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 .
DETAILED ACTION
This action is responsive to the following communication: Non-Provisional Application filed Jun. 30, 2024.
Claims 1-20 are pending in the case. Claims 1, 8 and 15 are independent claims.
Claim Rejections - 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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “ obtaining … an input query; in response to the input query: generating a token sequence associated with the input query by applying a beam search algorithm on generated bound knowledge graphs; applying the input query to a large language model (LLM) of the question-response mechanism to obtain first outputs” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “obtaining, by a data system implementing the question-response mechanism” is mere insignificant extra solution activity and something the courts have recognized as being well-understood, routine and conventional.
Yes, the limitation “applying the generated token sequence to a graph neural network (GNN) to obtain second outputs; applying fusion layers on the first outputs and the second outputs to generate a response associated with the input query and generated token sequence; and performing a remediation using the generated response, wherein the remediation comprises updating the LLM and the GNN based on the generated response.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a data system implementing the question-response mechanism” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “query to a large language model (LLM) of the question-response mechanism to obtain first outputs; applying the generated token sequence to a graph neural network (GNN) ” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a data system” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “applying the input query to a large language model (LLM) of the question-response mechanism to obtain first outputs; applying the generated token sequence to a graph neural network (GNN) to obtain second outputs;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “ obtaining … an input query; in response to the input query: generating a token sequence associated with the input query by applying a beam search algorithm on generated bound knowledge graphs; applying the input query to a large language model (LLM) of the question-response mechanism to obtain first outputs” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “obtaining, by a data system implementing the question-response mechanism” is mere insignificant extra solution activity and something the courts have recognized as being well-understood, routine and conventional.
Yes, the limitation “applying the generated token sequence to a graph neural network (GNN) to obtain second outputs; applying fusion layers on the first outputs and the second outputs to generate a response associated with the input query and generated token sequence; and performing a remediation using the generated response, wherein the remediation comprises updating the LLM and the GNN based on the generated response.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “ A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “query to a large language model (LLM) of the question-response mechanism to obtain first outputs; applying the generated token sequence to a graph neural network (GNN) ” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “ non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor ” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “applying the input query to a large language model (LLM) of the question-response mechanism to obtain first outputs; applying the generated token sequence to a graph neural network (GNN) to obtain second outputs;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 15:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “ obtaining … an input query; in response to the input query: generating a token sequence associated with the input query by applying a beam search algorithm on generated bound knowledge graphs; applying the input query to a large language model (LLM) of the question-response mechanism to obtain first outputs” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “obtaining, by a data system implementing the question-response mechanism” is mere insignificant extra solution activity and something the courts have recognized as being well-understood, routine and conventional.
Yes, the limitation “applying the generated token sequence to a graph neural network (GNN) to obtain second outputs; applying fusion layers on the first outputs and the second outputs to generate a response associated with the input query and generated token sequence; and performing a remediation using the generated response, wherein the remediation comprises updating the LLM and the GNN based on the generated response.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a processor; and memory including instructions” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “query to a large language model (LLM) of the question-response mechanism to obtain first outputs; applying the generated token sequence to a graph neural network (GNN) ” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a processor; and memory including instructions” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “applying the input query to a large language model (LLM) of the question-response mechanism to obtain first outputs; applying the generated token sequence to a graph neural network (GNN) to obtain second outputs;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
Claims 2-7, 9-14 and 16-20 are dependent claims. These claims recite additional limitations, but do not otherwise add any meaningful limits beyond the abstract idea.
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.
Claims 1, 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (hereinafter Yu) U.S. Patent Publication No. 2021/0406669 in view of Wu et al. (hereinafter Wu) U.S. Patent Publication No. 2022/0108188.
