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 .
Response to Arguments
Applicant’s remarks filed 15 July 2026 have been fully considered. The rejections under section 112 are overcome by amendment. However, the amendments raise the issue of new matter, and new grounds of rejection are therefore made. Applicant argues Dinh does not teach the amendments. Examiner respectfully disagrees. It does, as explained in the updated grounds of rejection below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 and 10-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The disclosure as originally filed does not contain sufficient written disclosure to show that Applicant was in possession of “performing a third operation comprising evaluating a function that can be used through the function calling mechanism of the large language model.” This is generic functional language because it specifies a result of evaluating a function that can be used through the function calling mechanism of the large language model without reciting particular steps to accomplish the function. Looking at the disclosure, this claim language solely appears in ipsis verbis in the specification, and there is nothing else in the specification that demonstrates that the applicant has made a generic invention that achieves the result of evaluating a function that can be used through the function calling mechanism of the large language model. By specifying this desired result without any description of how this function is performed, Applicant lacks sufficient written disclosure to a claim directed to performing the generic function. MPEP § 2161.01. This concern is particularly acute where the question of whether the claims are directed to patentable subject matter turns on the ability of the function calling mechanism to determine, in the first place, what functions are capable of answering the received nature language query information based on the received industrial process monitoring system information, present these functions to the user, and receive a selection as part of a process to “provide a more targeted, more professional and more feasible solution” and thereby improving question-answer technology. See MPEP § 2106.05(a) (“An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome.”).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6 and 10-15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dinh et al., US 2025/0335479 A1.
As per claims 1 and 10, Dinh teaches:
utilizing a processor to obtain the large language model and one of industrial process monitoring system information and nature language query information from a memory, wherein the industrial process monitoring system information is different from the nature language query information, Dinh ¶¶ 0028-29, where an industrial process monitoring system and natural language query information are both used to generate answers to a query but kept separately;
utilizing the processor to perform a specific domain function operation, the specific domain function operation comprising performing a function calling mechanism according to the large language model to generate a function corresponding to the one of the industrial process monitoring system information and the nature language query information, Dinh ¶¶ 0028-29, where an industrial process monitoring system and natural language query information are both used to generate answers to a query;
utilizing the processor to perform a retrieval augmented generation operation, the retrieval augmented generation operation comprising performing an embedding transfer mechanism to generate a plurality of embedding vectors, Dinh ¶ 0027, where the encoding is the embedding transfer mechanism, and retrieving at least one of the embedding vectors and establishing a vector store, Dinh ¶ 0027, where the vector database is the vector store, wherein the at least one of the embedding vectors is most relevant to the one of the industrial process monitoring system information and the nature language query information, Dinh ¶ 0027, where a vector is retrieved to answer a query; and
utilizing the processor to perform a knowledge and information management operation, the knowledge and information management operation comprising managing the knowledge base and the vector store, Dinh ¶ 0031, where the knowledge base and vector store is managed to determine if the system can respond to the query;
wherein the processor generates an answer corresponding to the one of the industrial process monitoring system information and the nature language query information according to the function and at least one of the embedding vectors, Dinh ¶ 0028, where the “very domain specific solutions” are the answer,
wherein the specific domain function operation further comprises:
performing a first operation comprising receiving the one of the industrial process monitoring system information and the nature language query information from a question dispatch mechanism through a user interface, Dinh ¶¶ 0028-29, where the temperature of equipment is received and a query is received;
performing a second operation comprising transmitting the one of the industrial process monitoring system information and the nature language query information to the function calling mechanism of the large language model in a data format through the user interface, Dinh ¶ 0028, where the observed temperature and the query are considered to determine if there is a document in the vector database that includes the information to solve the current situation;
performing a third operation comprising evaluating a function that can be used through the function calling mechanism of the large language model, Dinh ¶ 0028, where it is determined to prompt an expert to add rules;
performing a fourth operation comprising notifying the user interface of the function that can be used in the data format through the function calling mechanism of the large language model, Dinh ¶ 0028, the expert is prompted to add rules;;
performing a fifth operation comprising calling the function that can be used through the user interface, wherein the function that can be used is a fault detection and classification chart acquisition function, Dinh ¶ 0028, where the expert adding rules is a fault detection and classification chart acquisition function under a broadest reasonable interpretation;
performing a sixth operation comprising receiving a return result through the user interface, Dinh ¶ 0032, where the nearest neighbor is used to obtain a result which is used to answer the query; and
performing a seventh operation comprising sending the return result back to the question dispatch mechanism through the user interface, Dinh ¶ 0032, where the query is answered;
wherein the first operation, the second operation, the third operation, the fourth operation, the fifth operation, the sixth operation and seventh operation are performed in sequence, Dinh ¶¶ 0028-32;
wherein the retrieval augmented generation operation further comprises:
performing an equipment manual retrieval, the equipment manual retrieval comprising performing the embedding transfer mechanism on an equipment manual to generate an equipment manual vector of the embedding vectors, Dinh ¶ 0029 (“documents stored in a vector database to determine what the available information is relevant to the query 205”);
wherein the embedding transfer mechanism further comprises converting text data into embedding vector data, Dinh ¶ 0029 (“documents stored in a vector database to determine what the available information is relevant to the query 205”);
wherein the retrieval augmented generation operation further comprises:
performing a question-answer retrieval, the question-answer retrieval comprising performing the embedding transfer mechanism on a question-answer pair data to generate a question-answer vector of the embedding vectors, Dinh ¶ 0029 (“the system generates an embedding based on the query 205”);
wherein the vector store comprises the equipment manual vector and the question-answer vector of the embedding vectors, and the processor performs vector similarity search through the vector store to retrieve the answer corresponding to the one of the industrial process monitoring system information and the nature language query information, Dinh ¶ 0029 (“the system generates an embedding based on the query 205 and performs nearest neighbor search through the vector database using a similarity metric (e.g., cosine similarity) to identify relevant documents.”).
