DETAILED ACTION
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 .
This application is a CIP of 18/407,415 01/08/2024 PAT 12450570
Claim Objection
Claim 13 is object for the following informality – “perform an architecture modification analysis” should be “performing an architecture modification analysis” to keep consistency.
Claim Rejection – 35 U.S.C. 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.
Claim 13 is 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. Independent claim 13 recites “LLM” and “SLM” without spelling out the acronyms, and this could potentially create confusion. For example, “LLM” could also mean “Master of Laws”. Claims 14-19 are also rejected for their dependency on claim 13.
Claim Rejection – 35 U.S.C. 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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The rationale for this finding is explained below. In the instant case, the claims are directed towards providing financial advisory. The concept is clearly related to managing human financial activities, thus the present claims fall within the Certain Method of Organizing Human Activity grouping. The claims do not include limitations that are “significantly more” than the abstract idea because the claims do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Note that the limitations, in the instant claims, are done by the generically recited computer device. The limitations are merely instructions to implement the abstract idea on a computer and require no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. Therefore, claims 1-19 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Step 1: The claims 1-19 are directed to a process, machine, manufacture, or composition matter.
In Alice Corp. Pty. Ltd. v. CLS Bank Intern., 134 S. Ct. 2347 (2014), the Supreme Court applied a two-step test for determining whether a claim recites patentable subject matter. First, we determine whether the claims at issue are directed to one or more patent-ineligible concepts, i.e., laws of nature, natural phenomenon, and abstract ideas. Id. at 2355 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1296–96 (2012)). If so, we then consider whether the elements of each claim, both individually and as an ordered combination, transform the nature of the claim into a patent-eligible application to ensure that the patent in practice amounts to significantly more than a patent upon the ineligible concept itself.
Claims 1-12 are directed to a machine (i.e., device/system claims).
Claims 13-19 are directed to a process (i.e., method claims).
Step 2A: The claims are directed to an abstract idea.
Prong One
The present claims are directed towards providing financial advisory. The concept comprises obtaining user profile data, obtaining client profile data, obtaining financial advisor data, feeding these data as training data into a large language model fine-tuning engine, performing hyperparameter optimization, performing an architecture modification analysis, performing validation check, creating multiple fine-tuned specialized language models each specialized in a specific area of financial expertise, generating a digital avatar that mimics a specific human financial advisor, receiving a client query, analyzing the query, determining if the query’s complexity exceeds a predetermined threshold, if so escalating the query to human financial advisor, otherwise generating response using the relevant specialized SLMs and presenting to user, and recording the interaction for continuous learning. Financial advisory is a fundamental economic activity and managing human financial activities, thus the present claims clearly fall within the Certain Method of Organizing Human Activity grouping. The performance of the claim limitations using generic computer components (i.e., a processor and a memory) does not preclude the claim limitation from being in the certain methods of organizing human activity grouping. Accordingly, the present claims recite an abstract idea.
Prong Two
Independent claim 1 recites a processor coupled with a memory and a communication interface as additional hardware elements. Claim 1 also recites software elements, such as a user profile datastore, a client profile datastore, a digital advisory application, a data fusion suite, a large language model fine-tuning engine, a knowledge base, multiple specialized language models, a collaboration middleware, an advisor review interface. Independent claim 13 does not recite hardware element but recites the same additional software elements. Dependent claims 2-12 and 14-19 do not recite any other additional element. The additional elements are claimed to perform basic machine learning functions, such as obtaining data, processing data, feeding the processed data as training data to a LLM, performing calculations and analysis, creating multiple SLM models, generating digital avatar, receiving query, generating and presenting responses, and recording interactions for continuous learning. Federal Circuit states in the Recentive v. Fox decision, “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101”. In this case, the present claims only recite training generic machine learning models with financial advisory data, and using the trained machine learning models to answer questions. Combining LLM and SLM, and recording interaction with human for continuous learning were also known techniques in machine learning too. There is no indication that the present claims recite improvement to existing computer technology or machine learning. The recitation of the computer elements amounts to mere instruction to implement an abstract concept on computers. The present claims do not solve a problem specifically arising in the realm of computer networks. The present claims do not recite limitation that improve the functioning of computer, effect a physical transformation, or apply the abstract concept in some other meaningful way beyond generally linking the use of the abstract concept to a particular technological environment. As such, the present claims fail to integrate into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the abstract idea.
As discussed earlier, independent claim 1 recites a processor coupled with a memory and a communication interface as additional hardware elements. Claim 1 also recites software elements, such as a user profile datastore, a client profile datastore, a digital advisory application, a data fusion suite, a large language model fine-tuning engine, a knowledge base, multiple specialized language models, a collaboration middleware, an advisor review interface. Independent claim 13 does not recite hardware element but recites the same additional software elements. Dependent claims 2-12 and 14-19 do not recite any other additional element. The additional elements are claimed to perform basic machine learning functions, such as obtaining data, processing data, feeding the processed data as training data to a LLM, performing calculations and analysis, creating multiple SLM models, generating digital avatar, receiving query, generating and presenting responses, and recording interactions for continuous learning. According to MPEP 2106.05(d), “performing repetitive calculations”, “receiving, processing, and storing data”, “electronically scanning or extracting data from a physical document”, “electronic recordkeeping”, “storing and retrieving information in memory”, and “receiving or transmitting data over a network, e.g., using the Internet to gather data” are considered well-understood, routine, and conventional functions of computer. The present claims do not improve the functioning of computer or machine learning itself. Simply implementing the abstract idea on a generic computer or using a computer as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the present claims are ineligible for patent.
Prior Arts Cited Not Applied
Gonsalves et al. (Pub. No.: US 2025/0045336) is cited, because the prior art teaches combining LLM and SLM trained for specific subjects to make recommendation (see paragraph 0055, “The use of this ‘student-teacher’ model with the LLM providing training data for the SLM can result in a trained SLM that provides results that are 90-93% as accurate as those of full-size language model”). However, Gonslves does not teach any of the detailed steps recited in independent claim 1 and 13.
Gholami et al. (Pub. No.: US 2025/0190712) is cited, because the prior art teaches transferring knowledge from a LLM to a SLM through training (see abstract). However, Gholami is not directed to providing financial advisory.
Matsuoka et al. (Pub. No.: US 2023/0077130) is cited, because the prior art teaches generating task recommendations to user based on collected data (see paragraph 0002-0004). However, Matsuoka does not teach any language model (LLM or SLM).
The cited prior arts, whether individually or combined, fail to teach “obtaining user profile data from a user profile datastore; obtaining client profile data from a client profile datastore; obtaining financial advisor data; processing the user profile data, client profile data, and financial advisor data to create processed training data; providing the processed training data to a LLM fine-tuning engine; performing hyperparameter optimization; perform an architecture modification analysis; performing validation checks; creating multiple fine-tuned SLM models, each specialized in a specific area of financial expertise; generating a digital avatar that mimics a specific human financial advisor's expertise and communication style; receiving a client query through a user interface on a user device; analyzing the query to determine its nature and complexity; determining if the query's complexity exceeds a predetermined threshold; if the threshold is exceeded, escalating the query to the human financial advisor; generating responses using the relevant specialized SLMs; presenting the response through one or more digital avatars representing the relevant areas of expertise; recording the interaction for continuous learning and improvement of the SLM,” as recited in independent claim 1 and 13. Therefore, no prior art based rejection is cited in this Office Action.
Conclusion
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/HAO FU/Primary Examiner, Art Unit 3695
MAR-2026