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 IDS, filed June 09, 2026, has been considered.
Claims 1-20, filed April 03, 2025, are examined on the merits.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1, 7-12, 13, and 18-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 7-12, 13, and 18-20 of U.S. Patent No. US 12299008 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the allowed claims are directed to an embodiment of the instant claim invention.
Instant Application: 19169986
U.S. Patent No. US 12299008 B2
1. A computer-implemented method, comprising: receiving a delegation from a domain to manage authorizations on behalf of the domain;
generating a first set of data through the authorizations, the first set of data comprising authorization records between user accounts of the domain and third-party named entities;
storing the first set of data as multi-dimensional data that comprises a plurality of dimensions, the plurality of dimensions comprising the user accounts and the third-party named entities as two of the dimensions;
receiving a second set of data from one or more external data sources;
causing to display a graphical user interface that visualizes an aggregation of the first set of data and the second set of data as a two-dimensional temporal graph; receiving, from the graphical user interface, a filtering selection of the aggregation of data by a first dimension;
filtering the aggregation of data by the first dimension to generate a first subset of data; receiving a time period selection to generate a second subset of data, the second subset of data comprising the first subset of data in the selected time period;
applying a model to the first subset of data to predict accumulative information of the first subset of data in an upcoming time period; and displaying, in the graphical user interface, the two-dimensional temporal graph comprising the second subset of data and the predicted accumulative information of the first subset of data.
1. A computer-implemented method, comprising: serving as a server that manages authorizations on behalf of a domain; generating a first set of data through the authorizations, the first set of data comprising authorization records between user accounts of the domain and third-party named entities; storing the first set of data as multi-dimensional data that comprises a plurality of dimensions, the plurality of dimensions comprising the user accounts and the third-party named entities as two of the dimensions; receiving a second set of data from one or more external data sources; causing to display a graphical user interface that visualizes an aggregation of the first set of data and the second set of data as a two-dimensional temporal graph; receiving, from the graphical user interface, a filtering selection of the aggregation of data by a first dimension; filtering the aggregation of data by the first dimension to generate a first subset of data; receiving, from the graphical user interface, a grouping selection to group the first subset of the data by a second dimension, the second dimension of the first subset of data comprising a plurality of second-dimensional values; and causing to modify the two-dimensional temporal graph in the graphical user interface, the modified two-dimensional temporal graph comprising accumulative information of the first subset of data that is separated by the plurality of second-dimensional values, each second-dimensional value of the second dimension being associated with a visual element that is distinguishable from other second-dimensional values, and wherein a totality of the visual elements of the plurality of second-dimensional values forms a visual element of the accumulative information in the modified two-dimensional temporal graph.
7. The computer-implemented method of claim 1, further comprising: receiving, from the graphical user interface, a second filtering selection of the aggregation of data by a third dimension;
filtering the aggregation of data by the first dimension and the third dimension to generate a second subset of data; and
displaying, in the graphical user interface, the two-dimensional temporal graph comprising accumulative information of the second subset of data.
7. The computer-implemented method of claim 1, further comprising: receiving, from the graphical user interface, a second filtering selection of the aggregation of data by a third dimension; filtering the aggregation of data by the first dimension and the third dimension to generate a second subset of data; and displaying, in the graphical user interface, the two-dimensional temporal graph comprising accumulative information of the second subset of data.
8. The computer-implemented method of claim 1, further comprising: receiving, from the graphical user interface, a user interaction to select a time point on the two-dimensional temporal graph; and
displaying a graphical element in the graphical user interface, the graphical element comprising information of the first subset of data for each second-dimensional value at the selected time point.
8. The computer-implemented method of claim 1, further comprising: receiving, from the graphical user interface, a user interaction to select a time point on the two-dimensional temporal graph; and displaying a graphical element in the graphical user interface, the graphical element comprising information of the first subset of data for each second-dimensional value at the selected time point.
9. The computer-implemented method of claim 1, wherein the plurality of dimensions further includes one or more of: vendor, cardholder, category, department, location, and payment type.
