DETAILED ACTION
Claims 1-22 are presented for consideration.
Claim Objections
Claim 19 is objected to because of the following informalities:
As per claim 19, “the AI model” should be corrected as “the generative AI model”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 9, 12, 17, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "a user" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "a query" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "the prompt" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "a user" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "the user device" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation "a query" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-22 are rejected under 35 U.S.C. 103 as being unpatentable over Venkateswaran et al. [ US Patent Application No 2024/0194194 ], in view of Strottner et al. [ US Patent Application No 2025/0148672 ].
As per claim 1, Venkateswaran discloses the invention as claimed including a method comprising:
obtaining a query [ i.e. natural language communications articulating a command, instruction, or query making a demand or request to the system ] [ Abstract; and paragraphs 0016-0018 ];
obtaining at least one detail related to a user of a client device [ i.e. query the user profile for additional preference information, the user’s profile ] [ paragraphs 0021, and 0027 ];
automatically enhancing the query to include the at least one detail related to the user [ i.e. metadata may comprise any data associated with a user ] [ paragraphs 0021, and 0054 ], thereby obtaining an enhanced query adapted to the user [ i.e. appending metadata to the newly synthesized software agent ] [ paragraphs 0024, 0056, and 0057 ], wherein said enhancing is performed by the client device [ i.e. software agent synthesis program ] [ 145, Figure 1; and paragraph 0036 and 0051 ];
submitting the enhanced query to a generative Artificial Intelligence (AI) model [ i.e. input parameters to the language model, and generator which may be anything from a rule-based machine learning model to more complicated generators that leverage generative artificial neural networks ] [ paragraphs 0019, 0022, 0023, and 0027 ];
receiving a response from the generative AI model; and providing an output based on the response [ i.e. the system may execute the three agents, and then output the message “I have booked …” ] [ paragraphs 0027, and 0056 ].
Venkateswaran does not specifically disclose thereby protecting a privacy of the user.
Strottner discloses protecting a privacy of the user [ i.e. acquire and process visual data with a focus on privacy requirements ] [ Abstract; and paragraphs 0042, and 0108 ].
It would have been obvious to a person skill in the art before the effective filing date of the claimed invention to combine the teaching of Venkateswaran and Strottner because the teaching of Strottner would enable to provide a mean to acquire and process visual data with a focus on privacy requirements [ Strottner, paragraph 0042 ].
As per claim 2, Venkateswaran discloses wherein the at least one detail is a characteristic of the user [ i.e. user profiles ] [ paragraphs 0024, and 0054 ].
As per claim 3, Venkateswaran in view of Strottner discloses the method of claim 1, furthermore, Venkateswaran discloses wherein the characteristic is selected from the group consisting of: education, profession, occupation, work address, marital status, family member details, hobbies, purchases, previous address, current location, date or time of arrival to current location, mobility information of the user device, and a travel destination [ i.e. booking a flight, shopping, restaurant, manager ] [ paragraphs 0010, 0027, and 0031 ], and Strottner discloses residential address, age, gender [ i.e. age, gender, gym, work, home ] [ paragraphs 0032, 0035, and 0043 ].
As per claim 4, Venkateswaran discloses wherein the at least one detail related to a user is determined based on monitoring user activity of the user, the monitoring is performed locally at the user device, information gathered based on the monitoring is retained locally in at the user device [ i.e. monitor using sensors ] [ paragraphs 0023, and 0055 ].
As per claim 5, Venkateswaran discloses wherein said obtaining the at least one detail comprises selecting the at least one detail from a set of locally retained details, wherein the selection is based on the query, thereby selecting relevant details to the query [ i.e. save Boston to user’s profile, the system may first query the user profile for additional preference information ] [ paragraph 0027 ].
As per claim 6, Venkateswaran in view of Strottner discloses the method of claim 1, furthermore, Venkateswaran discloses wherein said obtaining the at least one detail comprises selecting the at least one detail from a set of locally retained details [ paragraph 0027 ], and Strottner discloses wherein said selecting excludes at least one detail, whereby protecting the privacy of the user [ i.e privacy requirements ] [ Abstract; and paragraphs 0042, and 0108 ].
