DETAILED ACTION
This Office Action is in response to the application 19/190477, filed on 04/25/2025.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
As per preliminary amended, submitted on 06/17/2025, claims 21-40 have been examined and are pending in this application. Claims 21, 25, and 33 are independent.
Priority/Continuity
This application is a continuation of application 18/923219, filed on 10/22/2024, currently patent US 12,309,164.
Information Disclosure Statement
The information disclosure statement (IDS), submitted on 05/13/2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
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 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 nonstatutory 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).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of U.S. Patent No. US 12,309,164. Although, the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are anticipated by the reference claims.
The following claims are presented side by side for comparison. The comparison shows how the broader scoped independent claims 21, 25, and 33 of the instant application are anticipated by the narrower scoped independent claims 1, 6, and 14 of the reference patent. The dependent claims of the instant application, are also anticipated, by the claims of the reference patent, respectively.
Instant Application 19/190477
Reference Patent US 12,309,164
21. A computer-implemented method, comprising:
receiving a set of attributes associated with a set of computing resources;
generating a first data structure indicating first correlations among a first subset of the set of attributes;
generating a second data structure indicating second correlations between the first data structure and a second subset of the set of attributes based, at least in part, on previous access data associated with the one or more computing resources; and
as a result of receiving a request for an entity to obtain access to at least one of the set of computing resources, select one or more portions from the second data structure based, at least in part, on information associated with the entity.
24. (New) The computer-implemented method of claim 21, further comprising: generating access permissions corresponding to the entity based, at least in part, on the one or more selected portions of the second data structure.
1. A system, comprising: one or more processors; and one or more non-transitory, computer-readable mediums comprising instructions recorded thereon that, as a result of execution by the one or more processors, causes the system to at least:
obtain, from a storage device that includes previous access permissions data associated with one or more computing resources, a plurality of attributes associated with the previous access permissions data and a plurality of roles associated with the previous access permissions data;
generate a first graph data structure indicating first correlations between attributes of the plurality of attributes;
identify second correlations between the first graph data structure and the plurality of roles; generate a second graph data structure indicating an association between at least one of the plurality of attributes and at least one of the plurality of roles based, at least in part, on the second correlations; and
in response to a request for an access token representing access permissions to the one or more computing resources to be granted to a user:
provide a list of attributes and roles associated with the user, the list being determined, at least in part, on the second graph data structure; and
generate the access token based, at least in part, on an attribute or a role selected from the list of attributes and roles, wherein the user is granted access to the one or more computing resources upon verification of the access token.
25. A system, comprising:
one or more processors; and one or more non-transitory, computer-readable mediums comprising executable instructions recorded thereon that, as a result of execution by the one or more processors, causes the system to at least:
obtain a set of attributes associated with one or more computing resources;
generate a data structure indicating first correlations among a first subset of the set of attributes;
identify second correlations between the data structure and a second subset of the set of attributes based, at least in part, on previous access data associated with the one or more computing resources;
update the data structure based, at least in part, on the second correlations; and
identify a portion of the data structure to be used to determine permissions for an entity to access the one or more computing resources based, at least in part, on information associated with the entity.
27. (New) The system of claim 25, wherein the set of attributes is generated, at least in part, according to an attribute-based access control (ABAC) model or a role-based access control (RBAC) model.
31. The system of claim 25, wherein the executable instructions further include instructions that further cause the system to: generate one or more access tokens for the entity based, at least in part, on the determined permissions.
6. A computer-implemented method, comprising:
receiving, from a storage device that includes information associated with one or more computing resources, a plurality of attributes and a plurality of roles;
generating a first data structure indicating first correlations between attributes of the plurality of attributes;
determining second correlations between the first data structure and the plurality of roles based, at least in part, on the information from the storage device; and
generating a second data structure indicating an association between at least one of the plurality of attributes and at least one of the plurality of roles based, at least in part, on the second correlations; and
as a result of receiving a request to access the one or more computing resources:
providing a list of attributes and roles based, at least in part, on the second data structure; and
generating an access token usable to access the one or more computing resources as a result of receiving an indication of an attribute or a role that is selected from the list of attributes and roles, wherein a user is granted access to the one or more computing resources upon verification of the access token.
33. One or more non-transitory computer-readable storage media having stored thereon computer-executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:
receive a set of attributes associated with a set of computing resources;
generate a first data structure indicating first correlations among a first portion of the set of attributes;
determine second correlations between the first data structure and a second portion of the set of attributes based, at least in part, on previous access data associated with the set of computing resources;
generate a second data structure based, at least in part, on the second correlations; and
in response to a request for an entity to obtain access to at least one of the set of computing resources, identify a portion from the second data structure to be used to generate one or more permissions based, at least in part, on information associated with the entity.
