DETAILED ACTION
The following is a Final Office action. In response to Non-Final communications received 12/29/2025, Applicant, on 3/9/2026, amended Claims 1, 10, and 17. Claims 1-20 are pending in this action, have been considered in full, and are rejected below.
Response to Arguments
Arguments regarding the Non-Statutory Double Patenting – Applicant stated they will file a terminal disclaimer in the future.
Arguments regarding 35 USC §103 – The rejection is hereby removed in light of Applicant’s amendments for the reasons found in the “Allowable Subject Matter” section found below.
Arguments regarding 35 USC §101 Alice – Applicant asserts that the claims are not directed at an abstract idea and are integrated into a practical application, by stating that the amended limitations improve computer functionality and thus are not directed at an abstract idea due to Desjardins. Applicant also states the claims determine how the user persona clusters are clustered, how the training data is generated, how the machine learning model is trained, and how the clusters are revised are more than merely being performed in the mind or any fundamental economic process, by stating that there is an improvement as stated in the specification in [0003-4]. Examiner disagrees as first the claims and the Desjardins cases have different fact patterns, and here there is no improvement to a computer as the computer is not improved by performing the abstract limitations of the claims. The claims recite clear abstractions of both mental processes and certain methods of organizing human activity as per the rejection below. This is stated clearly by the office action, and the use of a supervised and trained machine learning model does not change the fact there are two abstract ideas which are identified in the Claims, nor does it make the claims practically integrated. Further, at best this is utilization of current technologies to analyze and calculate an overall score of a cluster, as there is no improvement to the machine learning model, any technology, or technological process, and thus “Applying It” similar to Alice, not practically integrated, nor significantly more, and not eligible by the MPEP.
Applicant asserts the there is an improvement to the training of machine learning model, as there is no 102 or 103 rejections, and states that the claims recite features which exceed “well-understood, routine, conventional activities”. Examiner disagrees as the totality of the amended limitations are part of the abstraction, and at best this is utilization of current technologies, “Applying It”, similar to that of Alice, and does not make these limitations eligible under 101, as there is no improvement to the machine learning model or any additional element, alone or in combination, such as the system, memory, processor, etc. Further, there is no improvement to a technology or any technological process and performing these actions on a computer would be utilization of current technologies to perform the abstract limitations of the Claims, and any inventive concept would be contained wholly within the abstraction.
Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 of the current application, hereby known as ‘220, are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,073,423, hereby known as ‘423. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Regarding Claims 1, 10, and 17, Claims 1, 10, and 17 of ‘220 recite substantially similar steps of '423 Claims 1, 11, and 16.
Claims 1, 10, and 17 of ‘220 recite the limitations of:
generate a plurality of time series metrics based on one or more parameters that correspond to each one of a plurality of users;
perform feature selection based on characteristics of the plurality of users;
gather, according to the feature selection, a set of relevant scalar and vectorial data among scalar and vectorial data for each one of the plurality of users;
gather, according to the feature selection, a set of relevant time series metrics among the plurality of time series metrics for each one of the plurality of users based on one or more parameters over a time period;
calculate one or more aggregated metrics for each of a plurality of subperiods of the time period based on the set of relevant time series metrics, wherein each aggregated metric comprises at least one numeric value for a corresponding subperiod,
cluster the plurality of user to generate a plurality of user persona clusters based at least in part on the calculated one or more aggregated metrics for each user and each respective set of relevant scalar and vectorial data, wherein each of the plurality of user persona clusters includes a subset of the plurality of users,
calculate an overall score for each user persona cluster in the plurality of user persona clusters, wherein the overall score indicates separation of the plurality of user persona clusters;
in response to determining the overall score is greater than or equal to a predetermined threshold value:
generate a respective target label for each of the plurality of user persona clusters; and
apply each respective target label to the subset of users of each respective user persona cluster to generate training data for a supervised machine learning model,
and train the supervised machine learning model based on the training data; and in response to determining the overall score is less than the predetermined threshold value:
remove at least a portion of the calculated one or more aggregated metrics to reduce a maximum size of each of the plurality of user persona clusters;
generate a revised plurality of user persona clusters based at least in part on a remaining portion of the calculated one or more aggregated metrics for each user; and
recalculate the overall score for each user persona cluster in the plurality of user persona clusters.
