Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This Office Action is in response to the application 18/973,209 filed on 12/10/2024.
Claims 21-40 have been examined and are pending in this application. As per the Preliminary Amendment filed on 12/10/2024, claims 1-20 were cancelled.
Priority
This application is a divisional application of U.S. application No. 17/584,370 filed on January 25th, 2022 (Now U.S. Patent No. 12,189,809) and claims priority to application 63/141,286 filed on January 25th, 2021.
Examiner’s notes
In attempt to accelerate the prosecution process, the Examiner called (also emailed several times) on 03/24/2026 and later to Kevin J. McNeely (Reg. No. 52,018) to propose to amendments to the claims. However, No response has been received.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 21-40 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claims 21 and 39-40, claims 21 and 39-40 recite “calculating an expanded search area for the geographic location of the user; identifying a second set of geographical locations of interest within the expanded search area ……...” which is unclear. It is not clear how the expanded search area for the geographic location of the user is calculated.
Regarding claims 21 and 39-40, claims 21 and 39-40 recite “determining a weighted relevance of the second set of local data source jurisdictions; quantifying a relative value in searching for public records within each of the weighted second set of local data source jurisdictions relative to a search cost…. .” this phrase is unclear as it doesn’t further limit the claim and the claim scope is unclear.
Regarding claims 21 and 39-40, claims 21 and 39-40 recite “quantifying a relative value in searching for public records within each of the weighted second set of local data source jurisdictions relative to a search cost ……...” which is unclear. It is not clear how a relative value in searching for public records within each of the weighted second set of local data source jurisdictions relative to a search cost is quantified.
Regarding claims 21 and 39-40, claims 21 and 39-40 recite “performing an optimized search of public records in the first set of local data source jurisdictions and a selection of the second set of local data source jurisdictions based on the quantified relative value……...” which is unclear. It is not clear how an optimized search of public records in the first set of local data source jurisdictions is performed based on the quantified relative value .
Regarding claims 22-38; claims 22-38 are dependent on claim 21, and therefore inherit 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph issues of the independent claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C.
102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
Claims 21-29 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Padmanabham (US 2020/0134656) and in view of O’Malley (US 2020/0104939).
Regarding claim 1, Padmanabham discloses a method of creating a trust profile with a computer (Padmanabhan par. 0243; Receiving validation information from the unknown customer and creating a validated customer profile for the previously unknown customer), the method comprising:
receiving personal information from a user at the computer (Padmanabhan par. 0240; Prompting the unknown customer to provide validation information includes requesting the unknown customer to provide one or more of: a first and last name. See also par. 0157);
gathering biographical information about the user (Padmanabhan par. 0240; Prompting the unknown customer to provide validation information includes requesting the unknown customer to provide one or more of: a first and last name; an email address; a cellular telephone number; a biometric fingerprint scan; a biometric retina scan; and in which the validation information provided by the unknown customer is associated with the unknown customer's new global ID as part of new validated customer profile);
validating the personal information from the gathered biographical information (Padmanabhan par. 0240 and 0243; Receiving validation information from the unknown customer and creating a validated customer profile for the previously unknown customer. the unknown customer to provide validation information includes requesting the unknown customer to provide one or more of: a first and last name; an email address; a cellular telephone number; a biometric fingerprint scan; a biometric retina scan; and in which the validation information provided by the unknown customer is associated with the unknown customer's new global ID as part of new validated customer profile);
identifying a first set of geographic locations of the user from the gathered biographical information and the validated personal information (Padmanabhan par. 0225 and 0247; processing logic receives limited transaction data with the first e-commerce customer transaction including at least a transaction date, a transaction ID, a transaction amount, and a transaction location).
calculating an expanded search area for the geographic location of the user (Padmanabhan par. 0229 and 0230; Processing logic matches at least a portion of the transaction source information for the first and second e-commerce customer transactions and responsively prompting the matched unknown customer to confirm they are associated with both the first and second e-commerce customer transactions. At block 640, processing logic invites the matched unknown customer to create a single sign-on ID and to participate in a commerce rewards program offered by the commerce cloud platform).
