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 .
DETAIL ACTION
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 4/16/2025 and 8/12/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant invention encompasses three sets of claims: a system in claims 1-10 (i.e., a manufacture), a method in claims 11-17 (i.e., a process) and a system in claims 18-20 (i.e., a manufacture). All claims are directed to one of the four statutory categories and meet the requirements of step 1.
Step 2A
Prong One
The claimed invention is directed to an abstract idea without significant more. The instant invention is broadly directed to “performing a spatial reasoning task by using language models”. Claim 1 recites the following (with emphasis added):
Claim 1: A computing system comprising:
a storage subsystem comprising one or more data storage devices; and
a logic subsystem comprising one or more logic devices, wherein the storage subsystem comprises code executable by the logic subsystem to perform a spatial reasoning task by:
at a reasoner agent,
generating a reasoner prompt containing task information and a schema of scene data, and inputting the reasoner prompt into a reasoner language model,
receiving a reasoner response from the reasoner language model, and
sending a query to a retriever agent based upon the reasoner response;
at the retriever agent,
receiving the query from the reasoner agent,
generating a retriever prompt containing the schema of the scene data and the query, and inputting the retriever prompt into a retriever language model,
receiving a retriever output from the retriever language model, the retriever output comprising code representing a query to query scene data represented by the schema of the scene data,
querying the scene data using the code representing the query and receiving one or more results of the query, and
sending the one or more results of the query to the reasoner agent; and
the reasoner agent, using the one or more results of the query.
The bold portions of claim 1 encompass the abstract idea, which is also encompassed by the dependent claims 2-10, and substantially also encompassed by claims 11-17 and 18-20.
Claims 1, 11, and 18 recite the steps to perform a spatial reasoning task by using language models including a language processing. These limitations, when given their broadest reasonable interpretation, are directed to certain performing of organizing human activity and mental processes, which is abstract idea.
Prong Two
This judicial exception is not integrated into a practical application because mere instruction to implement on computers (i.e. storage medium or logic device in claim 1) or a computer model (language model, reasoner agent, and retriever agent here in claim 1), or merely using computers as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment for field of use is not considered integration into a practical application. Claim 1 recites using language prompt to generate output data of the trained natural language model. Using input data to a trained machine-learning or language model is a generic feature of language modeling process, which does not represent a technological improvement. The using of the computer and language modeling process does not add improvement to the functioning of a computer or to any other technology field, which failed to enable the abstract idea to integrate into a practical application. The claims are drafted in a result-oriented fashion, without the requisite specificity needed to provide a nonabstract technological solution. The computing system and language modeling process are directed to the components of a system amount to merely field of use type limitations and/or extra solution activity to implement the abstract idea as presented.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires 'more than simply stat[ing] the [abstract idea] while adding the words 'apply it."' Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include 'additional features' to ensure 'that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].'" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims include the additional elements other than the abstract idea which include a logic subsystem, storage medium, language models (or agents) and user interface (in claim 1). These additional elements are merely conventional computer and computer model. Any potentially technical aspects of the claims are well-known generic computer components performing conventional functions (e.g., a processor performing a mental process). The present claims have been analyzed both individually and in combination and, the instant claims do not provide any improvement of the functioning of the computer or improvement to computer technology or any other technical field. There do not appear to be any meaningful limitations other than those that are well-understood, routine and conventional in the field. Thus, the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claims 1-10 are not patent eligible.
Claims 11-17 and 18-10 recite similar limitations of claims 1-10, thus are abstract idea and not patent eligible.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Castrejon Subira et al (US 20250239066 A1; hereinafter “Castrejon”) in view of Peters (US 20250298974 A1).
Regarding claim 1, Castrejon discloses a computing system [e.g. FIG. 1 and 12-13; computing devices] comprising:
a storage subsystem comprising one or more data storage devices [e.g. FIG. 12; memory]; and
a logic subsystem comprising one or more logic devices [e.g. processors], wherein the storage subsystem comprises code executable by the logic subsystem to perform a spatial reasoning task [e.g. FIG. 2; [0015]; machine-learned agents can be configured for spatial reasoning] by:
at a reasoner agent [e.g. FIG.1-2; agent 204],
generating a reasoner prompt [e.g. 106; an agent can be prompted with a plurality of example task inputs] containing task information [e.g. task input] and a schema of scene data [e.g. FIG. 2 and 6; [0219]; data type of input or output in any format or schema; e.g. geographical data from sensors], and inputting the reasoner prompt into a reasoner language model [e.g. retrieval agent 226],
receiving a reasoner response [e.g. 234] from the reasoner language model 226], and sending a query [e.g. 244] to a retriever agent [236] based upon the reasoner response;
at the retriever agent [236],
receiving the query from the reasoner agent [244],
generating a retriever prompt [250] containing the schema of the scene data and the query [e.g. FIG. 1-2 and 6], and inputting the retriever prompt into a retriever language model [e.g. 236],
receiving a retriever output [250] from the retriever language model, the retriever output comprising code representing a query to query scene data [e.g. FIG. 1 and 6; user querying; specialized task like spatial reasoning],
querying the scene data using the code representing the query and receiving one or more results of the query [e.g. 120], and
sending the one or more results of the query to the reasoner agent [e.g. 204]; and
at the reasoner agent, using the one or more results of the query [e.g. 120 to 122].
Although Castrejon discloses input or output data of language mode containing the schema of the scene data [e.g. FIG. 2 and 6; [0219]; data type of input or output in any format or schema; e.g. geographical data from sensors], it is noted that Castrejon differs to the present invention in that Castrejon fails to explicitly disclose retriever output.
