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 .
The following is a Final Office Action in response to communications received May 06, 2026. Claim(s) 9 has been canceled. Claims 1-7, 10-14 and 16-19 have been amended. No new claims have been added. Therefore, claims 1-8 and 10-20 are pending and addressed below.
Priority
Application No. 18/234,240 filed 08/15/2023 is a Continuation of 17582905 , filed 01/24/2022, now abandoned 17582905 is a Continuation of 16365145 , filed 03/26/2019.
Applicant Name/Assignee: American Express Travel Related Services Company Inc.
Inventor(s): Markikar Upendra et al
Response to Arguments/Amendments
Claim Rejections - 35 USC § 112
Applicant’s amendments in response to the 112(a) rejection set forth in the previous Office Action for failing to comply with written description requirement is sufficient to overcome the 112(a) rejection of claims 1-8 and 10-20. The examiner withdraws the 112(a) rejection of claims 1-9 and 11-20.
Claim Rejections - 35 USC § 101
Applicant's arguments filed May 06, 2026 have been fully considered but they are not persuasive.
In the remarks applicant argues that in view of the amendments that claimed subject matter is patent eligible. Specifically, applicant points to 2B by reciting significantly more than any alleged abstract idea. Applicant points to MPEP 2106.05 and Ex parte Yi (2025). Applicant argues the additional elements individually and in combination constitute an inventive concept that is not well understood conventional application of technology or insignificant extra solution activity implemented at a high level. Specifically, applicant points to the limitation “generating by the …server before completion of the pending interaction session, an interaction-control signal that includes the …score”, “aggregates, by the …server, transaction context…inputs comprising…data, …records, , wherein the aggregating is performed during pending interaction session”. Applicant argues the “generating” and “aggregating” constitutes a technical process that is technically specific and unique providing significantly more than high-level application of technology and therefore, patent eligible under 2B. Applicants’ argument is not persuasive. The limitation “generating….signal that includes the …score” is high level without details of technical implementation. The specification describes applying “ analysis machine” that “leverages input data …to generate …risk assessment and metric (score), which quantifies the level and type of risk (para 0039, para 0040) lacking any technical disclosure. Instead the limitations and specification recite high level operations with an expected result. The limitation “aggregating, by the …server, transaction-context-specific…inputs comprising …text data…historical interaction records”. The common definition of the term “aggregate” is to combine. The specification discloses “collect as much data about users…then to aggregate that data with other data collected…typically this aggregation is performed by aggregation services that receive …data from multiple sources, combine it with other data….” (para 0008). The specification further describes “applying machine learning…to data aggregation process…”(para 0009) and “sharing collected…information …the data aggregation services…(para 0058) and “aggregation services are…collecting, analyzing and combining inputs from a variety of sources (para 0061). It is clear that both the specification and limitations lack technical disclosure as to how the aggregation of data is performed as a technical process. When considered as a combination the “generating …score” in combination with the “aggregating” data does not provide an inventive concept or provide significantly more than when considered individually. The rejection is maintained.
In the remarks applicant argues that the “transaction …inputs” are technical signal constructs analogous to “user engagement signals” found in Ex parte Yi. Applicant argues the amended limitations recitation of inputs comprising…text data…” and “historical interaction record” that are “aggregated” during pending interaction session and then processed using ML model to compute and adjust trust score is not abstract or generic data collection. Applicant argues the analogous eligibility in light Yi is the recited generating of multiple types of “user engagement signal” including click-based browsing signals, post-click dwell time signals with content dependent thresholds and reformulation based negative signals. The board in consideration of Yi found the examiner did not provide evidence that the signal generation steps of Yi are insignificant. Applicant’s argument is not persuasive. The specification or claim limitation do not provide any signal generation steps unlike Yi. The limitations recite an interaction control signal that includes a score and a transmission of the interaction request based on the …control signal satisfying a predefined condition…. The specification also is silent with respect to step or technical processes for generating signals. According to MPEP 2106.05 (g) and/(h), the courts have recognized, or those of ordinary skill in the art would recognize, as elements that describe well‐understood, routine activities when they are claimed in a merely generic manner (e.g. at a high level of generality). Computer functions recognized as insignificant extra solution activity or well understood include “receiving or transmitting data over a network… “. The claimed signals unlike as found in Yi are high level and generic with an expected result.
In the remarks applicant argues that according to USPTO 101 guidelines under 2B, analysis, factual support must be provided for the determination of generic well understood computer elements and processes. Applicant points to Yi where the courts confirmed that when rejections fail to provide evidence as required, the rejection is invalid. The examiner respectfully disagrees with the premise of applicant’s argument. The previous Office action explicitly recognizes the additional elements recited in the claim beyond the abstract idea including “a merchant device”, “user device”, “computer based system”, distributive ledger” , “peer to peer network” and “predictive model”. The claimed computer system to perform the operation “receiving” data. The previous Office action found that the limitations “determining …information”, “retrieving …record”, “inputting information” “ applies weight to keywork” , “determine trust score…”, “outputs …score” and “sending…score” is not tied to technology and therefore does not provide any technical process. The examiner notices that applicant does not rebut this or discuss this. The applicant does not address the guidance by Electric Power Group which held that applying technology to collect data, analyze data and output the result to be conventional application of technology. The rejection is maintained.
In the remarks applicant argues that the ordered combination of the additional elements supplies an inventive concept beyond generic analysis and presentation of information. Applicant argues that the claimed combination provides concrete technical solution with the retrieval of verified identity information from ledger/database, aggregates transaction signals (data), executes ML model to parse data, apply weights and computes and adjust scores and generate and transmits signals to prevent completion of interaction when risk condition is met, arguing that the limitations are not a sequence of receive, analyze and display. This is because the limitations describe technical pipeline with inputs, ML model transformation and control results affecting whether a transaction completion messages is transmitted. Applicant’s argument is not persuasive. The combination of the providing information that is retrieved/inputted, analyzed using a model that generates scores and outputs/transmits messages. This is explicitly analogous to Electric Power Group analysis and determination of conventional application of technology. The rejection is maintained.
Claim Rejections - 35 USC § 103
Applicant's arguments filed May 06, 2026 have been fully considered but they are not persuasive.
In the remarks applicant argues that the prior art references fail to teach “aggregating, by the trust score server, transaction context specific reputation inputs comprising (i) reputational text data associated with the user at one or more third party network services and (ii) historical interaction records associated with the user device, wherein the aggregating is performed in real time during the pending interaction session”
In the remarks applicant argues the prior art references fail to teach “generating, by the trust-score server before completion of the pending interaction session, an interaction-control signal that includes the adjusted trust score”, the examiner respectfully disagrees. The prior art McCown teaches in at least Col 3 lines 5-11 wherein the prior art teaches the purpose of the reputation scores is to verify transaction and show whether user is trustworthy in advance of any interactions; Col 7 lines 19-36 wherein the prior art teaches the creation of a reputation score and use reputation of other identities to boost reputations resulting in updated identity score, Col 8 lines 5-38 wherein the prior art teaches calculating reputation score; Col 11 lines 45-53, Col 13 lines 22-27 wherein the prior art teaches calculated reputation score based on legal identity when determining reputation score in digital personal identity processing, lines 32-67 wherein the prior art teaching generating initial reputation score and when the personal identity undertakes activities that will result in updated reputation score and stored in blockchain ensuring to any requester the level of trust; Col 15 lines 55-Col 16 lines 55-Col 17 lines 1-5 wherein the prior art teaches applying prediction score to predict likelihood of behavior before it happens; Col 17 lines 50-Col 18 lines 1-60)
In the remarks applicant argues that the prior art references fail to teach “temporal aspect of real-time, session-specific processing and the functional aspect of network-level interaction control”, the examiner respectfully disagrees with the premise of applicant’s argument. Applicant is arguing a limitation not claimed. The claim limitation recites the wherein clause further limiting the aggregating step “wherein the aggregating is performed in real time during the pending interaction session”. The prior art reference teaches Col 15 lines 5-10, wherein the prior art teaches the data collected and aggregated and teaches Col 19 lines 53-58 wherein the prior art teaches the reputation system provides to multiple parties realtime results providing the benefit of instant information change if necessary. The rejection is maintained.