40. (New) The one or more non-transitory computer-readable storage media of claim 33, wherein the set of attributes is generated, at least in part, according to an attribute-based access control (ABAC) model or a role-based access control (RBAC) model.
14. A non-transitory computer-readable storage medium storing computer-executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:
receive, from a storage device that includes information associated with one or more computing resources, a plurality of attributes and a plurality of roles;
generate a first data structure indicating first correlations between attributes of the plurality of attributes;
determine second correlations between the first data structure and the plurality of roles based, at least in part, on the information from the storage device; and
generate a second data structure indicating an association between at least one of the plurality of attributes and at least one of the plurality of roles based, at least in part, on the second correlations; and
as a result of receiving a request to access the one or more computing resources:
provide a list of attributes and roles based, at least in part, on the second data structure; and
generate an access token usable to access the one or more computing resources as a result of receiving an indication of an attribute or a role that is selected from the list of attributes and roles, wherein a user is granted access to the one or more computing resources upon verification of the access token.
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 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.
This application currently names joint inventors. In considering patentability of the claims the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the Examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 21-40 are rejected under 35 U.S.C. 103 as being unpatentable over Mardikar et al (“Mardikar,” 2012/0060207, published on 03/08/2012), in view of Cook (“Cook,” US 2024/0126794, published on 04/182024)).
As to claim 21, Mardikar teaches a computer-implemented method (Mardikar: pars 0007-0009, a system/method of access control, where a unique combination of authentication context is used for implementing in an underlying role-based attribute based access control (RABAC) model), comprising:
receiving a set of attributes associated with a set of computing resources (Mardikar: pars 0007-0008, 0016, where in an Attribute Based Access Control (ABAC) a subject/user is granted access based on attributes. Where in a Role Based Access Control (RBAC), a subject/user is assigned one or more roles, and the authorization is based on each resource and action in the system requiring membership in a particular role in order to proceed. Each role is assigned one or more privileges that are permitted to users in that role);
generating a first data structure indicating first correlations among a first subset of the set of attributes (Mardikar: pars 0007-0008, 0016, where in an Attribute Based Access Control (ABAC) a subject/user is granted access based on attributes. Where in a Role Based Access Control (RBAC), a subject/user is assigned one or more roles, and the authorization is based on each resource and action in the system requiring membership in a particular role in order to proceed. Each role is assigned one or more privileges that are permitted to users in that role);
generating a second data structure indicating second correlations between the first data structure and a second subset of the set of attributes based (Mardikar: pars 0016, 0019-0020, the access control security is enhanced through the use of a unique combination of authentication context, in an underlying role-based attribute based access control (RABAC) model; rules to render decisions using a combination of role and attribute based expressions); and
as a result of receiving a request for an entity to obtain access to at least one of the set of computing resources, select one or more portions from the second data structure based, at least in part, on information associated with the entity (Mardikar: pars 0011, 0016, 0019-0020, an authorization services receives the token, allow access to the system according to input from the token, associating with the role based access control model and policies, and the attribute based access control model and policies. Rules to render decisions using a combination of role and attribute based expressions. Generated a token based on the attributes and incorporating the role based expression).
Mardikar does not explicitly teach at least in part, on previous access data associated with the one or more computing resources.
However, in an analogous art, Cook teaches at least in part, on previous access data associated with the one or more computing resources (Cook: 00135-0136; Fig 4, where system uses a neural network for implementing a machine-learning or deep learning model. Where, one or more intermediate layers is correlated with an input layer of nodes, producing an output layer node. Inputs, those are connected in the neural network, are weighted in generating one or more outputs).
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 Cook with the method/system of Mardikar to include the limitation(s), at least in part, on previous access data associated with the one or more computing resources, where one would have been motivated for the benefit of correlating, using a neural network graph, attribute relationship of user access to one or more role of the user in association with resource, for implementing the combination of role-based attribute based access control (RABAC) model (Cook: 00135-0136).
As to claim 22, the combination of Mardikar and Cook teaches the computer-implemented method of claim 21,
Mardikar and Cook teaches of further comprising: using an encoder of one or more neural networks that generate a set of vectors that correspond to different types of attributes of the set of attributes; and using a decoder of the one or more neural networks to identify the second correlations based, at least in part, on the set of vectors (Mardikar: pars 0009-0010, authorization service allow access to the system by the access device according to the token, and input from a risk based access control, a role based access control, and an attribute based access control. Cook: pars 0024, 0048 in the neural network layer, the embedding layer receives the tokens as an input sequence of tokens representing the network data and generates an input vector based on the input sequence of tokens. Trained to generate an output vector based on the input vector).