Whereas Claims 1, 11, and 16 of ‘423 states:
receive one or more parameters that correspond to each one of a plurality of sellers,
generate a plurality of time series metrics based on the one or more parameters,
receive scalar and vectorial data for each one of the plurality of sellers, wherein the scalar and vectorial data for each seller includes one or more of the following information of the respective seller: geolocation, payment account information, or categories of items sold;
the processing system is configured to:
perform a feature selection based on characteristics of the plurality of sellers,
transmit the feature selection as a feedback back to the input system; the input system is further configured to:
receive the feedback of the feature selection from the processing system,
gather, according to the feature selection from the processing system, a set of relevant scalar and vectorial data among the scalar and vectorial data for each one of the plurality of sellers, and
gather, according to the feature selection from the processing system, a set of relevant time series metrics among the plurality of time series metrics for each one of the plurality of sellers based on the respective seller's one or more parameters over a time period, wherein the time period comprises a plurality of subperiods;
calculate one or more aggregated metrics for each corresponding subperiod based on each seller's respective set of relevant time series metrics, wherein each aggregated metric comprises at least one numeric value for the corresponding subperiod,
cluster the plurality of sellers using a Gaussian Mixture Model to generate a plurality of seller persona clusters based at least in part on the calculated one or more aggregated metrics for each seller and each seller's respective set of relevant scalar and vectorial data, wherein each of the plurality of seller persona clusters includes a group of sellers among the plurality of sellers,
calculate an overall score for all of the generated plurality of seller persona clusters, wherein the overall score indicates how well the seller persona clusters are separated, and
transmit the plurality of seller persona clusters to the output system when the overall score is greater than or equal to a predetermined threshold value;
receive the plurality of seller persona clusters from the processing system,
generate a respective target label for each of the plurality of seller persona clusters, and
apply each respective target label to the group of sellers within each respective seller persona cluster to generate training data for a supervised machine learning model, wherein the generated target labels comprise: bad seller, high volume seller, seasonal seller, high customer dispute seller, and fraudulent seller;
train the supervised machine learning model based on the training data, and
apply the trained supervised machine learning model to classify and detect sellers on a retail platform.
These are obvious variants of each other as both recite substantially the same limitations. Further, elimination of an element or its functions is deemed to be obvious in light of prior art teachings of at least the recited element or its functions (see In re Karlson, 136 USPQ 184, 186; 311 F2d 581 (CCPA 1963)), thereby rendering the elimination of any elements recited in the claims of the related patent (that are not recited in the instant claims) obvious.
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.
Alice - Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 10, and 17 recite limitations to generate a plurality of time series metrics based on one or more parameters that correspond to each one of a plurality of users (Collecting and Analyzing Information, an Observation and Evaluation, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), perform feature selection based on characteristics of the plurality of users (Collecting and Analyzing Information, an Observation and Evaluation, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), gather, according to the feature selection, a set of relevant scalar and vectorial data among scalar and vectorial data for each one of the plurality of users (Collecting Information, an Observation, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), gather, according to the feature selection, a set of relevant time series metrics among the plurality of time series metrics for each one of the plurality of users based on one or more parameters over a time period (Collecting Information, an Observation, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), calculate one or more aggregated metrics for each of a plurality of subperiods of the time period based on the set of relevant time series metrics, wherein each aggregated metric comprises at least one numeric value for a corresponding subperiod (Analyzing Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), cluster the plurality of user to generate a plurality of user persona clusters based at least in part on the calculated one or more aggregated metrics for each user and each respective set of relevant scalar and vectorial data, wherein each of the plurality of user persona clusters includes a subset of the plurality of users Analyzing Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), calculate an overall score for each user persona cluster in the plurality of user persona clusters, wherein the overall score indicates separation of the plurality of user persona clusters (Analyzing Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), in response to determining the overall score is greater than or equal to a predetermined threshold value: generate a respective target label for each of the plurality of user persona clusters; and apply each respective target label to the subset of users of each respective user persona cluster to generate training data for a supervised machine learning model (Transmitting and Analyzing Information, an Evaluation and Judgment, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), and train the supervised machine learning model based on the training data (Analyzing Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), and in response to determining the overall score is less than the predetermined threshold value: remove at least a portion of the calculated one or more aggregated metrics to reduce a maximum size of each of the plurality of user persona clusters ;generate a revised plurality of user persona clusters based at least in part on a remaining portion of the calculated one or more aggregated metrics for each user; and recalculate the overall score