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, 4-10, 12-18, and 20-23 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Final Office action. Applicant amended claims, and previously cancelled claims 3, 11, and 19. Claims 1, 2, 4-10, 12-18, and 20-23 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The 103 Rejections was withdrawn in view of the amended claims limitations and arguments presented by the Applicant in the Final Office Action dated 06/10/2025.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 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).
Applicant further contends the limitations support computational efficiency. Applicant further points to the specification in paragraphs 33 and 34 when discussing a set of cluster models and further calculating similarity scores.
Examiner respectfully disagrees. 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.
Applicant further contends pointing to Desjardins that the claims improve a computer by allowing a computer to generate lookalike segments using less computational resources.
Examiner respectfully disagrees and points to the responses seen above. The generation of segments is a concept capable of being performed in the human mind but is applied to a general purpose computer for the known benefit of speed and efficiency in calculations that a computer provides (See MPEP 2106.05(f)).
Applicant contends with regards to Claims 6 and 14 that the claims integrate the abstract idea into a practical application by the use of different scoring metrics for scoring between users. Applicant further contends that the combinatorial models provide a technological solution through integrated mechanisms such as calculating divergence scores and calibrating different model outputs to enable direct comparison. Applicant further contends that the ranking function may identify … features… associated with higher contribution to respective scores and as such improve similarity scoring.
Examiner respectfully disagrees. Claims 6 and 14 do not recite additional elements as the claims are directed towards calculating divergence scores, calculating combinatorial scores, and calculating a set of similarity scores all of which are concepts capable of being performed in the human mind (i.e. via pen and paper). Although not claimed using different scoring metrics would further be an additional mental process. Examiner further notes the use of machine learning models is recited at a high level of generality. The claims merely recite combinatorial scores and identifying segments which is further a mental process as a human could generate combinatorial scores and further identify scores through use of pen and paper. The Examiner further notes that a cluster model does not inherently imply combining models, but even if it the claims did recite a combinatorial model1 it does not provide further detail as to how the models are combined and through what process.
Applicant further points to Desjardins and notes the combinatorial model features integrate the abstract idea into a practical application as the models improves the computer and reduces system complexity.
Examiner respectfully disagrees and points to the responses seen above. The claims merely recite the use of a cluster of models and do not recite a combinatorial model but merely using the cluster of models to produce scores. The machine learning models and cluster models are recited at a high level of generality and are addressed below in the full 101 Rejection.
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, 4-10, 12-18, and 20-23 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, 4-10, 12-18, and 20-23 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;
generating, using a set of cluster models and based at least in part on a seed segment and a corpus of entity data, 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 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 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;
generating, based at least in part on a projection of the entities of each of the candidate segment of the set of candidate segments to a respective one-dimensional array, visual representations associated with the set of candidate segments, the visual representations indicating the distribution of the entities within a respective candidate segment;
displaying, via a user interface of a user device, one or more of the visual representations associated with the set of candidate segments, wherein each of the visual representations comprises a dark band to indicate a high similarity between a first portion of the entities within an associated candidate segment and a light band to indicate a low similarity between a second portion of the entities within the associated candidate 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
identifying, from the set of candidate segments and based at least in part on the set of similarity scores, 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 two cluster models of different machine learning model types (recited at a high level of generality), 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; and A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by a processor; two cluster models of different machine learning model types; 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 displaying, via a user interface of a user device, one or more of the visual representations. 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) 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). Examiner notes that generating a visual representation, which under the broadest reasonable interpretation is a concept that could be performed in the human mind (i.e. via pen and paper)
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).
Regarding Claim(s) 2, 4-8, 10, 12-16, 18, and 20-23, the claim further narrows the abstract idea or recite additional elements previously rejected in the independent claims..
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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/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>