As to claim 23, the combination of Mardikar and Cook teaches the computer-implemented method of claim 22,
Mardikar and Cook further teaches of wherein the one or more neural networks comprise a transformer neural network (Cook: pars 0135-0136 Fig 4, input-output relationship graph is created using a neural network).
As to claim 24, the combination of Mardikar and Cook teaches the computer-implemented method of claim 21,
Mardikar and Cook teaches of further comprising: generating access permissions corresponding to the entity based, at least in part, on the one or more selected portions of the second data structure (Mardikar: pars 0009-0010, authorization service allow access to the system by the access device according to the token, and input from a risk based access control, a role based access control, and an attribute based access control. Cook: pars 0135-0136; Fig 4, one or more intermediate layers is correlated with an input layer of nodes, producing an output layer node. Inputs, those are connected in the neural network, are weighted in generating one or more outputs).
As to claim 25, Mardikar teaches a system, comprising: one or more processors; and one or more non-transitory, computer-readable mediums comprising executable instructions recorded thereon that, as a result of execution by the one or more processors (Mardikar: pars 0007-0009, a system/method of access control, where a unique combination of authentication context is used for implementing in an underlying role-based attribute based access control (RABAC) model), causes the system to at least:
obtain a set of attributes associated with one or more computing resources (Mardikar: pars 0007-0008, 0016, where in an Attribute Based Access Control (ABAC) a subject/user is granted access based on attributes. Where in a Role Based Access Control (RBAC), a subject/user is assigned one or more roles, and the authorization is based on each resource and action in the system requiring membership in a particular role in order to proceed. Each role is assigned one or more privileges that are permitted to users in that role);
generate a data structure indicating first correlations among a first subset of the set of attributes (Mardikar: pars 0007-0008, 0016, where in an Attribute Based Access Control (ABAC) a subject/user is granted access based on attributes. Where in a Role Based Access Control (RBAC), a subject/user is assigned one or more roles, and the authorization is based on each resource and action in the system requiring membership in a particular role in order to proceed. Each role is assigned one or more privileges that are permitted to users in that role);
identify second correlations between the data structure and a second subset of the set of attributes based (Mardikar: pars 0016, 0019-0020, the access control security is enhanced through the use of a unique combination of authentication context, in an underlying role-based attribute based access control (RABAC) model; rules to render decisions using a combination of role and attribute based expressions);
update the data structure based, at least in part, on the second correlations; and identify a portion of the data structure to be used to determine permissions for an entity to access the one or more computing resources based, at least in part, on information associated with the entity (Mardikar: pars 0011, 0016, 0019-0020, an authorization services receives the token, allow access to the system according to input from the token, associating with the role based access control model and policies, and the attribute based access control model and policies. Rules to render decisions using a combination of role and attribute based expressions. Generated a token based on the attributes and incorporating the role based expression).
Mardikar does not explicitly teach at least in part, on previous access data associated with the one or more computing resources.
However, in an analogous art, Cook teaches at least in part, on previous access data associated with the one or more computing resources (Cook: 00135-0136; Fig 4, where system uses a neural network for implementing a machine-learning or deep learning model. Where, one or more intermediate layers is correlated with an input layer of nodes, producing an output layer node. Inputs, those are connected in the neural network, are weighted in generating one or more outputs).
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 Cook with the method/system of Mardikar to include the limitation(s), at least in part, on previous access data associated with the one or more computing resources, where one would have been motivated for the benefit of correlating, using a neural network graph, attribute relationship of user access to one or more role of the user in association with resource, for implementing the combination of role-based attribute based access control (RABAC) model (Cook: 00135-0136).
As to claim 26, the combination of Mardikar and Cook teaches the system of claim 25,
Mardikar and Cook further teaches of wherein the second correlations are identified using one or more neural networks that generates one or more vectors corresponding to the set of attributes (Mardikar: pars 0009-0010, authorization service allow access to the system by the access device according to the token, and input from a risk based access control, a role based access control, and an attribute based access control. Cook: pars 0024, 0048 in the neural network layer, the embedding layer receives the tokens as an input sequence of tokens representing the network data and generates an input vector based on the input sequence of tokens. Trained to generate an output vector based on the input vector).
