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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15, 18-20, and 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claims recite an abstract idea of observation, judgment and evaluation. This judicial exception is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are combination of generic computer hardware in combination with extra-solution activity used to execute the abstract idea. See the analysis below for further details.
Claim 1 and 22
Step 1: The claim recites a method and system, therefore, it falls into the statutory category of a method and apparatus.
Step 2A Prong 1: The claim recites, inter alia:
Generate one or more personalized responses to the input based on the acquired task specific data and the respective one or more historical interaction patterns. (This amounts to a mental process of observation, evaluation and judgment where a user answers a question (response to input) using past interaction data (historical interaction pattens) and task specific data. For example, a person is asked by a co-worker should they go to lunch today. The person knows that they have a meeting at 11:30am today that will go into lunch time and from past interaction with the co-worker they can not be late to lunch. So based on the task data and past interaction the person would say no he can’t do lunch today. This is a personalized response based on task data and historical interaction.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
a storage subsystem that maintains a user registry mapping a plurality of user identifiers to a plurality of user profiles, wherein each user profiles defines: one or more credential for accessing applications, one or more knowledge base access permissions to specific resources, and one or more historical interaction patterns for a user associated with one of the plurality of user identifiers; and (This is memory storing user profiles and associated user identifiers, credentials, and knowledge bases, and historical data, thus is it generic hardware performing generic computer functions used to execute the abstract idea, see MPEP 2106.05(f).)
one or more processors that instantiate an AI agent for a first of the user identifiers from the plurality of user identifiers; (This is generic hardware used to implement the abstract idea above, see MPEP 2106.05(f). Also the AI agent is cited at high level of generality result in it being used a tool in implement the abstract idea, see MPEP 2106.05(f).)
wherein the AI agent: receivers an input indicative of a task from a first user associated with the first user identifier, performs access actions using the one or more stored credentials and the one or more knowledge base permissions associated with the first user identifier to acquire specific task data, (This amount to a processor (AI agent) receiving data which is data transmitting and access memory wherein the credentials and knowledge bases are stored. Thus, this is extra-solution activity of transmitting data and accessing memory, see MPEP 2106.05(g).)
wherein the task-specific data acquired by the access actions is limited to resources authorized by the one or more knowledge bases access permissions associated with the first user identifier, and data outside of the one or more access permission associated with the first user identifier is excluded from the acquired task-specific data. (This is extra-solution activity of accessing/receiving data based user credentials such as username & password, role-based access credentials, etc.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “receivers an input indicative of a task from a first user associated with the first user identifier, performs access actions using the one or more stored credentials and the one or more knowledge base permissions associated with the first user identifier to acquire specific task data” amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. Also, the accessing the memory is well-understood, routine and convention, see MPEP 2106.05(d)(IV) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”. T
he additional limitation of “wherein the task-specific data acquired by the access actions is limited to resources authorized by the one or more knowledge bases access permissions associated with the first user identifier, and data outside of the one or more access permission associated with the first user identifier is excluded from the acquired task-specific data.” is extra-solution activity of accessing/receiving data based user credentials such as username & password, role-based access credentials, etc., which is access control. Access control well-understood, routine and conventional in computer systems. See the reference title “Access Control Methods: What They Are, How They Work, and How to Choose the Right Approach” which teaches access control means managing permissions and authorizations of people who need resources for these jobs on page 2 and on page 3 it give s the most common models of access control are attribute-based Policy control, Discretionary Access Control (DAC), Mandatory Access Control (MAC), logical access control methods, access control list (ACLs), and passwords. As these are common then they are well-understood, routine and conventional. Another reference by Immuta.com titled “What is RBAC, (Role-Based Access Control) – And is it Right for You?” teaches on page 1 that DAC and MAC was well-known and widely used in the 1960s and 1970s. On page 2 last it teaches RBAC was have been in use for over 30 years is inescapable today in data space. This would also indicate that DAC, MAC, RBAC are all well-understood, routine and conventional. As such that examiner finds that controlling access to data based on credentials is well-understood, routine and conventional.)
The limitations of “a storage subsystem that maintains a user registry mapping a plurality of user identifiers to a plurality of user profiles, wherein each user profiles defines: one or more credential for accessing applications, one or more knowledge base access permissions to specific resources, and one or more historical interaction patterns for a user associated with one of the plurality of user identifiers; and one or more processors that instantiate an AI agent for a first of the user identifiers from the plurality of user identifiers” amounts to using machine learning as tool to apply an abstract idea, see MPEP 2106.05(f). When viewing the claim as a whole it does not amount to significantly more than the abstract idea.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 2
Step 2A Prong 1: The claim recites, inter alia:
Claim 2 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the AI agent maintains a continuous memory state and operational context across multiple application sessions. (This is linking the abstract idea to a technological field, see MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they merely link the abstract idea to a technological field.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “wherein the AI agent maintains a continuous memory state and operational context across multiple application sessions.” Amoun to linking the abstract idea to a technological field, see MPEP 2106.05(h).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as merely link the abstract idea to technological field of use.
Claim 3
Step 2A Prong 1: The claim recites, inter alia:
wherein prior to generating the one or more personalized responses, the AI agent processes and optimizes the task-specific data to reduce computational token consumption while maintaining response accuracy. (This a mental process of observation, evaluation and judgement wherein a user scores data and pick out segments of data to reduce amount of data and computation load.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, there are no additional elements.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. There are not additional elements of the claim.
Claim 4
Step 2A Prong 1: The claim recites, inter alia:
Claim 4 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
comprising cross-application conversation continuity functionality, wherein the AI agent automatically presents conversation history from a first application within an interface of a second application when the first user transitions between applications. (This amounts to extra-solution activity of presenting data, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “comprising cross-application conversation continuity functionality, wherein the AI agent automatically presents conversation history from a first application within an interface of a second application when the first user transitions between applications.” amounts presenting data which is well understood, routine and conventional. See MPEP 2106.05(d)(IV) which cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; ”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as extra-solution activity in combination with the abstract idea.
Claim 5
Step 2A Prong 1: The claim recites, inter alia:
Claim 5 inherits the abstract idea of claim 4.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
comprising cross-application conversation continuity functionality, wherein the AI agent automatically presents conversation history from a first application within an interface of a second application when the first user transitions between applications. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).;
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “comprising cross-application conversation continuity functionality, wherein the AI agent automatically presents conversation history from a first application within an interface of a second application when the first user transitions between applications.” is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely instructions to implement the abstract idea using the computer as tool.
