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, 3, 6-11, 13, and 16-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination (RCE) 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 RCE submission filed on 7/3/26, with claims corresponding to 6/4/26, has been entered.
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, 3, 6-11, 13, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claims 1 and 20 are to a method (i.e., a process), claim 11 to a system (i.e., a machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1 and 11 is:
determining coefficients for a plurality of risk factors for a legal entity,
wherein said coefficients are updated by submitting previously recorded combinations of coefficients and risk scores to a model and retrieving updated coefficients, wherein said model is trained by operations comprising:
receiving training datasets comprising training coefficients, training risk factors and training trust indicators; and
[determining] said training coefficients from said training trust indicators and said training risk factors;
wherein said risk factors indicate risks associated with one or more of: a transaction said legal entity is taking part in, and a transaction type, and
wherein said coefficients determine a relative impact of each of said plurality of risk factors in the calculation of a risk score for said legal entity;
calculating said risk score from said coefficients and said plurality of risk factors;
assessing data incompleteness for values of said plurality of risk factors and calculating a data incompleteness score, wherein the data incompleteness score is calculated using cardinalities of risk factors whose values are missing;
generating a trust indicator for said legal entity from said risk score and data incompleteness score; and
based on said trust indicator, controlling whether said legal entity is permitted to execute a transaction, wherein when said trust indicator is less than a threshold value, blocking said legal entity from executing said transaction.
The abstract idea of claim 20 is:
determining weights for a plurality of risk factors for a corporate body,
wherein said weights are updated by submitting previously recorded combinations of weights and risk scores to a model and retrieving updated weights, wherein said model is trained by operations comprising:
receiving training datasets comprising training weights, training risk factors and training trust indicators; and
[determining] said training weights from said training trust indicators and said training risk factors;
wherein said risk factors indicate risks associated with one or more of: a transaction said corporate body is taking part in, and a transaction type, and
wherein said weights determine a relative impact of each of said plurality of risk factors in the calculation of a risk score for said corporate body;
calculating said risk score from said weights and said plurality of risk factors;
identifying data completeness for values of said plurality of risk factors and calculating a data completeness score, wherein the data completeness score is calculated using cardinalities of risk factors whose values are missing;
generating a trust indicator for said corporate body from said risk score and data completeness score; and
based on said trust indicator, controlling whether said corporate body is permitted to execute a transaction, wherein when said trust indicator is less than a threshold value, blocking said corporate body from executing said transaction.
The abstract idea steps italicized above describe a business relation that pertains to determining the trust score of a corporate body and controlling whether said corporate body is permitted to execute a transaction, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by [0003] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions that pertain to the trust score of a corporate body and controlling whether said corporate body is permitted to execute a transaction, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0003] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the steps that pertain to determining the trust score of a corporate body and controlling whether said corporate body is permitted to execute a transaction (i.e., mitigating risk), which constitutes a process that, under its broadest reasonable interpretation, covers fundamental economic principles or practices. This is further supported by [0003] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers limitations relating to hedging, insurance, and/or mitigating risk, then it falls within the Certain Methods of Organizing Human Activity – Fundamental Economic Principles or Practices grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
Claims 1 and 20 recite the following additional elements: machine learning [ML]; processor; training, by the processor, said ML model using said training datasets.
Claim 11 recites the following additional elements: computing device; memory; processor; machine learning [ML]; training said ML model using said training datasets.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0044] of applicant’s specification as filed.
Regarding the machine learning and training features, MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(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); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
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(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "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. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, the machine learning and training features are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system, applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f), they do not integrate the abstract idea into a practical application.
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well.
Dependent claims 3, 6-10, 13, and 16-19 narrow the abstract idea(s) with additional steps and/or information. This narrowing of the abstract idea does not integrate into practical application and/or add significantly more. (Additionally, claims 6-10 and 16-19 could be considered under the Mathematical Concepts grouping of abstract ideas. This modification of the abstract idea grouping does not integrate into practical application and/or add significantly more.)
Accordingly, claims 1, 3, 6-11, 13, and 16-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's RCE arguments filed 7/3/26 contain no additional arguments. For the purposes of compact prosecution, examiner will offer additional comments to applicant’s after final response filed 6/4/26. Applicant’s headings are used for consistency.