In the remarks applicant argues that combination of the prior art references applies hindsight and therefore is improper. Applicant’s argument is not persuasive. Applicant has not explained the error in the reason to combine the prior art references. The prior art Crane provides motivation of applying categories to keywords where the categories are given different weights, whereas McCown teaches classifying behavior and applying coefficients to those the behavior class for use in calculating the score. Applicant has not explained how such motivation is hindsight. The combination of McCown and Villa provides the motivation oar applying a risk score in order to determine whether to terminate a transaction process. Applicant has not explained how this motivation is hindsight. The combination of McCown and Butler provides the motivation of applying pair key technology written to blockchain in order to enable parties to locate and retrieve data without compromising sensitive data. Applicant has not explained how this motivation is hindsight. Applicant’s argument is not persuasive, the rejection is maintained.
Claim Interpretation
The specification does not provide a definition or description of the term “distributed user-identity data”, however the limitations do recite the data is from “distributed ledger”. Accordingly the examiner is interpreting “distributed user identity data” to be data stored in distributed ledgers.
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 1-8 and 10-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
In reference to Claims 1-8:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 1 and the dependent claims. Such methods fall under the statutory category of "process." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. Method claim 1 recites a method steps (1) receiving interaction data and a request likelihood for user to complete interaction (2) retrieving data (3) aggregating …inputs (4) executing ML model to parse and extract data (5) apply weight to a keyword of reputational data (6) compute trust score (7) adjust trust score (8) generating signal that includes trust score (9) controlling transmission of interaction request by inhibiting completion of pending interaction session based on satisfying predefined risk condition where inhibiting is preventing interaction based on adjusted trust score. The wherein clause does not remove the limitations from mental processes as the wherein clause merely determines when the data is analyzed and the results outputted.
The claimed limitations which under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic algorithm, merchant device, user device and computer. The claimed physical and/or computer elements (computer based system, user device and predictive model) are generic computer components and tools to perform the mental processes. The computer components are recited at a high level of generality and merely automates functions that could reasonable be performed using mental concepts, therefore acting as a generic computer to perform the abstract idea.
That is, other than reciting the operations being performed “by a computer based system” and “a predictive model”, nothing in the claim limitations precludes the recited steps from practically being performed in the mind. The steps recite steps that can easily be performed in the human mind as mental processes because the steps of obtain data which mimics mental processes of observation. The steps (1) receiving request data and (2) retrieving data mimics mental processes of observation. The steps (3) aggregating data collected mimics the human mind combining data in memory (4) parsing to extracting data -mimics mental process of reading and understanding data for use in calculation (5) applies weight to a keyword of reputational data -mimics mental process of analyze data by noting which data should be considered according to weights (6) computes trust score (7) adjust trust score- mimics mental processes of analyzing data for use and then observing, remembering data which is analyzed where specific data information is given more importance and calculating a trust score that is adjusted – mimics mental process of using the mental process of analyzing data where certain data is give more importance and the data as a whole is given a measure, which is adjusted based on giving different importance to data analyzed. The limitation (9) “inhibiting completion of transaction based on conditions not met by preventing …interaction “ mimics mental concept of decision to not perform an action. Therefore, the limitations, mimic human thought processes of observation, evaluation and opinion, and communication of result which, where the data interpretation is perceptible only in the human mind. See In re TLI Commc'ns LLC Patent Litig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. latric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)
The Specification is titled “Dynamic Trust Score”, and discusses in the background addressing the negative consequences for merchants when establishing a relationship/transaction with a consumer (see background). The specification disclose that to address the problem may receive a trust score request that has been calculated for a transaction based on the transaction data, merchant data, third party data and the user data to be utilized by the merchant to authorize the transaction.(spec para 0003). Here, it is clear from the Specification (including the claim language) that claim 1 focuses on an abstract idea, and not on an improvement to technology and/or a technical field. The Specification describes that it also is challenging for merchants to determine whether a client is worthy for a transaction process. Accordingly, when considered as a whole and in light of the specification, the claimed subject matter is directed toward receiving, analyzing user data in order to determine a score representing the level of trust for facilitating authorization of interactions. Such concepts can be found in the abstract category of analyzing business behaviors. Such concepts can be found in the abstract category of commercial behaviors. These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category of mental processes and methods of organizing human activity.
STEP 2A Prong 2: The identified judicial exception is not integrated into a practical application because the claims fail to provide indications of patent eligible subject matter that integrate the alleged abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include server over a network and machine learning model.
The additional element “server” applied to perform the method step operations “receiving…interaction request data” and “retrieving…data ”. According to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) the courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) where technology is merely applied to perform the abstract idea or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93
The claim limitations (receiving and retrieving) are recited at a high level of generality without details of technical implementation and thus are insignificant extra solution activity.
The additional element “server” is applied to perform at a high level the operations “aggregating” data, “executing” model, “generating” signal that includes score and controlling network transmission request by inhibiting transaction completion based on satisfying conditions. The limitations are not directed toward technology but rather the analysis of data collected for scoring risk and applying the risk determination as a condition for completing a transaction.
The additional element “machine learning model” to perform at a high level the operation “parse and extract” data for analysis, “apply a weight to a keyword”, “compute a trust score”, “adjust score” based on human behavior.
The server and model operations are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic system processor. The steps performed by the additional elements is generally used to apply the abstract idea. The ML model only recites outcomes of “parse” and “extract” data; “apply weight”, “computes trust score”, “adjust trust score” without any details about how the outcomes are accomplished. Taking the claim elements separately, the operation performed by the system processor at each step of the process is purely in terms of results desired and devoid of implementation of details. Technology is not integral to the process as the claimed subject matter is so high level that any generic programming could be applied and the functions could be performed by any known means. Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application).
When the claims are taken as a whole, as an ordered combination, the combination of limitations (1)-(2) and (3) are directed toward collecting and aggregating transactional data without technical details on how the data is collected and aggregated as a technical operation related to improving technology, providing solution to technology or applying technology in a manner that imposes meaningful limits upon the judicial exception as performed by the server. The combination of limitations (1)-(3) and (4)-(7) are directed toward applying a machine learning model as a tool at a high level to parse, extract and weight data collected and aggregated in limitations 1-3 for use in analyzing risk to compute and adjust a transaction risk score- mere data analysis for risk mitigation. The combination of limitations (1)-(7) and (8)-(9) is directed toward controlling a transmission request based on risk score computed in steps (1)-(7) to prevent transmission based on risk score analysis. The combinations of parts is not directed toward any technical process or technological technique or technological solution to a problem rooted in technology or improvement to technology.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. This is because the claimed subject matter fails to provide additional elements or combination or elements to apply or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
The functions recited in the claims recite the concept of inputting collected data, weighting data that is analyzed to determine a risk/trust score that is outputted and sent, which is a process directed toward a business practice. The claim provides no technical details regarding how the “embedding” operation is performed. Instead, similar to the claims at issue in Intellectual Ventures I LLC v. Capital One Financial Corp., 850 F.3d 1332 (Fed. Cir. 2017), “the claim language . . . provides only a result-oriented solution with insufficient detail for how a computer accomplishes it. Our law demands more.” Intellectual Ventures, 850 F.3d at 1342 (citing Elec. Power Grp. LLC v Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016)).
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how computers carry out one of their basic functions. The integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using an application. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments apply what generic computer functionality in the related arts. The steps are still a combination made to calculate a trust score and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes that goes beyond merely confining the abstract idea in a particular technological environment, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include server over a network and machine learning model.
The additional elements recited in the claim beyond the abstract idea include a server to perform the operation of receiving data. The limitations “receiving” data, “retrieving” data, “aggregating” data, “execute” machine learning model and “generating” signal including score computed by ML model which are conventional rudimentary generic computer operation high level operations applied to collect, analyze and output result for risk mitigation. The additional element “machine learning model” applied to “parse” and “extract” data, “apply weights” to data for analysis which is used to “compute” and “adjust” risk scores for use in completion of a transaction according to risk measurement which is not an unconventional user of machine learning technology or operations. Taking the claim elements separately, the function performed by the server and machine learning model at each step of the process is purely conventional performing ordinary operations of server and model technology. The wherein clause is not directed toward technology itself but rather directed toward the transaction process sequence.