Claim 6
Step 2A Prong 1: The claim recites, inter alia:
Claim 6 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein when the AI agent is automatically launched in the second application, the AI agent automatically retrieves and loads conversation context from the first application to continue conversation in the second application environment. (This amounts to extra-solution activity of data collection and presenting data, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “wherein when the AI agent is automatically launched in the second application, the AI agent automatically retrieves and loads conversation context from the first application to continue conversation in the second application environment.” amounts transmitting data and presenting data which is well understood, routine and conventional. See MPEP 2106.05(d)(IV) which cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; ” and See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as extra-solution activity in combination with the abstract idea.
Claim 7
Step 2A Prong 1: The claim recites, inter alia:
Conflict resolution logic that determines precedence when user-configured agent settings conflict with organizational policies stored in the storage subsystem. (This is a mental process of observation, judgement and evaluation wherein a user determines what policy to following when there is conflict the users settings/preferences and the organization policies.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
There are no additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. There are no additional limitations.
Claim 8
Step 2A Prong 1: The claim recites, inter alia:
Claim 8 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the AI agent supports distributed deployment across computing environments including one or more of: local execution on user devices, network-based execution through communication interfaces, cloud platforms, edge devices, and hybrid configurations while maintaining consistent agent behavior and capabilities. (This is linking the abstract idea to a technological field, see MPEP 2106.05(h).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they merely link the abstract idea to a technological field.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “wherein the AI agent supports distributed deployment across computing environments including one or more of: local execution on user devices, network-based execution through communication interfaces, cloud platforms, edge devices, and hybrid configurations while maintaining consistent agent behavior and capabilities.” amount to linking the abstract idea to a technological field, see MPEP 2106.05(h).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as merely link the abstract idea to technological field of use.
Claim 9
Step 2A Prong 1: The claim recites, inter alia:
Claim 9 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
a historical analysis module configured to maintain behavioral profiles capturing the one or more historical interaction patterns, agent intervention levels, and communication preferences from user interactions with the AI agent. (This amounts to memory storing data and as such is generic computer hardware used to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they merely generic computer hardware performing generic functions in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “a historical analysis module configured to maintain behavioral profiles capturing the one or more historical interaction patterns, agent intervention levels, and communication preferences from user interactions with the AI agent.” amount using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is merely generic computer hardware performing generic functions in combination with the abstract idea.
Claim 10
Step 2A Prong 1: The claim recites, inter alia:
Claim 10 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
a visual configuration interface operatively connected to the historical analysis module for assembling AI agent configurations from modular components through interactive graphical elements. (This is cited at a high level of generality and result is a GUI for interacting with the system, thus it is generic computer hardware used to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they merely generic computer hardware performing generic functions in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “a visual configuration interface operatively connected to the historical analysis module for assembling AI agent configurations from modular components through interactive graphical elements.” amount using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is merely generic computer hardware performing generic functions in combination with the abstract idea.
Claim 11
Step 2A Prong 1: The claim recites, inter alia:
Claim 11 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the visual configuration interface enables users to customize behavior of the AI agent through graphical elements including sliders, toggles, and selection controls that translate user preferences into agent response parameters stored in the storage subsystem. (This is cited at a high level of generality and is GUI interface that enables selection controls using slider, toggles, and other selection control likes buttons to make selection. This is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they merely extra-solution activity in combination with generic computer hardware to execute the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “wherein the visual configuration interface enables users to customize behavior of the AI agent through graphical elements including sliders, toggles, and selection controls that translate user preferences into agent response parameters stored in the storage subsystem.” amount to a GUI interface that enables selection controls using slider, toggles, and other selection control likes buttons to make selection, which is well-understood, routine and conventional activity supported under Berkheimer. See the attached references Friedman and Babich that disclose radio buttons, checkbox, toggle, droplist, and sliders all selection control buttons that are commonly used and discusses scenarios for using each in a GUI. Babich page 2 cites “radio buttons, checkboxes, toggles, and dropdowns are UI controls that allow users to make a selection. Although they have been in user interfaces for a long time, product designers still have a lot of trouble choosing the proper control for their tasks. This article will review 4 popular types of selectors, teach general rules on when and how to use them, and explore 7 common scenarios of using selection control." and Friedman page X cites “Another common use case where sliders are almost omnipresent is banks and energy suppliers. Most banks will offer house loans, car loans and mortgage calculators, and most energy companies will help you choose your plan based on your household’s energy consumption.”. Thus the use of the slides, toggles, radio buttons and other selection on a GUI is well-known, routine and conventional.)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is merely extra-solution activity in combination with generic computer hardware performing generic functions to execute the abstract idea.
Claim 12
Step 2A Prong 1: The claim recites, inter alia:
Monitoring user interaction patterns to identify individual configuration preferences. (This is a mental process of observation, judgement and evaluation, wherein user actions are observed and preferences are determined from user actions.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
a visual configuration interface (This is cited at a high level of generality and result is a GUI for interacting with the system, thus it is generic computer hardware used to execute the abstract idea, see MPEP 2106.05(f).)
stores the identified preferences in the user profile associated with the first user identifier. (This amount to saving data to memory, which extra-solution activity of data gathering, see MPEP 2106.05(g).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they merely generic computer hardware performing generic functions in combination extra-solution activity to execute the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “stores the identified preferences in the user profile associated with the first user identifier.” is well understood, routine and conventional. See MPEP 2106.05(d)(II)(iv) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;.” The additional limitation of “a visual configuration interface” amounts to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is merely generic computer hardware performing generic functions in combination with extra-solution activity to execute the abstract idea.
Claim 13
Step 2A Prong 1: The claim recites, inter alia:
Claim 13 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
comprising natural language-based personalization functionality including a conversational interface that enables the first user to customize behavior of the AI agent through natural language feedback that is processed into technical configuration parameters stored in the user profile. (This claim is cited at high level of generality and results in adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).;
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “comprising natural language-based personalization functionality including a conversational interface that enables the first user to customize behavior of the AI agent through natural language feedback that is processed into technical configuration parameters stored in the user profile.” is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely instructions to implement the abstract idea using the computer as tool.
Claim 14
Step 2A Prong 1: The claim recites, inter alia:
Claim 14 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
dynamic knowledge base combination functionality that combines modular knowledge bases to provide unified information access to the AI agent. (This claim is cited at high level of generality and results in adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).;
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “dynamic knowledge base combination functionality that combines modular knowledge bases to provide unified information access to the AI agent.” is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely instructions to implement the abstract idea using the computer as tool.
