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 .
Detailed Action
This action is in response to the application filed on 3/13/2026.
Priority
No claim for priority has been made in this application.
Restriction
Newly submitted claim 22 directed to an invention that is independent or distinct from the invention originally claimed for the following reasons: claims 1-21 are computer systems & method obtaining a supervised machine-learned (ML) model trained to determine values of a service event trust parameter for respective service events, computer program, claims 22 is direct to first and second dataset.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, the above cited newly submitted claims are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
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.
All claims 1-9,11-18 and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are directed to a system, method, or product, which are/is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent method Claim 1 (herein called the Primary Independent Claim) as the claim that represents the claimed invention for analysis and is similar to independent system Claim 11 and product Claim 20 (herein called Additional Independent Claims). The Primary Independent Claim recites the limitations of:
A computer-implemented method comprising: obtaining, by one or more processors, a supervised machine-learned (ML) model trained to determine values of a service event trust parameter for respective service events, the supervised ML model being trained based on a labeled model training dataset including labels indicating values of the service event trust parameter for a first plurality of service events, the labeled model training dataset being arranged according to a first data structure; and the service event trust parameter being a parameter indicating a likelihood of fraud or abuse in a corresponding service event;
obtaining, by the one or more processors, an input dataset indicative of a second plurality of service events, the input dataset being arranged according to a second data structure different from the first data structure;
transforming, by the one or more processors, the input dataset into a transformed input dataset arranged according to the first data structure; and
wherein transforming the input dataset into the transformed input dataset includes rescaling values of one or more parameters from the input dataset such that distributions of the values of at least one of the one or more parameters align with distributions of corresponding values of at least one corresponding one or more parameters from the labeled model training dataset;
Outputting, by the one or more processors applying the supervised ML model to the transformed input dataset, a plurality of respective values of the service event trust parameter for the second plurality of service events.
in response to outputting the plurality of respective values of the service event trust parameter, associating, by the one or more processors, the plurality of respective values of the service event trust parameter with the transformed input dataset.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Certain Methods of Organizing Human Activity”. The limitation of at least “obtaining, transforming and determining the supervised ML model to the transformed input dataset, a plurality of respective values of the service event trust parameter for the second plurality of service events.” recites a fundamental economic practice. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The limitation of at least “processors and a supervised machine-learned (ML) model” in the Primary Independent Claim is just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. The Additional Independent Claims are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. The examiner did not find any additional elements that would cause further analysis. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, all the independent claims are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware and software per se amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more as well as MPEP 2106.05(d). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, all independent claims are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims further define the abstract idea that is present in their respective independent claims, and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, all the claims are not patent-eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9,11-18 and 20-22 listed below are rejected under 35 U.S.C. 103 as being unpatentable over Rajan (U.S. Patent Pub 20160246931) in view of Tal=Talvola; Erik (U.S. Patent 11636497) and Pan=Pandian; Manickkam (U.S. Patent 11625723)
Re claim 1 & 11 & 20: Rajan discloses:
A computer-implemented method comprising: obtaining, by one or more processors, a supervised machine-learned (ML) model trained to determine values of a service event trust parameter for respective service events, (See Rajan Fig 1-4 + para 0063)
the supervised ML model being trained based on a labeled model training dataset including labels indicating values of the service event trust parameter for a first plurality of service events, the labeled model training dataset being arranged according to a first data structure, the service event trust parameter being a parameter indicating a likelihood of fraud or abuse in a corresponding service event; (See Rajan Fig 1-4 + para 0063)
A computing system comprising: one or more processors; and one or more non-transitory memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: (See Rajan Fig 1-4 + para 0063)
One or more non-transitory computer-readable media storing processor- executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: (See Rajan Fig 1-4 + para 0063)
obtaining, by the one or more processors, an input dataset indicative of a second plurality of service events, the input dataset being arranged according to a second data structure different from the first data structure; (See Rajan Fig 3 item 302 + para 0063 + 0003-0005)
transforming, by the one or more processors, the input dataset into a transformed input dataset arranged according to the first data structure; and (See Rajan Fig 3 item 306 + para 0063 + 0032 + 0052-0053)
wherein transforming the input dataset into the transformed input dataset includes rescaling values of one or more parameters from the input dataset such that distributions of the values of at least one of the one or more parameters align with distributions of corresponding values of at least one corresponding one or more parameters from the labeled model training dataset; (See Rajan Fig 3 item 306 + para 0063 + 0032 + 0052-0053)
outputting, by the one or more processors applying the supervised ML model to the transformed input dataset, a plurality of respective values of the service event trust parameter for the second plurality of service events. (See Rajan Fig 3 item 308 + para0025 + 0063 + 0064)
in response to outputting the plurality of respective values of the service event trust parameter, associating, by the one or more processors, the plurality of respective values of the service event trust parameter with the transformed input dataset. (See Rajan Fig 3 item 308 + para0025 + 0063 + 0064)
Although Rajan does not have service provider, Tal and Pan claims “Service provider”
While examiner believes, Rajan teaches the features of applicant, should the limitations be argued and for the sake of compact prosecution additional reference, Tal and Pan additionally teaches the limitations of the applicant.