for each user persona cluster in the plurality of user persona clusters (Analyzing Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. clustering sellers for pattern recognition; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of a Fundamental Economic Process, i.e. Grouping sellers for pattern recognition, but for the recitation of generic computer components. That is, other than reciting a system, memory, processor, and medium, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of a Fundamental Economic Process. For example, generating a plurality of time series metrics based on one or more parameters that correspond to each one of a plurality of users encompasses a supervisor or manager looking at different parameters for competitors, such as if they are seasonal sellers, and coming up with metrics based on the time in which these parameters occur, which is an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for a Fundamental Economic Process, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The system, memory, processor, and medium are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmitting steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0033]In addition, the methods and system described herein can be at least partially embodied in the form of computer-implemented processes and apparatus for practicing those processes. The disclosed methods may also be at least partially embodied in the form of tangible, non-transitory machine-readable storage media encoded with computer program code. For example, the steps of the methods can be embodied in hardware, in executable instructions executed by a processor (e.g., software), or a combination of the two. The media may include, for example, RAMs, ROMs, CD-ROMs, DVD-ROMs, BD-ROMs, hard disk drives, flash memories, or any other non-transitory machine-readable storage medium. When the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the method. The methods may also be at least partially embodied in the form of a computer into which computer program code is loaded or executed, such that, the computer becomes a special purpose computer for practicing the methods. When implemented on a general-purpose processor, the computer program code segments configure the processor to create specific logic circuits. The methods may alternatively be at least partially embodied in application specific integrated circuits for performing the methods.”
Which shows that any generic computer can be used to perform the abstract limitations, such as a laptop, phone, desktop, etc., and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving and transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the system, processor, memory, etc., nor the receiving or transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-9, 11-16, and 18-20 contain the identified abstract ideas, further narrowing them, with no new additional elements and any being used being highly generic when considered as part of a practical application or under prong 2 of the Alice analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Allowable Subject Matter
Claims 1-20 have overcome the prior art and would be allowable if amended to overcome the 35 USC 101 rejection and any other rejections.
The closest prior art of record are Saias (U.S. Publication No. 2003/001,4379) in view of Wu (U.S. Publication No. 2011/000,4509) in further view of Bellala (U.S. Publication No. 2017/014,7930). Saias, an adaptive and reliable system and method for operations management, teaches a system comprising: a memory having instructions stored thereon; and a processor, to generate a plurality of time series metrics based on one or more parameters that correspond to each one of a plurality of users, perform feature selection based on characteristics of the plurality of users, gather, according to the feature selection, a set of relevant scalar and vectorial data among scalar and vectorial data for each one of the plurality of users, gather, according to the feature selection, a set of relevant time series metrics among the plurality of time series metrics for each one of the plurality of users based on one or more parameters over a time period, to calculate one or more aggregated metrics for each of a plurality of subperiods of the time period based on the set of relevant time series metrics, wherein each aggregated metric comprises at least one numeric value for a corresponding subperiod, in response to determining the overall score is greater than or equal to a predetermined threshold value generate a respective target label for each of the plurality of user persona groups, and to group the plurality of user to generate a plurality of user persona groups based at least in part on the calculated one or more aggregated metrics for each user and each respective set of relevant scalar and vectorial data, wherein each of the plurality of user persona clusters includes a subset of the plurality of users, it does not explicitly state an overall score being calculated. Psota, a transaction facilitating marketplace platform, teaches use of clustering for training and scoring of records, to calculate an overall score for each user persona cluster in the plurality of user persona clusters, wherein the overall score indicates separation of the plurality of user persona clusters, and to apply each respective target label to the subset of users of each respective user persona cluster to generate training data for a supervised machine learning model, but neither Saias nor Wu teaches a Gaussian Mixture Model. Bellala, a system and method for performance testing based on variable length segmentation and clustering of time series data, teaches the application of a gaussian mixture model, but it does not teach the explicit manner as to which the supervised machine learning is trained, and then in response to determining the score is below a threshold, performing more calculations by reducing the size of each of the user persona clusters. None of the above prior art explicitly teaches this explicit manner as to which the supervised machine learning is trained, and then in response to determining the score is below a threshold, performing more calculations by reducing the size of each of the user persona clusters, along with the other limitations state limitations, which Applicant points out on pgs. 3 and 4 of the remarks of 3/9/2026, and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1-20 are allowable over the prior art of record, and are objected to as provided above.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 3/24/2026