However, Peters teaches the well-known concept of receiving a retriever output from the retriever language model [e.g. FIG. 4-5; the response from LLM], the retriever output comprising code representing a query [element “query”] to query scene data [e.g. “Location” processor] represented by the schema of the scene data [e.g. FIG. 4-5; [0113-0114]; JSON/or XML format or element].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the machine learning process system disclosed by Castrejon to exploit the well-known data format of response of LLM technique taught by Peters as above, in order to provide improved operation of LLM prompt pre-processing and LLM response post-processing [See Peters; [0102]].
Regarding claim 2, Castrejon and Peters further disclose using the one or more results at the reasoner agent comprises generating a next reasoner prompt comprising the one or more results [e.g. Castrejon: FIG. 1-3], inputting the next reasoner prompt into the reasoner language model, and receiving a next reasoner response [e.g. Castrejon: FIG. 2-3].
Regarding claim 3, Castrejon and Peters further disclose information regarding a tool executable to perform a corresponding task [e.g. Castrejon: FIG. 2-3; tools to perform the task], and wherein the reasoner output comprising the next reasoner response comprises code for executing the tool [e.g. Castrejon: FIG. 2-3 and 9].
Regarding claim 4, Castrejon and Peters further disclose a self-debugging mechanism [e.g. Castrejon: FIG. 1-2 and 9; [0058]; debugging including finetuning], the self-debugging mechanism comprising, at one or more of the reasoner agent or the retriever agent, code executable to generate a prompt to request a review of a history of code execution attempts and code execution outcomes for errors [e.g. reviewing failure], and code executable to generate a prompt to request a correction of the errors based on results of the review [e.g. Castrejon: FIG. 1-2 and 9; [0049 and 0058]; the parsed output can be checked for correctness (e.g., correct syntax, valid tool name, valid tool inputs].
Regarding claim 5, Castrejon and Peters further disclose a reasoner prompt history, and wherein the next reasoner prompt comprises a next reasoner prompt history [e.g. Castrejon: FIG. 1-3 and 9; [0112-0114]; prior inference iterations; retrieval tool 240; Peters: FIG. 4-5; [0014]; historic data].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the machine learning process system disclosed by Castrejon to exploit the well-known data format of response of LLM technique taught by Peters as above, in order to provide improved operation of LLM prompt pre-processing and LLM response post-processing [See Peters; [0102]].
Regarding claim 6, Castrejon and Peters further disclose the retriever prompt does not include a prompt history [e.g. Castrejon: FIG. 1-2 and 5-6; [0256]; zero-shot prompts].
Regarding claim 7, Castrejon and Peters further disclose the reasoner language model and the retriever language model are a same language model [e.g. Castrejon: FIG. 1-2 and 6, pretrained language (machine) model].
Regarding claim 8, Castrejon and Peters further disclose an error prevention mechanism [e.g. Castrejon: FIG. 1-2, 5 and 9; [0058]], the error prevention mechanism comprising, at the reasoner agent, code executable to generate a prompt to request a review of code comments for one or more keywords [Castrejon: FIG. 1-2; [0121]; keyword data], the one or more keywords indicating one or more assumptions in the code, and code executable to generate a prompt to request a removal of the one or more assumptions [e.g. Castrejon: FIG. 1-2, 5 and 9; [0074-0075]; loss function based on a comparison between the training output and a ground truth output, etc.), one or more parameter updates for the dispatcher agent 104; and updating the dispatcher agent 104 according to the parameter updates].
Regarding claim 9, Castrejon and Peters further disclose the reasoner prompt further comprises one or more task examples [e.g. Castrejon: FIG. 1-2; using tools to perform tasks].
Regarding claim 10, Castrejon and Peters further disclose the reasoner prompt further comprises an environment description [e.g. Castrejon: FIG. 1 and 9; [0219]; Peters: FIG. 3-4; [0066]; environmental noises, environmental images, sensor readings].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the machine learning process system disclosed by Castrejon to exploit the well-known data format of response of LLM technique taught by Peters as above, in order to provide improved operation of LLM prompt pre-processing and LLM response post-processing [See Peters; [0102]].
Regarding claim 11, this is a method that includes same limitation as in claim 1 above, the rejection of which are incorporated herein. Furthermore, Castrejon and Peters disclose performing a spatial reasoning task [e.g. Castrejon: FIG. 1-2 and 9], comprising: iteratively, for an iteration [n], where n=1 to x, at a reasoner agent, receiving one or more query results [n-1] from a retriever agent, based upon the one or more query results [n-1] [e.g. Castrejon: FIG. 2 and 5; [0112-0113 and 0211], repeating process or a plurality of inference iterations; Peters: FIG. 4-5]; generating a reasoner prompt [n] [e.g. Castrejon: FIG. 2 and 5-6], the reasoner prompt [n] comprising a reasoner prompt history, task information, a schema of scene data, and an environment description [e.g. Castrejon: FIG. 1 and 9; [0219]; Peters: FIG. 4-5; [0066]].
Regarding claim 12-13, 15 and 17, this is a method that includes same limitation as in claim 11 with 3, 6, 7-8 above respectively, the rejection of which are incorporated herein.
Regarding claim 14, Castrejon and Peters further disclose the schema of scene data further comprises information defining a format of a database [e.g. Castrejon: FIG. 1-2; Peters: FIG. 4-5; [0113-0114]; JSON/or XML format or element].
Regarding claim 16, Castrejon and Peters further disclose the reasoner language model and the retriever language model are two or more separate language models [e.g. Castrejon: FIG. 1-2 and 6-9].
Regarding claim 18-20, this is a computer system that includes same limitation as in claim 1, 8 and 4 above respectively, the rejection of which are incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HAN et al (US 20230169075 A1).
Shah et al (US 20230282358 A1).
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/ZHUBING REN/ Primary Examiner, Art Unit 2658
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