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 .
Status of Claims
This Office action is in reply to filing by applicant on 10/28/2025.
Claims 1, 11, and 20 were amended by Applicant.
Claims 2 – 5, 7 – 10, 12 – 15, and 17 – 19 were previously presented by Applicant.
Claims 6 and 16 remain as original.
Claims 1 – 20 are currently pending and have been examined.
The prior 35 USC 112(b) rejection of all claims set forth in the Non-Final rejection of 07/31/2025 is withdrawn in view of Applicant’s arguments and amendments.
The prior 35 USC 103 claim rejections set forth in the Non-Final rejection of 07/31/2025 as to claims 1 – 10 are maintained. Note that claims 11 – 20 have no art rejection. Please see “Allowable Subject Matter” paragraph below.
THIS ACTION IS MADE FINAL.
Response to Arguments
Applicant makes no arguments. Applicant rather concludes that claim 20 (which was expressly deemed allowable subject matter in the prior office action) has now been incorporated into claim 1 (see claims of 10/28/2025), and, thereby, claims 1 – 10 ought to be allowed. Remarks 11. There appears to be at least one error in the above Applicant conclusion. Namely, that the allowable subject matter of claim 20 was not incorporated into claim 1. For example, where independent claim 20 previously used “environmental impact”, independent claim 1 now uses “environmental risk scores”, both for determining / ranking user scores. These claim terms are not the same. See below 35 USC 101 analysis.
Moreover, claim 20 was never cancelled, and it is otherwise unclear what Applicant meant in its arguments that the:
the allowable subject matter of claim 20 has been incorporated into independent claim 1. Remarks 11.
Had “environmental impact” actually been incorporated from claim 20 to claim 1 as Applicant stated, then the same 35 USC 112(b) rejection as to such claim term would have also been added to this office action in claims 1 – 10. The “environmental impact” claim term was already held as indefinite in the last office action, noting that the prior 35 USC112(b) rejection included all claims. That said, the “incorporation” of the allowable subject matter of claim 20 into claim 1 has not occurred.
Generally as to obviousness, examiner submits that it is determined on the basis of the evidence as a whole and the relative persuasiveness of the arguments. See In re Oetiker, 977 F.2d 1443, 1445, 24 USPQ2d 1443, 1444 (Fed. Cir. 1992); In re Hedges, 783 F.2d 1038, 1039, 228 USPQ 685,686 (Fed. Cir. 1992); In re Piasecki, 745 F.2d 1468, 1472, 223 USPQ 785,788 (Fed. Cir. 1984); and In re Rinehart, 531 F.2d 1048, 1052, 189 USPQ 143,147 (CCPA 1976). Using this standard, examiner submits that the burden of presenting a prima facie case of obviousness was successfully established in the prior Office Action of 07/31/2025, and also respecting the pending amended claim set of 10/28/2025, as seen below.
Examiner recognizes that references cannot be arbitrarily altered or modified, and that there must be some reason why a person having ordinary skill in the relevant art would be motivated to make the proposed modifications. Although the motivation or suggestion to make modifications must be articulated, it is respectfully submitted that there is no requirement that the motivation to make modifications must be expressly articulated within the references themselves. References are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969).
Examiner also notes that the motivation to combine the applied references is, where appropriate in the below detailed analysis pursuant to 35 USC 103, additionally accompanied by select passages from the respective references which specifically support that particular motivation. It is also respectfully submitted that motivation based on the logic and scientific reasoning of one ordinarily skilled in the art at the time of the invention, which evidence can also support a finding of obviousness, is otherwise provided in the detailed 35 USC 103 analysis of the claim set below. In re Nilssen, 851 F.2d 1401, 1403, 7 USPQ2d 1500, 1502 (Fed. Cir. 1988) (references do not have to explicitly suggest combining teachings); Ex parte Clapp, 227 USPQ 972 (Bd. Pat. App. & Inter. 1985) (examiner must present convincing line of reasoning supporting rejection); and Ex parte Levengood, 28 USPQ2d 1300 (Bd. Pat. App. & Inter. 1993) (reliance on logic and sound scientific reasoning).
Examiner recognizes that obviousness can only be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to a person of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988) and In re Jones, 958 F.2d 347.