Claim 15
Step 2A Prong 1: The claim recites, inter alia:
the dynamic knowledge base combination functionality dynamically combines the modular knowledge bases based on contextual parameters associated with user sessions, generates semantic linkages between related concepts, (This is a mental process of observation, evaluation and judgement wherein a user combines data based on similar features or attributes. It can be don with aid of pen and paper. The generating semantic linkage between related concepts is also is a mental process of observation, evaluation and judgement wherein a user creates a semantic graph with links between related concepts, this can be done with the aid of pen and paper.)
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
provides a unified knowledge base interface that integrates the combined modules. (This claim limitation amount to retrieving data from memory, and presenting it which is extra-solution activity, see MPEP 2106.05(g).;
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “provides a unified knowledge base interface that integrates the combined modules.” is accessing/retrieving data from memory and presenting it, which are both well-understood, routine and conventional. For accessing and retrieving data from memory see MPEP 2106.06(d)(II)(iv), wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”; and for presenting data see MPEP 2106.06(d)(II)(iv) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;”
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it mere extra-solution activity that is well-known, understood, routine and conventional combined with the abstract idea.
Claim 18
Step 2A Prong 1: The claim recites, inter alia:
Inherits the abstract idea of claim 1.
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
the AI agent is automatically associated with an existing user profile from the plurality of user profiles, enabling personalized operation without user reconfiguration by inheriting the one or more historical interaction patterns and preferences through an initial setup procedure that requests confirmation of communication preferences and interaction style (This claim amounts to saving information that indicates an AI agent is associated with a user in the user profile. This is essentially saving data and later accessing it. Thus is extra-solution activity and accessing/retrieving data which is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “the AI agent is automatically associated with an existing user profile from the plurality of user profiles, enabling personalized operation without user reconfiguration by inheriting the one or more historical interaction patterns and preferences through an initial setup procedure that requests confirmation of communication preferences and interaction style” amounts to saving information that indicates an AI agent is associated with a user in the user profile. This is essentially saving data and later accessing it, and Thus is extra-solution activity that is well-understood, routine and conventional. See MPEP 2106.06(d)(II)(iv), wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it mere extra-solution activity that is well-known, understood, routine and conventional combined with the abstract idea.
Claim 19
Step 2A Prong 1: The claim recites, inter alia:
Inherits the abstract idea of claim 1.
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
agent profile sharing functionality, wherein the storage subsystem enables copying and sharing of user profiles, behavioral adaptation rules, and interaction preferences between different AI agent instances within an organization while implementing access control mechanisms to prevent unauthorized profile access. (This claim amounts to data collection or transmitting data, as it is sending a copy or sharing stored, as such it is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “agent profile sharing functionality, wherein the storage subsystem enables copying and sharing of user profiles, behavioral adaptation rules, and interaction preferences between different AI agent instances within an organization while implementing access control mechanisms to prevent unauthorized profile access.” amounts to data collection or transmitting data, as it is sending a copy or sharing stored, and thus is well-understood, routine and conventional. See MPEP 2106.06(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it mere extra-solution activity that is well-known, understood, routine and conventional combined with the abstract idea.
Claim 20
Step 2A Prong 1: The claim recites, inter alia:
Claim 20 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the AI agent delivers user-specific assistance that simultaneously reflects the first user's access rights defined by the one or more credentials, knowledge scope defined by the one or more knowledge base access permissions, personalized interaction style derived from the one or more historical interaction patterns, and real-time contextual needs to provide integrated user experiences. (This claim is cited at high level of generality and results in adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).;
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “wherein the AI agent delivers user-specific assistance that simultaneously reflects the first user's access rights defined by the one or more credentials, knowledge scope defined by the one or more knowledge base access permissions, personalized interaction style derived from the one or more historical interaction patterns, and real-time contextual needs to provide integrated user experiences. ” is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely instructions to implement the abstract idea using the computer as tool.
Claim 23
Step 2A Prong 1: The claim recites, inter alia:
Claim 23 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
wherein the AI agent instantiated for the first user identifier operates within a sandbox execution environment that prevents the AI agent from accessing resources outside the one or more knowledge base access permissions associated with the first user identifier. (This claim is cited at high level of generality and results in adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).;
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they extra-solution activity in combination with the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “wherein the AI agent instantiated for the first user identifier operates within a sandbox execution environment that prevents the AI agent from accessing resources outside the one or more knowledge base access permissions associated with the first user identifier. (This claim is cited at high level of generality and results in adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).;
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it merely instructions to implement the abstract idea using the computer as tool.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 8-10, 12-15, 18, 20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Cabrera-Cordon et al. (US 2018/0077088 A1 – hereinafter Cordon) in view of Yamane et al. (US 10,956,300 B1 – hereinafter Yamane) in view of Latka et al. (US 2022/044266 A1 – hereinafter Latka) and further in view of Badr et al. (US 2018/0329889 A1 – hereinafter Badr).
In regards to claim 1, Cordon discloses a system for personalized artificial intelligence agent responses based on user profiles, comprising:
a storage subsystem that maintains a user profile mapping a plurality of user identifiers to the user profiles, and one or more processors that instantiate an AI agent for a first of the user identifiers from the plurality of user identifiers; (Cordon para. [0018] cites “As illustrated, the example environment 100 includes an automated agent system 150, operative to generate a personalized automated agent 116 personalized to a user (agent owner 120) for receiving a query and providing a response on behalf of the user.” And the abstract cites “For example, the automated agent is operative to provide a response on behalf of an agent owner. A knowledge database is generated based on the agent owner's context (e.g., email conversations, calendar data, organizational chart, document database).”, these together teach a personalized AI agent being instantiated for a user based on the user data.)
wherein the AI agent:
receives an input indicative of a task from a first user associated with the first user identifier, performs access actions using the one or more knowledge base permissions associated with the first user identifier to acquire task-specific data, and (Cordon para. [0022] cites “When communicating with a user 130, the personalized automated agent 116 is operative to receive a natural language query.” This is receiving input indicative a task. Cordon para. [0029-0030] teaches query understanding subsystem 104 uses linguistic service 114 to determine intent and semantic content and uses semantic analyzer 112 that monitors frustration. In para. [0044] it cites “..the personalized automated agent 116 queries the knowledge database 110 for determining an appropriate answer to provide the user 130.” and para. [0030-0031 and 0044] goes on the teach the agent getting additional inputs from configuration settings 118 and specifying which calendars to reference, how and when to reach the owner, which user may interrupt the owner, and etc. This teaches acquiring task specific data from knowledgebases according to access rules. For example, see fig. 3 and para. [0038-0039] wherein a query is received and the AI agent accesses a calendar that it has permission to suggest scheduling a meeting.)
