DETAILED ACTION
Remarks
This communication is in response to the amendment/arguments filed on August 3, 2026 has been fully considered. The rejection is made final. Claims 1-15 are pending for examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
The examiner requests, in response to this Office action, supports are shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
If Applicant has any question or concern before filing a response to this Office Action, Applicant may call Examiner to have a discussion in an interview.
Response to Amendment
Objection to claim(s) 1-15 imposed in the previous office action has been withdrawn because of the amendment to the claim(s).
Response to Arguments
Applicant's arguments filed August 3, 2026 have been fully considered but they are not persuasive.
In response to applicant’s argument on pages 8-11 that there is no teaching, suggestion, or motivation to combine the references in order to teach the limitations of the amended claim 1 “the resulting combination would merely provide encrypted storage of user information. The cited combination still fails to teach or suggest the claimed combination of generating personalized recommendations based on the plurality of data sets, iteratively refining the logic of categorization and recommendation generation by analyzing logged outcomes and feedback from prior interactions, and securely storing the prior interactions and historical data within the database of the secure cloud-based enclave”, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Liu [0047-0049], [0062], [0066-0067] discloses that the proactive inference layer 280 may infer user interests and preferences based on the user profile that is retrieved from the user context engine 225. The proactive inference layer 280 may further communicate with proactive agents 285 regarding the inference. The proactive agents 285 may execute proactive tasks based on the inference. The proactive tasks may comprise sending content objects or providing services to the user. Each proactive task may be associated with an agenda item. The agenda item may comprise a recurring item such as a daily digest. A proactive agent 285 may retrieve the user profile (user profile data may include personal information that is useful for training a robust ranker model, … Training the ranker model using a plurality of training samples (i.e., plurality of data sets) from both historical data and real-time data associated with user profile data makes the ranker model robust [0066] (i.e., historical feedback data for the user)) from the user context engine 225 when executing the proactive task based on a user profile. Therefore, the proactive agent 285 may execute the proactive task in a personalized and context-aware manner. As an example, the proactive inference layer may infer that the user likes the band Maroon 5 and the proactive agent 285 may generate a recommendation of Maroon 5's new song/album to the user (i.e., generating categorization and personalized recommendation). The ranking may be based on the similarities between a user's interests and the candidate entities. … The ranking may be alternatively based on a ranking model that is trained based on user feedback data. … the proactive task may comprise recommending the candidate entities to a user. The proactive agent 285 may schedule the recommendation, thereby associating a recommendation time with the recommended candidate entities (i.e., feedback history). Therefore, Liu discloses generating personalized recommendations based on the plurality of data sets, iteratively refining the logic of categorization and recommendation generation by analyzing logged outcomes and feedback from prior interactions. Whereas, Ambroz [0018] discloses that provide users with complete control of their encrypted data saved on remote devices and computers connected to the Internet by storing their encrypted data values in the database and their encrypted files in the cloud storage space, for complete data privacy and security, including all system and logs. … The encrypted cloud database and encrypted cloud storage can be utilized by other system-authorized applications or apps that are available on the remote devices or computers. … These cloud-based applications can securely store encrypted data values such as encrypted user history and logs (i.e., prior interactions and the historical data being securely stored within the database of the secure cloud-based storage), encrypted user emails, and encrypted user chat, voice and video logs for complete privacy of user data. Therefore, Ambroz discloses securely storing the prior interactions and historical data within the database of the secure cloud-based enclave. Thus, combination of Liu and Ambroz discloses the above argued limitation of amended claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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, 3-6, 8, 10-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US Patent Publication No. 2019/0325081 A1, ‘Liu’, hereafter) in view of Ambroz et al. (US Patent Publication No. 2016/0028699 A1, ‘Ambroz’, hereafter).