Claim Rejections – 35 USC 103
In the event the determination of the status of the application as subject to AIA 35 USC 102 and 103 is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 USC 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 USC 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating
obviousness or nonobviousness.
Claims 1 and 3 - 10 are rejected pursuant to 35 USC 103 as being unpatentable over Nandakumar (US20200051058A1) in view of Sasha (US20080288405A1) and in further view of Abdelaziz (US20200089848A1).
Regarding claim 1:
Nandakumar discloses:
receiving a user data set for a user, wherein the user data set includes a user identifier (“In some embodiments of the inventive subject matter, a method comprises performing by a processor: receiving a transaction request associated with a first user from a first device”, [[3]) and A user or shopper may provide payment information to the POS terminal 110 using a mobile device 105 a that may include links to financial accounts thereon and/or data identifying various financial accounts, such as credit card accounts, store credit accounts, debit card accounts, checking accounts, loyalty program accounts, rewards accounts, and the like. The mobile device 105 may also include identification credentials, such as a photo identification, driver's license, passport, insurance card, home and/or work address information, home and/or work telephone numbers, and the like. In some embodiments, the financial information and/or personal identification information may be managed using a digital wallet application residing on the phone or in a cloud server on the Internet or other network.”, [020]), and see Abstract, published 02/13/2020; user identifiers may be received;
modifying a data store server to store the user identifier and the user score; (“a data store server to store the user identifier”, [020]) and see [003]);
and one or more user authentications for one or more entities, (“Similarly, when a device is used for a transaction the first time, a user may receive enhanced authentication and/or transaction authorization scrutiny in the form of, for example, multi-factor authentication techniques, access codes received through another device or system … As the number of successful transactions using a device increases, a risk assessment system of a merchant and/or financial institution may lessen authentication and/or transaction authorization scrutiny based on the device's history.”, [002], note that a successful transaction for an entity may occur, and said entity’s transaction risks go down as the number of successful transactions (authentications/authorizations) goes up;
receiving, from a ranking entity, an entity score for each entity of the one or more entities, the entity score based on an environmental risk score; for the purposes of compact prosecution, examiner interprets the above mentioned claim term “entity score”, throughout these claims, to include in its meaning any “risk” based score of any sort, … (“In some embodiments, the risk assessment server 140 may be configured to generate a risk assessment score that that is indicative of the relative risk associated with a requested transaction.”, [023]);
determining a user score for the user based on the one or more user authentications and the entity score for each of the one or more entities included in the one or more user authentications; (“In some embodiments, the risk assessment server 140 may be configured to generate a risk assessment score that that is indicative of the relative risk associated with a requested transaction”, [023]) and (“Similarly, when a device is used for a transaction the first time, a user may receive enhanced authentication and/or transaction authorization scrutiny in the form of, for example, multi-factor authentication techniques, access codes received through another device or system, … As the number of successful transactions using a device increases, a risk assessment system of a merchant and/or financial institution may lessen authentication and/or transaction authorization scrutiny based on the device's history.”, [002]) and (“For example, the risk score engine may evaluate a transaction risk based on the credit score of a user, payment history, whether the user is using a recognized device or an unrecognized device, etc.”, [031]). a risk score for a potential transaction authentication is generated;
Nandakumar does not expressly disclose but Sasha teaches;
and transmitting, to a graphical user interface of a user device of the user, the targeted event via an actionable button, wherein the targeted event is automatically initiated upon receiving input from the user. (“Referring now to FIG. 2, a data processing system 200 that may be used to implement the risk assessment server 140 of FIG. 1, in accordance with some embodiments of the inventive subject matter, display 204, and comprises input device(s) 202, such as a keyboard or keypad, a a memory 206 that communicate with a processor 208.”, [027]) and (“If the Billing date has arrived 424, all transaction parameters are pushed for a second iteration into the FFT Analysis Module 500 (of FIG. 5) that is configured to calculate scores related to the probability of Fraud and/or Charge back types of activity”, [085]) and (“If P (F) is greater then a preset fraud rejection threshold, FFT will auto reject the transaction. Else if P (F) is greater then some fraud review threshold and smaller then the fraud rejection threshold, transaction will be sent to Manual order review for Fraud assessment 636. … if P (CB) is greater then some charge back review threshold and smaller then the rejection threshold, transaction will be sent to Manual order review for charge back assessment 634.”, [091]) and (“When the issuing bank receives this authorization request it will identify the device and consumer application associated with the credit card on the issuing bank server, and initiate the user authentication and transaction verification process. The user can then either approve or decline the transaction. This method presents a robust and redundant strategy for preventing online fraud.”, [083]) and (“The amount to be billed appears in the ‘amount field’ 780, and the ‘confirm payment’ button 782 is used to complete the payment transaction and initiate the eFFT authorization process. This example shows some of the great benefits of the eFFT service. After linking the card with an e-identity, subsequent purchases simply require the push of a button (and entering the credit card information if the client is new to the merchant site). The eFFT server will automatically lookup the e-identity which is associated with the card number and will forward the payment validation request to the client, who can click to ‘accept’ or ‘reject’ the payment within seconds.”, [0133]) and (“For example, the business rules determine the thresholds by which e-transaction parameters are weighted and the method by which, the decision to accept, reject or push into manual review is reached; new products that are introduced by the seller and which are thought to be high risk products, can have a custom weight or score which initially forces these to be manually reviewed.”, [047]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Nandakumar to incorporate the teachings of Sasha because Nandakumar would be more efficient and versatile if it could target users with suggested goods and services based on perceived risks of transactions. (“Links may be included for allowing consumers to easily access Java-based presentations of software, or samples of music or other digital media being offered for purchase. The FFT therefore contains a ‘targeted advertising and product offering’ or ‘strategic marketing’ module 292 e which provides this functionality and provides user friendly graphical interface for interacting with the consumer on operations related to this feature.”, [71]) of Sasha.
The combination of Nandakumar and Sasha do not expressly disclose, but Abdelaziz teaches:
generating, by a trained machine learninq model, a targeted event associated with a user medium, based on the user score; (“The computing system also iteratively and dynamically updates the first user identity risk score or generates a new first user identity risk score by reapplying the third machine learning tool to the quantified risk level set of sign-ins. By generating and storing multiple different user scores, it is possible to evaluate risk trends associated with different users.”, [0115]), risk levels of user associated with scores via machine learning.
the trained machine learninq model trained usinq entity scores as inputs; (“Systems are provided for improving computer security systems that are based on user risk scores. These systems can be used to improve both the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to different the user risk profile components used to generate the user risk scores and in such a manner as to dynamically generate and modify the corresponding user risk scores.”, [see Abdelaziz Abstract, published 3/19/2020]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Nandakumar to incorporate the teachings of Abdelaziz because Nandakumar would be more efficient and versatile if it could target users with suggested goods and services based machine learning processed perceived risks of transactions, as done in Abdelaziz. (“Disclosed embodiments are directed to systems and methods for improving user identity protection and for further improving computer security systems that are based on user risk profiles and, even more particularly, to embodiments for improving the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to the user risk profile components in order to dynamically generate and modify the corresponding user risk scores.”, [011]) of Abdelaziz;
Regarding claim 3:
The combination of Nandakumar, Sasha and Abdelaziz have the limitations of claim 1:
Nandakumar further teaches:
wherein the targeted event comprises: identifying a proposed entity based on the user score and a proposed entity score. (“In some embodiments, the risk assessment server 140 may be configured to generate a risk assessment score that that is indicative of the relative risk associated with a requested transaction.”, [023]), note once again that, for the purposes of compact prosecution, examiner interprets the above mentioned claim term “eco-friendly score”, throughout these claims, to include in its meaning any “risk” related authentication score of any sort (see above claims 1, 11, 20 for details).