35 U.S.C. § 101 Rejections
After summarizing remarks and court decisions argued in the response, applicant offers, regarding Step 2A Prong One:
The claims as amended are not directed to an abstract idea.
Applicant respectfully submits that amended independent claim 1 is not directed to an abstract idea, as it recites "based on said trust indicator, controlling whether said legal entity is permitted to execute a transaction, wherein when said trust indicator is less than a threshold value, blocking said legal entity from executing said transaction." This limitation transforms the claims from mere data processing into a specific technological solution for transaction control. As disclosed in the specification, the present invention provides "a more adaptive risk assessment framework responsive to developments in the conduction of financial crime compared to existing methodologies that depend on predefined rules and thresholds." As-Filed Specification, paragraph [0007]. This represents a technical improvement over prior systems, not merely an abstract concept.
The claims are analogous to those found patent eligible in Enfish, LLC v. Microsoft Corp., where the Federal Circuit held that claims directed to a specific improvement in computer functionality are not abstract. Here, the claims recite a specific technical approach: training an ML model using training datasets comprising training coefficients, training risk factors, and training trust indicators, then using the trained model to update coefficients that determine relative impact of risk factors. Unlike claims that merely collect and analyze information, amended claim 1 recites specific technical steps including "assessing data incompleteness for values of said plurality of risk factors and calculating a data incompleteness score, wherein the data incompleteness score is calculated using cardinalities of risk factors whose values are missing." This specific technical methodology for handling incomplete data cannot practically be performed in the human mind.
The Examiner has alleged that these steps could be performed "mentally, including with pen and paper." Office Action, page 5. Applicant respectfully submits that this characterization is incorrect. The combination of training an ML model, updating coefficients through the trained model, calculating data incompleteness scores using cardinalities, and controlling transaction execution based on the generated trust indicator requires computer implementation and cannot be practically performed mentally.
Further, similar to USPTO Example 39 (Method for Training a Neural Network for Facial Detection), the claims here recite a specific process for training an ML model and applying the trained model to achieve a particular result, which is not an abstract idea.
Accordingly, the Applicant respectfully submits that amended independent claim 1 does not recite an abstract idea under Prong One of Step 2A.
Examiner first notes that the “directed to” inquiry is performed at Step 2A Prong Two, not Step 2A Prong One, which seeks to answer the question: do the claims recite an abstract idea? Examiner maintains that 1) an abstract idea is recited; 2) the additional elements of the claims do not integrate said abstract idea into practical or add significantly more.
Examiner’s position is that the amended claim language – i.e., based on said trust indicator, controlling whether said legal entity is permitted to execute a transaction, wherein when said trust indicator is less than a threshold value, blocking said legal entity from executing said transaction – is still part of the abstract idea. Simply “controlling” a transaction, without any recitation of the underlying technology, is an abstract step an administrator could perform and considered part of the Certain Method of Organizing Human Activity (e.g., a Commercial Interaction).
The additional elements, which are considered at Step 2A Prong Two, do not appear to demonstrate an improvement to technology in a manner akin to Enfish or Example 39. Specifically, any recitation of machine learning is done at a high level of generality, without technical specificity. Applicant’s specification does not provide any sort of detail regarding the machine learning mechanism or training steps; instead, applicant has taken off-the-shelf machine learning tools and/or packages and used them to facilitate the tasks of the abstract idea. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(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); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
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(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "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. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, the machine learning and training features are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Examiner therefore maintains that the claim is directed to an abstract idea, contrary to applicant’s assertion.
Applicant continues, regarding Step 2A Prong Two:
Notwithstanding the above, even assuming arguendo that the claims recite a judicial exception, the amended claims integrate any such exception into a practical application under the 2019 Revised Patent Subject Matter Eligibility Guidance.
Amended claim 1 now recites "based on said trust indicator, automatically controlling whether said legal entity is permitted to execute a transaction, wherein when said trust indicator is less than a threshold value, blocking said legal entity from executing said transaction." This limitation applies the trust indicator to effect a real-world consequence - controlling whether a transaction is permitted or blocked.