identifying a second set of geographical locations of interest within the expanded search area (Padmanabhan par. 0228; Processing logic receives a second e-commerce customer transaction for processing at the cloud commerce platform wherein receiving the second e-commerce customer transaction includes receiving the limited transaction data for the second e-commerce customer transaction and receiving the transaction source information for the second e-commerce customer transaction);
mapping spatiotemporal data associated with the first set of geographic locations to produce a first set of local data source jurisdictions and to the second set of geographic locations of interest to a second set of local data source jurisdictions (Padmanabhan par. 0229; Processing logic matches at least a portion of the transaction source information for the first and second e-commerce customer transactions and responsively prompting the matched unknown customer to confirm they are associated with both the first and second e-commerce customer transactions);
determining a weighted relevance of the second set of local data source jurisdictions (Padmanabhan par. 0245; Receiving the second purchase transaction further includes: creating a second new global ID based on the second purchase transaction; allocating commerce rewards points to the unknown customer via the second new global ID based on the second purchase transaction);
quantifying a relative value in searching for public records within each of the weighted second set of local data source jurisdictions relative to a search cost (Padmanabhan par. 0042 and 0274; Further depicted is the host organization 110 receiving input and other requests 115 from customer organizations 105A-C via network 125 (such as a public Internet). For example, incoming search queries, database queries, API requests, interactions with displayed graphical user interfaces and displays at the user client devices 106A-C, or other inputs may be received from the customer organizations 105A-C to be processed against the database system 130, or such queries may be constructed from the inputs and other requests 115 for execution against the databases 155A and 155B or the query interface 180, pursuant to which results 116 are then returned to an originator or requestor, such as a user of one of a user client device 106A-C at a customer organization 105A-C. For example, the user interface device can be used to access data and applications hosted by system 916, and to perform searches on stored data, and otherwise allow a user to interact with various GUI pages that may be presented to a user);
performing an optimized search of public records in the first set of local data source jurisdictions and a selection of the second set of local data source jurisdictions based on the quantified relative value (Padmanabhan par. 0043; Host organization 110 may receive a variety of requests for processing by the host organization 110 and its database system 130. Incoming requests 115 received at web-server 175 may specify which services from the host organization 110 are to be provided, such as query requests, search request, status requests, database transactions, graphical user interface requests and interactions, processing requests to retrieve, update, or store data on behalf of one of the customer organizations 105A-C);
assessing collected user information and validated personal information that includes the optimized search of public (Padmanabhan par. 0243; Generating a validation score for the validated customer profile, in which the validation score indicates a degree of confidence); and
enabling the user to share a trust score or the trust profile with an evaluator (Padmanabhan par. 0157; If the user creates a new single sign-on ID, then the commerce cloud platform 195 may additionally prompt the unknown customer requesting that they share their email, telephone number, full name, etc., which each piece of personal information voluntarily shared by the customer corresponding to a reward or a bounty for sharing such details with the commerce cloud platform).
Padmanabham teaches, validating the personal information from the gathered biographical information and collecting risk information regarding the user according to the validated personal information from at least one data source (Padmanabhan par. 0240 and 0243). However, Padmanabham does not explicitly disclose assessing the collected risk information and validated personal information; creating the trust profile for the user from the assessed risk information and validated personal information and enabling the user to share a trust score or the trust profile with an evaluator.
However, in an analogous art, O’Malley teaches assessing the collected risk information and validated personal information (O’Malley par. 0019; The system electronically validates the personal information of the applicant. At Step 210, authorization is obtained then electronically underwrites a potential life insurance policy for the applicant including a risk assessment).
creating a trust profile for the user from the assessed user information and validated personal information (O’Malley par. 0019; The system electronically validates the personal information of the applicant. At Step 210, authorization is obtained, from the applicant, to retrieve externally sourced data related to the applicant. At Step 212, the applicant replies to reflexive questions from the question driver (this step may be done in conjunction with, or in addition to, the collection of personal information in Step 206). At Step 214, the system collects the externally sourced data authorized by the applicant in Step 210. At Step 216, the system electronically underwrites a potential life insurance policy for the applicant including a risk assessment. At Step 218, if the rating (and/or risk assessment) determined at Step 216 is acceptable, the adviser is prompted, and the adviser then uploads the illustration (an insurance profile, perhaps including predictive modeling, understood by those skilled in the art).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of O’Malley with the method and system of Padmanabham, wherein assessing the collected risk information and validated personal information to provide users with a means for electronically managing certain life insurance policy (profile) determinations (O’Malley par. 0002).
Regarding claim 22, Padmanabham and O’Malley disclose the method of claim 21,
Padmanabham further discloses wherein identifying first or second set of geographic locations includes identifying vacation homes, rental properties, education, mailing/post office boxes, arrest locations and travel history (Padmanabhan par. 0182; If the customer visits a brick and mortar physical store location, many of which are participating merchants that utilize the commerce cloud platform 195 of the host organization, then again the customer can identify themselves and authenticate with the brick-and-mortar's physical location systems simply by scanning a QR code or entering their phone number or email at check out, or alternatively, by authenticating on their customer user device 499 via which the customer can pay for the transaction using stored payment data).
Regarding claim 23, Padmanabham and O’Malley disclose the method of claim 21,
Padmanabham further discloses wherein calculating the expanded search area includes adjusting the search area based on local geographic considerations and proximity of hot spot locations having higher amounts of criminal activity (Padmanabhan par. 0229 and 0230; Processing logic matches at least a portion of the transaction source information for the first and second e-commerce customer transactions and responsively prompting the matched unknown customer to confirm they are associated with both the first and second e-commerce customer transactions. At block 640, processing logic invites the matched unknown customer to create a single sign-on ID and to participate in a commerce rewards program offered by the commerce cloud platform).