When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these computer functions are generic, routine, conventional computer activities that are performed only for their conventional uses. See Elec. Power Grp. v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Also see In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011) Absent a possible narrower construction of the terms “receiving”, “retrieving”, “aggregating”, “executing …model”, “generate trust score” and “controlling transmission request” of the server and the terms “parse” data, “apply weights” to data, “compute” scores, “adjust scores” performed by the model ... are functions can be achieved by any general purpose computer without special programming. None of these activities are used in some unconventional manner nor do any produce some unexpected result. In short, each step does no more than require a generic computer to perform generic computer functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
Electric Power Group which held that applying technology to gather data, analyze data and output the result.
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Similar to Electric Power Group, the claimed limitations do not go beyond requiring the collection, analysis, and display/output of available/result information in a particular field, stating those functions in general terms, without limiting them to technical means for performing the functions that are arguably an advance over conventional computer and network technology.
The specification nominally mentions the server and its use
[0044] The RAE functionality may be presented in many different embodiments, including but not limited to the following:
1. In one embodiment, the above solution is used for users interacting with systems (e.g., web browser to web server).
2. In another embodiment, the above solution is used for systems interacting with other
systems (e.g., IoT device to server, server to server, IoT to IoT, etc.)…
The specification make clear that any of a laundry list of learning algorithms can be applied without technical specificity.
[0009] The impact of collecting user data through VCs and linking it to their DID can entice
those currently collecting user data to collect even more data, generate more analytics, more
uniquely target users, etc. It is also apparent that the extent and impact of applying machine
learning or artificial intelligence to the data aggregation process can further put users at
incalculable risk. Since DIDs may be collected and linked to data, a separate mitigating process
is necessary for protecting user privacy.
[0039] The disclosed solution introduces a Risk Analysis Engine (RAE) that leverages input
data (e.g., historical, new data, stored, received, acquired from external sources, etc.) to
generate a privacy risk assessment and metric, which quantifies the level and type of risks
associated with performing a VC presentation proof response, making connections to other
entities, exchanging or remitting data items, etc. In one embodiment, the RAE perform its
analysis and calculations by incorporating machine learning or artificial intelligence techniques
to assist in determining the level of risk for a given action or potential. The RAE automates
the processes for performing the recording, combinatory, analysis, and correlation steps (as
outlined below). The RAE also creates a rapid risk metric and description to quantify the level
of risk associated with performing a given activity or potential activity.
[0050] The Risk Analysis Engine compares an incoming verifiable credential request with the
rules maintained by a Persona and calculates how likely a given request is to facilitate a data
collection entity to correlate collected pieces of information, correlate two or more Personas
that are owned by the same Legal Identity, or correlate the owning Legal Identity to a Persona
when one of its Persona(s) is in use. The resulting calculation is quantified by a weighted score
that indicates how a particular action may result in adverse effects to a user's privacy. This
calculated score is presented to a user using a wide range of user interface design elements, so
as to convey the information with easy-to-understand presentations and elicit a user response.
In some embodiments, this score may be presented along with a recommended action for the
user (e.g., "low risk, so accept the request" or "high risk, so reject the request"). The user
response is used to govern the immediate action as well as to be fed back into the Risk Analysis
Engine's processing for future evaluations using a supervised learning paradigm.
Alternatively, this calculated score may be automatically applied to an incoming verifiable
credential request so that an action is taken without user interaction. The incoming verifiable
credential proof request and automatically selected actions are also recorded and fed back into
the Risk Analysis Engine's processing for future action evaluations using an un-supervised
learning paradigm.
Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional server, network, machine learning and display technology for gathering, analyzing, and presenting the desired information. the claims, defining a desirable information-based result and not limited to inventive means of achieving the result, fail under § 101.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2-8 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim 2 is directed toward applying PKI technology to retrieve and obtain data- well understood technology. Dependent claim 3 is directed toward parsing the textual data to determine the keyword indicating positive/negative connotation with the user- data manipulation. Dependent claim 4 is directed toward data - non-functional descriptive subject matter. Dependent claim 5 is directed toward data content - non-functional descriptive subject matter. Dependent claim 6 is directed toward receiving data and request- insignificant extra solution activity. Dependent claim 7 is directed toward storing trust score-insignificant extra solution activity. Dependent claim 8 is directed toward encrypting data with a private key and sending a key – well understood and routine application of technology.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-8 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 10-15:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a system, as in independent Claim 10 and the dependent claims. Such systems fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The functions of system claim 10 corresponds to steps of method claim 1. Therefore, claim 10 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The functions of system claim 10 corresponds to steps of method claim 1. The additional elements beyond the abstract idea include a system comprising a memory, at least one processor coupled to the memory and sever to perform the operations corresponding to claim 1. Therefore, claim 10 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a system comprising a memory, at least one processor coupled to the memory and sever to perform the operations corresponding to claim 1–is purely functional and generic. Nearly every computer system for implementing a method will include a “memory”, “processor coupled to the memory” capable of performing the basic computer functions corresponding to the steps (“receiving”, “determining”, “retrieving”, “inputting”, “applies a weight”, “determines the trust score”, “adjust the trust score”, “outputs the trust score”, “sending…the trust score”) of method claim 1-----is some of the most basic functions of a processor. As a result, none of the system components recited by the system claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.
The functions of system 10 corresponds to steps of method claim 1. Therefore, claim 10 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Similar to Electric Power Group, the claimed limitations do not go beyond requiring the collection, analysis, and display/output of available/result information in a particular field, stating those functions in general terms, without limiting them to technical means for performing the functions that are arguably an advance over conventional computer and network technology.
The specification nominally mentions the server and its use
[0044] The RAE functionality may be presented in many different embodiments, including but not limited to the following:
1. In one embodiment, the above solution is used for users interacting with systems (e.g., web browser to web server).
2. In another embodiment, the above solution is used for systems interacting with other
systems (e.g., IoT device to server, server to server, IoT to IoT, etc.)…
The specification make clear that any of a laundry list of learning algorithms can be applied without technical specificity.
[0009] The impact of collecting user data through VCs and linking it to their DID can entice
those currently collecting user data to collect even more data, generate more analytics, more
uniquely target users, etc. It is also apparent that the extent and impact of applying machine
learning or artificial intelligence to the data aggregation process can further put users at
incalculable risk. Since DIDs may be collected and linked to data, a separate mitigating process
is necessary for protecting user privacy.
[0039] The disclosed solution introduces a Risk Analysis Engine (RAE) that leverages input
data (e.g., historical, new data, stored, received, acquired from external sources, etc.) to
generate a privacy risk assessment and metric, which quantifies the level and type of risks
associated with performing a VC presentation proof response, making connections to other
entities, exchanging or remitting data items, etc. In one embodiment, the RAE perform its
analysis and calculations by incorporating machine learning or artificial intelligence techniques
to assist in determining the level of risk for a given action or potential. The RAE automates
the processes for performing the recording, combinatory, analysis, and correlation steps (as
outlined below). The RAE also creates a rapid risk metric and description to quantify the level
of risk associated with performing a given activity or potential activity.
[0050] The Risk Analysis Engine compares an incoming verifiable credential request with the
rules maintained by a Persona and calculates how likely a given request is to facilitate a data
collection entity to correlate collected pieces of information, correlate two or more Personas
that are owned by the same Legal Identity, or correlate the owning Legal Identity to a Persona
when one of its Persona(s) is in use. The resulting calculation is quantified by a weighted score
that indicates how a particular action may result in adverse effects to a user's privacy. This
calculated score is presented to a user using a wide range of user interface design elements, so
as to convey the information with easy-to-understand presentations and elicit a user response.
In some embodiments, this score may be presented along with a recommended action for the
user (e.g., "low risk, so accept the request" or "high risk, so reject the request"). The user
response is used to govern the immediate action as well as to be fed back into the Risk Analysis
Engine's processing for future evaluations using a supervised learning paradigm.
Alternatively, this calculated score may be automatically applied to an incoming verifiable
credential request so that an action is taken without user interaction. The incoming verifiable
credential proof request and automatically selected actions are also recorded and fed back into
the Risk Analysis Engine's processing for future action evaluations using an un-supervised
learning paradigm.