As per claim 7, Strottner discloses encoding the enhanced query by the client device prior to said submitting, whereby protecting the privacy of the user [ i.e. obfuscated image with placeholder data replacing the facial image data ] [ Abstract ].
As per claim 8, Venkateswaran discloses wherein the at least one detail is obtained from a query submitted by the user to the generative AI model during a previous session, and wherein the at least one detail, or a prompt or response provided during the previous session are locally retained on the client device [ i.e. historical data ] [ Abstract; and paragraphs 0047, and 0051 ].
As per claim 9, Venkateswaran discloses wherein the at least one detail is obtained from a query submitted by the user to another generative AI model during a previous session, and wherein the at least one detail, or a prompt or response exchanged during the previous session are locally retained on the client device [ i.e. prompt based learning ] [ paragraphs 0025, and 0056 ].
As per claim 10, Venkateswaran discloses wherein the enhanced query is submitted automatically and without user intervention, in response to a trigger event, wherein the query is pre- defined to be submitted in response to the trigger event [ i.e. automatically provided to a software agent ] [ paragraphs 0010, and 0021 ].
As per claim 11, Venkateswaran discloses wherein the trigger event is detected based on a reading from a sensor comprised in the client device or based on user activity [ i.e. sensor ] [ 125, Figure 1; and paragraph 0018 ].
As per claim 12, Venkateswaran discloses wherein the at least one detail used for enhancing the prompt is related to the trigger event [ paragraphs 0010, and 0024 ].
As per claim 13, Venkateswaran discloses wherein the trigger event is selected from the group consisting of: arriving at a specific location or at a location of specific type, leaving a home of the user, leaving a location the user is at, a phone call the user has made or received, an e-mail or message the user has sent or received, a social media activity of the user, a person the user has met, a purchase of an item made by the user, meeting a predetermined person, a predetermined date or time [ i.e. trip dates, origin city, destination city, reservation at a restaurant, buying, etc… ] [ paragraphs 0010, 0012, and 0027 ].
As per claim 14, Venkateswaran discloses wherein the query is a predefined query [ i.e. detect trigger and execute series of tasks ] [ paragraphs 0010, and 0027 ].
As per claim 15, Venkateswaran discloses wherein the query is selected from a collection of preset queries [ i.e. carry out one or more tasks ] [ Abstract; and paragraph 0017 ].
As per claim 16, Venkateswaran discloses wherein the query is automatically selected from the collection of preset queries based on a trigger event [ i.e. one or more natural language user inputs ] [ Abstract; and paragraph 0056 ].
As per claim 17, Venkateswaran discloses wherein the query is a user provided query that is manually defined by the user using the user device as a textual prompt to be provided to the generative AI model at a future time [ i.e user prompts for personalization through the language model to obtain new software agent definitions ] [ paragraphs 0023-0025 ].
As per claim 18, Venkateswaran discloses receiving from a server an invitation to participate in a campaign [ i.e. book flights online, making a reservation at a restaurant, buying a product ] [ paragraph 0010, 0012, and 0027 ], the campaign dependent upon a trigger event, the invitation comprising a template of a query; storing the invitation in a storage device associated with the client device; upon identifying an occurrence of the trigger event, and upon verifying that the campaign is relevant to the user, completing the template into the query [ i.e. the language model may be provided with the following training data during the training phase ] [ paragraphs 0028-0032 ].
As per claim 19, Venkateswaran discloses wherein the client device comprises a sensor, and wherein the trigger event is fired in response to a reading from the sensor [ Figure 1; and paragraphs 0023, and 0036 ].
As per claim 20, Venkateswaran discloses wherein the AI model is a Large Language Model (LLM) [ i.e. language model may be a deep neural network ] [ paragraphs 0019, and 0022 ].
As per claim 21, it is rejected for similar reasons as stated above in claim 1.
As per claim 22, it is rejected for similar reasons as stated above in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bakshi et al. [ US Patent Application No 2024/0031159 ] discloses generating synthetic invisible fingerprints for metadata security and document verification using generative AI
Ruelke et al. [ US Patent Application No 2023/0252178 ] discloses artificial intelligence query system for protecting private personal information
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/DUSTIN NGUYEN/Primary Examiner, Art Unit 2445
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