This is analogous to the claims found eligible in DDR Holdings, LLC v. Hotels.com, where the Federal Circuit found that claims providing a technical solution to a problem specifically arising in computer networks integrated the abstract idea into a practical application. Here, the claims provide a technical solution for controlling financial transactions based on computed trust indicators. Under the 2019 PEG, a claim integrates a judicial exception into a practical application when it effects a transformation or reduction of a particular article to a different state or thing. The amended claims transform the state of a transaction from "pending" to either "blocked" or "permitted" based on the trust indicator threshold comparison.
The specification discloses that "when the trust indicator is < than a threshold value, blocking the legal entity associated with the trust indicator from executing a transaction" and "when the trust indicator is >= than a threshold value, permitting the legal entity associated with the trust indicator to execute a transaction." As-Filed Specification, paragraphs [0010]-[0011]. This demonstrates a concrete, tangible application of the trust indicator. The claims are similar to USPTO Example 42 (Method for Transmission of Notifications When Medical Records Are Updated), which was found eligible because it applied the result of data analysis to effect a real- world action. Here, the trust indicator is applied to control transaction execution.
Amended claim 11 recites a system comprising "a computing device; a memory; and a processor" configured to perform the claimed operations including "control whether said legal entity is permitted to execute a transaction." This is not merely generic computer implementation but rather a specific technical system that controls real-world transactions. The Exparte Desjardins (Appeal No. 2024-000567 (PTAB Decision Sept. 26, 2025)) decision from the Patent Trial and Appeal Board supports eligibility where claims recite specific technical implementations that go beyond merely automating mental processes. The amended claims here recite specific ML training operations and transaction control that constitute such technical implementations.
Accordingly, even assuming arguendo that previously presented independent claim 1 recites a judicial exception, the Applicant respectfully submits that amended independent claim 1 integrates such exception into a practical application under Prong Two of Step 2A.
As noted above, examiner’s position is that the amended claim language – i.e., based on said trust indicator, controlling whether said legal entity is permitted to execute a transaction, wherein when said trust indicator is less than a threshold value, blocking said legal entity from executing said transaction – is still part of the abstract idea. Simply “controlling” a transaction, without any recitation of the underlying technology, is an abstract step an administrator could perform and considered part of the Certain Method of Organizing Human Activity (e.g., a Commercial Interaction).
Applicant’s citation to the specification is not especially helpful, as an administrator could still “block” or “approve” a transactions in accordance with certain criteria (e.g., a trust indicator being above or below a threshold value).
Examiner maintains that any recitation of the associated computing elements and machine learning are done at a high level of generality, without technical specificity. Applicant’s specification does not provide any sort of detail regarding the machine learning mechanism or training steps, contrary to applicant’s assertions; instead, applicant has taken off-the-shelf machine learning tools and/or packages and used them to facilitate the tasks of the abstract idea.
MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner. Applicant’s additional elements – and the resulting improvement to technology – are not comparable to Enfish, the PTO eligibility examples that demonstrate eligibility at Step 2A Prong Two, or the Desjardins decision, which bolsters eligibility for improvements to machine learning, not their routine application.
Applicant continues, regarding Step 2B:
Further, and again assuming arguendo that further analysis under Step 2B is required, the Applicant respectfully submits that amended independent claim 1 recites additional elements that amount to significantly more than any alleged abstract idea.
The claims recite that "the coefficients are updated by submitting previously recorded combinations of coefficients and risk scores to a machine learning (ML) model and retrieving updated coefficients." As-Filed Specification, paragraph [0012]. This is not merely "applying" machine learning as a generic tool, but rather recites a specific iterative process for coefficient optimization using historical data.
The ML model is trained by specific operations including "receiving, by a processor, training datasets including training coefficients, training risk factors and training trust indicators; and training, by the processor, the ML model using the training datasets to determine the training coefficients from the training trust indicators and the training risk." As-Filed Specification, paragraph [0013]. This recites a specific training methodology, not merely invoking ML as a black box.
Under Berkheimer v. HP Inc., whether additional elements are well-understood, routine, and conventional is a factual determination. The Office Action has not provided any evidence that the specific combination of: (1) training an ML model with training datasets comprising training coefficients, training risk factors, and training trust indicators; (2) calculating data incompleteness scores using cardinalities of missing risk factor values; and (3) automatically blocking transactions based on trust indicator thresholds, is well-understood, routine, or conventional. In fact, the Office Action does not establish, with evidentiary support, that the claimed ordered combination is well- understood, routine, and conventional.