The specification make clear that any of a laundry list of learning algorithms can be applied without technical specificity. Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information. he claims, defining a desirable information-based result and not limited to inventive means of achieving the result, fail under § 101.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 11-14 these dependent claim have also been reviewed with the same analysis as independent claim 10. Dependent claim 11 is directed toward applying PKI technology to retrieve data. Dependent claim 12 is directed toward parsing the textual data to determine the keyword indicating positive/negative connotation with the user- data manipulation. Dependent claim 13 is directed toward data - non-functional descriptive subject matter. Dependent claim 14 is directed toward receiving data and request- insignificant extra solution activity. Dependent claim 15 is directed toward encrypting the trust score, and sending data- well known and understood technology.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 10. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 11-15 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 16-20:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a non-transitory computer-readable medium, as in independent Claim 16 and the dependent claims. Such mediums fall under the statutory category of "manufacture." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The instructions of medium claim 16 corresponds to steps of method claim 1. Therefore, claim 16 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The instructions of medium claim 16 corresponds to steps of method claim 1. The additional elements beyond the abstract idea include a non-transitory computer readable medium manufacture having instructions stored on and executed on a computing device, server and machine learning model performing operation/instructions corresponding to claim 1. Therefore, claim 16 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a non-transitory computer readable medium manufacture having instructions stored on and executed on a computing device, server and machine learning model. The instructions corresponding to the operations corresponding to steps of method claim 1–is purely functional and generic. Nearly every computer readable medium having instructions corresponding to the steps (“receiving”, “determining”, “retrieving”, “inputting”, “applies a weight”, “determines the trust score”, “adjust the trust score”, “outputs the trust score”, “sending…the trust score”) of method claim 1-----is some of the most basic functions of a processor. As a result, none of the instructions recited by the manufacture claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.
The instructions of medium claim 16 corresponds to steps of method claim 1. Therefore, claim 16 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Similar to Electric Power Group, the claimed limitations do not go beyond requiring the collection, analysis, and display/output of available/result information in a particular field, stating those functions in general terms, without limiting them to technical means for performing the functions that are arguably an advance over conventional computer and network technology.
The specification nominally mentions the server and its use
[0044] The RAE functionality may be presented in many different embodiments, including but not limited to the following:
1. In one embodiment, the above solution is used for users interacting with systems (e.g., web browser to web server).
2. In another embodiment, the above solution is used for systems interacting with other
systems (e.g., IoT device to server, server to server, IoT to IoT, etc.)…
The specification make clear that any of a laundry list of learning algorithms can be applied without technical specificity.
[0009] The impact of collecting user data through VCs and linking it to their DID can entice
those currently collecting user data to collect even more data, generate more analytics, more
uniquely target users, etc. It is also apparent that the extent and impact of applying machine
learning or artificial intelligence to the data aggregation process can further put users at
incalculable risk. Since DIDs may be collected and linked to data, a separate mitigating process
is necessary for protecting user privacy.
[0039] The disclosed solution introduces a Risk Analysis Engine (RAE) that leverages input
data (e.g., historical, new data, stored, received, acquired from external sources, etc.) to
generate a privacy risk assessment and metric, which quantifies the level and type of risks
associated with performing a VC presentation proof response, making connections to other
entities, exchanging or remitting data items, etc. In one embodiment, the RAE perform its
analysis and calculations by incorporating machine learning or artificial intelligence techniques
to assist in determining the level of risk for a given action or potential. The RAE automates
the processes for performing the recording, combinatory, analysis, and correlation steps (as
outlined below). The RAE also creates a rapid risk metric and description to quantify the level
of risk associated with performing a given activity or potential activity.
[0050] The Risk Analysis Engine compares an incoming verifiable credential request with the
rules maintained by a Persona and calculates how likely a given request is to facilitate a data
collection entity to correlate collected pieces of information, correlate two or more Personas
that are owned by the same Legal Identity, or correlate the owning Legal Identity to a Persona
when one of its Persona(s) is in use. The resulting calculation is quantified by a weighted score
that indicates how a particular action may result in adverse effects to a user's privacy. This
calculated score is presented to a user using a wide range of user interface design elements, so
as to convey the information with easy-to-understand presentations and elicit a user response.
In some embodiments, this score may be presented along with a recommended action for the
user (e.g., "low risk, so accept the request" or "high risk, so reject the request"). The user
response is used to govern the immediate action as well as to be fed back into the Risk Analysis
Engine's processing for future evaluations using a supervised learning paradigm.
Alternatively, this calculated score may be automatically applied to an incoming verifiable
credential request so that an action is taken without user interaction. The incoming verifiable
credential proof request and automatically selected actions are also recorded and fed back into
the Risk Analysis Engine's processing for future action evaluations using an un-supervised
learning paradigm.
The specification make clear that any of a laundry list of learning algorithms can be applied without technical specificity. Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information. he claims, defining a desirable information-based result and not limited to inventive means of achieving the result, fail under § 101.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 17-20 these dependent claim have also been reviewed with the same analysis as independent claim 16. Dependent claim 17 is directed toward parsing the textual data to determine the keyword indicating positive/negative connotation with the user- data manipulation. Dependent claim 18 is directed toward data - non-functional descriptive subject matter. Dependent claim 14 is directed toward receiving data and request- insignificant extra solution activity. Dependent claim 19 is directed toward receiving data and the request for trust score is directed toward insignificant extra solution activity. Dependent claim 20 is directed toward encrypting the trust score, and sending data- well known and understood technology.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 16. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 17-20 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1, 3-6; Claims 10, 12-14; Claim(s) 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 11,177,937 B1 by McCown et al. (McCown), in view of WO 2015/192086 by Crane et al. (Crane) and further in view of US Pub. No. 2018/0309752 A1 by Villavicencio et al. (Villa).
In reference to Claim 1:
McCown teaches:
(Currently Amended) A computer implemented method for controlling network transmission of an interaction request between a …[transaction parties] and a user device((McCown) in at least Col 3 lines 1-19) comprising:
receiving, by a computer-based trust score server over a network, interaction request data generated by the user device and identifying a pending interaction session between the …[transaction party] and the user device ((McCown) in at least FIG. 2, FIG. 4; Col 2 lines 67-Col 3 lines 1-12 wherein the prior art teaches analyzing activities from ratings and reviews submitted by other parties of verified transactions , lines 47-62 wherein the prior art teaches prompting user for information, Col 6 lines 1-3 wherein the prior art teaches applying a server for communication over a network; Col 7 lines 9-53 wherein the prior art teaches gathering information form third party legal service, Col 8 lines 5-29 wherein the prior art teaches gathering information representative of user actions including purchases items, Col 10 lines 33-51 wherein the prior art teaches creating quantifiable reputation based on commerce activities including purchases or sales , Col 14 lines 9-14 wherein the prior art teaches scraping data from public database, commercial database access);
retrieving, trust-score server, in response to the interaction request data, distributed user-identity data, from a distributed ledger maintained by a plurality of peer nodes the distributed user-identify data indicating that an identity of the user device has been cryptographically verified by a verifying entity ((McCown) in at least FIG. 5, FIG. 6A-B, FIG. 8B; Col 1 lines 25-30 wherein the prior art teaches in blockchain environment data is typically cryptographically hashed; Col 9 lines 52-Col 10 lines 1-25, lines 62-Col 11 lines 1-4, Col 11 lines 31-46, Col 17 lines 52-62, Col 18 lines 10-22);
aggregating, by the trust-score server, transaction-context-specific reputation inputs ((McCown) in at least Col 15 lines 5-10), comprising (i) reputational text data associated with the user at one or more third-party network services ((McCown) in at least FIG. 2 ref # 210, FIG 3 ref # 300; Col 1 lines 49-51 wherein the prior art teaches government third party providing information for validation; Col 7 lines 30-67 wherein the prior art teaches third party legal identity services seed reputation data, Col 8 lines 60-67); and (ii) historical interaction records associated with the user device, wherein the aggregating is performed in real time during the pending interaction session ((McCown) in at least Col 3 lines 5-12 wherein the prior art teaches information submitted by other parties includes purchases, rentals, enrollments, Col 6 lines 65 wherein the prior art teaches overtime personal identities reflective of previous activities, Col 10 lines 41-65 wherein the prior art teaches utilizing resumes, online purchases, communication in forums, memberships, services contracts, activity online, Col 15 lines 5-10);