Regarding claim 4:
The combination of Nandakumar, Sasha and Abdelaziz have the limitations of claim 3:
Sasha further teaches:
wherein identifying the proposed entity is based on a determined subsequent increase to the user score based on subsequent authentications at the proposed entity. (“If the transaction is legitimate 446, then all transaction parameters are also pushed to the Transactions/Consumers depository DB 448 where they are stored. If the transaction is not valid, all transaction parameters are pushed into the FFT Analysis Module 500 for a probability of fraud/Charge back Assessment 452 that is performed specifically in relation to the transaction parameters. The FFT Real Time DB 116 (of FIG. 1 a) is then updated with new weights/scores for all transaction parameters.’, [087]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Nandakumar to incorporate the teachings of Sasha because Nandakumar would be more efficient and versatile if it could target users with suggested goods and services based on perceived risks of transactions, as done in Sasha . (“Links may be included for allowing consumers to easily access Java-based presentations of software, or samples of music or other digital media being offered for purchase. The FFT therefore contains a ‘targeted advertising and product offering’ or ‘strategic marketing’ module 292 e which provides this functionality and provides user friendly graphical interface for interacting with the consumer on operations related to this feature.”, [71]) of Sasha.
Regarding claim 5:
The combination of Nandakumar, Sasha and Abdelaziz have the limitations of claim 3:
Abdelaziz further teaches:
receiving a subsequent user authentication at the proposed entity; (“The authentication and validation of a user account and their access rights may be performed, in some instances, by the user providing a required combination of credentials, which may include any combination of user login credentials (e.g., name and password), certificates, digital signatures, device identifiers, tokens and so forth. In some instances, no credentials are required.”, [039]);
providing an indication of the subsequent user authentication to a machine learning model; (“The sign-in data 116 may also be directly sent to the machine learning engine 130, for analysis and application by the machine learning engine 130 (which itself may be a part of the cloud/service portal 110). This is shown, for example, by lines 117 and 119.”, [041]);
receiving an updated user score as an output of the machine learning model; and Please note as above examiner’s interpretation of “eco-friendly score” … (“This label data is used as crowdsourcing feedback by one or more machine learning models/algorithms to further train the machine learning and to generate a new or updated risk profiles report that maps the various user patterns and sign-in patterns to different risk valuations.”, [058]) and (“As described below, with reference to FIGS. 7-10, this machine learning engine may apply a tiered approach generate/modify risk scores by quantifying the relative risks associated with a user and/or sign-in event based on the crowdsourcing feedback”, [059]);
identifying an updated proposed entity based on the updated user score. (“If a user is later discovered to be high risk, their profile can be updated to reflect their heightened risk level and to restrict their access. However, when these types of systems can still be circumvented, such as when a bad entity creates or uses an account with a lower level risk and/or performs a bad act before the corresponding user risk profile can be updated to reflect the risky behavior.”, [005]) and (“Thereafter, the computer system iteratively and dynamically updates the first user identity risk score or generates a new user identity risk score by reapplying the third machine learning tool to the quantified risk level set of sign-ins.”, [016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Nandakumar to incorporate the teachings of Abdelaziz because Nandakumar would be more efficient and versatile if it could target users with suggested goods and services based machine learning processed perceived risks of transactions, as done in Abdelaziz. (“Disclosed embodiments are directed to systems and methods for improving user identity protection and for further improving computer security systems that are based on user risk profiles and, even more particularly, to embodiments for improving the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to the user risk profile components in order to dynamically generate and modify the corresponding user risk scores.”, [011]) of Abdelaziz;
Regarding claim 6:
The combination of Nandakumar, Sasha and Abdelaziz have the limitations of claim 1:
Nandakumar further teaches:
wherein the user medium includes a user account, a browser, or an extension. (“A service may be provided using Software as a Service (SaaS), Platform as a Service (PaaS), and/or Infrastructure as a Service (IaaS) delivery models. In the SaaS model, customers generally access software residing in the cloud using a thin client, such as a browser, for example.”, [015]).
Regarding claim 7:
The combination of Nandakumar, Sasha and Abdelaziz have the limitations of claim 1:
Abdelaziz further teaches:
wherein determining the user score comprises receiving a machine learning output from a trained machine learning model configured to output the user eco-friendly score based on one or more trends identified from the user authentications. (“Disclosed embodiments are directed to systems and methods for improving user identity protection and for further improving computer security systems that are based on user risk profiles and, even more particularly, to embodiments for improving the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to the user risk profile components in order to dynamically generate and modify the corresponding user risk scores.”, [011]) and (“The computing system also iteratively and dynamically updates the first user identity risk score or generates a new first user identity risk score by reapplying the third machine learning tool to the quantified risk level set of sign-ins. By generating and storing multiple different user scores, it is possible to evaluate risk trends associated with different users.”, [0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Nandakumar to incorporate the teachings of Abdelaziz because Nandakumar would be more efficient and versatile if it could target users with suggested goods and services based machine learning processed perceived risks of transactions, as done in Abdelaziz. (“Disclosed embodiments are directed to systems and methods for improving user identity protection and for further improving computer security systems that are based on user risk profiles and, even more particularly, to embodiments for improving the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to the user risk profile components in order to dynamically generate and modify the corresponding user risk scores.”, [011]) of Abdelaziz.
