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 action is responsive to the application filed 6/17/2024. Claims 1-20 are presented for examination.
Priority
Applicant’s claim for the benefit of a prior filed application CN202311763895.X, filed 12/20/2023, is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted 04/08/2025, 07/01/2025, & 11/20/2025 have been considered by the examiner.
Claim Rejections - 35 USC § 102
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 9 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. ("Self-Instruct: Aligning Language Models with Self-Generated Instructions", arXiv:2212.10560v2, May 25, 2023), hereafter Wang.
Wang was cited in the IDS submitted 11/20/2025.
Regarding independent claim 9, Wang teaches a method comprising:
acquiring a to-be-solved question ([Sec. 2 & Fig. 2] discusses acquiring a to-be-solved question);
inputting the to-be-solved question into a step planning model in a question solving model to obtain a solving step output by the step planning model ([Sec. 2 & Fig. 2] discusses inputting the question into a step planning model to obtain a solving step output);
and inputting the to-be-solved question and the solving step into a large language model in the question solving model to obtain an answer output by the large language model ([Sec. 2 & Fig. 2] discusses inputting the to-be-solved question and solving step output into a LLM to obtain an answer output);
wherein the question solving model is obtained by performing training with the method according to claim 1 ([Sec. 3-4 & Tables 1-3] discusses the question solving model is obtained by performing training with the method through a series of experiments and benchmarks).
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.
Claims 1, 7-8, 10, & 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hsieh et al. (“Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes”, Google, ACL) (2023), hereafter Hsieh, in view of Wang et al. ("Self-Instruct: Aligning Language Models with Self-Generated Instructions", arXiv:2212.10560v2, May 25, 2023), hereafter Wang, and further in view of Zelikman et al. (“STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning”, Stanford, Google, arXiv) (2022), hereafter Zelikman.
Wang was cited in the IDS submitted 11/20/2025.
Regarding independent claim 1, Hsieh teaches a method comprising:
acquiring a first sample question ([Sec. 3.1] discusses drawing a sample question from a dataset);
inputting the first sample question and a solving step grabbing template into a large language model to obtain a first sample solving step output by the large language model ([Sec. 3.1] discusses taking a solving step template and sample question and feeding it to a LLM to obtain a first solving step output);
pre-training a step planning model according to the first sample question and the first sample solving step ([Sec. 3.2] discusses training a step planning model according to the sample question and sample solving step);
and acquiring the question solving model according to the step planning model and the large language model obtained by pre-training ([Sec. 4.2] discusses experiments in which the trained step planning model and LLM model are acquired together to perform as one system pipeline).
Hsieh does not explicitly teach inputting the first sample question, the first sample solving step and an answer grabbing template into the large language model to obtain a first sample answer output by the large language model; pre-training the large language model according to the first sample question, the first sample solving step and the first sample answer.
However, in a similar field of endeavor, Wang teaches a method for training a question solving model using a set of rationales to input into a LLM to obtain sample answer outputs ([Sec. 2.2] discusses taking the sample question, solving step, and output of a first LLM and inputting the data into a the LLM to obtain a first sample answer output).
Because Hsieh teaches acquiring a first sample question, inputting the first sample question and solving step grabbing template into a large language model to obtain a first sample solving step, pre-training a step planning model using the first sample question and first sample solving step, and acquiring the question solving model comprising the step planning model and the large language model; and Wang teaches using a set of rationales to input into a LLM to obtain sample answer outputs, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate inputting the first sample question, first sample solving step, and an answer grabbing template into the LLM to obtain a first sample answer output as taught by Wang into Hsieh’s method, with a reasonable expectation of success, to teach acquiring a first sample question; inputting the first sample question and a solving step grabbing template into a large language model to obtain a first sample solving step output by the large language model; inputting the first sample question, the first sample solving step and an answer grabbing template into the large language model to obtain a first sample answer output by the large language model; pre-training a step planning model according to the first sample question and the first sample solving step; and acquiring the question solving model according to the step planning model and the large language model obtained by pre-training. This combination would have been motivated by the desire to improve answer quality by reducing calculation errors and missing-step errors (Wang [Sec. 4.2]).
The combination of Hsieh and Wang does not explicitly teach pre-training the large language model according to the first sample question, the first sample solving step and the first sample answer.
However, in a similar field of endeavor, Zelikman teaches a method for a self-taught reasoner wherein a LLM is pre-trained using a first sample question, first sample solving step, and first sample answer ([Abstract & Sec. 3] discusses repeatedly training the large language model on the sample question, sample solving step, and sample answer).