executing, by the trust-score server, a trained machine learning predictive model ((McCown) in at least Col 15 lines 23-44 model activities including learning reputation phase, Col 16 lines 34-45, Col 18 lines 39-60) that is configured to:
parse the reputational text data to extract one or more interaction-relevant keywords ((McCown) in at least Col 14 lines 7-21 wherein the prior art teaches after data collected using NLP model to identify and parse data and using neural networks to perform the operations, lines 45-60, Col 15 lines 1-18, Col 16 lines 6-18);
apply interaction specific weights to the extracted …[behavior/activity] based on a relevance of each …[behavior/activity] to the pending interaction session ((McCown) in at least Col 16 lines 19-45 wherein the prior art teaches classifier applied to interpret coefficients to applied to defines behavior patterns for fraud, criminal, defamatory or good reputation; Col 19 lines 9-15);
compute a trust score from the weighted …[behavior/activity] the historical interaction records, and the distributed user identity data; ((McCown) in at least FIG. 5, FIG. 6A-B, FIG. 8B; Col 3 lines 1-12, lines 17-25, Col 4 lines 41-44, Col 8 lines 36-38, Col 9 lines 52-Col 10 lines 1-25, Col 11 lines 31-46, Col 12 lines 5-17, Col 13 lines 54-Col 14 lines 1-6, Col 17 lines 5-13, lines 52-62, Col 18 lines 10-22): and…
adjust the computed trust score based on prior failed or negative interaction outcomes correlated with the weighted [specific measurement] keywords to provide an adjusted trust score ((McCown) in at least FIG. 2-3; Col 7 lines 19-29 wherein the prior art teaches the user can create and update the reputation with new personal identity score, Col 13 lines 43-67, wherein the prior art teaches personal identity will undertake activity resulting in updated reputation score and teaches reputation data augmented by data collector where the reputation calculation service periodically calculates/recalculates reputation score, Col 15 lines 33-45),
generating by the trust-score server, before completion of the pending interaction session, an interaction-control signal that includes the adjusted trust score ((McCown) in at least Col 3 lines 5-11 wherein the prior art teaches the purpose of the reputation scores is to verify transaction and show whether user is trustworthy in advance of any interactions; Col 7 lines 19-36 wherein the prior art teaches the creation of a reputation score and use reputation of other identities to boost reputations resulting in updated identity score, Col 8 lines 5-38 wherein the prior art teaches calculating reputation score; Col 11 lines 45-53, Col 13 lines 22-27 wherein the prior art teaches calculated reputation score based on legal identity when determining reputation score in digital personal identity processing, lines 32-67 wherein the prior art teaching generating initial reputation score and when the personal identity undertakes activities that will result in updated reputation score and stored in blockchain ensuring to any requester the level of trust; Col 15 lines 55-Col 16 lines 55-Col 17 lines 1-5 wherein the prior art teaches applying prediction score to predict likelihood of behavior before it happens; Col 17 lines 50-Col 18 lines 1-60 ); and…
McCown suggest but does not explicitly teach:
… an interaction between a merchant device and a user device ((McCown) in at least Col 2 lines 67-Col 3 lines 1-12 wherein the prior art teaches analyzing activities from ratings and reviews submitted by other parties of verified transactions, Col 8 lines 5-29 wherein the prior art teaches gathering information representative of user actions including purchases items, Col 10 lines 33-51 wherein the prior art teaches creating quantifiable reputation based on commerce activities including purchases or sales , Col 13 wherein the prior art teaches maintaining interactions between identities and applications by a process for calculating reputation scores where the user enrolls in reputation system without requiring interaction between parties where the reputation system collects reputation data, passes it to blockchain holding queue after personal identify submitted while the reputation scoring module generates initial reputation score and digital identity data updated causing recalculation or reputation score in response to the enrollment (not an interaction completion between parties, Col 14 lines 9-14 wherein the prior art teaches scraping data from public database, commercial database access, Col 15 lines 10-32, col 16 lines 6-33, Col 20 lines 34-43 wherein the prior art teaches connecting the reputation services to third party application for open access to the reputation systems)
receiving, by a computer-based system, interaction information associated with the interaction between the merchant device and the user device…((McCown) in at least Col 2 lines 67-Col 3 lines 1-12 wherein the prior art teaches analyzing online activities from ratings and reviews submitted by other parties of verified transactions, Col 8 lines 5-29 wherein the prior art teaches gathering information representative of user actions including purchases items, Col 10 lines 33-51 wherein the prior art teaches creating quantifiable reputation based on commerce activities including purchases or sales , Col 14 lines 9-14 wherein the prior art teaches scraping data from public database, commercial database access)
Although McCown does not explicitly recite the parties in a transaction as including a merchant, in light of KSR and common sense rationale, based on the prior art providing some teaching, suggestion or motivation that would have led of ordinary skill to modify the prior art reference, it would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the prior art references to arrive at the claimed invention. The prior art McCown teaches that the data received includes commerce activities of purchases or sales and teaches analyzing activities from ratings and reviews submitted by other parties of verified transactions which suggest and based on knowledge generally available to one of ordinary skill in the art, to modify the reference with a reasonable expectation of success to arrive at the claimed invention.
McCown does not explicitly teach:
apply interaction specific weights to the extracted keywords based on a relevance of each keyword…
controlling network transmission of the interaction request by inhibiting completion of the pending interaction session based on the interaction-control signal satisfying a predefined adjusted trust score indicating a risk condition, the inhibiting with the interaction, the preventing the interaction comprising sending the adjusted trust score preventing transmission of a transaction-completion message to the merchant device.
Crane teaches:
apply interaction specific weights to the extracted keywords based on a relevance of each keyword ((Crane) in at least para 0045-0047),
adjust the computed trust score based on prior failed or negative interaction outcomes correlated with the weighted [specific measurement] keywords to provide an adjusted trust score ((Crane) in at least FIG. 8C; para 0046, para 0054-0055, para 0057-0058, para 0060-0061, para 0075-0076, para 0087, para 0107-0112, para 0115, para 0120-0124, para 0133,
output the adjusted trust score ((Crane) in at least para 0125, para 0127, para 0136-0137)
According to KSR, known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. The prior art reference McCown teaches keyword nodes which are assigned specific reputation measurements (Col 15 lines 33-36). The prior art reference Crane included a similar application (applying weights to keywords for calculating reputation scores) of technology that is analogous to the claimed invention. The prior art Crane provide design incentives or market forces which would have prompted adaptation of the known method (modify the attributes to apply weights to be keyword categories). The prior art Crane provides evidence that the difference between the claimed invention and the prior art McCown where encompassed in known variations or a principle known in the prior art. Therefore, according to common sense rationale, one of ordinary skill in the art, in view of the identified design incentives or other market forces, could have implemented the claimed variation of the prior art, and the claimed variation would have been predictable to one of ordinary skill in the art.
The prior art McCown explicitly teaches calculating and outputting on a display a trust/reputation score in order to represent a probabilistic score representing good trust/bad trust to facilitate human understanding of behavior and to minimize false/negative instances when models fail to identify mal-intentioned personal identity (fraudulent). The prior art McCown in combination with Crane teaches that scores representing potential behavior and sending the results in order to display to the user to results of the recalculated score based on received additional information.
Both McCown and Crane are directed toward a process to calculate reputations scores applying keywords and weights when calculating reputation scores. Crane teaches the motivation of applying categories of keywords such as finance, bankruptcy, credit crisis, flood, labor, etc where the categories of keywords are given different weights based on the effect of the category on the merchant. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the application of keywords and which elements for which weights are applied to calculate reputation scores of McCown to include the application of keywords and weights as taught by Crane since Crane teaches the motivation of applying categories of keywords such as finance, bankruptcy, credit crisis, flood, labor, etc where the categories of keywords are given different weights based on the effect of the category on the merchant.
Both McCown and Crane are directed toward a process to calculate reputations scores and outputs the result. Crane teaches the motivation of outputting the updated calculated reputation score results and sending the results in order to display to the user to results of the recalculated score based on received additional information. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the sending of the results of the recalculated score to the database to include sending the results to a user display as taught by Crane since Crane teaches the motivation of outputting the updated calculated reputation score results and sending the results in order to display to the user to results of the recalculated score based on received additional information.