9. The computer-implemented method of claim 1, wherein the plurality of dimensions further includes one or more of: vendor, cardholder, category, department, location, and payment type.
10. The computer-implemented method of claim 1, wherein the aggregation of data
comprises transaction amounts associated with one or more of the dimensions.
10. The computer-implemented method of claim 1, wherein the aggregation of data comprises transaction amounts associated with one or more of the dimensions.
11. The computer-implemented method of claim 10, further comprising: ranking, the first subset of data based on the transaction amounts associated with a third dimension, the third dimension of the first subset of data comprising a plurality of third-dimensional values; and
causing to display in the graphical user interface, the ranked subset of data.
11. The computer-implemented method of claim 10, further comprising: ranking, the first subset of data based on the transaction amounts associated with a third dimension, the third dimension of the first subset of data comprising a plurality of third-dimensional values; and causing to display in the graphical user interface, the ranked subset of data.
12. The computer-implemented method of claim 10, further comprising: setting a threshold for a transaction amount in a third dimension, the third dimension of the subset of data comprising a plurality of third-dimensional values; and in response to a transaction amount associated with a third-dimensional value exceeding the threshold, causing to send a notification to a user device to inform the user about the transaction amount associated with the third- dimensional value.
12. The computer-implemented method of claim 10, further comprising: setting a threshold for a transaction amount in a third dimension, the third dimension of the subset of data comprising a plurality of third-dimensional values; and in response to a transaction amount associated with a third-dimensional value exceeding the threshold, causing to send a notification to a user device to inform the user about the transaction amount associated with the third-dimensional value.
Claims 13 and 18-20 are directed to a system comprising the same steps in claims 1 and 7-12.
Claims 13 and 18-20 are directed to a system comprising the same steps in claims 1 and 7-12.
BASIS FOR DOUBLE PATENTING REJECTION
The allowed claims do not recite “a time period selection…”, however, the recitation of “a grouping selection to group the first subset of the data by a second dimension, the second dimension of the first subset of data comprising a plurality of second-dimensional values; and causing to modify the two-dimensional temporal graph in the graphical user interface” is an obvious variation of the instant embodiment. Further, the allowed claims do not recite “a model to the first subset of data to predict accumulative information of the first subset of data in an upcoming time period”, however, the “two-dimensional temporal graph” reasonable reads on the instant limitation of “model.” Therefore, it would have been obvious for one of ordinary skill in the art at the time before to the effective filing date of the instant invention to use the instant invention as described by U.S. Patent No. US 12299008 B2.
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.
Claim(s) 1-10 and 12-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (Tiwari hereafter, US 20240095743 A1) in view of Fitzpatrick et al. (US 20180165347 A1).
Claim 1, Tiwari discloses a computer-implemented method, comprising:
receiving a delegation from a domain to manage authorizations on behalf of the domain ([0042], e.g. When the service provider server 130 receives a transaction request (e.g., a request to log in to a corresponding account, a request to access data, a request to perform an electronic payment transaction, etc.) associated with an account from a device (e.g., the user device 110, the user device 180, the merchant server 120, etc.), the service provider server 130 may request the data analysis module 132 to analyze the transaction request and/or the account);
generating a first set of data through the authorizations, the first set of data comprising authorization records between user accounts of the domain and third-party named entities (page 7, [0054], e.g. used by the corresponding entity when interacting with the service provider server 130 (e.g., browsing a website hosted by the service provider server 130, logging in to an account with the service provider server 130, and Figure 3C);
storing the first set of data as multi-dimensional data that comprises a plurality of dimensions, the plurality of dimensions comprising the user accounts and the third-party named entities as two of the dimensions (page 6, [0045], e.g. service provider server 130 may store data associated with each of its registered entities in the account database 136) that comprises a plurality of dimensions, the plurality of dimensions comprising the user accounts and the third-party named entities as two of the dimensions (page 6, [0047], e.g. Each of the intermediate code may be two-dimensional, having a first dimension (e.g., a dimension 212) corresponding to the time period, and a second dimension (e.g., a dimension 214) corresponding to the characteristic dimension);;