generates one or more personalized responses to the input based on the acquired task- specific data and the respective one or more historical interactions. (Cordon para. [0019-0020] teaches providing personalized responses to query input based on task specific data and past user interaction wherein it cites “Generally, the automated agent system 150 learns about the agent owner 120 from the agent owner's interactions, communications, relationships, etc. For example, the system learns who the agent owner 120 interacts with, understands topics discussed with other individuals, learns how the agent owner 120 interacts with specific types of content, learns how the agent owner 120 interacts with other individuals in association with specific requests or topics, etc. [0020] As an example, a category identified in the agent owner's context based on the agent owner's emails 122 or conversations in the agent owner's mail folder(s) may be "text analytics." Other keywords identified as related to "text analytics" in the agent owner's context may include "topic detection" or "sentiment analysis." Based on an organizational chart, or the agent owner's contacts or emails 122, Bob, who is a program manager, may be identified in the agent owner's context as an entity related to "text analytics." Accordingly, when a user 130 asks the personalized automated agent 116 a question on "text analytics," or any of its related keywords, the personalized automated agent 116 is enabled to understand that Bob might be a person to whom to recommend routing the question.” Also see fig. 2 wherein a first response provide a document, the agent got feedback and determined it to be negative and then provided a second more customized response that took into past historical interaction and database to find an expert for the user. Also see figure 3 where it did a similar process but accessed a calendar and proposed a meeting with an expert and time.)
However, Cordon does not explicitly discloses using the one or more stored credentials for accessing applications and a storage subsystem that maintains a user registry mapping a plurality of user identifiers to a plurality of user profiles.
Yamane disclose using one or more stored credentials for accessing applications. (Yamane claim 1 teaches providing credentials to a system for using AI agent wherein it cites “providing to the AI agent, by the computing device, login credentials that grant the AI agent access to the online software product;”.)
It would have been obvious to one of ordinary skill before the earlies effective filing date of the claimed invention to modify the teachings of the Cordon with that of Yamane in order to allow for using one or more user credentials for accessing applications as both reference deal with the use of AI agents and the benefit of doing so it creates a secure application wherein user information and the application is protected from unauthorized use.
However, Cordon in view of Yamane does not explicitly disclose a storage subsystem that maintains a user registry mapping a plurality of user identifiers to a plurality of user profiles.
Latka discloses a storage subsystem that maintains a user registry mapping a plurality of user identifiers to a plurality of user profiles. (Latka para. [0003] cites “A smart feedback system (SFS) stores a plurality of user profiles. Each user profile is associated with a corresponding user and a corresponding personalized avatar.”, also see fig. 1 element 150 that stores user profiles, element 152. Also see Latka para. [0040] which cites “Each user profile 152 of the plurality of user profiles 152 may include user information, device information, a set of interaction patterns, and a set of preferences, among others. The user information may include, for example, a user name, a user identification (ID), a unique avatar ID associated with the personalized avatar, an age of the user, a gender of the user, or other user identifying features, among others. The device information may include a list of devices associated with the user, where each device in the list of devices may include, for example, a device name, a device ID, and an address associated with the device, among others. Each interaction pattern of the set of interaction patterns may include, for example, a type of interaction pattern, an offer associated with a participant associated with an interaction with the personalized avatar, a learned behavior of the user based on an analysis of feedback captured by a device associated with the user in response to a feedback request during the interaction with the personalized avatar based on the feedback request associated with the offer, and a machine learning algorithm, among others. Each preference of the set of preferences may include, for example, a type of preference, an offer associated with a participant associated with an interaction with the personalized avatar, a learned preference of the user based on an analysis of feedback captured by a device associated with the user in response to a feedback request during the interaction with the personalized avatar based on the feedback request associated with the offer, and a machine learning algorithm, among others.”. This teaches maintaining a plurality of user profiles mapped with user identifiers as well as historical user interaction patterns. )
It would have been obvious to one of ordinary skill before the earlies effective filing date of the claimed invention to modify the teachings of the Cordon in view of Yamane with that of Latka in order have a storage subsystem storing a plurality of user profiles mapping a plurality of user identifiers to a plurality of user profiles as all the references deal with the use of AI agents and the benefit of doing so it creates a more robust that can be used a plurality of user and can provide customized responses to each user based on their profile.
However Cordon in view of Yamane in view of Latka does not explicitly disclose performing, by the AI agent, access actions using one or more credentials and the one or more knowledgebase access permissions associated with the first user identifier to acquire task-specific data, where the task-specific data acquired by the access actions is limited to resources authorized by the one or more knowledgebase access permissions associated with the first user identifier, and data outside of the one or more knowledge base access permissions associated with the first user identifier is excluded from the acquired task-specific data.