Applicant respectfully submits that the ordered combination of elements in amended claim 1 provides significantly more than an abstract idea. The claims do not merely recite generic computer components performing generic functions, but rather recite a specific technical process for generating trust indicators and controlling transactions based on those indicators. The specification discloses that embodiments provide "automatically generating a trust indicator for assessing a legal entity that combines risk factors and assesses the quality of data present in the risk factors by determining a data incompleteness score" which "may more accurately determine a trust indictor for a legal entity." As-Filed Specification, paragraph [0005]. This technical improvement provides significantly more than the alleged abstract idea.
Similar to USPTO Example 37 (Relational Database), which was found to recite significantly more because it provided a specific technical improvement, the amended claims here provide a specific technical improvement in transaction risk assessment through the combination of ML-optimized coefficients, data incompleteness scoring, and automatic transaction control.
Accordingly, when considered as an ordered combination, the elements of amended independent claim 1 amount to significantly more than a judicial exception, thereby satisfying Step 2B of the eligibility analysis.
Examiner first notes that the terms “well-understood, routine, or conventional” were not used in the eligibility analysis. Instead, the Step 2A Prong Two conclusion, where it was determined that the additional elements are generic computing components and claimed in a results-oriented manner, was “carried over,” per MPEP 2106.
Similar to Step 2A Prong Two, at Step 2B it was determined that the machine learning and training features are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f). In particular, applicant’s “training” step includes no technical detail, other than describing the informational details of the training set.
The improvements that are detailed in the remarks and the claims, while being relevant to the abstract idea, do not describe an improvement to technology. Applicant may potentially arrive at novel abstract idea that includes calculating data incompleteness scores using cardinalities of missing risk factor values and automatically blocking transactions based on trust indicator thresholds; however, per MPEP 2106, an improved abstract idea is still abstract.
Accordingly, examiner maintains the rejections under 35 U.S.C. § 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Machine Learning Based Trust Computational Model for IoT Services” (NPL attached), which teaches: Therefore, in this paper, a quantifiable trust assessment model is proposed. Built on this model, individual trust attributes are then calculated numerically. Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making. Finally, our model's effectiveness is verified through a simulation. The results show that our method has advantages over other aggregation methods.
US 20200045064, which teaches: Embodiments described include a computing device for generating risk scores of network entities. The computing device can include one or more processors configured to detect a plurality of risk indicators. Each of the risk indicators identify one of a plurality of activities of a network entity of an organization. The network entity includes a device, an application or a user in the organization's network. The one or more processors can generate a risk score of the network entity, by combining a risk value, an amplification factor and a dampening factor of each of the plurality of risk indicators, and adding an adjustment value for the plurality of risk indicators. The one or more processors can determine, using the generated risk score, a normalized risk score of the network entity. The one or more processors can initiate an action according to the normalized risk score.
US 20220358516, which teaches: An automated system for detecting risky entity behavior using an efficient frequent behavior-sorted list is disclosed. From these lists, fingerprints and distance measures can be constructed to enable comparison to known risky entities. The lists also facilitate efficient linking of entities to each other, such that risk information propagates through entity associations. These behavior sorted lists, in combination with other profiling techniques, which efficiently summarize information about the entity within a data store, can be used to create threat scores. These threat scores may be applied within the context of anti-money laundering (AML) and retail banking fraud detection systems. A particular instantiation of these scores elaborated here is the AML Threat Score, which is trained to identify behavior for a banking customer that is suspicious and indicates high likelihood of money laundering activity.
US 20230046601, which teaches: A method can be used to predict risk using machine learning models having efficient feature learning. A risk prediction model can be applied to time-series data associated with a target entity to generate a risk indicator. The risk prediction model can include a feature learning model for generating features from the time-series data. The risk prediction model can also include a risk classification model for generating the risk indicator. The feature learning model can include filters and can be trained. Parameters of the risk prediction model can be adjusted to minimize a loss function associated with risk indicators. An updated risk prediction model can be generated by removing a filter from an original set of filters based on influencing scores of the original filters. The risk indicator can be transmitted to a computing device for use in controlling access of the target entity to a computing environment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
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