As per claims 2 and 11, the rejection of claims 1 and 10 is incorporated, and Dinh further teaches:
utilizing an industrial process monitoring system to transmit the industrial process monitoring system information to the memory, Dinh ¶ 0028 (“The system may observe a particular situation in an industrial setting, for example, the temperature of some equipment exceeds a threshold.”);
utilizing a cyber physical agent of a data collection device to collect the industrial process monitoring system information, Dinh ¶ 0066, where data collection is performed by an observer; and
utilizing an user interface of the data collection device to collect the nature language query information, and transmit the nature language query information to the memory through the cyber physical agent, Dinh ¶ 0064, where the natural language query information is transmitted;
wherein the industrial process monitoring system is connected to the memory and the processor, the data collection device is connected to the memory and the processor, and the user interface is connected to the cyber physical agent, Dinh ¶ 0028, where, e.g., the temperature is observed.
As per claims 3 and 12, the rejection of claims 2 and 11 is incorporated, and Dinh further teaches:
utilizing the industrial process monitoring system to generate the industrial process monitoring system information by monitoring at least one equipment of a production line through the cyber physical agent, Dinh ¶ 0028, where, e.g., the temperature is monitored;
wherein the production line is connected to the cyber physical agent of the data collection device, and comprises the at least one equipment, Dinh ¶ 0028, where, e.g., the temperature is monitored from equipment.
As per claims 4 and 13, the rejection of claims 2 and 11 is incorporated, and Dinh further teaches:
wherein the industrial process monitoring system information comprises fault detection and classification alarm data, the industrial process monitoring system comprises a fault detection and classification system for a semiconductor bump process, the fault detection and classification alarm data comes from the fault detection and classification system of the semiconductor bump process, Dinh ¶ 0074, the nature language query information comprises a natural language query question from the user interface, the large language model comprises a generative pre-trained transformer, Dinh ¶ 0021, and any one of the specific domain function operation, the retrieval augmented generation operation, and the knowledge and information management operation is a software operation, Dinh ¶ 0077.
As per claims 5 and 14, the rejection of claims 2 and 11 is incorporated, and Dinh further teaches:
utilizing the processor to receive the one of the industrial process monitoring system information and the nature language query information in one of a first method and a second method to form a demand layer, and the first method is different from the second method, Dinh ¶ 0028, where natural language queries and industrial process monitoring, e.g., temperature monitoring, are both performed;
utilizing the processor to forward the one of the industrial process monitoring system information and the nature language query information to the specific domain function operation or the retrieval augmented generation operation according to a question dispatch mechanism to form a transfer layer, Dinh ¶ 0028, where this information is used to perform RAG; and
utilizing the processor to perform the function calling mechanism or the embedding transfer mechanism to the one of the industrial process monitoring system information and the nature language query information according to the specific domain function operation or the retrieval augmented generation operation to form an application layer, Dinh ¶ 0029, where cosine similarity in an embedding space is used to identify relevant documents.
As per claims 6 and 15, the rejection of claims 5 and 14 is incorporated, and Dinh further teaches:
wherein the first method comprises utilizing natural language for querying through the user interface to obtain a demand question, Dinh ¶ 0027, where a query is received; and
the second method comprises querying through an alarm of the industrial process monitoring system to obtain the demand question, Dinh ¶ 0028, where temperature is monitored.
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) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dinh et al., US 2025/0335479 A1.
As per claims 7 and 16, the rejection of claims 2 and 11 is incorporated, but Dinh does not teach:
wherein the data format comprises a JSON format.
However, official notice is taken of JSON.
It would therefore have been obvious to one of ordinary skill in the art to modify the teachings of Dinh to use JSON as a data format for responses as a design choice in order to ensure that the responses are intelligible by the computer system.
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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WILLIAM SPIELER
Primary Examiner
Art Unit 2159
/WILLIAM SPIELER/Primary Examiner, Art Unit 2159
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