Badr discloses performing, by the AI agent, access actions using one or more credentials and the one or more knowledgebase access permissions associated with the first user identifier to acquire task-specific data, where the task-specific data acquired by the access actions is limited to resources authorized by the one or more knowledgebase access permissions associated with the first user identifier, and data outside of the one or more knowledge base access permissions associated with the first user identifier is excluded from the acquired task-specific data. (Badr para. [0001-0003] teaches users/people using AI agents (intelligent personal assistants) to perform various task and answer queries by accessing various databases (See fig. 1 and para. [0028] wherein it cites “While user-controlled resources 128 is depicted in FIG. 1 as a single database, this is not to suggest that all user-controlled resources is stored in a single location. To the contrary, in many implementations, user-controlled resources may be stored ( or otherwise available) in part on client devices 106 (e.g., sensor signals such as GPS), and/or may be distributed across a variety of cloud-based systems, each which may serve a different purpose ( e.g., one set of one or more servers may provide email functionality, another set of one or more servers may provide calendar functionality, etc.).”. Para. [0003] teaches the AI agent accessing online data and user account (profile) data, and para. [0021] teaches each user having their own AI agent. Fig. 9 element 902, 904, 906 teaches the AI agent receiving a query/task from the user. Para. [0026] teaches user credentials associated with a user and user account (username, password, biometrics, and any other type of credentials) and allowing the AI agent to access anything the user has access to. Also, para. [0024] teaches user-controlled resources engine which has access-control entries for each user, thus it controls what can be accessed by each user and their AI agents. Para. [0065] teaches the system checking if a user has access rights to a resources, wherein it cites “In some implementations, permissions may be checked on an individual user basis. In some implementations, the system may determine whether the first user has appropriate access rights as regards the second user by determining that the first user is a member of a first group and determining that the first group has appropriate access rights as regards the second user. In various implementations, a task request may include multiple sub tasks or queries, some of which for which permission is governed by access control list 126 and other for which no permission is required.”) Figure 9 element 910 teaches the AI executing the task or performing the access action. In para. [0049] it teaches accessing knowledgebase that user has permission for to acquire task specific data, in particular, it acquires data regarding Sarah’s schedule that Dave has permission to access to answer the question is Sarah available for lunch wherein it cites “[0049] In FIG. 2A, user 101 ("Dave") provides natural language input 280 of "Is Sarah available for lunch on Tuesday?" in a human-to-computer dialog session between the user 101 and automated assistant 120. In response to the natural language input 280, automated assistant 120 interacts with user-controlled resources engine 130 to determine whether access control list 126 permits Dave, and more particularly, an automated assistant 120 serving Dave, to read data from Sarah's schedule. For example, Dave's automated assistant 120 may interact with schedule service 132, which may determine whether Dave has appropriate access rights as regards Sarah. Assuming Dave has such rights, schedule service 132 may either permit Dave's automated assistant to analyze Sarah's calendar, or may analyze Sarah's calendar itself. Either way, a determination may be made, e.g., by Dave's automated assistant 120 and/or schedule service 132, that Sarah is available for lunch on Tuesday at 1 PM. Accordingly, Dave's automated assistant 120 (executing on client device 206A) may provide responsive natural language output 282 of "Let me check . . . Sarah appears to be available for lunch at 1 PM.”. In para. [0057] it discloses wherein a user does not have permission to access another users grocery list, and thus the system does not access and edit the grocery list but instead notifies the user that it does not have permission. Thus it was not accessed or restricted from the task specific data.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Cordon in view of Yamane in view of Latka wit the teachings of Badr in order to allow for accessing and restricting an AI agents access to databases based on user credentials as Cordon Yamane and Badr deal with using AI agents. The benefit of allow and restricting access to information or knowledgebases based on user credentials of the user it creates a more secure system to restricting users from accessing information they are not authorized to as well as creating an efficient system wherein the AI agent can perform tasks for users and thus saving time.
In regards to claim 2, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 1, wherein the AI agent maintains a continuous memory state and operational context across multiple application sessions. (Cordon para. [0019 and 0021] teaches a personalize knowledge base built using agent owners’ context (user information and context), wherein the knowledgebase is operative to store the extracted keywords, topics, categories, and entities, as well as the identified relationships and calculated degrees of similarity between the keywords, topics, categories, and entities. In some examples, a knowledge graph is used to represent keywords, topics, categories, and entities as nodes, and attributes and relationships between the nodes as edges, thus providing a structured schematic of entities and their properties and how they relate to the agent owner 120. Cordon para. [0029-0031, 0043-0045] teaches the query understanding and resolution subsystem receives natural language query, classifies the query into intent, and then queries the knowledgebase to determine an appropriate answer to provide to the user. Thus, in step 418 of fig. 4B it queries the knowledge base, also figure. 4A and para. [0012] shows a process for continually update the knowledgebase, thus it used over time and different session as it updated.)
In regards to claim 3, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 1, wherein prior to generating the one or more personalized responses, the AI agent processes and optimizes the task-specific data to reduce computational token consumption while maintaining response accuracy. (Cordon para. [0018-0019] teaches building a knowledge database based on agents own text using texting mining machine learning engine to mine various data collection such as emails, calendar, org. chart, documents, and configuration settings. Cordon para. [0019-0021, 0040-0041] teaches an engine for analyzes the data collections and extracting keywords, topics, categories, entities, and degree of similarity between items. It also teaches storing this data as knowledge graphs and semantic graphs, wherein this pre-processing/optimizing as it takes raw data and reduced it knowledge graph and sematic structures. Also, Cordon para. [0029-0031 and 0043-0044] teaches using the knowledge base and structures to determine appropriate answer, thus maintaining response accuracy.)
In regards to claim 8, Cordon in view of Yamane in view of Latka in view of Badr disclose the system of claim 1, wherein the AI agent supports distributed deployment across computing environments including one or more of: local execution on user devices, network-based execution through communication interfaces, cloud platforms, edge devices, and hybrid configurations while maintaining consistent agent behavior and capabilities. (Cordon fig. 7 shows a distributed deployment system wherein multiple edge devices (General computing device, element 705a; tablet computing device, element 705b; and mobile device, element 705c) access the automated agent system through a server. )
In regards to claim 9, Cordon in view of Yamane in view of Latka in view of Badr disclose the system of claim 1, further comprising a historical analysis module configured to maintain behavioral profiles capturing the one or more historical interaction patterns, agent intervention levels, and communication preferences from user interactions with the AI agent. (Latka para. [0040-0043] teaches the SFS repository store a plurality of user profiles each associated with user and personalized avatar. Each user profiles further comprises user information, device information, a set of interaction patterns, and a set of preferences. Latka para. [0004, 0029-0031] teaches the SFS system captures feedback of the user and para. [0042-0044, 0050-0053 and 0114-0115] teaches a feedback analysis engine uses machine learning algorithms to map feedback to meanings and to update interaction patterns and preferences in the user profiles.)
In regards to claim 10, Cordon in view of Yamane in view of Latka in view of Badr disclose the system of claim 9, further comprising a visual configuration interface operatively connected to the historical analysis module for assembling AI agent configurations from modular components through interactive graphical elements. (Examiner interprets this claim to mean there is visual interface with graphical elements for the user interact with the AI agent. Latka figures 2A-2J discloses a user device with the AI agent on it and various buttons for providing feedback and interaction with the AI agent. See Latka para. [0009-0018] that further gives descriptions. Also see Cordon para. [0042-0044 and 0050-0053] that teaches user feedback information is stored in the knowledge base and user profile and used in further future recommendations. )
In regards to claim 12, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 10, wherein the visual configuration interface monitors user interaction patterns within the configuration interface to identify individual configuration preferences and stores the identified preferences in the user profile associated with the first user identifier. (Latka para. [0040-0043, 0050-0053, 0114-0115] teaches the system updating the user’s profiles set of user interaction patterns and set of patterns based on feedback from the user interface interactions.)
In regards to claim 13, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 1, further comprising natural language-based personalization functionality including a conversational interface that enables the first user to customize behavior of the AI agent through natural language feedback that is processed into technical configuration parameters stored in the user profile. (Cordon para. [0023-0025] teaches a natural language personalization function wherein the user tells the system what it needs to learn wherein it cites “A user 130 (or the agent owner 120) is enabled to enrich the personalized automated agent 116 by telling the agent the information it needs to learn (e.g., "Carl is working on the ABC project"; "Joe is Anna's husband"; "Johanna is an expert on Semantic Graphs"; "There is good information on machine learning at www.machinelearning.com'').” It further teaches the system identifies the entities and relations and saving it a semantic graph data, which is saved in the knowledge base of the user and used to create answers queries. This is customizing behavior of the AI agent via natural language as it tells the system what it needs to learn and configured to parameters later used by the system for making responses.)