Regarding claim 8:
The combination of Nandakumar, Sasha and Abdelaziz have the limitations of claim 7:
Abdelaziz further teaches:
wherein the one or more trends are identified by comparing the one or more user authentications with one or more other user data sets for one or more other users, (“It will also be appreciated that the user identity risk score may be represented as a numeric value, a label, or any other representation that enables the user identity risk score to quantity or otherwise reflect a relative measure or level of risk, as compared to different user identity risk scores.”, [035]),
each of the one or more other user data sets including one or more other user authentications for one or more other entities having corresponding other entity scores. (“It will also be appreciated that the user identity risk score may be represented as a numeric value, a label, or any other representation that enables the user identity risk score to quantity or otherwise reflect a relative measure or level of risk, as compared to different user identity risk scores.”, [035])
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Nandakumar to incorporate the teachings of Abdelaziz because Nandakumar would be more efficient and versatile if it could target users with suggested goods and services based machine learning processed perceived risks of transactions, as done in Abdelaziz. (“Disclosed embodiments are directed to systems and methods for improving user identity protection and for further improving computer security systems that are based on user risk profiles and, even more particularly, to embodiments for improving the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to the user risk profile components in order to dynamically generate and modify the corresponding user risk scores.”, [011]) of Abdelaziz.
Regarding claim 9:
The combination of Nandakumar, Sasha and Abdelaziz have the limitations of claim 1:
Abdelaziz further teaches:
wherein the user score is based on a percentage of user authentications at entities having respective entity scores that exceed an entity score threshold in comparison to a total number of user authentications. (“the precision of a security system, which indicates how accurately the system is able to accurately identify a bad actor/actions as being bad, is inversely correlated with the recall effectiveness of the security system, which indicates the total percentage of bad actors/actions that are identified and prevented from harming a system.”, [008]) and (“These full user and sign-in reports (142/144) can also be parsed and filtered to generate corresponding risky user reports (146) and risky sign-in reports (148) that only contain users and sign-in events that are determined to meet or exceed a predetermined certain risk threshold.”, [057]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Nandakumar to incorporate the teachings of Abdelaziz because Nandakumar would be more efficient and versatile if it could target users with suggested goods and services based machine learning processed perceived risks of transactions, as done in Abdelaziz. (“Disclosed embodiments are directed to systems and methods for improving user identity protection and for further improving computer security systems that are based on user risk profiles and, even more particularly, to embodiments for improving the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to the user risk profile components in order to dynamically generate and modify the corresponding user risk scores.”, [011]) of Abdelaziz
Regarding claim 10:
The combination of Nandakumar, Sasha and Abdelaziz have the limitations of claim 1:
Abdelaziz further teaches:
wherein the user score is based on a number of user authentications at entities having respective entity scores that exceed an entity score threshold within a threshold period of time. (“This sign-in data, which is stored for a predetermined period of time, comprising one or more sign-in event. Then, from the stored sign-in data, and based on risk profiles associated with the stored sign-in data, the computer system identifies a set of one or more sign-in detectors for each sign-in event. These sign-in detectors comprise risk features and attributes associated with the sign-in event.”, [013]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Nandakumar to incorporate the teachings of Abdelaziz because Nandakumar would be more efficient and versatile if it could target users with suggested goods and services based machine learning processed perceived risks of transactions, as done in Abdelaziz. (“Disclosed embodiments are directed to systems and methods for improving user identity protection and for further improving computer security systems that are based on user risk profiles and, even more particularly, to embodiments for improving the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to the user risk profile components in order to dynamically generate and modify the corresponding user risk scores.”, [011]) of Abdelaziz.