With respect to independent claim 1, Yu teaches a method for updating a question-response mechanism in a data system (see e.g., Para [17] – “Machine reading comprehension (MRC) is a task of textual question answering (QA), in which each question is provided context from which to infer an answer. The main objective is to extract a correct answer from the context or to generate an answer based on the context. In other words, given the context C and question Q, MRC tasks ask a model to provide an answer”), the method comprising:
obtaining, by a data system implementing the question-response mechanism, an input query (see e.g., Para [25] – “the neuro-symbolic rules learning system can input a query with corresponding texts to determine an answer.”);
in response to the input query (see e.g., Para [17][25]):
generating a token sequence associated with the input query by applying a beam search algorithm on generated bound knowledge graphs (see e.g., Para [43][54] – “For each potential path, there is a probability associated with each passage edge. In some embodiments, a Grady search is performed. A Grady search selects the edges with the highest probability to determine the potential path with the correct answer. In some embodiments, a beam search is performed.”);
applying the input query to a large language model (LLM) of the question-response mechanism to obtain first outputs (see e.g., Para [34] – “The neural model 140 can include embedding, feature extraction, context-query interaction, and answer prediction modules. The embedding module is configured to encode input natural language words into fixed-length vectors, which the neural model 140 can understand. The embedding module can employ several types of embedding methods. These methods include, for example, conventional, contextual, and multiple granularity methods. Conventional methods include, for example, One-Hot and distributed. Contextual methods include, for example, Contextual Word Vectors (CoVE), Embeddings from language models (ELMo), generative pre-training (GPT), and bidirectional encoder representation from Transformers (BERT).”);
applying fusion layers on the first outputs and the second outputs to generate a response associated with the input query (see e.g., Para [24][55] – “The neural model outputs textual rules from the inputted texts. Neuro-symbolic rules are learned by combining potential symbolic rules with neural textual rules generated by the neural model.” “The edges along the reasoning path are converted into symbolic rules and are then combined to form a neuro-symbolic rule that reflects the relations between the head entity and the answer.”) and generated token sequence (see e.g., Para [34] – “The embedding module is configured to encode input natural language words into fixed-length vectors, which the neural model 140 can understand.”); and
performing a remediation using the generated response, wherein the remediation comprises updating the LLM based on the generated response (see e.g., Para [55] and Claim 9 – “converting a reasoning path with a highest score into a neuro-symbolic rule; and combining the neuro-symbolic rule with the neuro-symbolic rules already learned by the neural model.”).
Yu does not expressly show applying the generated token sequence to a graph neural network (GNN) to obtain second outputs and updating the GNN based on the generated response. However, Wu teaches similar feature (see e.g. para [30] [56] – “one or more embodiments described herein can have a practical application by casting a querying KG task as a graph based learning to rank task and leveraging neural network embeddings to encode graph structural information regarding semantic complexities. For example, one or more embodiments described herein can control a first bidirectional GNN (e.g., a bidirectional gated graph sequence neural network) for encoding the question graph and a second bidirectional GNN (e.g., a bidirectional message passing neural network) for encoding the KG subgraphs. Thereby, the one or more embodiments, can perform subgraph matching and ranking techniques that incorporate the graph structure information.”). Both Yu and Wu are directed to AI question answering systems. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Yu and Wu in front of them to modify the system of Yu to include the above feature. The motivation to combine Yu and Wu comes from Wu. Wu discloses the motivation to improve matching by incorporate the GNN (see e.g. para [30]). This motivation for combination also applies to the remaining claims which depend on this combination.
Claim 8 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 15 is rejected for the similar reasons discussed above with respect to claim 1.
Claims 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yu in view of Wu and further in view of Menaged et al. (hereinafter Menaged) U.S. Patent Publication No. 2024/0143583.
With respect to claim 7, Yu does not expressly show the generated response is based on a set of response templates associated with the LLM. However, Menaged teaches similar feature (see e.g. Abstract para [58][130] [188][198] – “populating, via an action server, a response template with the one or more entity values based on one or more prestored entity values and one or more specifics to form a populated response,”). Both Yu and Menaged are directed to AI question answering systems. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Yu and Menaged in front of them to modify the system of Yu to include the above feature. The motivation to combine Yu and Menaged comes from Menaged. Menaged discloses the motivation to streamlined responses in a coherent and easily navigable interface by applying templates. (see e.g. para [3]).
Claim 14 is rejected for the similar reasons discussed above with respect to claim 7.
Claim 20 is rejected for the similar reasons discussed above with respect to claim 7.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