Regarding claim 24, Padmanabham and O’Malley disclose the method of claim 21,
Padmanabham further discloses wherein determining the weighted relevance of the second set of local data source jurisdictions includes considering a centroid distance to a user's known location, an expanse of geographic coverage, a recency and duration of time spent in a location, a population density of a location, a crime rate of a location, and the per capita alcohol consumption or drug usage (Padmanabhan par. 0178; The commerce cloud platform 195 may flag transactions for further validation randomly, or based on minor issues, such as the customer utilizing a new merchant, the customer spending outside of a normal range, the customer spending above or below a threshold, the customer initiating more transactions than usual within a defined period of time, the customer initiating a transaction after a long period of time, and other criteria, which may be defined manually or which may be generated and defined by a machine learning model which screens for potentially fraudulent transactions).
Regarding claim 25, Padmanabham and O’Malley disclose the method of claim 21,
Padmanabham further discloses wherein determining the weighted relevance of the second set of local data source jurisdictions includes estimating a center point to a user's known locations, an expanse of geographic coverage, a recency and duration of time spent in a location, a population density of a location, a normalized crime rate of a location, and the per capita alcohol consumption or drug usage (Padmanabhan par. 0178; The commerce cloud platform 195 may flag transactions for further validation randomly, or based on minor issues, such as the customer utilizing a new merchant, the customer spending outside of a normal range, the customer spending above or below a threshold, the customer initiating more transactions than usual within a defined period of time, the customer initiating a transaction after a long period of time, and other criteria, which may be defined manually or which may be generated and defined by a machine learning model).
Regarding claim 26, Padmanabham and O’Malley disclose the method of claim 21,
Padmanabham further discloses further comprising applying additional geographic related reference data to the spatiotemporal data for the second set of geographic locations of interest to a second set of local data source jurisdictions; and wherein determining the weighted relevance of the second set of local data source jurisdictions comprises determining the weighted relevance of the second set of local data source from the applied additional geographic related reference data (Padmanabhan par. 0178; The commerce cloud platform 195 may flag transactions for further validation randomly, or based on minor issues, such as the customer utilizing a new merchant, the customer spending outside of a normal range, the customer spending above or below a threshold, the customer initiating more transactions than usual within a defined period of time, the customer initiating a transaction after a long period of time, and other criteria, which may be defined manually or which may be generated and defined by a machine learning model which screens for potentially fraudulent transactions).
Regarding claim 27, Padmanabham and O’Malley disclose the method of claim 26,
Padmanabham further discloses wherein applying additional geographic related reference data includes consideration of the user's frequency-based mobility, traffic patterns to nearby dense populations, and the geographic distribution of location real estate types as commercial, industrial and residential (Padmanabhan par. 0229 and 0230; Processing logic matches at least a portion of the transaction source information for the first and second e-commerce customer transactions and responsively prompting the matched unknown customer to confirm they are associated with both the first and second e-commerce customer transactions. At block 640, processing logic invites the matched unknown customer to create a single sign-on ID and to participate in a commerce rewards program offered by the commerce cloud platform).
Regarding claim 28, Padmanabham and O’Malley disclose the method of claim 21,
Padmanabham further discloses wherein quantifying the relative value in searching for public records includes assimilating the jurisdiction attributes from each weighted set of relevant jurisdictions into a model to evaluate the value/risk metric of a targeted jurisdiction (Padmanabhan par. 0178; The commerce cloud platform 195 may flag transactions for further validation randomly, or based on minor issues, such as the customer utilizing a new merchant, the customer spending outside of a normal range, the customer spending above or below a threshold, the customer initiating more transactions than usual within a defined period of time, the customer initiating a transaction after a long period of time, and other criteria, which may be defined manually or which may be generated and defined by a machine learning model which screens for potentially fraudulent transactions).
Regarding claim 29, Padmanabham and O’Malley disclose the method of claim 21,
Padmanabham further discloses wherein quantifying the relative value in searching for public records includes selecting an optimal jurisdiction search to balance a maximum search value and a minimal cost (
Regarding claim 39; claim 39 is directed to a method associated with the method claimed in claim 21. Claim 39 is similar in scope to claim 21, and is therefore rejected under similar rationale. Padmanabhan further teaches enabling the user to share the digital trust profile (Padmanabhan par. 0157; If the user creates a new single sign-on ID, then the commerce cloud platform 195 may additionally prompt the unknown customer requesting that they share their email, telephone number, full name, etc., which each piece of personal information voluntarily shared by the customer corresponding to a reward or a bounty for sharing such details with the commerce cloud platform).
Allowable Subject Matter
Claims 30-38 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and amend to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
Claim 40 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANCHIT K SARKER whose telephone number is (571)270-7907. The examiner can normally be reached M-F 8:30 AM-5:30 PM.
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/SANCHIT K SARKER/Primary Examiner, Art Unit 2495
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