As to claim 27, the combination of Mardikar and Cook teaches the system of claim 25,
Mardikar and Cook further teaches of wherein the set of attributes is generated, at least in part, according to an attribute-based access control (ABAC) model or a role-based access control (RBAC) model (Mardikar: pars 0007-0009, the access control security is enhanced through the use of a unique combination of authentication context, in an underlying role-based attribute based access control (RABAC) model; rules to render decisions using a combination of role and attribute based expressions. Where in in an Attribute Based Access Control (ABAC) a subject/user is granted access based on attributes, in a Role Based Access Control (RBAC), a subject/user is assigned one or more roles).
As to claim 28, the combination of Mardikar and Cook teaches the system of claim 25,
Mardikar and Cook further teaches of wherein the executable instructions further include instructions that further cause the system to: add the determined permissions to be part of previous access data (Cook: pars 0041-0042, user/entity’s preferences, historical interactions, and behavioral patterns are associated with).
As to claim 29, the combination of Mardikar and Cook teaches the system of claim 25,
Mardikar and Cook teaches of wherein the data structure comprises a directed acyclical graph (DAG) (Cook: pars 0128, 0135-0136; Fig 4, uses acyclic graph or the like. Input-output relationship graph is one directional to output [i.e., a DAG graph]).
As to claim 30, the combination of Mardikar and Cook teaches the system of claim 25,
Mardikar and Cook further teaches of wherein the data structure is further updated based, at least in part, on one or more policies associated with a plurality of entities (Mardikar: pars 0016, 0019-0020, an authorization services receives the token, allow access to the system according to input from the token, associating with the role based access control model and policies, and the attribute based access control model and policies).
As to claim 31, the combination of Mardikar and Cook teaches the system of claim 25,
Mardikar and Cook further teaches of wherein the executable instructions further include instructions that further cause the system to: generate one or more access tokens for the entity based, at least in part, on the determined permissions (Mardikar: pars 0011, 0016, 0019-0020, rules to render decisions using a combination of role and attribute based expressions. Generation of an access a token based on the attributes and incorporating the role based expression).
As to claim 32, the combination of Mardikar and Cook teaches the system of claim 25,
Mardikar and Cook further teaches of wherein the entity is associated with another entity that transmitted a request to access the one or more computing resources on behalf of the entity, the request comprising the information (Mardikar: pars 0019-0020, uses an authorization service and authentication service in the process).
As to claim 33, Mardikar teaches one or more non-transitory computer-readable storage media having stored thereon computer-executable instructions that, as a result of being executed by one or more processors of a computer system (Mardikar: pars 0007-0009, a system/method of access control, where a unique combination of authentication context is used for implementing in an underlying role-based attribute based access control (RABAC) model), cause the computer system to at least:
receive a set of attributes associated with a set of computing resources (Mardikar: pars 0007-0008, 0016, where in an Attribute Based Access Control (ABAC) a subject/user is granted access based on attributes. Where in a Role Based Access Control (RBAC), a subject/user is assigned one or more roles, and the authorization is based on each resource and action in the system requiring membership in a particular role in order to proceed. Each role is assigned one or more privileges that are permitted to users in that role);
generate a first data structure indicating first correlations among a first portion of the set of attributes (Mardikar: pars 0007-0008, 0016, where in an Attribute Based Access Control (ABAC) a subject/user is granted access based on attributes. Where in a Role Based Access Control (RBAC), a subject/user is assigned one or more roles, and the authorization is based on each resource and action in the system requiring membership in a particular role in order to proceed. Each role is assigned one or more privileges that are permitted to users in that role);
determine second correlations between the first data structure and a second portion of the set of attributes based (Mardikar: pars 0016, 0019-0020, the access control security is enhanced through the use of a unique combination of authentication context, in an underlying role-based attribute based access control (RABAC) model; rules to render decisions using a combination of role and attribute based expressions);
generate a second data structure based, at least in part, on the second correlations; and in response to a request for an entity to obtain access to at least one of the set of computing resources, identify a portion from the second data structure to be used to generate one or more permissions based, at least in part, on information associated with the entity (Mardikar: pars 0011, 0016, 0019-0020, an authorization services receives the token, allow access to the system according to input from the token, associating with the role based access control model and policies, and the attribute based access control model and policies. Rules to render decisions using a combination of role and attribute based expressions. Generated a token based on the attributes and incorporating the role based expression).
Mardikar does not explicitly teach at least in part, on previous access data associated with the one or more computing resources.
However, in an analogous art, Cook teaches at least in part, on previous access data associated with the one or more computing resources (Cook: 00135-0136; Fig 4, where system uses a neural network for implementing a machine-learning or deep learning model. Where, one or more intermediate layers is correlated with an input layer of nodes, producing an output layer node. Inputs, those are connected in the neural network, are weighted in generating one or more outputs).