In regards to claim 14, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 1, further comprising dynamic knowledge base combination functionality that combines modular knowledge bases to provide unified information access to the AI agent. (Cordon para. [0019] teaches multiple modular data sources (email conversation, calendar data, organization chart, documents, configuration settings, social networking threads and contact list. These are distinct, structured and unstructured stored data. Further para. [0019-0021] teaches mining the various knowledge bases using machine learning to creating dynamic unified structured knowledgebase, that is queried to answer users queries. Also figure 4 teaches the knowledgebase is updated continually in a loop.)
In regards to claim 15, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 14, wherein the dynamic knowledge base combination functionality dynamically combines the modular knowledge bases based on contextual parameters associated with user sessions, generates semantic linkages between related concepts, and provides a unified knowledge base interface that integrates the combined modules. (Cordon para. [0018-0019] teaches building a knowledge database based on agents own text using texting mining machine learning engine to mine various data collection such as emails, calendar, org. chart, documents, and configuration settings. Cordon para. [0019-0021, 0040-0041] teaches an engine for analyzes the data collections and extracting keywords, topics, categories, entities, and degree of similarity between items. It also teaches storing this data as knowledge graphs and semantic graphs, wherein this pre-processing/optimizing as it takes raw data and reduced it knowledge graph and sematic structures. Also, Cordon para. [0029-0031 and 0043-0044] teaches using the knowledge base and structures to determine appropriate answer. These exerts shows that various knowledge bases are combined into a unified knowledge base across user session and creates semantic linkages in the sematic graph.)
In regards to claim 18, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 1, wherein the AI agent is automatically associated with an existing user profile from the plurality of user profiles, enabling personalized operation without user reconfiguration by inheriting the one or more historical interaction patterns and preferences through an initial setup procedure that requests confirmation of communication preferences and interaction style. (Latka para. [0047-0049 teaches that at initial registration, the user interaction engine creates a user profile, including a unique personalized avatar ID, based on user and device information. This is stored in the SFS repository associated with the user. Latka para. [0040-0034 and 0054-0058] teaches each user profile contains a set of interaction patterns and preferences which are derived from historical feedback. This enables instant personalization without reconfiguration when user is identified.)
In regards to claim 20, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 1, wherein the AI agent delivers user-specific assistance that simultaneously reflects the first user's access rights defined by the one or more credentials, (Yamane claim 1 teaches providing credentials to a system for using AI agent wherein it cites “providing to the AI agent, by the computing device, login credentials that grant the AI agent access to the online software product;”.) knowledge scope defined by the one or more knowledge base access permissions, (Cordon In para. [0044] it cites “..the personalized automated agent 116 queries the knowledge database 110 for determining an appropriate answer to provide the user 130.” and para. [0030-0031 and 0044] goes on the teach the agent getting additional inputs from configuration settings 118 and specifying which calendars to reference, how and when to reach the owner, which user may interrupt the owner, and etc. This teaches acquiring task specific data from knowledgebases according to access rules/permissions) personalized interaction style derived from the one or more historical interaction patterns, and real-time contextual needs to provide integrated user experiences. (Cordon para. [0019 and 0031] teaches tone and cordiality level from configuration settings and learning how the agent owner interacts with specific types of content and other individuals from historical context. Thus, it teaches personalized interaction style derived from historical interaction patterns. Also see fig. 3 for real-time contextual needs to provide integrated user experiences as proper responses. )
In regards to claim 22, it is the method embodiment of claim 1 with similar limitations and as such is rejected using the same reasoning in claim 1.
Claims 4 -6 are rejected under 35 U.S.C. 103 as being unpatentable over Cabrera-Cordon et al. (US 2018/0077088 A1 – hereinafter Cordon) in view of Yamane et al. (US 10,956,300 B1 – hereinafter Yamane) in view of Latka et al. (US 2022/044266 A1 – hereinafter Latka) in view of Badr et al. (US 2018/0329889 A1 – hereinafter Badr) and further Boyd et al. (US 20240015126 A1 – hereinafter Boyd).
In regards to claim 4, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 1, but does not disclose further comprising cross-application conversation continuity functionality, wherein the AI agent automatically presents conversation history from a first application within an interface of a second application when the first user transitions between applications.
Boyd disclose cross-application conversation continuity functionality, wherein the AI agent automatically presents conversation history from a first application within an interface of a second application when the first user transitions between applications. (Boyd para. [0109] teaches presenting a conversation history from a first application within an interface of a second application when the first user transitions between applications. In para. [0109] Body cites “In some examples, the conversation access module 512 receives a request from a client device 102 to access a conversation interface of the messaging client 104. For example, the conversation access module 512 receives a request to launch the conversation interface on a first web session (e.g., a first window of a web browser application, a first tab of the browser application, or a first computing device). The conversation access module 512 can detect that the request has been received from a particular type of client device 102 (e.g., desktop computer) and/or from a particular type of software application (e.g., a web browser application). Namely, rather than launching the messaging client 104 directly from an application that runs on a mobile device that includes the code for executing the messaging client 104, the conversation access module 512 can enable a user to access content of the messaging client 104 via other types of client applications, such as web browsers.”, thus it transfer a conversation from a mobile device to desktop computer and vice versa.)
It would have been obvious to one of ordinary skill before the earliest effective filing date of the claimed invention to modify the teachings of Cordon in view of Yamane in view of Latka in view of Badr with that of Boyd in order to allow for transferring past conversations between applications as Cordon, Yamane and Boyd all deal with user profile with user historical data. The benefit of doing so it allows a user to maintain conversation between devices allowing for a more user friendly integration of devices.
In regards to Claim 5, Cordon in view of Yamane in view of Latka in view of Badr in view of Boyd discloses the system of claim 4, wherein the cross-application conversation continuity functionality further comprises intra-application context transitions that enable the AI agent to maintain contextual awareness and provide relevant information when the first user transitions between different areas within the same application. (Boyd para. [0057] cites “ The conversation management system 224 monitors web sessions used by a client device 102 to access and present a conversation interface of a messaging client 104 displayed on a web browser implemented on a particular type of client device 102, such as a desktop computer. The conversation management system 224 conditionally and intelligently transfers the conversation interface from one web session to another web session in response to determining that a request is received to access the conversation interface from the other web session. Conversation elements can include user identifiers, chat input regions, messages exchanged, presence or user status indicators, phone call status indicators and options for placing phone calls, group names, and various other conversation related information. While the present disclosure describes the concept of adjusting the display of a web-browser window or tab in relation to conversation elements, this is just one example and is not meant to be limiting.”, which teaches maintaining a conversation history and context awareness when transitioning between different areas.)
