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 .
Drawings
The drawings were received on 1/30/2025. These drawings are accepted.
Claim Rejections - 35 USC § 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 1-3,5-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea in the form of mental process without significantly more. The claim(s) recite(s) prompting a generic device, such as a large language model, to predict complexity and outputting such predicted complexity, where the prompt includes information such as standard, stored data content, and context. This judicial exception is not integrated into a practical application because the recited limitations is merely directed towards the abstract idea without integrating the abstract idea into practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited claimed language is merely directed towards the judicial exception without positively recited language indicating significantly more than the judicial exception.
Although the specification discloses an improvement of preventing hallucinations generated by the LLM, the recited claimed language is merely directed towards the abstract idea without positively recited language integrating the abstract idea into practical application. For example, paragraphs 46-47 disclose depending on the predicted complexity, the data contents or structured language data structure can be routed to other software, where execution of the data contents of the structured language data structure occurs, as opposed to the task implemented by the LLM. Unfortunately, the recited claimed language does not recite language reflecting such portion of the disclosure.
Claims 2-3,5-11 merely recite language adding to the abstract idea without positively recited language indicating significantly more and/or integrating the abstract idea into practical application.
Claims 12-17,19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea in the form of mental process without significantly more. The claim(s) recite(s) prompting a generic device, such as a large language model, to predict complexity and outputting such predicted complexity, where the prompt includes information such as standard, stored data content, and context. Additional limitation of “a computer processor; a language model, executed by the computer processor; a data repository …; a server controller …” are directed towards generic device performing the abstract idea as opposed to language integrating the abstract idea into practical application and/or indicating significantly more. This judicial exception is not integrated into a practical application because the recited limitations is merely directed towards the abstract idea without integrating the abstract idea into practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited claimed language is merely directed towards the judicial exception without positively recited language indicating significantly more than the judicial exception.
Although the specification discloses an improvement of preventing hallucinations generated by the LLM, the recited claimed language is merely directed towards the abstract idea without positively recited language integrating the abstract idea into practical application. For example, paragraphs 46-47 disclose depending on the predicted complexity, the data contents or structured language data structure can be routed to other software, where execution of the data contents of the structured language data structure occurs, as opposed to the task implemented by the LLM. Unfortunately, the recited claimed language does not recite language reflecting such portion of the disclosure.
Claims 13-17,19 merely recite language adding to the abstract idea without positively recited language indicating significantly more and/or integrating the abstract idea into practical application.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea in the form of mental process without significantly more. The claim(s) recite(s) prompting a generic device, such as a large language model, to predict complexity and outputting such predicted complexity, where the prompt includes information such as standard, stored data content, and context. This judicial exception is not integrated into a practical application because the recited limitations is merely directed towards the abstract idea without integrating the abstract idea into practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited claimed language is merely directed towards the judicial exception without positively recited language indicating significantly more than the judicial exception.
Although the specification discloses an improvement of preventing hallucinations generated by the LLM, the recited claimed language is merely directed towards the abstract idea without positively recited language integrating the abstract idea into practical application. For example, paragraphs 46-47 disclose depending on the predicted complexity, the data contents or structured language data structure can be routed to other software, where execution of the data contents of the structured language data structure occurs, as opposed to the task implemented by the LLM. Unfortunately, the recited claimed language does not recite language reflecting such portion of the disclosure.
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.
Claim 6 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.
Claim 6 recites the limitation “a fourth command …” and is dependent on claim 1. It is unclear and indefinite as to whether there are 4 commands since claim 1 includes a first and second command, but does not include a third command. What is considered the third command since there is a fourth command? Is claim 6 supposed to be dependent on claim 5, where claim 5 recites a third command?
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1-2,5,8,12,16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Veeramani et al (NPL: Large language model-based pipeline for item difficulty and response time estimation for educational assessments) in view of Kim et al (KR 20250054242a).
