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 .
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.
DETAILED ACTION
This action is in response to applicant’s original submittal made on 07/01/2025. Claims 1-20 are pending.
Double Patenting
The non-statutory 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 non-statutory double patenting rejection is appropriate where the claims at issue 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); and 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 a non-statutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1, 11 and 19 are rejected on the ground of non-statutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,401,650 and 650’ hereinafter. Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are drawn to the following:
(19/257389) 1. A method comprising: generating, by a computing system, a dataset based on a first digital identity profile of a first entity and a second digital identity profile of a second entity; inputting, by the computing system, the dataset to an artificial intelligence (Al) agent to generate a linkage definition and a set of activation elements, the Al agent having been trained by applying one or more machine learning models to a set of session logs corresponding to digital identity profiles of a plurality of linked entities, wherein the linkage definition identifies one or more assets of one or both of the first entity or the second entity, and wherein the set of activation elements identifies one or more states; determining, by the computing system, that at least one activation element of the set of activation elements has been triggered; in response to determining that the at least one activation element of the set of activation elements has been triggered, generating, by the computing system, one or more security access tokens based on the linkage definition, the one or more security access tokens indicating that access to the one or more assets is granted; and transmitting, by the computing system, the one or more security access tokens to at least one of a first device identified in the first digital identity profile or a second device identified in the second digital identity profile.; maps to (650’) retrieving, by a computing system comprising one or more processors, from a first digital identity profile of a first entity, a first set of identity elements and a first set of metadata corresponding to the first set of identity elements; retrieving, by the computing system, from a second digital identity profile of a second entity, a second set of identity elements and a second set of metadata corresponding to the second set of identity elements; generating, by the computing system, a dataset based on a plurality of the first set of identity elements, the second set of identity elements, the first set of metadata, and the second set of metadata; inputting, by the computing system, the dataset to an artificial intelligence (AI) agent to generate a linkage definition and a set of activation elements, the AI agent having been trained by applying one or more machine learning models to a set of session logs corresponding to digital identity profiles of a plurality of linked entities, wherein the linkage definition identifies one or more physical or digital assets of one or both of the first entity or the second entity, and wherein the set of activation elements identifies one or more states; receiving, by the computing system, from a plurality of computing devices, a set of inputs corresponding to the first entity and the second entity; determining, by the computing system, based on the set of inputs, that the set of activation elements has been triggered; in response to determining that the set of activation elements has been triggered, generating, by the computing system, a set of one or more security access tokens based on the linkage definition, the set of one or more security access tokens indicating that access to select digital or physical assets are granted for specified time periods; and transmitting, by the computing system, the set of one or more security access tokens to at least one of a first device identified in the first digital identity profile or a second device identified in the second digital identity profile.
Claim Rejections - 35 USC § 103
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Handelman et al. (US Patent No. 12,554,797 and Handelman hereinafter) in view of Chen (EP-3742700-A1).
As to claims 1, 11 and 19, Handelman teaches a method comprising:
generating, by a computing system, a dataset based on a first digital identity profile of a first entity and a second digital identity profile of a second entity (i.e., …teaches in col. 2 lines 45-60 the following: “identify different users who are sharing a user account by analyzing past transactions associated with the user account and different user devices that were used to conduct the past transactions.” …teaches in col. 10 lines 35-60 the following: “the account decomposition module 132 may determine that the user account is shared among multiple users.”. …teaches in col. 15 lines 45-65 the following: “the account decomposition manager 202 may generate multiple user profiles 214a and 214b for the user account. Each user profile may correspond to a cluster and a distinct user of the user account. For example, the account decomposition manager 202 may generate the user profile 214a based on the cluster 350 and the user profile 214b based on the cluster 360. In some embodiments, the account decomposition manager 202 may generate the user profile 214a based on the past transactions of the user account within the cluster 350. For example, the account decomposition manager 202 may derive transaction pattern(s) for the user profile 214a based on the past transactions within the cluster 350”.);