Villa teaches:
controlling network transmission of the interaction request by inhibiting completion of the pending interaction session based on the interaction-control signal satisfying a predefined adjusted trust score indicating a risk condition, the inhibiting with the interaction, the preventing the interaction comprising sending the adjusted trust score preventing transmission of a transaction-completion message to the merchant device..((Villa) in at least Abstract; para 0033, para 0037, para 0040-0041, para 0056-0058)
Both McCown and Villa are directed toward scoring authentication risk. McCown teaches that the embodiments of use for the reputation system includes enabling others of ordinary skill in the art to utilize the reputation generating score system to be modified as are suited to the particular use contemplated. Villa teaches the motivation of applying the risk score in order to determine whether to terminate a transaction process where based on risk score level determines the termination of transaction. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the activity when receiving a risk score for use as taught by McCown to include preventing a transaction as taught by Villa since Villa teaches the motivation of applying the risk score in order to determine whether to terminate a transaction process where based on risk score level determines the termination of transaction.
In reference to Claim 3:
The combination of McCown, Crane and Villa discloses the limitations of dependent claim 1. Crane further discloses the limitations of dependent claim 3.
(Currently amended) The method of claim 1 (see rejection of claim 1 above), further comprising
wherein the reputational text data comprises textual review of the user ((McCown) in at least Fig. 6A-E; Col 3 lines 4-7, Col 4 lines 27-35, Col 6 lines 30-37, Col 8 lines 22-29) , the method further comprising:
McCown does not explicitly teach:
parsing the textual review to determine the weighted keyword, wherein the weighted keywords indicate a positive connotation or a negative connotation with the user.
Crane teaches:
parsing (categorize) the textual review to determine the weighted keywords, wherein the weighted keywords indicate a positive connotation or a negative connotation with the user.((Crane) in at least para 0040, para 0046-0047, para 0061, para 0120, para 0123-0124, para 0133)
Both McCown and Crane are directed toward calculating reputation scores and analyzing different categories for which weights can be applied. Crane teaches the motivation of categorizing different that provide negative and positive impact to a business so that the negative and positive calculated weights can be summed to calculate an overall score. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the calculation of reputations scores and the categories of McCown to include assigning negative and positive to different categories as taught by Crane since Crane teaches the motivation of categorizing different that provide negative and positive impact to a business so that the negative and positive calculated weights can be summed to calculate an overall score.
In reference to Claim 4:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 1. Crane further discloses the limitations of dependent claim 4.
(Currently Amended) The method of claim 1 (see rejection of claim 1 above), wherein the historical interaction records comprise
at least one of demographic records, an initial risk profile underwriting, a loan history, an indication of timeliness of payments, an interaction dispute history, a revolving interaction account balance, a delinquency history, a fraud score, a credit score, an income level, an education history, or a tax history. ((McCown) in at least Col 4 lines 27-31, Col 14 lines 54-59, Col 18 lines 41-50)
In reference to Claim 5:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 1. Crane further discloses the limitations of dependent claim 5.
(Currently Amended) The method of claim 1 (see rejection of claim 1 above),
McCown does not explicitly teach:
wherein the historical interaction records comprise at least one of line item records, interaction authorization records, interaction submission records, or recent interaction records.
Crane teach:
wherein the historical interaction records comprise at least one of line item records, interaction authorization records, interaction submission records, or recent interaction records. ((Crane) in at least para 0074, para 0102, para 0118, para 0131-0132)
Both McCown and Crane are directed toward calculating reputation scores applying historical data. Crane teaches the motivation of applying the history of the buyer in order to use the data as part of the data for inputting data elements to calculate the reputation score. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the historical data for calculation of reputations scores of McCown to include the historical data as taught by Crane since Crane teaches the motivation of applying the history of the buyer in order to use the data as part of the data for inputting data elements to calculate the reputation score.
In reference to Claim 6:
The combination McCown, Crane and Villa discloses the limitations of independent claim 1. Crane further discloses the limitations of dependent claim 6
(Currently Amended) The method of claim 1 (see rejection of claim 1 above),
wherein the receiving the interaction request data and the identifying the pending interaction session is further based on an interaction with a user interface.((McCown) in at least Col 3 lines 5-11 wherein the prior art teaches the purpose of the reputation scores is to verify transaction and show whether user is trustworthy in advance of any interactions; Col 11 lines 45-53, Col 13 lines 32-67 wherein the prior art teaching generating initial reputation score and when the personal identity undertakes activities that will result in updated reputation score and stored in blockchain ensuring to any requester the level of trust; Col 15 lines 55-Col 16 lines 55-Col 17 lines 1-5 wherein the prior art teaches applying prediction score to predict likelihood of behavior before it happens; Col 17 lines 50-Col 18 lines 1-60)
In reference to Claim 10:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 10.
The functions of system claim 10 functional processes correspond to the method steps of method claim 1. The additional limitations recited in claim 10 that go beyond the limitations of claim 1 include the system ((McCown) in at least FIG. 1, Col 5 lines 65-Col 6 lines 1-18) to perform the operation that correspond to claim 1 include the structure comprising:
a memory ((McCown) in at least FIG. 1, Col 5 lines 65-Col 6 lines 1-18);
at least one processor coupled to the memory ((McCown) in at least FIG. 1, Col 5 lines 65-Col 6 lines 1-18) to perform the operations corresponding to claim 1; and
Therefore, claim 10 has been analyzed and rejected as previously discussed with respect to claim 1.
The prior art McCown explicitly teaches calculating and outputting on a display a trust/reputation score in order to represent a probabilistic score representing good trust/bad trust to facilitate human understanding of behavior and to minimize false/negative instances when models fail to identify mal-intentioned personal identity (fraudulent). The prior art McCown in combination with Crane teaches that scores representing potential behavior and sending the results in order to display to the user to results of the recalculated score based on received additional information.
In reference to Claim 12:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 10. Crane further discloses the limitations of dependent claim 12.
(Currently Amended) The system of claim 11 (see rejection of claim 11 above), further comprising
wherein the reputational text data comprises textual review of the user ((McCown) in at least Fig. 6A-E; Col 3 lines 4-7, Col 4 lines 27-35, Col 6 lines 30-37, Col 8 lines 22-29) , the operations further comprising:
McCown does not explicitly teach:
parsing the textual review to determine the weighted keywords, wherein the weighted keywords indicate a positive connotation or a negative connotation with the user.
Crane teaches:
parsing (categorize) the textual review to determine the keyword, wherein the keyword indicates a positive connotation or a negative connotation with the user.((Crane) in at least para 0040, para 0046-0047, para 0061, para 0120, para 0123-0124, para 0133)
Both McCown and Crane are directed toward calculating reputation scores and analyzing different categories for which weights can be applied. Crane teaches the motivation of categorizing different that provide negative and positive impact to a business so that the negative and positive calculated weights can be summed to calculate an overall score. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the calculation of reputations scores and the categories of McCown to include assigning negative and positive to different categories as taught by Crane since Crane teaches the motivation of categorizing different that provide negative and positive impact to a business so that the negative and positive calculated weights can be summed to calculate an overall score.
In reference to Claim 13:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 10. Crane further discloses the limitations of dependent claim 13.
(Currently amended) The system of claim 10 (see rejection of claim 10 above), wherein the historical interaction records comprise
McCown does not explicitly teach:
at least one of demographic records, an initial risk profile underwriting, a loan history, an indication of timeliness of payments, an interaction dispute history, a revolving interaction account balance, a delinquency history, a fraud score, a credit score, an income level, an education history, a tax history, line item records, interaction authorization records, interaction submission records, or recent interaction records.
Crane teaches:
at least one of demographic records, an initial risk profile underwriting, a loan history, an indication of timeliness of payments, an interaction dispute history, a revolving interaction account balance, a delinquency history, a fraud score, a credit score, an income level, an education history, a tax history, line item records, interaction authorization records, interaction submission records, or recent interaction records. ((Crane) in at least para 0074, para 0102, para 0118, para 0131-0132)
Both McCown and Crane are directed toward calculating reputation scores applying historical data. Crane teaches the motivation of applying the history of the buyer in order to use the data as part of the data for inputting data elements to calculate the reputation score. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the historical data for calculation of reputations scores of McCown to include the historical data as taught by Crane since Crane teaches the motivation of applying the history of the buyer in order to use the data as part of the data for inputting data elements to calculate the reputation score.
In reference to Claim 14:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 10. Crane further discloses the limitations of dependent claim 14.