Regarding claim 1. Liu teaches a method for managing interaction of data between a plurality of Artificial Intelligent (AI) agents within a secure cloud-based enclave, comprising:
initiating, by a single Al agent of the plurality of Al agents, an interaction request with at least one shared Al agent of the plurality of Al agents (see Liu, Abstract, [0041] and [0051], for an assistant system for processing/responding to a request using one or more agents selected from a plurality of agents (e.g., first-party agents, third-party agents, or proactive agents). Please also see [0061-0061]);
triggering, by the at least one shared Al agent, a negotiation and authorization process to the single Al agent based on the interaction request, wherein the negotiation and authorization process determines whether the single Al agent is eligible to interact with the shared Al agent (see Liu [0062] for triggering/communicating to one or more agents, wherein each of the one or more agents can be interpreted as either a category-specific AI agent or a support AI agent as recited, and the information returned from each agent can be interpreted as either the relevant information or the auxiliary information as broadly recited; also see Liu [0062] for the dialog engine accessing the user context engine to retrieve the context information wherein the dialog engine or the user context engine can be interpreted as equivalent to a support AI agent as broadly recited, and the context information can be interpreted as auxiliary information as recited);
based on the determination that the single Al agent is eligible to interact with the shared Al agent, receiving, by the at least one shared Al agent, user data from the single Al agent (see Liu [0062] for receiving the information returned from the one or more agents);
fetching, by the at least one shared Al agent, feedback from prior interactions and historical data related to the user data from a database of the secure cloud- based enclave (see Liu [0062] for receiving the information returned from the one or more agents. Also see Liu [0048-0049] for the ranking may be based on the similarities between a user's interests and the candidate entities. … The ranking may be alternatively based on a ranking model that is trained based on user feedback data. … the proactive task may comprise recommending the candidate entities to a user. The proactive agent 285 may schedule the recommendation, thereby associating a recommendation time with the recommended candidate entities (i.e., feedback history));
categorizing, by the at least one shared Al agent, the user data into a plurality of data sets based on a type of the user data, the feedback from prior interactions, and the historical data (see Liu [0062] for determining one or more agents from a plurality of agents for executing one or more tasks associated with the one or more one or more dialog-intents (i.e., categories) associated with the request, wherein each of one or more agents identified as disclosed can be interpreted as a category-specific AI agent as recited); and
generating, by the at least one shared Al agent, personalized recommendations based on the plurality of data sets, wherein a logic of the categorization of the user data and the generation of the personalized recommendations are iteratively refined by analyzing logged outcomes and the feedback from prior interactions (Liu [0047-0049], [0062], [0066-0067] discloses that the proactive inference layer 280 may infer user interests and preferences based on the user profile that is retrieved from the user context engine 225. The proactive inference layer 280 may further communicate with proactive agents 285 regarding the inference. The proactive agents 285 may execute proactive tasks based on the inference. The proactive tasks may comprise sending content objects or providing services to the user. Each proactive task may be associated with an agenda item. The agenda item may comprise a recurring item such as a daily digest. A proactive agent 285 may retrieve the user profile (user profile data may include personal information that is useful for training a robust ranker model, … Training the ranker model using a plurality of training samples (i.e., plurality of data sets) from both historical data and real-time data associated with user profile data makes the ranker model robust [0066] (i.e., historical feedback data for the user)) from the user context engine 225 when executing the proactive task based on a user profile. Therefore, the proactive agent 285 may execute the proactive task in a personalized and context-aware manner. As an example, the proactive inference layer may infer that the user likes the band Maroon 5 and the proactive agent 285 may generate a recommendation of Maroon 5's new song/album to the user (i.e., generating categorization and personalized recommendation). The ranking may be based on the similarities between a user's interests and the candidate entities. … The ranking may be alternatively based on a ranking model that is trained based on user feedback data. … the proactive task may comprise recommending the candidate entities to a user. The proactive agent 285 may schedule the recommendation, thereby associating a recommendation time with the recommended candidate entities (i.e., feedback history)).
Liu does not explicitly teach
secure cloud- based enclave and wherein the prior interactions and the historical data being securely stored within the database of the secure cloud-based enclave.
However, Ambroz teaches
secure cloud- based enclave and wherein the prior interactions and the historical data being securely stored within the database of the secure cloud-based enclave (provide users with complete control of their encrypted data saved on remote devices and computers connected to the Internet by storing their encrypted data values in the database and their encrypted files in the cloud storage space, for complete data privacy and security, including all system and logs. … The encrypted cloud database and encrypted cloud storage can be utilized by other system-authorized applications or apps that are available on the remote devices or computers. … These cloud-based applications can securely store encrypted data values such as encrypted user history and logs (i.e., prior interactions and the historical data being securely stored within the database of the secure cloud-based storage), encrypted user emails, and encrypted user chat, voice and video logs for complete privacy of user data, Ambroz [0018]).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Liu and Ambroz before him/her, to modify Liu with the teaching of Ambroz’s encrypted network storage space. One would have been motivated to do so for the benefit of providing increased security and anonymity when remotely accessing data and databases on devices and computers connected to the Internet (Ambroz, Abstract, [0004]).