inputting, by the computing system, the dataset to an artificial intelligence (Al) agent to generate a linkage definition and a set of activation elements (i.e., …teaches in col. 18 lines 10-35 the following: “The process 500 begins by accessing (at step 505) a plurality of transactions conducted through a user account. For example, the risk manager 202 may obtain data associated with past transactions conducted through a user account from the account database 136. (67) The process 500 then generates (at step 510), for each transaction, a word based on an attribute associated with the transaction. For example, the transaction analysis module 204 may extract one or more attributes from each past transaction of the user account, and may use the word generation module 208 to generate a word for each of the extracted attributes. The attributes extracted from the past transactions may include product categories, amounts, merchant identities, etc. (68) After generating a word for each past transaction, the process 500 determines (at step 515) a vector in a multi-dimensional space based on the word. For example, the transaction analysis module 204 may train a machine learning model (e.g., a word2vec model) for mapping a word to a vector within the multi-dimensional space using words generated for past transactions associated with different user accounts.”),
the Al agent having been trained by applying one or more machine learning models to a set of session logs corresponding to digital identity profiles of a plurality of linked entities (i.e., …teaches in col. 13 lines 40-50 the following: “use the trained machine learning model to map each past transaction to a vector (e.g., a position) within the vector space based on the extracted attributes.” … the past transactions associated with the user account have been mapped to different positions 304-334 within the vector space 300. By analyzing the different vectors 304-334 within the vector space 300, the transaction analysis module 204 may identify different users who share the user account.),
wherein the linkage definition identifies one or more assets of one or both of the first entity or the second entity (i.e., ...teaches in col. 18 lines 30-55 the following: “The transaction analysis module 204 may then use the machine learning model to map the word generated for each past transaction to a vector within the multi-dimensional space. (69) The process 500 then clusters (at step 520) the vectors using a constrained clustering technique. For example, the account decomposition manager 202 may determine one or more constraint attributes (e.g., device identifiers, mouse movement, etc.), and may use the clustering module 210 to cluster the past transactions of the user account based on the vectors in the multi-dimensional space using a constrained clustering technique. Using the constrained clustering technique, the clustering modules 210 is configured to group past transactions having common attribute values corresponding to the one or more constraint attributes in the same cluster. For example, when the constraint attribute includes device identifiers,” …”the clustering module 210 may group past transactions conducted through the same device in the same cluster. The clustering module 210 may then perform clustering for the past transactions based on the constraint. If the clustering module 210 cannot determine a cluster or can only determine a single cluster, the account decomposition manager 202 may determine that the user account is used by only a single user. However, if the clustering module 210 determines multiple clusters for the past transactions of the user account using the constrained clustering technique, the account decomposition manager 202 may determine that multiple users have been sharing and using the same user account.”),
and wherein the set of activation elements identifies one or more states (i.e., …teaches in col. 14 lines 1-20 the following: “The transaction analysis module 204 may use the trained machine learning model to map the past transactions to different vectors (e.g., different positions) within the vector space 300 based on the extracted attributes. As shown in FIG. 3, the past transactions associated with the user account have been mapped to different positions 304-334 within the vector space 300. By analyzing the different vectors 304-334 within the vector space 300, the transaction analysis module 204 may identify different users who share the user account. As shown in FIG. 3, the vectors 304-334 indicate two distinct transaction patterns based on the past transactions—one near the bottom left corner of the vector space 300 and another near the upper right corner of the vector space 300. Using a clustering technique (e.g., a k-means clustering technique, a fuzzy clustering technique, etc.), the clustering module 210 may identify two distinct groups of past transactions, which may indicate either two different users who are sharing the user account or a single user who has drastically different interests or buying patterns. (53) In order to prevent mis-identification of multiple users for the user account when a single user who has different interests is using the user account, in some embodiments, the clustering module 210 may use a constrained clustering technique to cluster the past transactions based on their vectors within the vector space 300. The account decomposition manager 202 may select one or more constraint attributes for use in the constrained clustering process to determine that the different transaction patterns correspond to different users (instead of a single user having different interests). Possible constraint attributes may include device attributes (e.g., device identifiers, IP addresses, screen resolutions, memory capacities, etc.) and user-device interaction patterns (e.g., typing speed, mouse movement patterns, etc.)”);