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 Cook with the method/system of Mardikar to include the limitation(s), at least in part, on previous access data associated with the one or more computing resources, where one would have been motivated for the benefit of correlating, using a neural network graph, attribute relationship of user access to one or more role of the user in association with resource, for implementing the combination of role-based attribute based access control (RABAC) model (Cook: 00135-0136).
As to claim 34, the combination of Mardikar and Cook teaches the one or more non-transitory computer-readable storage media of claim 33,
Mardikar and Cook further teaches of wherein the computer-executable instructions to determine the second correlations further include executable instructions that further cause the computer system to: use an encoder of one or more neural networks that generate a set of vectors that correspond to different types of attributes of the set of attributes; and use a decoder of the one or more neural networks to identify the second correlations based, at least in part, on the set of vectors (Mardikar: pars 0009-0010, authorization service allow access to the system by the access device according to the token, and input from a risk based access control, a role based access control, and an attribute based access control. Cook: pars 0024, 0048 in the neural network layer, the embedding layer receives the tokens as an input sequence of tokens representing the network data and generates an input vector based on the input sequence of tokens. Trained to generate an output vector based on the input vector).
As to claim 35, the combination of Mardikar and Cook teaches the one or more non-transitory computer-readable storage media of claim 34,
Mardikar and Cook further teaches of wherein the one or more neural networks comprise a transformer neural network (Cook: pars 0135-0136 Fig 4, input-output relationship graph is created using a neural network).
As to claim 36, the combination of Mardikar and Cook teaches the one or more non-transitory computer-readable storage media of claim 33,
Mardikar and Cook further teaches of wherein the computer-executable instructions further include executable instructions that further cause the computer system to: update the second data structure based, at least in part, on additional attributes that are associated with a new computing resource (Mardikar: pars 0007, 0016, where in a Role Based Access Control (RBAC), a subject/user is assigned one or more roles, and the authorization is based on each resource and action in the system requiring membership in a particular role in order to proceed. Each role is assigned one or more privileges that are permitted to users in that role).
As to claim 37, the combination of Mardikar and Cook teaches the one or more non-transitory computer-readable storage media of claim 33,
Mardikar and Cook further teaches of wherein the second correlations are further determined based, at least in part, on a group of policies associated with a plurality of entities (Mardikar: pars 0007, 0016-20, Each role is assigned one or more privileges that are permitted to users in that role. Associating with the role based access control model and policies, and the attribute based access control model and policies. Cook: par 0128, group of entities collectively acting as the time stamping authority with a requirement.).
As to claim 38, the combination of Mardikar and Cook teaches the one or more non-transitory computer-readable storage media of claim 33,
Mardikar and Cook further teaches of wherein the computer-executable instructions to identify the portion from the second data structure further include executable instructions that further cause the computer system to: generate a list to be displayed to a device associated with the entity based, at least in part, on the identified portion of the second data structure, wherein the list indicates a degree to which individual attribute is relevant to the entity (Mardikar: pars 0007, 0016-20, Each role is assigned one or more privileges that are permitted to users in that role. Associating with the role based access control model and policies, and the attribute based access control model and policies. Cook: pars 0128, 0135-0136; Fig 4, uses acyclic graph or the like. Input-output relationship graph is one directional to output).
As to claim 39, the combination of Mardikar and Cook teaches the one or more non-transitory computer-readable storage media of claim 33,
Mardikar and Cook further teaches of wherein the computer-executable instructions further include executable instructions that further cause the computer system to: cause data indicating the one or more permissions to be part of the previous access data (Cook: pars 0041-0042, user/entity’s preferences, historical interactions, and behavioral patterns are associated with).
As to claim 40, the combination of Mardikar and Cook teaches the one or more non-transitory computer-readable storage media of claim 33,
Mardikar and Cook further teaches of wherein the set of attributes is generated, at least in part, according to an attribute-based access control (ABAC) model or a role-based access control (RBAC) model (Mardikar: pars 0007-0009, the access control security is enhanced through the use of a unique combination of authentication context, in an underlying role-based attribute based access control (RABAC) model; rules to render decisions using a combination of role and attribute based expressions. Where in in an Attribute Based Access Control (ABAC) a subject/user is granted access based on attributes, in a Role Based Access Control (RBAC), a subject/user is assigned one or more roles).
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Jahangir Kabir whose telephone number is (571) 270-3355. The Examiner can normally be reached on 9:00- 5:00 Mon-Thu.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Luu Pham can be reached on (571) 270-5002. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAHANGIR KABIR/ Primary Examiner, Art Unit 2439
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