receiving a second set of data from one or more external data sources (Figure 3C, e.g. row 334);
causing to display a graphical user interface that visualizes an aggregation of the first set of data and the second set of data as a two-dimensional temporal graph (Figures 4A through 4C);
receiving a time period selection to generate a second subset of data, the second subset of data comprising the first subset of data in the selected time period ([0014], e.g. the code may indicate changes across the different account characteristics for the account over a time period (e.g., 30 days, 6 months, a year, etc.). The time period may be divided into multiple time frames. For example, when the time period is 30 days, each time frame may represent a distinct day within the 30-day period. In another example, when the time period is 6 months, each time frame may represent a distinct week within the 6-month period);
applying a model to the first subset of data to predict accumulative information of the first subset of data in an upcoming time period ([0023], e.g. the data analysis system may use a machine learning model to predict a risk associated with the first user account based on attributes associated with the code, and ([0042])Each code generated by the data analysis module 132 may represent changes to various characteristics associated with a corresponding account over a time period); and
displaying, in the graphical user interface, the two-dimensional temporal graph comprising the second subset of data and the predicted accumulative information of the first subset of data (Figures 4A through 4C).
However, Tiwari does not disclose receiving, from the graphical user interface, a filtering selection of the aggregation of data by a first dimension; filtering the aggregation of data by the first dimension to generate a first subset of data…applying a model to the first subset of data to predict accumulative information of the first subset of data in an upcoming time period; and displaying, in the graphical user interface, the two-dimensional temporal graph comprising the second subset of data and the predicted accumulative information of the first subset of data.
Fitzpatrick discloses receiving, from the graphical user interface, a filtering selection of the aggregation of data by a first dimension (page 3, [0038], e.g. named filter dimension 138 may be explicitly selected by a user working through client device 110 from a list of dimensions for use in generating a report);
filtering the aggregation of data by the first dimension to generate a first subset of data (page 3, [0035], e.g. Original data further can refer to such data in a form or structure prior to aggregation and/or summarization. Thus, for example, a named filter specifies an expression that is operative on data 130, e.g., rows and/or columns of data 130, prior to aggregation and/or summarization. As noted, a named filter, once stored within data engine 115 and available for use in generating reports of data 130, may be referred to as a member of the named filter dimension).
Fitzpatrick discloses an improvement to the existing multi-dimensional analysis systems further aggregate data through levels of the hierarchies to provide summarized views of the data. The improvements allow a user to drill down beneath the aggregated data to view the lower levels of the hierarchies when more detail is needed (page 1, [0002]). One of ordinary skill in the art at the time before the effective filing date of the instant invention would have been motivated by Fitzpatrick to improve the system of Tiwari. Therefore, it would have been obvious for one of ordinary skill in the art to use the multi-dimensional analysis systems of Tiwari with the improvements described by Fitzpatrick. The benefit would be to allow a user to drill down beneath the aggregated data to view the lower levels of the hierarchies when more detail is needed.
Claim 2, Tiwari as modified discloses the two-dimensional temporal graph comprising a plurality of visual elements on a time-axis, and the visual elements represent the second subset of data and the predicted accumulative information of the first subset of data respectively (Tiwari, page 2, [0015], e.g. data analysis system may generate a code having a first dimension (e.g., a time dimension) corresponding to the time period. Thus, the code may be divided into multiple sections, where each section represents a respective time frame within the time period. When an account characteristic is changed during a particular time frame within the time period, the data analysis system may highlight a section within the code that corresponds to the particular time frame); and displaying, in the graphical user interface, the two-dimensional temporal graph comprising accumulative information of the second subset of data (Figures 4A-4C).