Regarding claim 3. Liu as modified teaches, wherein the interaction request is associated with access to a resource within the secure cloud-based enclave (Ambroz [0018]).
Regarding claim 4. Liu as modified teaches, wherein the plurality of data sets comprises at least one of factual immutable data, factual mutable data, historical preferences, current preferences, and inferred data (Liu [009], [0036], [0049]).
Regarding claim 5. Liu as modified teaches, wherein the plurality if data sets is stored in the database of the secure cloud-based enclave (Ambroz [0018]).
Regarding claim 6. Liu as modified teaches, wherein the secure cloud-based enclave securely retains the user data within the database without directly exposing to an external Al system (Ambroz [0018]).
Regarding claim 8. Liu teaches a system for managing interaction of data between a plurality of Artificial Intelligent (AI) agents within a secure cloud-based enclave, comprising: one or more processors associated with a plurality of Al agents; and a memory storing programmed instructions executable by the one or more processors, wherein the one or more processors execute the programmed instructions (for an assistant system for processing/responding to a request using one or more agents selected from a plurality of agents (e.g., first-party agents, third-party agents, or proactive agents), Liu, Abstract, [0041], [0051]. Processor includes hardware for executing instructions, … Instructions in the instruction caches may be copies of instructions in memory or storage, … instructions executing at processor or for writing to memory, Liu [0105-0107]) to:
although claim 8 directed to a system, it is similar in scope to claim 1. The method steps of claim 1 substantially encompass the medium recited in claim 8. Therefore; claim 8 is rejected for at least the same reason as claim 1 above.
Regarding claims 10-13, the method steps of claims 3-6 substantially encompass the system recited in claims 10-13. Therefore, claims 10-13 are rejected for at least the same reason as claims 3-6 above.
Regarding claim 15. Liu teaches a non-transitory machine-readable medium including data, which when used by a system for managing interaction of data between a plurality of Artificial Intelligent (AI) agents within a secure cloud-based enclave, causes the system to perform instructions that cause the system to perform operations (for an assistant system for processing/responding to a request using one or more agents selected from a plurality of agents (e.g., first-party agents, third-party agents, or proactive agents), Liu, Abstract, [0041], [0051]. Processor includes hardware for executing instructions, … Instructions in the instruction caches may be copies of instructions in memory or storage, … instructions executing at processor or for writing to memory, Liu [0105-0107]. Computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits, Liu [0112]) comprising:
although claim 15 directed to a medium, it is similar in scope to claim 1. The method steps of claim 1 substantially encompass the medium recited in claim 15. Therefore; claim 15 is rejected for at least the same reason as claim 1 above.
Claims 2, 7, 9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US Patent Publication No. 2019/0325081 A1, ‘Liu’, hereafter) in view of Ambroz et al. (US Patent Publication No. 2016/0028699 A1, ‘Ambroz’, hereafter) and further in view of Naanaa et al. (U.S. Patent No. 12,238,213 B1, ‘Naanaa’, hereafter).
Regarding claim 2. Liu as modified teaches, further comprising:
transmitting, by the at least one shared Al agent, the personalized recommendations to the user through a portal (Liu [0062]); and
Liu does not teach
terminating a communication between the single Al agent and at least one shared Al agent.
However, Naanaa teaches
terminating a communication between the single Al agent and at least one shared Al agent (Naanaa, Col 28, lines 53-67).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Liu and Ambroz before him/her, to further modify Liu with the teaching of Naanaa's system by implementing a feature for determining different metrics/scores associated with agents for ranking and selecting the agents. Ordinarily skilled artisan would have been motivated to do so to provide Liu’s system with an effective way to evaluate and rank the agents based on multiple factors and scores. In addition, both of the references (Liu and Naanaa) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, providing a system for processing/responding to a request using a plurality of agents. This close relation between both of the references highly suggests an expectation of success when combined.
Regarding claim 7. Liu as modified teaches, wherein the at least one shared Al agent filters, selects, and customizes the personalized recommendations based on general user's profile, relevance, user's consent, and preferences (Naanaa, Col 14, lines 26-36, Col 32, lines 36-58).
Regarding claims 9 and 14, the method steps of claims 2 and 7 substantially encompass the system recited in claims 9 and 14. Therefore, claims 9 and 14 are rejected for at least the same reason as claims 2 and 7 above.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASANUL MOBIN whose telephone number is (571)270-1289. The examiner can normally be reached on 9:30AM to 6:00PM EST M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HASANUL MOBIN/
Primary Examiner, Art Unit 2168