determining, by the computing system, that at least one activation element of the set of activation elements has been triggered (i.e., …teaches in col. 16 lines 40 the following: “60) When an incoming transaction request associated with the user account (e.g., a request to log in to the user account, a payment transaction using the user account, etc.) is received by the service provider server 130, the service provider server 130 may use the account decomposition module 132 to determine a risk associated with the incoming transaction request. In some embodiments, the risk assessment module 206 may select one of the user profiles 214a or 214b associated with the user account to determine a risk for the incoming transaction request based on one or more attribute values corresponding to the one or more constraint attributes used to perform the constrained clustering process. For example, when the device identifiers attribute is used as the constraint attribute, the risk assessment module 206 may determine a device identifier of the device (e.g., the user device 110, 180, or 190) used to submit/conduct the incoming transaction request. The risk assessment module 206 may select one of the user profiles 214a or 214b for assessing the risk of the incoming transaction request based on the device identifier of the device. In another example, when the mouse movement attribute is used as the constraint attribute, the risk assessment module 206 may detect a mouse movement pattern based on interactions between a user and the device (e.g., the user device 110, 180, or 190) used to submit/conduct the incoming transaction request. The risk assessment module 206 may select one of the user profiles 214a or 214b for assessing the risk of the incoming transaction request based on the detected mouse movement pattern.”);
in response to determining that the at least one activation element of the set of activation elements has been triggered (i.e., …teaches in col. 16 lines 40 the following: “60) When an incoming transaction request associated with the user account (e.g., a request to log in to the user account, a payment transaction using the user account, etc.) is received by the service provider server 130, the service provider server 130 may use the account decomposition module 132 to determine a risk associated with the incoming transaction request. In some embodiments, the risk assessment module 206 may select one of the user profiles 214a or 214b associated with the user account to determine a risk for the incoming transaction request based on one or more attribute values corresponding to the one or more constraint attributes used to perform the constrained clustering process. For example, when the device identifiers attribute is used as the constraint attribute, the risk assessment module 206 may determine a device identifier of the device (e.g., the user device 110, 180, or 190) used to submit/conduct the incoming transaction request. The risk assessment module 206 may select one of the user profiles 214a or 214b for assessing the risk of the incoming transaction request based on the device identifier of the device. In another example, when the mouse movement attribute is used as the constraint attribute, the risk assessment module 206 may detect a mouse movement pattern based on interactions between a user and the device (e.g., the user device 110, 180, or 190) used to submit/conduct the incoming transaction request. The risk assessment module 206 may select one of the user profiles 214a or 214b for assessing the risk of the incoming transaction request based on the detected mouse movement pattern.” …teaches in col. 17 lines 1-20 the following: “The risk assessment module 206 may also determine a risk associated with the transaction request 402 based on whether the transaction request 402 matches transaction pattern(s) included in the user profile 214a that were derived from the cluster of past transactions 350. The account decomposition manager 202 may then determine a process for processing the transaction request 402 based on the risk. For example, when the risk is above a threshold, the account decomposition manager 202 may require additional authentication (e.g., biometrics and/or two-factor authentication in additional to user name and password) from the user 140 before the transaction request is processed. In some embodiments, the risk assessment module 206 may authorize or deny the transaction request 402 based on whether the transaction request 402 matches (e.g., within predetermined tolerances, correlations, or thresholds) the transaction pattern(s) included in the user profile 214a.” …teaches in col. 19 lines 35-55 the following: “provide different authentication or authorization for transaction requests based on a predicted user of the user account who submitted the transaction request. The user account decomposition techniques can be used in other applications for providing personalized experience (e.g., user interfaces, content, etc.) to users of the same user account. Consider an example where multiple users are sharing a user account of a content providing service (e.g., video streaming services, content subscription services, etc.). The user account decomposition techniques described herein can be used to identify the different users who are sharing the user account. By separating the different users who are sharing the user account, more personalized content or user interface experience can be provided to the different users (e.g., identified by a device identifier, user-device interactions, etc.).”).
The system of Handelman does not expressly teach:
generating, by the computing system, one or more security access tokens based on the linkage definition,
the one or more security access tokens indicating that access to the one or more assets is granted,
transmitting, by the computing system, the one or more security access tokens to at least one of a first device identified in the first digital identity profile or a second device identified in the second digital identity profile.
In this instance the examiner notes the teachings of prior art reference Chen.