Claim 3, Tiwari as modified discloses the second subset of data further comprises the first subset of data in a second time period prior to the selected time period (Tiwari, page 6, [0047], e.g. Each of the intermediate code may be two-dimensional, having a first dimension (e.g., a dimension 212) corresponding to the time period, and a second dimension (e.g., a dimension 214) corresponding to the characteristic dimension).
Claim 4, Tiwari as modified discloses receiving, from the graphical user interface, a selection of a second-dimensional value (Tiwari, page 2, [0015], e.g. data analysis system may generate a code having a first dimension (e.g., a time dimension) corresponding to the time period. Thus, the code may be divided into multiple sections, where each section represents a respective time frame within the time period. When an account characteristic is changed during a particular time frame within the time period, the data analysis system may highlight a section within the code that corresponds to the particular time frame); and displaying, in the graphical user interface, the two-dimensional temporal graph comprising information of the second subset of data having the selected second-dimensional value in the selected time period and the second time period (Tiwari, Figures 4A-4C).
Claim 5, Tiwari as modified discloses applying a model to the first subset of data to predict accumulative information of the first subset of data in an upcoming time period; and displaying, in the graphical user interface, the two-dimensional temporal graph comprising the predicted accumulative information of the first subset of data (Tiwari, page 2, [0019], e.g. the data analysis system may determine that accounts that have experienced similar change patterns share common characteristics, such that a behavior of an account can be inferred and/or predicted based on behavior of other accounts that have similar change behavior); and displaying, in the graphical user interface, the two-dimensional temporal graph comprising the predicted accumulative information of the first subset of data (Figures 4A-4C).
Claim 6, Tiwari as modified discloses the selected time period is a week, a month (Tiwari, page 1, [0014], e.g. when the time period is 30 days, each time frame may represent a distinct day within the 30-day period. In another example, when the time period is 6 months, each time frame may represent a distinct week within the 6-month period), or a year.
Claim 7, Tiwari as modified discloses receiving, from the graphical user interface, a second filtering selection of the aggregation of data by a third dimension (Tiwari, page 2, [0020], e.g. the data analysis system may generate a code (also referred to as a “multi-dimensional code,” “a three-dimensional code,” or a “merged code”) that represents changes across multiple categories (e.g., multiple domains) in different time frames for an account such that the changes can be presented, viewed, and analyzed in an efficient manner. In this regard, the data analysis system may generate a code that includes a third dimension (e.g., a category dimension). To implement this third dimension, the data analysis system may generate multiple two-dimensional codes for the respective account categories); filtering the aggregation of data by the first dimension and the third dimension to generate a second subset of data (Fitzpatrick, page 3, [0035], e.g. Original data further can refer to such data in a form or structure prior to aggregation and/or summarization. Thus, for example, a named filter specifies an expression that is operative on data 130, e.g., rows and/or columns of data 130, prior to aggregation and/or summarization. As noted, a named filter, once stored within data engine 115 and available for use in generating reports of data 130, may be referred to as a member of the named filter dimension); and displaying, in the graphical user interface, the two-dimensional temporal graph comprising accumulative information of the second subset of data (Tiwari, Figures 4A-4C).
Claim 8, Tiwari as modified discloses receiving, from the graphical user interface, a user interaction to select a time point on the two-dimensional temporal graph (Tiwari, page 2, [0015], e.g. data analysis system may generate a code having a first dimension (e.g., a time dimension) corresponding to the time period. Thus, the code may be divided into multiple sections, where each section represents a respective time frame within the time period. When an account characteristic is changed during a particular time frame within the time period, the data analysis system may highlight a section within the code that corresponds to the particular time frame); and displaying a graphical element in the graphical user interface, the graphical element comprising information of the first subset of data for each second-dimensional value at the selected time point (Tiwari, Figures 4A-4C).
Claim 9, Tiwari as modified discloses the plurality of dimensions further includes one or more of: vendor, cardholder, category, department, location, and payment type (Tiwari, [0018], e.g. a payment method, a payment status, whether a credit is used, etc.).