Therefore it would have been obvious to one of ordinary skill in the art at the effect filling date was made to modify Rajan by adapting any features of Tal and Pan.
Specifically the “service provider” see Tal figure 13 item 8337 providers and “arranged according to a second data structure different from the first data structure” see “ML model to the transformed input dataset” see Tal abstract + figure 12 item 104 and “respective values of the service event trust parameter” see Tal Figure 13 item 8337-8339 providers.
Specifically the “service provider” see Pan figure 1 item 110 and “arranged according to a second data structure different from the first data structure” see Pan Figure 1 item 160 & 170 and “respective values of the service event trust parameter” see pan Figure 7 item 710 merchant account.
It is clear that one would be motivated by the teaching in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. Specifically, both Rajan teaches 3 that is adapted in Tal and Pan system see Tal Fig 13 and Pan Figure 1.
Re claim 2 & 12: see claim 1 +
further comprising: training, by the one or more processors, the supervised ML model to determine the values of the service event trust parameter for service events, based on the labeled model training dataset indicating the values of the service event trust parameter for the first plurality of service events. (See Rajan Fig 3 item 306 + para 0063 + 0032 + 0052-0053)
Re claim 3 & 13: see claim 1 +
wherein training the supervised ML model comprises obtaining the labeled model training dataset by: obtaining a first dataset indicating the first plurality of service events associated with respective first entities; obtaining a second dataset indicating second entities associated with an indicator of distrust; comparing the first dataset to the second dataset to determine matches between the respective first entities and the second entities; and labeling the first plurality of service events with respective values of the service event trust parameter based on whether the respective first entities match the second entities, thereby producing the labeled model training dataset arranged according to the first data structure. (See Rajan Fig 3 item 306 + para 0063 + 0032 + 0052-0053)
Re claim 4 & 14: see claim 1 +
wherein the service event trust parameter indicates whether respective entities associated with the first plurality of service events are associated with at least one instance of fraud or abuse. (See Rajan Fig 3 item 306 + para 0063 + 0032 + 0052-0053)
Re claim 5 & 15: see claim 1 +
wherein a service event from among the second plurality of service events corresponds to a plurality of service actions by a service provider. (See Rajan Fig 3 item 308 + para0025 + 0063 + 0064 + see Tal figure 13 item 8337 providers)
Re claim 6 & 16: see claim 1 +
wherein the plurality of service actions by the service provider serve different respective ones of a plurality of service recipients. (See Rajan Fig 3 item 308 + para0025 + 0063 + 0064 + see Tal figure 13 item 8337 providers)
Re claim 7 & 17: see claim 1 +
wherein determining a trust parameter value from among the plurality of respective values of the service event trust parameter includes determining a type of potential fraud or abuse associated with a corresponding service event from among the second plurality of service events. (See Rajan Fig 3 item 308 + para0025 + 0063 + 0064 + see Tal figure 13 item 8337 providers)
Re claim 8 & 18: see claim 1 +
wherein transforming the input dataset includes removing one or more fields for each of the respective service events from among the second plurality of service events. (See Rajan Fig 3 item 306 + para 0063 + 0032 + 0052-0053 + see Tal Figure 13 item 8337-8339 providers)
Re claim 9 & 19: see claim 1 +
wherein transforming the input dataset includes merging two or more service events from among the second plurality of service events. (See Rajan Fig 3 item 306 + para 0063 + 0032 + 0052-0053 + see Tal Figure 13 item 8337-8339 providers)
Re claim 21: see claim 1 +
wherein the instructions, when executed by the one or more processors, further cause the one or more processors to perform operations comprising: training the supervised ML model to determine the values of the service event trust parameter for service events, based on the labeled model training dataset indicating the values of the service event trust parameter for the first plurality of service events. (See Rajan Fig 3 item 306 + para 0063 + 0032 + 0052-0053)
Response to Arguments
Applicant’s arguments have been fully considered and are not persuasive. Answers to the arguments on the amended limitations which change the scope of the claims, will be addressed in the action above. Applicant's art arguments are considered moot due to new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Grisby et al., U.S. Patent Pub 2025/0004732, discloses systems and techniques for optimizing attribute accesses include receiving a first data structure, the first data structure including a first sequence of statements representing programming functions having an input and an output.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kirsten Apple whose telephone number is (571)272-5588. The examiner can normally be reached on 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, Michael Anderson can be reached on (571) 270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KIRSTEN S APPLE/Primary Examiner, Art Unit 3693
Prosecution Insights