With regards to applicant’s claim limitation element of, “generating, by the computing system, one or more security access tokens based on the linkage definition”, Chen teaches in par. 0077 the following: “The learning phase of the detection engine 112 may include profiling entity access patterns and clustering entities based on, at least, entity relationships represented by the profiles. In some embodiments, profiles comprise an aggregation of the entity relationships identified from access request data corresponding to a plurality of access requests. As will be discussed further below, the detection engine operates on feature data extracted from token/authorization request/response data”. Chen teaches in par. 0081 the following: “Access to the resources 125 is managed by an authentication service 122. In some embodiments, the authentication service 122 is implemented by one or more host(s) 110. The authentication service 122 maintains or has access to a dataset to determine which requests from which accounts should be provided a positive response (e.g. a token or authorization) to allow access to a requested resource.”. Teaches in par. 0083 the following: “A client host transmits a token/authorization request 151 on behalf of an account to the authentication service over one or more switches 106. The authentication service 122 will process the token/authorization request 151 to determine whether a token or authorization should be provided to the host. Depending on the result of that determination, the authentication service 122 will return a denial or a token/authorization granting the requested access at 152. If the token is provided to the host, (e.g. a host of host(s) 104a-n) the host will use the token/authorization to access the internal network resource 125 at 156.”. Teaches in par. 0012 the following: “The learning phase of the detection engine may include profiling entity access patterns and clustering entities based on, at least, entity relationships represented by the profiles. In some embodiments, profiles comprise an aggregation of the entity relationships identified from access request data corresponding to a plurality of access requests.”.
With regards to applicant’s claim limitation element of, “the one or more security access tokens indicating that access to the one or more assets is granted”, Chen teaches in par. 0081 the following: “Access to the resources 125 is managed by an authentication service 122. In some embodiments, the authentication service 122 is implemented by one or more host(s) 110. The authentication service 122 maintains or has access to a dataset to determine which requests from which accounts should be provided a positive response (e.g. a token or authorization) to allow access to a requested resource.”. Teaches in par. 0083 the following: “A client host transmits a token/authorization request 151 on behalf of an account to the authentication service over one or more switches 106. The authentication service 122 will process the token/authorization request 151 to determine whether a token or authorization should be provided to the host. Depending on the result of that determination, the authentication service 122 will return a denial or a token/authorization granting the requested access at 152. If the token is provided to the host, (e.g. a host of host(s) 104a-n) the host will use the token/authorization to access the internal network resource 125 at 156.”. Further teaches in par. 0016 the following: “The authentication service maintains or has access to a dataset to determine which requests from which accounts should be provided a positive response (e.g. a token or authorization) to allow access to a requested resource.”.
With regards to applicant’s claim limitation element of, “transmitting, by the computing system, the one or more security access tokens to at least one of a first device identified in the first digital identity profile or a second device identified in the second digital identity profile”, Chen teaches in par. 0081 the following: “…If the token is provided to the host, (e.g. a host of host(s) 104a-n) the host will use the token/authorization to access the internal network resource 125 at 156.”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to implement the teachings of Handelman with the teachings of Chen by having their system comprise an enhanced authorization process. One would have been motivated to do so to provide a simple and effective means to control resource access, wherein the enhanced authorization process helps facilitate security within the network and makes it easier to control network access.
As to claims 2, 12 and 20, the system of Handelman and Chen as applied to claim 1 above teaches access control, specifically Handelman teaches a method of claim 1, further comprising: retrieving, by the computing system, a first set of identity elements and a first set of metadata from the first digital identity profile (i.e., …teaches in col. 7 lines 20-40 the following: “For example, the account decomposition system may determine a risk for the incoming transaction request based in part on the particular user profile. When the transaction request is a login request, the account decomposition system may determine whether attributes of the login request (e.g., a location of the user device, a time of the day, etc.) match (e.g., within a threshold location distance, a threshold time period, etc.) the past login attempts associated with the particular user profile.” When the login request does not match the past login attempts associated with the particular user profile, the account decomposition system may deny the login request or may prompt the user for additional authentication credentials (e.g., biometrics in addition to user name and password, etc.) before authorizing the login request.);
and generating, by the computing system, the dataset based at least on the first set of identity elements and the first set of metadata (i.e., …teaches in col. 6 lines 7-25 the following: “the account decomposition system may determine a risk for the incoming transaction request based in part on the particular user profile.”).
As to claims 3 and 13, the system of Handelman and Chen and as applied to claim 1 above teaches access control, specifically Handelman teaches a method of claim 1, further comprising: retrieving, by the computing system, a second set of identity elements and a second set of metadata from the second digital identity profile (i.e., …teaches in the Abstract the following: “The account decomposition system may determine different user profiles for the different users, and may use the different user profiles to process incoming transaction requests initiated by different users of the user account.”);
and generating, by the computing system, the dataset based at least on the second set of identity elements and the second set of metadata (i.e., …teaches in col. 6 lines 7-25 the following: “the account decomposition system may determine a risk for the incoming transaction request based in part on the particular user profile.”).