Claim 10, Tiwari as modified discloses the aggregation of data comprises transaction amounts associated with one or more of the dimensions (Tiwari, [0018], e.g. characteristics related to transactions conducted by a user (e.g., a transaction pattern, an average transaction amount).
Claim 12, Tiwari as modified discloses setting a threshold for a transaction amount in a third dimension, the third dimension of the subset of data comprising a plurality of third-dimensional values; and in response to a transaction amount associated with a third-dimensional value exceeding the threshold, causing to send a notification to a user device to inform the user about the transaction amount associated with the third- dimensional value (Tiwari, page 3, [0025], e.g. if the data analysis system determines that over a threshold number (or percentage) of user accounts within the cluster corresponds to malicious user accounts (e.g., user accounts through which fraudulent transactions have been conducted), the data analysis system may increase the risk associated with the first user account and take appropriate actions, such as requiring additional authentication).
Claims 13-19, Tiwari as modified discloses the system comprising the same steps as cited above. Therefore, claims 13-19 are rejected for the same rationale and citations above.
Claim(s) 11 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (Tiwari hereafter, US 2024/0095743 A1) in view of Fitzpatrick et al. (Fitzpatrick hereafter, US 2018/0165347 A1), as applied to claims 1-10, 12, and 13-19 above, in further view of Cole (US 20190034951 A1).
Claim 11, Tiwari as modified discloses the claimed invention except for the limitation of “ranking, the first subset of data based on the transaction amounts…the ranked subset of data.” Cole discloses ranking, the first subset of data based on the transaction amounts associated with a third dimension, the third dimension of the first subset of data comprising a plurality of third-dimensional values (page 3, [0044], e.g. the customer's transaction data is analyzed. The customer's transactions data is analyzed for all the key drivers that the retailer has selected, in this customer behavioral model example (501) only four key drivers are depicted (store promotional items, price of goods, quality of goods, and choice of goods); however it should be appreciated that in a full customer focus system all key drivers are analyzed. (Currently six dimensions are used, namely Price, Promotions, The Circular, Choice, Quality, and Direct Communication, but more may be used in the future.) For some key drivers the customer behavioral model 501 ranks the transaction according to given criteria (507, 525), while for other key drivers the customer behavioral model 501 may only check the transaction for a key driver 547 and then label the transaction as having the driver (551) or not (549). For key driver 547 there are a multitude of possible labels: a label may indicate that the product is a new product, that that customer buys a large repertoire of products, that the product is a niche product, or another label indicating that the product is of a choice type. It is possible for a transaction to have multiple labels); and causing to display in the graphical user interface, the ranked subset of data (Figures 6A and 6B).
Cole discloses an invention improved profitability of retail operations and improved competitive advantage in battleground markets (page 5, [0058]-[0060]). One of ordinary skill in the art at the time befor the effective filing date of the instant invention would have been motivated by Cole to improve the system of Tiwari as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use ranking to Cole with the system of Tiwari as modified. The benefit would be for improved profitability of retail operations and improved competitive advantage in battleground markets.
PERTINENT PRIOR ART
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Moffett discloses describe how access authority can be delegated from the original owners of objects, through managers and security administrators, to system users (page 12).
M. Jarke et al. discloses the multidimensional view of data considers that information is stored in a multidimensional array or cube. A cube is a group of data cells ar ranged by the dimensions of the data, where a dimension is a list of members, all of which are of a similar type in the user's perception of the data. Each dimension has an associated hierarchy of levels of aggregated data, i.e., it can be viewed at different levels of detail (e.g., Time can be detailed as Year, Month, Week, or Day). Navigation (often improperly called slicing and dicing) is a term used to de scribe the processes employed by users to explore a cube interactively by drilling, rotating, and screening, generally using a graphical OLAP client connected to an OLAP server (page 90).
CONCLUSION
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Dune Ly, whose telephone number is (571) 272-0716. The examiner can normally be reached on Monday-Friday from 8 A.M. to 4 PM ET.
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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
6/11/2026
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