In regards to Claim 6, Cordon in view of Yamane in view of Latka in view of Badr in view of Boyd discloses the system of claim 4, wherein when the AI agent is automatically launched in the second application, the AI agent automatically retrieves and loads conversation context from the first application to continue conversation in the second application environment. (Boyd para. [0109] teaches a request is sent and then past conversation are automatically transfer from a first device and application to second device and application.)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Cabrera-Cordon et al. (US 2018/0077088 A1 – hereinafter Cordon) in view of Yamane et al. (US 10,956,300 B1 – hereinafter Yamane) in view of Latka et al. (US 2022/044266 A1 – hereinafter Latka) ) in view of Badr et al. (US 2018/0329889 A1 – hereinafter Badr) and further Mascaro et al. (US 11,069,001 B1 – hereinafter Mascaro).
In regards to claim 7, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 1, but does not explicitly disclose further comprising conflict resolution logic that determines precedence when user-configured agent settings conflict with organizational policies stored in the storage subsystem.
Mascaro discloses conflict resolution logic that determines precedence when user-configured agent settings conflict with organizational policies stored in the storage subsystem. (Mascaro column 11 lines 22- column 12 line 15 teaches customizing an experience based on user preferences (user-configure settings). It also teaches when user preferences conflict with business rules or policy that the user preferences and business rules are compared and any non-compliant user preferences are filtered out to provide business rules compliant user experiences. This is conflict resolution logic as fixes the conflict between user preferences and business rules.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Cordon in view of Yamane in view of Latka with that of Mascaro as both Cordon and Mascaro deal with customization based on user preferences. The benefit of doing so it creates a more robust system that can fulfil user preferences as well as follow business rule and guidelines, thus making both the user and business happy.
Claims 11 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Cabrera-Cordon et al. (US 2018/0077088 A1 – hereinafter Cordon) in view of Yamane et al. (US 10,956,300 B1 – hereinafter Yamane) in view of Latka et al. (US 2022/044266 A1 – hereinafter Latka) ) in view of Badr et al. (US 2018/0329889 A1 – hereinafter Badr) and further Vasylyev (US 2024/0412720 A1).
In regards to claim 11, Cordon in view of Yamane in view of Latka in view of Badr disclose the system of claim 10, but does not disclose wherein the visual configuration interface enables users to customize behavior of the AI agent through graphical elements including sliders, toggles, and selection controls that translate user preferences into agent response parameters stored in the storage subsystem.
Vasylyev disclose wherein the visual configuration interface enables users to customize behavior of the AI agent through graphical elements including sliders, toggles, and selection controls that translate user preferences into agent response parameters stored in the storage subsystem. (Vasylyev para. [0405] teaches customizing the personality of AI agent wherein it cites “According to one embodiment, assistant system 2 may be configured to act as a specific personality and further provide the user with the option to select which type of personality he or she prefers. For example, assistant system 2 can be configured to emulate the personality of a professional business consultant, offering concise, fact-based advice and prioritizing efficiency in communication. In a further example, in response to the user's request, assistant system 2 can be configured to respond in a more casual and friendly tone, using colloquial language and inserting humor into its responses. … In addition to providing preset personalities, assistant system 2 may be configured to allow users to customize the personality traits according to their preferences and provide responses in a certain style (e.g., formal or informal), exhibiting certain attitudes (e.g., optimistic or realistic), or showing certain behaviors (e.g., proactive or reactive). …. The parameters associated with such learnt behavior can be stored in non-volatile, long-term system memory unit 118 to allow for reusing such parameters in further conversations. Various preferences and parameters can be stored in system memory unit 118 and associated with specific user IDs such that when the user IDs are identified in further conversations, the respective preferences and parameters can be applied automatically to those conversations.” And para. [0619] teaches a GUI display with toggles, slider, and other input selection types for customizing the system. Para. [0619] cites “The user interface may include a dedicated privacy settings menu, accessible through voice commands and/or the graphical user interface (GUI). Within this menu, users can configure their privacy preferences using a series of toggles, sliders, checkboxes, and/or voice feedback.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of Cordon in view of Yamane in view of Latka in view of Badr with that of Vasylyev in order to allow for customizing the behavior of an AI agent as Cordon, Yamane, Badr and Vasylyev all deal with using AI agents and as Vasylyev has GUI with toggles, sliders, and other selection inputs for customization it would be obvious to use a GUI with toggles, sliders, and other selection inputs for customizing the personality of the AI agent as Vasylyev also teaches user selecting AI agent personality traits. It provides the benefit of further customizing the AI agent to user liking in a simple and intuitive manner.
In regards to claim 16, Cordon in view of Yamane in view of Latka in view of Badr disclose the system of claim 10, but does not explicitly disclose wherein the interactive graphical elements include a communication style slider interface ranging from formal to casual settings that automatically adjusts AI agent response tone and terminology, wherein the visual configuration interface presents the communication style slider interface within a chat environment for real-time configuration.
Vasylyev discloses disclose wherein the interactive graphical elements include a communication style slider interface ranging from formal to casual settings that automatically adjusts AI agent response tone and terminology, wherein the visual configuration interface presents the communication style slider interface within a chat environment for real-time configuration. (Vasylyev para. [0405] teaches customizing the personality of AI agent wherein it cites “According to one embodiment, assistant system 2 may be configured to act as a specific personality and further provide the user with the option to select which type of personality he or she prefers. For example, assistant system 2 can be configured to emulate the personality of a professional business consultant, offering concise, fact-based advice and prioritizing efficiency in communication. In a further example, in response to the user's request, assistant system 2 can be configured to respond in a more casual and friendly tone, using colloquial language and inserting humor into its responses. … In addition to providing preset personalities, assistant system 2 may be configured to allow users to customize the personality traits according to their preferences and provide responses in a certain style (e.g., formal or informal), exhibiting certain attitudes (e.g., optimistic or realistic), or showing certain behaviors (e.g., proactive or reactive). …. The parameters associated with such learnt behavior can be stored in non-volatile, long-term system memory unit 118 to allow for reusing such parameters in further conversations. Various preferences and parameters can be stored in system memory unit 118 and associated with specific user IDs such that when the user IDs are identified in further conversations, the respective preferences and parameters can be applied automatically to those conversations.” And para. [0619] teaches a GUI display with toggles, slider, and other input selection types for customizing the system. Para. [0619] cites “The user interface may include a dedicated privacy settings menu, accessible through voice commands and/or the graphical user interface (GUI). Within this menu, users can configure their privacy preferences using a series of toggles, sliders, checkboxes, and/or voice feedback.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of Cordon in view of Yamane in view of Latka in view of Badr with that of Vasylyev in order to allow for interactive graphical elements include a communication style slider interface ranging from formal to casual settings that automatically adjusts AI agent response tone and terminology, customizing the behavior of an AI agent as Cordon, Yamane, Badr and Vasylyev all deal with using AI agents and as Vasylyev has GUI with toggles, sliders, and other selection inputs for customization it would be obvious to use a GUI with toggles, sliders, and other selection inputs for customizing the personality of the AI agent as Vasylyev also teaches user selecting AI agent personality traits ranging from formal to informal, professional to casual, and etc. It provides the benefit of further customizing the AI agent to user liking in a simple and intuitive manner.