Claim 1, Veeramani et al discloses
Receiving data contents (Section 4.1.1,4.1.2 discloses context including labeled named entities and annotated semantic role) stored in a structured language data (4.1.2,4.1.3 discloses data contents (structured language), stored or buffered according to the LLM generating the data contents.);
Executing a language model (Section 4.1.3 discloses LLM) with a prompt (Section 4.1.3 discloses a prompt) to generate an output comprising a predicted complexity of the data contents (Section 4.1.3 discloses a prompt instructing the LLM to generate or predict the item difficulty.), wherein the prompt comprises:
A first command (Section 4.1.3 discloses a prompt commands including predicted complexity.) to predict the predicted complexity (Paragraph 1 of Section 4.1.3) according to a standard defined outside the structured language data structure (Section 4.1.3 discloses a prompt including range of 0 to 1.4 for the assigned score of the context, a range separate from the data contents, hence separate from the structured language data structure. The prompt also includes the data contents.), and
A second command (Section 4.1.3 discloses a prompt with 2 commands: 1) summarization and 2) difficulty level in the form of a score.) to the language model (Section 4.1.3 discloses an LLM is prompted to execute an action.) to reference a context when the language model executes the first command (Section 4.1.3 discloses the prompt instructs the LLM to reference the content via “input context consisting of an input sentence … depending on the difficulty level of the linkages between input content and answer options, assign the input context ….”. The second command is inputted into the LLM as the first command is inputted hence executed when the first command is executed by the LLM.), wherein the context comprises the standard (Section 4.1.3 discloses a prompt including range of 0 to 1.4 for the assigned score of the context.); and
Returning the predicted complexity as output of the language model (Paragraph 1 of Section 4.1.3 discloses the LLM is prompted to predicted item difficulty. Fig. 1, label item difficulty as the output.).
Although Veeramani et al discloses generation of the data contents (Section 4.1.1,4.1.2) generated by the LLM, where the LLM’s output indicates the structured data structure of the data contents (Section 4.1.1,4.1.2), Veeramani et al does not disclose a data structure. Kim et al discloses natural language analysis provide natural language analysis for the user’s query or input in the form of structured data such as JSON (javascript object notation), where the natural language analysis unit is a language model that performs named entity analysis and assign semantic role. (Page 3, paragraph 9-10)
It would be obvious to one skilled in the art before the effective filing date of the application to modify Veeramani et al’s data contents by incorporating buffering in a data structure as disclosed by Kim et al so to enable easy access to contents for the further processing.
Claim 2, Veeramani et al discloses modifying, prior to executing the language model, the structured language data structure (Section 4.1.1,4.1.2 discloses named entities as output by the LLM is input into the LLM to output semantic role labels. By generating semantic role labels via the LLM, the structured language data structure is modified.).
Claim 5, Veeramani et al discloses the prompt further comprises a third command comprising a system message defining parameters for the language model to apply when executing the language model executes the first command (Section 4.1.3 discloses the prompt comprises a system message (instruction such as “summarize the association … assign input context score …” defining parameters for the language model (“answer option set … collection of named entities and semantic role information …) to apply when executing the language model executes the first command (the prompt includes request for difficulty based on the complexity according to the parameters).).
Claim 8, Veeramani et al discloses the context further comprises rules defining the standard and a grading scale for indicating the predicted complexity (Section 4.1.3 discloses the prompt includes a score range of 0 to 1.4.).