(Currently Amended) The system of claim 10 (see rejection of claim 10 above),
wherein the receiving the interaction request data and the identifying the pending interaction session is further based on an interaction with a user interface. ((McCown) in at least FIG. 6A, FIG. 6C, FIG. 6E; Col 3 lines 5-11 wherein the prior art teaches the purpose of the reputation scores is to verify transaction and show whether user is trustworthy in advance of any interactions; Col 5 lines 65-Col 6 lines 1-35, Col 11 lines 45-53, Col 13 lines 32-67 wherein the prior art teaching generating initial reputation score and when the personal identity undertakes activities that will result in updated reputation score and stored in blockchain ensuring to any requester the level of trust; Col 15 lines 55-Col 16 lines 55-Col 17 lines 1-5 wherein the prior art teaches applying prediction score to predict likelihood of behavior before it happens; Col 17 lines 50-Col 18 lines 1-60, Col 20 lines 38-43)
In reference to Claim 16:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 16.
The instructions of manufacture claim 16 correspond to the method steps of method claim 1. The additional limitations recited in claim 16 that go beyond the limitations of claim 1 include the non-transitory computer readable medium having instructions executed by a processor ((McCown) in at least Col 20 lines 44-67) to perform the operation that correspond to claim 1:
Therefore, claim 16 has been analyzed and rejected as previously discussed with respect to claim 1.
According to KSR, common sense rationale, it is obvious to try- when choosing from a finite number of identified predictable solutions with a reasonable expectation of success. The prior art at the time of the invention recognized need to apply computer readable medium memory comprising instructions executed by processors in order to perform operations. There is a finite number of identified, predictable solutions to the recognized need of applying computer readable medium memories storing instructions executed by processors (i.e. transitory and/or non-transitory). Accordingly, since there are only two options for computer readable mediums (transitory/non-transitory) one of ordinary sill in the art could have pursued the known potential solutions with a reasonable expectation of success. Therefore, the prior art provides some teaching/suggestion that would have led one of ordinary skill to arrive at the claimed invention.
The prior art McCown explicitly teaches calculating and outputting on a display a trust/reputation score in order to represent a probabilistic score representing good trust/bad trust to facilitate human understanding of behavior and to minimize false/negative instances when models fail to identify mal-intentioned personal identity (fraudulent). The prior art McCown in combination with Crane teaches that scores representing potential behavior and sending the results in order to display to the user to results of the recalculated score based on received additional information.
In reference to Claim 17:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 16. Crane further discloses the limitations of dependent claim 17.
(Currently Amended) The non-transitory computer-readable medium of claim 16 (see rejection of claim 16 above), wherein the reputational text data comprises textual review of the user ((McCown) in at least Fig. 6A-E; Col 3 lines 4-7, Col 4 lines 27-35, Col 6 lines 30-37, Col 8 lines 22-29) , the operations further comprising:
McCown does not explicitly teach:
parsing the textual review to determine the weighted keywords, wherein the weighted keywords indicate a positive connotation or a negative connotation with the user.
Crane teaches:
parsing (categorize) the textual review to determine weighted keywords, wherein the weighted keywords indicate a positive connotation or a negative connotation with the user.((Crane) in at least para 0040, para 0046-0047, para 0061, para 0120, para 0123-0124, para 0133)
Both McCown and Crane are directed toward calculating reputation scores and analyzing different categories for which weights can be applied. Crane teaches the motivation of categorizing different that provide negative and positive impact to a business so that the negative and positive calculated weights can be summed to calculate an overall score. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the calculation of reputations scores and the categories of McCown to include assigning negative and positive to different categories as taught by Crane since Crane teaches the motivation of categorizing different that provide negative and positive impact to a business so that the negative and positive calculated weights can be summed to calculate an overall score.
In reference to Claim 18:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 16. Crane further discloses the limitations of dependent claim 18.
(Currently Amended) The non-transitory computer-readable medium of claim 16 (see rejection of claim 16 above), wherein the historical interaction records comprise
McCown does not explicitly teach:
at least one of demographic records, an initial risk profile underwriting, a loan history, an indication of timeliness of payments, an interaction dispute history, a revolving interaction account balance, a delinquency history, a fraud score, a credit score, an income level, an education history, a tax history, line item records, interaction authorization records, interaction submission records, or recent interaction records.
Crane teaches:
at least one of demographic records, an initial risk profile underwriting, a loan history, an indication of timeliness of payments, an interaction dispute history, a revolving interaction account balance, a delinquency history, a fraud score, a credit score, an income level, an education history, a tax history, line item records, interaction authorization records, interaction submission records, or recent interaction records ((Crane) in at least para 0074, para 0102, para 0118, para 0131-0132)
Both McCown and Crane are directed toward calculating reputation scores applying historical data. Crane teaches the motivation of applying the history of the buyer in order to use the data as part of the data for inputting data elements to calculate the reputation score. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the historical data for calculation of reputations scores of McCown to include the historical data as taught by Crane since Crane teaches the motivation of applying the history of the buyer in order to use the data as part of the data for inputting data elements to calculate the reputation score.
In reference to Claim 19:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 16. Crane further discloses the limitations of dependent claim 19.
(Currently Amended) The non-transitory computer-readable medium of claim 16 (see rejection of claim 16 above),
wherein the receiving the interaction request data and the identifying the pending interaction session is further based on an interaction with a user interface. ((McCown) in at least FIG. 6A, FIG. 6C, FIG. 6E; Col 3 lines 5-11 wherein the prior art teaches the purpose of the reputation scores is to verify transaction and show whether user is trustworthy in advance of any interactions; Col 5 lines 65-Col 6 lines 1-35, Col 11 lines 45-53, Col 13 lines 32-67 wherein the prior art teaching generating initial reputation score and when the personal identity undertakes activities that will result in updated reputation score and stored in blockchain ensuring to any requester the level of trust; Col 15 lines 55-Col 16 lines 55-Col 17 lines 1-5 wherein the prior art teaches applying prediction score to predict likelihood of behavior before it happens; Col 17 lines 50-Col 18 lines 1-60, Col 20 lines 38-43)
Claim(s) 2 and 7-8; Claims 11 and 15 of claim 10; and Claim 20 of claim 16 above is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 11,177,937 B1 by McCown et al. (McCown), in view of WO 2015/192086 by Crane et al. (Crane) in view of US Pub. No. 2018/0309752 A1 by Villavicencio et al. (Villa) and further in view of US Pub No. 2018/0309581 A1 by Butler et al. (Butler)
In reference to Claim 2:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 1. Crane further discloses the limitations of dependent claim 2
(Currently Amended) The method of claim 1 (see rejection of claim 1 above, wherein
the distributed ledger is a blockchain ((McCown) in at least FIG. 4 ref 406, FIG. 5; Col 1 lines 26-35, lines 55-60, Col 6 lines 30-37),
the user device comprises a blockchain interface configured to access a blockchain node, of the blockchain, assigned to the user device ((McCown) in at least Col 14 lines 62-63, Col 15 lines 33-45, lines 54-57, Col 16 lines 6-18, Col 19 lines 16-58),…
the method further comprises:
updating, by the trust-score server, the blockchain node assigned to the user device to include the adjusted trust score ((McCown) in at least Col 14 lines 62-65, Col 16 lines 6-18, Col 17 lines 21-62, Col 19 lines 5-57), ,,,
McCown does not explicitly teach:
the blockchain node assigned to the user device is addressable using a public key of the user device,
the user record is retrieved from the blockchain using the public key of the user device, and
providing, by the computer-based system, the public key of the user device to the merchant device, and preventing, by the computer-based system, the interaction by sending the public key of the user device to the merchant device,
wherein the merchant device is configured to retrieve the adjusted trust score from the blockchain using the public key of the user device.
Butler teaches:
the blockchain node assigned to the user device is addressable using a public key of the user device ((Butler) in at least abstract; para 0017 wherein the prior art teaches public key are written to blockchain with information enabling users to locate information, para 0026, para 0030, para 0043),
the user record is retrieved from the blockchain using the public key of the user device ((Butler) in at least para 0043 wherein the prior art teaches using public key to retrieve data from blockchain) , and
providing, by the computer-based system, the public key of the user device to the merchant device … sending the public key of the user device to the merchant device ((Butler) in at least para 0017, para 0019 para 0023-0027, para 0030),
wherein the merchant device is configured to retrieve the adjusted trust score from the blockchain using the public key of the user device ((Butler) in at least para 0017, para 0019 para 0023-0027, para 0029-0030),.
