DETAILED ACTION
Response to Amendment
1. This office action is in response to applicant’s communication filed on 01/05/2026 in response to the non-final office action mailed on 10/08/2025. The Applicant’s remarks and amendments to the claims and/or the specification were considered with the results as follows.
2. In response to the last Office Action, claims 1-3 are amended. No claims are added or canceled. As a result, claims 1-6 are pending in this office action.
Response to Arguments
3. Applicant's arguments with respect to 35 USC 103 have been fully considered but are moot in view of new ground(s) of rejection.
Claim Rejections - 35 USC § 103
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.
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.
4. Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2022/0165267 A1) and in view of Nair (US 2024/0303496 A1) and further in view of Ahmadidaneshashtiani (US 2021/0249002 A1).
Referring to claim 1, Lee discloses a method for using a hybrid natural language processing system (See para. [0069] and para. [0070], using a natural language platform to identify the user’s intent) for outputting a customized response to a natural language formatted query (See Figure 11 and para. [0137], outputting a customized output “I will play on of bits” based on the natural language formatted query “Playing Bangtan Boys’ song”), the method comprising:
receiving, at a large language model, a natural language formatted query comprising a set of real-time conditions (See para. [0048], [0065], receiving, at an AI model system [e.g., neural network-based system], a query or a user request executing a function or a service, note in para. [0065], selecting a function or a service responding to the user request is generated in real-time) the large language model being an artificial intelligence model that uses one or more neural networks with a plurality of parameters (See para. [0065], para. [0071], the AI model system uses neural networks [e.g., FNN, RNN] with a plurality of plans, and the plans are intentions and parameters), trained on one or more large data stores to understand, summarize, generate and predict responses (See Figure 8, para. [ 0123] training on training data [e.g. NLU training data] and/or user log 810 include content and/ or corpus of a frequently website or community to classify user’s expression on a specific topic detected in the user log into a user-specific general expression and a user-specific named-entity expression);
processing, at the large language model, the natural language formatted query (See para. [0065], para. [0137] and Figure 11, processing, at the AI model system, the user request in natural language formatted query “Playing Bangtan Boys’ song”); outputting, at the large language model, a natural language formatted response template (See para [0065], para. [0097], para. [0098], the system outputs a template to be utilized by the natural language generation module as a response), the template […] for data responsive to the of real-time conditions (See para. [0004] and para. [0132], the template contains standardized values); receiving, at a dynamic, machine learning response application, the natural language formatted query and the natural language formatted response template (See para. [0134] and para. para. [0135], receiving, at a deep learning module or application, the generated response in response to the user request);
processing, using the dynamic, machine learning response application, the natural language formatted query and the natural language formatted response template (See para. [0134], using the deep learning module or application to add additional user characteristics in addition to the generated response and convert the same into a response appropriate to the user’s known current situation); inputting, using the dynamic, machine learning response application, one or more real-time data […] (See para. [0133]-para. [0135], inputting user characteristic [e.g. values] from a tone detection module or an expression detection module]; and outputting, using the dynamic, machine learning response application, a natural language formatted query response customized for the set of real-time conditions (See para. [0136]-para. [0138] and Figure 11, outputting, using the deep learning module or application, a natural language formatted query response customized with user’s characteristics).
Lee does not explicitly disclose the templates comprising one or more placeholders for data responsive to the set of real-time conditions and inputting, using the dynamic, machine learning module, one or more real-time data elements into the one or more placeholders.
Nair discloses the templates comprising one or more placeholders for data responsive to the set of real-time conditions (See para. [0060], para. [0061] the templates can include static portions and dynamic portions that receive specific information in placeholders) and inputting, using the dynamic, machine learning module, one or more real-time data elements into the one or more placeholders (See para. [0045] and para. [0061], para. [0066], the AI model input values filled into the placeholders [e.g., dynamic tokens]).