As to claims 4 and 14, the system of Handelman and Chen as applied to claim 1 above teaches access control, specifically Handelman teaches a method of claim 1, wherein the one or more assets comprises at least one of a digital asset or a physical asset (i.e., … teaches in col. 9 lines 65-67 the following: “access a user account”).
As to claims 5 and 15, the system of Handelman and Chen as applied to claim 1 above teaches access control, specifically Handelman teaches a method of claim 1, further comprising:
receiving, by the computing system, a set of inputs from one or more computing devices (i.e., …figure 4 illustrates receiving inputs from multiple devices);
and determining that at least one of the set of activation elements has been triggered based on the set of inputs (i.e., …teaches in col. 3 lines 45-55 the following: “and/or user-device interaction patterns such as mouse movement…”).
As to claims 6 and 16, the system of Handelman and Chen as applied to claim 1 above teaches access control, specifically Handelman teaches a method of claim 1, wherein the Al agent comprises a pattern recognition model or a classification model, and wherein the method further comprises inputting, by the computing system, the dataset to the pattern recognition model or to the classification model to detect normal or abnormal patterns of behavior (i.e. …teaches in col. 19 lines 35-55 the following: “provide different authentication or authorization for transaction requests based on a predicted user of the user account who submitted the transaction request. The user account decomposition techniques can be used in other applications for providing personalized experience (e.g., user interfaces, content, etc.) to users of the same user account. Consider an example where multiple users are sharing a user account of a content providing service (e.g., video streaming services, content subscription services, etc.). The user account decomposition techniques described herein can be used to identify the different users who are sharing the user account. By separating the different users who are sharing the user account, more personalized content or user interface experience can be provided to the different users (e.g., identified by a device identifier, user-device interactions, etc.).”).
As to claims 7 and 17, the system of Handelman and Chen as applied to claim 1 above teaches access control, specifically Handelman teaches a method of claim 1, wherein the Al agent comprises a regression model, and wherein the method further comprises inputting, by the computing system, the dataset to the regression model to identify causal factors for one or more identity elements or corresponding metadata in digital identity profiles (i.e., … teaches in col. 12 lines 9-20 the following: “the account decomposition manager 202 may train a machine learning model (e.g., a word2vec model) to generate linguistic contexts of words associated with different product categories.”).
As to claims 8 and 18, the system of Handelman and Chen as applied to claim 1 above teaches access control, specifically Handelman teaches a method of claim 1, further comprising updating, by the computing system, at least one of the first digital identity profile or the second digital identity profile to include the one or more security access tokens (i.e., …teaches in col. 7 lines 20-40 the following: “each of which may be associated with one or more profiles and may include account information associated with one or more individual users associated with the user accounts. For example, account information may include private financial information of users, such as one or more account numbers, passwords, credit card information, banking information, digital wallets used, or other types of financial information, transaction history, Internet Protocol (IP) addresses, device information associated with the user account.”).
As to claim 9, the system of Handelman and Chen as applied to claim 1 above teaches access control, specifically Handelman teaches a method of claim 1, wherein the one or more security access tokens grant access to at least one of a digital file, a smart device, a physical location, or an article of manufacture (i.e., ...teaches in col. 9 lines 55-67 & col. 10 lines 1-10 the following: “For example, the interface server 134 may store a log-in page and is configured to serve the log-in page to users for logging into user accounts of the users to access various service provided by the service provider server 130. The interface server 134 may also include other electronic pages associated with the different services (e.g., electronic transaction services, etc.) offered by the service provider server 130. As a result, a user may access a user account associated with the user and access various services offered by the service provider server 130, by generating HTTP requests directed at the service provider server 130.”).
As to claim 10, the system of Handelman and Chen as applied to claim 1 above teaches access control, specifically Handelman teaches a method of claim 1, further comprising receiving, by the computing system, a first set of identity elements and a first set of metadata from a first computing system associated with the first digital identity profile (i.e., … teaches in col. 12 lines 9-20 the following: “the account decomposition manager 202 may train a machine learning model (e.g., a word2vec model) to generate linguistic contexts of words associated with different product categories.”).
Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Smith et al. (US Patent Publication No. 2015/0293997).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN F WRIGHT whose telephone number is (571)270-3826.
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/BRYAN F WRIGHT/ Examiner, Art Unit 2497
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