Both McCown and Butler teach utilizing blockchain technology to store transaction and user data where the data is encrypted for security. Butler teaches the motivation of applying pair key technology written to the blockchain in order to enable parties to locate and retrieve information without compromising sensitive biometric user data. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the process for receiving data stored within blockchain of McCown to include applying PKI technology as taught by Butler since Butler teaches the motivation of applying pair key technology written to the blockchain in order to enable parties to locate and retrieve information without compromising sensitive biometric user data..
Villa teaches:
and preventing, by the computer-based system, the interaction by sending … [retrieving/receiving score risk data] of the user device to the merchant device ((Villa) in at least Abstract; para 0033, para 0037, para 0040-0041, para 0056-0058)
Both McCown and Villa are directed toward scoring authentication risk. McCown teaches that the embodiments of use for the reputation system includes enabling others of ordinary skill in the art to utilize the reputation generating score system to be modified as are suited to the particular use contemplated. Villa teaches the motivation of applying the risk score in order to determine whether to terminate a transaction process where based on risk score level determines the termination of transaction. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the activity when receiving a risk score for use as taught by McCown to include preventing a transaction as taught by Villa since Villa teaches the motivation of applying the risk score in order to determine whether to terminate a transaction process where based on risk score level determines the termination of transaction.
In reference to Claim 7:
The combination of McCown, Crane, Villa and Butler discloses the limitations of dependent claim 2. Crane further discloses the limitations of dependent claim 7.
(Currently Amended) The method of claim 2 (see rejection of claim 2 above), further comprising
updating, by the trained machine learning model, the adjusted trust score ….((McCown) in at least Col 3 lines 4-26 wherein the prior art teaches verified transaction data including transaction completions, lines 33-37 wherein the prior art teaches pattern of transaction are part of the reputation score , Col 6 lines 45-55; Col 7 lines 19-36 wherein the prior art teaches the user can create and update the reputation with new personal identity score, Col 13 lines 43-67, wherein the prior art teaches personal identity will undertake activity resulting in updated reputation score and teaches reputation data augmented by data collector where the reputation calculation service periodically calculates/recalculates reputation score, Col 19 lines 5-15),
McCown does not explicitly teach:
updating, …., the adjusted trust score based on the preventing the transmission of the transaction-completion message
Villa teaches:
updating, by the trained machine learning model, the adjusted trust score based on the preventing the transmission of the transaction-completion message ((Villa) in at least para 0033-0034, para 0037, para 0040-0041, para 0056-0058, para 0063, para 0065)
Both McCown and Villa are directed toward scoring authentication risk and updating the risk score in response to previous transaction. McCown teaches that the embodiments of use for the reputation system includes enabling others of ordinary skill in the art to utilize the reputation generating score system to be modified as are suited to the particular use contemplated. Villa teaches the motivation of applying updating the risk engine generating the risk score when a failed or successful authentication event has occurred in order to score user patterns for use in determining whether to terminate a transaction process where based on risk score level determines the termination of transaction. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the activity when receiving a risk score for use as taught by McCown to include updating risk score applied for determining whether to prevent a transaction as taught by Villa since Villa teaches the motivation of applying updating the risk engine generating the risk score when a failed or successful authentication event has occurred in order to score user patterns for use in determining whether to terminate a transaction process where based on risk score level determines the termination of transaction.
In reference to Claim 8:
The combination of McCown, Crane, Villa and Butler discloses the limitations of dependent claim 7. Crane further discloses the limitations of dependent claim 8.
(Previously Presented) The method of claim 7 (see rejection claim 7 above), further comprising:
encrypting the adjusted trust score … storing the adjusted trust score on the digital identity management blockchain ((McCown) in at least Col 1 lines 22-35, Col 9 lines 20-24, Col 9 lines 62-Col 10 lines 1-2, Col 18 lines 33-38)
McCown does not explicitly teach:
encrypting the adjusted trust score with a private key prior …; and
sending, to the first device, a public key associated with the private key, wherein the public key facilitates retrieval of the adjusted trust score from the digital identity management blockchain.
Butler teaches:
encrypting the … [data retrieved] with a private key prior to storing the adjusted [data] on the digital identity management blockchain ((Butler) in at least para 0017, para 0019 para 0023-0027, para 0029-0030)
sending, to the first device, a public key associated with the private key, wherein the public key facilitates retrieval of the adjusted trust score from the digital identity management blockchain ((Butler) in at least para 0017, para 0019 para 0023-0027, para 0030),
Both McCown and Butler teach utilizing blockchain technology to store transaction and user data where the data is encrypted for security. Butler teaches the motivation of applying pair key technology written to the blockchain in order to enable parties to locate and retrieve information without compromising sensitive biometric user data. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the process for receiving data stored within blockchain of McCown to include applying PKI technology as taught by Butler since Butler teaches the motivation of applying pair key technology written to the blockchain in order to enable parties to locate and retrieve information without compromising sensitive biometric user data
In reference to Claim 11:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 10. Crane further discloses the limitations of dependent claim 11.
The functions of System claim 11 corresponds to the steps of method claim 2. Therefore, claim 11 has been analyzed and rejected as previously discussed with respect to claim 2.
In reference to Claim 15:
The combination of McCown, Crane, Villa and Butler discloses the limitations of dependent claim 11. Crane further discloses the limitations of dependent claim 15
(Previously Presented) The system of claim 11 (see rejection of claim 11 above), the operations further comprising:
encrypting the adjusted trust score with the … of the user device to storing the adjusted trust score on the blockchain. ((McCown) in at least Col 1 lines 22-35, Col 9 lines 20-24, Col 9 lines 62-Col 10 lines 1-2, Col 18 lines 33-38)
McCown does not explicitly teach:
encrypting the adjusted trust score with the public key of the user device…
Butler teaches:
encrypting the … [data retrieved] with the public key of the user device prior to storing the adjusted [data] on the digital identity management blockchain ((Butler) in at least para 0017, para 0019 para 0023-0027, para 0029-0030)
Both McCown and Butler teach utilizing blockchain technology to store transaction and user data where the data is encrypted for security. Butler teaches the motivation of applying pair key technology written to the blockchain in order to enable parties to locate and retrieve information without compromising sensitive biometric user data. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the process for receiving data stored within blockchain of McCown to include applying PKI technology as taught by Butler since Butler teaches the motivation of applying pair key technology written to the blockchain in order to enable parties to locate and retrieve information without compromising sensitive biometric user data
In reference to Claim 20:
The combination of McCown, Crane and Villa discloses the limitations of independent claim 16. Crane further discloses the limitations of dependent claim 20.
(Previously Presented) The non-transitory computer-readable medium of claim 16 (see rejection of claim 16 above), the operations further comprising:
encrypting the adjusted trust score … prior to storing the adjusted trust score on a blockchain ((McCown) in at least Col 1 lines 22-35, Col 9 lines 20-24, Col 9 lines 62-Col 10 lines 1-2, Col 18 lines 33-38)
McCown teaches:
encrypting the adjusted trust score with the public key of the user device…
Butler teaches:
encrypting the … [data retrieved] with a private key prior to storing the adjusted [data] on the digital identity management blockchain ((Butler) in at least para 0017, para 0019 para 0023-0027, para 0029-0030)
Both McCown and Butler teach utilizing blockchain technology to store transaction and user data where the data is encrypted for security. Butler teaches the motivation of applying pair key technology written to the blockchain in order to enable parties to locate and retrieve information without compromising sensitive biometric user data. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the process for receiving data stored within blockchain of McCown to include applying PKI technology as taught by Butler since Butler teaches the motivation of applying pair key technology written to the blockchain in order to enable parties to locate and retrieve information without compromising sensitive biometric user data
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Patent No. 9,703,986 by Ashley et al, US Patent No. 10,270,748 B2 by Briceno et al; US Pub No. 2018/0374151 A1 by Joshi.
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 MARY M GREGG whose telephone number is (571)270-5050. The examiner can normally be reached M-F 9am-5pm.
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/MARY M GREGG/Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
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