multiple machine learning models; rather, it is a type of unsupervised machine learning algorithm designed to identify hidden patterns, similarities, or structures in unlabeled data. < https://www.google.com/cluster+model>
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