Claim 12, Veeramani et al discloses
A language model (Fig. 1, Section 4.1.1-4.1.3);
data contents (Section 4.1.1,4.1.2 discloses context including labeled named entities and annotated semantic role) stored in a structured language data (4.1.2,4.1.3 discloses data contents (structured language), stored or buffered according to the LLM generating the data contents.);
an output comprising a predicted complexity of the data contents (Section 4.1.3 discloses a prompt instructing the LLM to generate or predict the item difficulty.), wherein the predicted complexity (Paragraph 1 of Section 4.1.3) is defined according to a standard defined outside the structure language data structure (Section 4.1.3 discloses a prompt including range of 0 to 1.4 for the assigned score of the context, a range separate from the data contents, hence separate from the structured language data structure. The prompt also includes the data contents.),
a context readable by the language model (Section 4.1.3 discloses the prompt instructs the LLM to reference the content via “input context consisting of an input sentence … depending on the difficulty level of the linkages between input content and answer options, assign the input context ….”.) and defining the standard (Section 4.1.3 discloses a prompt including range of 0 to 1.4 for the assigned score of the context.)
a prompt comprising a first command to the language model to predict the predicted complexity (Section 4.1.3 discloses a prompt with commands to predict the predicted complexity.) and a second command to reference the context when the language model executes the first command (Section 4.1.3 discloses a prompt with commands including reference to context such as “input context consisting of an input sentence … depending on the difficulty level of the linkages between input content and answer options, assign the input context ….”. ….”. The second command is inputted into the LLM as the first command is inputted hence executed when the first command is executed by the LLM.); and
method comprising
Receiving the data contents (Section 4.1.1,4.1.2 discloses context including labeled named entities and annotated semantic role) stored in a structured language data (4.1.2,4.1.3 discloses data contents (structured language), stored or buffered according to the LLM generating the data contents.);
executing the language model (Section 4.1.3 discloses LLM) with the prompt (Section 4.1.3 discloses a prompt) to generate, as output of the language model, the predicted complexity (Section 4.1.3 discloses a prompt instructing the LLM to generate or predict the item difficulty.), and
returning the predicted complexity (Paragraph 1 of Section 4.1.3 discloses the LLM is prompted to predicted item difficulty. Fig. 1, label item difficulty as the output.).
Although Veeramani et al discloses generation of the data contents (Section 4.1.1,4.1.2) generated by the LLM, where the LLM’s output indicates the structured data structure of the data contents (Section 4.1.1,4.1.2), Veeramani et al does not disclose a data structure. Kim et al discloses natural language analysis provide natural language analysis for the user’s query or input in the form of structured data such as JSON (javascript object notation), where the natural language analysis unit is a language model that performs named entity analysis and assign semantic role. (Page 3, paragraph 9-10)
It would be obvious to one skilled in the art before the effective filing date of the application to modify Veeramani et al’s data contents by incorporating buffering in a data structure as disclosed by Kim et al so to enable easy access to contents for the further processing.
Veeramani et al fails to disclose a computer processor; a language model, executable by the computer processor; a data repository in communication with the computer processor and storing data; and a server controller programmed, when executed by the computer processor, to perform a computer-implemented the method.
Kim et al discloses
a computer processor (page 5);
a language model, executable by the computer processor (page 3, paragraph 9-10 discloses a natural language analysis unit or language model.);
a data repository in communication with the computer processor and storing data (page 5 discloses storing data and instructions in communication with a processor.); and
a server controller programmed, when executed by the computer processor, to perform a computer-implemented the method (Page 3, paragraph 7 discloses natural language understand int unit and server executing analysis of the input query to generate a response.).
It would be obvious to one skilled in the art before the effective filing date of the application to modify Veeramani et al’s processing system by incorporating computer components as disclosed by Kim et al so to enable implementation of Veeramani et al processing system.
Claim 16, Veeramani et al discloses modifying, prior to executing the language model, the structured language data structure (Section 4.1.1,4.1.2 discloses named entities as output by the LLM is input into the LLM to output semantic role labels. By generating semantic role labels via the LLM, the structured language data structure is modified.).
Allowable Subject Matter
Claim 20 is allowed over prior art.
Claims 3-4,6-7,9,11,13-15,17-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Note: All rejections must be overcome prior to placing the case in condition for allowance.
Conclusion
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/LINDA WONG/Primary Examiner, Art Unit 2655