Claim 2 is rejected pursuant to 35 USC 103 as being unpatentable over Nandakumar (US20200051058A1) in view of Sasha (US20080288405A1) in further view of Abdelaziz (US20200089848A1) and in further view of Marshall (US20030233278A1).
Regarding claim 2:
The combination of Nandakumar, Sasha and Abdelaziz have the limitations of claim 1:
That combination does not expressly disclose, but Marshall teaches:
determining increased rewards for subsequent authentications from a subsequent entity having an entity score that exceeds an entity score threshold; and (“An announced increase in the relative degree of perceived threat may correspond to an increase in the number of points that may be awarded to individuals who locate specified items or individuals.”, [0339]) and (“The methods and systems of the invention, as noted above, may be employed in with incentives for preparation and authentication of documents.”, [067]); connection transaction fraud risks are tied to increased rewards points;
providing the increased rewards for the subsequent authentications via a graphical user interface (GUI), (“At this point, a message may be displayed inviting the individual to provide the personal information in exchange for a coupon or other benefit good for a limited time at one of the displayed businesses selected by the individual.”, [0246]);
wherein the increased rewards are ordered, within the GUI, based on an ordering scheme. (“At this point, a message may be displayed inviting the individual to provide the personal information in exchange for a coupon or other benefit good for a limited time at one of the displayed businesses selected by the individual.”, [0242]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Nandakumar to incorporate the teachings of Marshall because Nandakumar would be more efficient and versatile if it could reward users as appropriate functions of authentications, as done in Marshall (“The methods and systems of the invention, as noted above, may be employed in with incentives for preparation and authentication of documents.”, [067] of Marshall).
Allowable Subject Matter
Claims 11 – 20 would be allowable if rewritten or amended to overcome the additional rejection herein pursuant to 35 U.S.C. 112(b), and otherwise put into an allowable form. The following is a statement of reasons for the indication of allowable subject matter: Independently, while the claims' limitations most recently set forth herein may individually be disclosed by the prior art, the claims as a whole are not obvious because the examiner would have to improperly use their separate limitations as a road map to combine them.
CONCLUSION
THIS ACTION IS MADE FINAL. 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 extension fee 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.
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form 892.
Harris (US20190333073A1) - A method includes: receiving information regarding a plurality of completed transactions from a plurality of users; receiving a query from a first user regarding a proposed transaction; determining at least one affinity between the first user and the plurality of users based on the information; determining a ranking or expectation of success for each of a plurality of potential entities for the proposed transaction based on the at least one affinity; selecting a plurality of selected entities based on the ranking or expectation of success for each of the potential entities; and sending, in response to the query, the plurality of selected entities to the first user.
Sliwaka (US20210082044A1) - According to embodiments of the present disclosure decentralized loan processes are disclosed. An example method includes receiving a request to initiate a loan process that indicates a collateral item. The method also includes generating a collateral token corresponding to the collateral item. The method further includes instantiating a loan process smart contract instance that is configured to manage a loan process in accordance with a loan process workflow, wherein the loan process smart contract instance that is configured to instantiate a loan smart contract receives a loan agreement notification indicating one or more loan term parameters of a loan that was agreed to by a lender and the borrower and to manage repayment of a loan based on the one or more loan term parameters. The collateral token is locked in an escrow account stored on a distributed ledger until the loan is fully repaid or in a default state.
Marsh (US20210256084A1) - Various aspects describe an information platform for consistently integrating and/or quantifying the underlying principles of ESG into financial analyses, analytical tools, metrics, and/or available information on reviewed companies, business entities, etc., and further provide integration of analysis with community-based insight, contextual information and tools for readily understanding both. Various embodiments implement machine learning tools for curating data sources and incorporating the data sources into the knowledge platform. The incorporation of AI moderated information sources enables succinct views of often massive information pools, and further provides for transitions between types of information (e.g., qualitative, quantitative, and interactive data source (e.g., engagements, collaborative information, etc.)). The platform facilitates user understanding and can eliminate the need to design and execute complicate queries by allowing users to transition between data types and view to develop better understanding and context of various information sources.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW COBB whose telephone number is (571) 272-3850. The examiner can normally be reached 9 - 5, M - F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to call examiner Cobb as above, or to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan, can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661