In regards to claim 17, Cordon in view of Yamane in view of Latka in view of Bard in view of Vasylyev discloses the system of claim 16, wherein the AI agent generates the communication style slider interface within a chat response, and user interaction with the communication style slider interface directly modifies backend configuration settings of the AI agent. (Vasylyev para. [0405] teaches customizing the personality of AI agent wherein it cites “According to one embodiment, assistant system 2 may be configured to act as a specific personality and further provide the user with the option to select which type of personality he or she prefers. For example, assistant system 2 can be configured to emulate the personality of a professional business consultant, offering concise, fact-based advice and prioritizing efficiency in communication. In a further example, in response to the user's request, assistant system 2 can be configured to respond in a more casual and friendly tone, using colloquial language and inserting humor into its responses. … In addition to providing preset personalities, assistant system 2 may be configured to allow users to customize the personality traits according to their preferences and provide responses in a certain style (e.g., formal or informal), exhibiting certain attitudes (e.g., optimistic or realistic), or showing certain behaviors (e.g., proactive or reactive). …. The parameters associated with such learnt behavior can be stored in non-volatile, long-term system memory unit 118 to allow for reusing such parameters in further conversations. Various preferences and parameters can be stored in system memory unit 118 and associated with specific user IDs such that when the user IDs are identified in further conversations, the respective preferences and parameters can be applied automatically to those conversations.” And para. [0619] teaches a GUI display with toggles, slider, and other input selection types for customizing the system. Para. [0619] cites “The user interface may include a dedicated privacy settings menu, accessible through voice commands and/or the graphical user interface (GUI). Within this menu, users can configure their privacy preferences using a series of toggles, sliders, checkboxes, and/or voice feedback.” This teaches directly modifying the AI agent based on user selections, and the system modifies it background or backend.)
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Cabrera-Cordon et al. (US 2018/0077088 A1 – hereinafter Cordon) in view of Yamane et al. (US 10,956,300 B1 – hereinafter Yamane) in view of Latka et al. (US 2022/044266 A1 – hereinafter Latka) in view of Badr et al. (US 2018/0329889 A1 – hereinafter Badr) and further Lewis et al. (US 2025/0335458 A1 – hereinafter Lewis).
In regards to claim 4, Cordon in view of Yamane in view of Latka in view of Badr discloses the system of claim 1, wherein the AI agent instantiated for the first user identifier operates such that it prevents the AI agent from accessing resources outside of the one or more knowledgebase access permissions associated with the first user identifier. (Badr para. [0049] teaches the AI agent access information or knowledgebases it has permission for and para. [0057] teaches restricting access to databases or data it does not have permission to.)
However, Cordon in view of Yamane in view of Latka in view of Badr does not disclose wherein a AI agent operates within a sandboxed execution environment.
Lewis discloses wherein a AI agent operates within a sandboxed execution environment. (Lewis para. [0483] teaches an AI agent executing in sandbox wherein it cites “The resulting context data (information assembled from permitted capsule contents) is then delivered back to the AI agent. The memory wallet ensures that this context data is packaged in a way that the agent cannot accidentally or maliciously obtain more data than intended. This could involve, for instance, running the AI agent's code in a secure enclave or sandbox that only has access to the data provided and not the raw capsules themselves.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Cordon in view of Yamane in view of Latka in view of Badr with that of the Lewis in order to restrict the AI agent to only information it is allowed as the reference Cordon, Yamane, Badr and Lewis all deal with using AI agents. The benefit of doing so is the Sandox is a secure enclave that restricts or isolates the AI agent from data it does not have access to, creating a more secure system.
Allowable Subject Matter
Claims 19 and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed on 17 June 2026 have been fully considered but they are not persuasive. The applicant argues that the amendment to the claim of allowing access to retrieve information to databases or sources that user has authorization/credentials to while restricting access to to sources the users does not have credentials to integrates the application into a practical application. The applicant also appears to argues the case is similar to that Desjardins, and such overcome the rejection under 35 USC 101. The examiner respectfully traverses the applicant’s arguments as the claism of the instant application are not similar to those found in Desjardins, as such the examiner fails to see how the cases are similar. Additionally, while the applicant is correct that allowing or restricting access to information based on credentials is not a mental process, it is something is well-understood, routine and conventional. The limitation is question allows for retrieving data based on a user having credentials or authorization to access the data, and is not the user or system can not access it. This is something is well known and widely used called access control. Access control well-understood, routine and conventional in computer systems. See the reference title “Access Control Methods: What They Are, How They Work, and How to Choose the Right Approach” which teaches access control means managing permissions and authorizations of people who need resources for these jobs on page 2 and on page 3 it gives the most common models of access control are attribute-based Policy control, Discretionary Access Control (DAC), Mandatory Access Control (MAC), logical access control methods, access control list (ACLs), and passwords. As these are common and so they are well-understood, routine and conventional. Another reference by Immuta.com titled “What is RBAC, (Role-Based Access Control) – And is it Right for You?” teaches on page 1 that DAC and MAC was well-known and widely used in the 1960s and 1970s. On page 2 last it teaches RBAC was have been in use for over 30 years is inescapable today in data space. This would also indicate that DAC, MAC, RBAC are all well-understood, routine and conventional methods of controlling users access to data. As such that examiner finds that controlling access to data based on credentials in computer systems is well-understood, routine and conventional. As such it does not amount to significantly more nor does it integrate the abstract idea into a practical application.
In regards to applicant Applicant’s arguments with respect the rejection of the claims under 35 USC 103, they have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127
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