Therefore, it 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 was made to modify the template of Lee to include one or more real-time data elements into one or more placeholders, taught by Nair. Skilled artisan would have been motivated to fine-tuning with some or all pre-trained parameters to achieve quality results (See Nair, para. [0002]). In addition, all references (Nair and Lee) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as training neural network language models for domain specific applications. This close relation between all references highly suggests an expectation of success.
Lee in view of Nair does not explicitly disclose a natural language formatted query comprising a stock portion and a set of real-time condition portions.
Ahmadidaneshashtiani discloses receiving a natural language formatted query comprising a stock portion and a set of real-time condition portions (See para. [0163] and Fig 1I receiving a new utterance “I want to buy 10 shares of RY at 86.23”); communicating with a large data store, and based on historical data stored at the large data store, formulating a natural language formatted response to the stock portion of the query; and inserting one or more place holders into a natural language formatted response for responses to the set of real-time conditions portion of the query (See para. [0163] and Figure 1I, communicating with data agents accessing different stored data profiles and inserting <Ticker> placeholder(s) into the natural language query); communicating with one or more private data stores, and based on real-time data stored at the one or more private data stores, formulating a real-time responsive element for each of the one or more placeholders included in the natural language formatted response; and inserting the real-time responsive element into each of the one or more placeholders included in the natural language formatted response; transmitting, at the dynamic, machine learning response application, the natural language formatted query response customized for the set of real-time conditions, to a responder; and responding, at the responder, with the natural language formatted query response customized for the set of real-time conditions (See para. [0164] and para. [0165], in the example above, the natural language query can be generated between the personal banking domain specific agent and the wealth management domain specific agents noting, e.g., “User would like to purchase 1000 shares of RY in a retirement savings plan the user currently has $ 10,000 in a checking account and can afford to purchase the shares without using a loan, note in para. [0138] and para. [0139], if an agent is designed as a primary “driving agent”, a string encapsulating the response from the agent, for example, the system generates using a machine learning model that establishes the next best word until a response is completed, the output response data structure encapsulates this response and can be provided to the chatbot for downstream output).
Therefore, it 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 was made to transform the natural language input of Lee to different types of conversation such as personal banking, direct investment, business banking and wealth management, taught by Ahmadidaneshashtiani. Skilled artisan would have been motivated to have different natural language processing agents which have different dictionaries and capabilities to handle multiple intent representations simultaneously (See Ahmadidaneshashtiani, para. [0017]). In addition, all references (Ahmadidaneshashtiani, Nair and Lee) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as training neural network language models using machine learning models. This close relation between all references highly suggests an expectation of success.
As to claim 2, Lee discloses displaying the natural language formatted query response before or after the inserting (See Figure 11 and para. [0137], outputting a customized output “I will play on of bits” based on the natural language formatted query “Playing Bangtan Boys’ song”).
As to claim 3, Lee discloses wherein the processing, using the dynamic, machine learning response application, the natural language formatted query and the natural language formatted response comprises: identifying a user account included in the set of real-time conditions; and retrieving user account information from the one or more private data stores (See para. [0120] and para. [0123], identing a user log to detect a user-specific expression and retrieving the user log from a user-specific expression database 930).
As to claim 4, Lee discloses wherein the one or more private data stores are continually updated in real-time (See para. [0065], selecting a function or a service responding to the user request is generated in real-time).
As to claim 5, Lee discloses wherein the large language model is trained on data from a large data store (See Figures 5, 8, para. [ 0123], the AI model system is trained on data from a system database 820).
As to claim 6, Lee discloses wherein the large data store is a fixed knowledge base that is updated periodically (See para. [0065] and para. [0074], the plans are generated in real time by the AI model system which is stored in a capsule database).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUK TING CHOI whose telephone number is (571)270-1637. The examiner can normally be reached Monday-Friday 9am-6pm.
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/YUK TING CHOI/ Primary Examiner, Art Unit 2164