Because the combination of Hsieh and Wang teaches acquiring a first sample question, inputting the first sample question and solving step grabbing template into a large language model to obtain a first sample solving step, inputting the first sample question, first sample solving step, and an answer grabbing template into the LLM to obtain a first sample answer output, pre-training a step planning model using the first sample question and first sample solving step, and acquiring the question solving model comprising the step planning model and the large language model; and Zelikman teaches pre-training a LLM using a first sample question, first sample solving step, and first sample answer, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate pre-training a LLM using a first sample question, first sample solving step, and first sample answer as taught by Zelikman into the combination of Hsieh and Wang’s method, with a reasonable expectation of success, to teach acquiring a first sample question; inputting the first sample question and a solving step grabbing template into a large language model to obtain a first sample solving step output by the large language model; inputting the first sample question, the first sample solving step and an answer grabbing template into the large language model to obtain a first sample answer output by the large language model; pre-training a step planning model according to the first sample question and the first sample solving step; pre-training the large language model according to the first sample question, the first sample solving step and the first sample answer; and acquiring the question solving model according to the step planning model and the large language model obtained by pre-training. This combination would have been motivated by the desire to train the LLM to perform accurately on multiple datasets by allowing the LLM to improve from its own reasoning (Zelikman [Abstract]).
Regarding dependent claim 7, the combination of Hsieh, Wang, and Zelikman teaches the claimed invention as claimed in claim 1, including:
inputting the first sample question into the step planning model to obtain a first prediction solving step output by the step planning model (Hsieh [Sec. 3.2] discusses inputting a sample question into the model to obtain a prediction output by the model);
obtaining a first loss function value according to the first sample solving step and the first prediction solving step (Hsieh [Sec. 3.2] discusses computing a loss function value using the sample solving step and prediction step);
and adjusting parameters of the step planning model according to the first loss function value to obtain the pre-trained step planning model (Hsieh [Sec. 3.2] discusses the loss enables the model to better learn to generate steps in a planning model by adjusting features or rationale; thus, the parameters are adjusted based on the first loss function value).
Regarding dependent claim 8, the combination of Hsieh, Wang, and Zelikman teaches the claimed invention as claimed in claim 1, including:
inputting the first sample question and the first sample solving step into the large language model to obtain a first prediction answer output by the large language model (Hsieh [Sec. 3.2] discusses inputting a sample question into the model to obtain a prediction output by the model);
acquiring a second loss function value according to the first sample answer and the first prediction answer (Hsieh [Sec. 3.2] discusses generating a first prediction answer, then using the sample answer and prediction answer in an equation to acquire another loss value);
and adjusting parameters of the large language model according to the second loss function value to obtain the pre-trained large language model (Hsieh [Sec. 3.2] discusses the loss enables the model to better learn to generate steps in a planning model by adjusting features or rationale; thus, the parameters are adjusted based on the first loss function value).
Regarding claims 10 & 16-17, claims 10 & 16-17 are system claims that are substantially the same as the method of claims 1 & 7-8, respectively. Therefore, claims 10 & 16-17 are rejected for the same reasons as claims 1 & 7-8, respectively.
Regarding claim 18, claim 18 is a computer-readable storage medium claim that is
substantially the same as the method of claim 1. Therefore, claim 18 is rejected for the same reasons as claim 1.
Claims 2, 11, & 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hsieh, in view of Wang, in view of Zelikman, as applied in claim 1, and further in view of Bai et al. ("Constitutional AI: Harmlessness from AI Feedback", Anthropic, arXiv) (Year: 2022), hereafter Bai.
Regarding dependent claim 2, the combination of Hsieh, Wang, and Zelikman teaches the claimed invention as claimed in claim 1, including:
inputting the first sample question, the first sample solving step, the first sample answer into the large language model to obtain output by the large language model (Zelikman [Sec. 3 & Alg. 1] discusses inputting data to a LLM and there is a step where the output is evaluated);
and in the case where the data evaluation result is determined to meet a preset requirement, taking the first sample question, the first sample solving step and the first sample answer as the pre-training data (Zelikman [Sec. 3 & Alg. 1] discusses filtering the evaluation results to only include the values meeting the requirement and using it as pre-training data along with the sample question and solving step).
The combination of Hsieh, Wang, and Zelikman does not explicitly teach inputting the first sample question, the first sample solving step, the first sample answer and a data evaluation template into the large language model to obtain a data evaluation result output by the large language model.
However, in a similar field of endeavor, Bai teaches a method for correcting a LLM’s reasoning using a data evaluation template and result ([Sec. 3.1] discusses providing a sample question, solving step, answer, and data evaluation template to a LLM and obtains a data evaluation result in the form of a harmfulness result).
Because the combination of Hsieh, Wang, and Zelikman teaches inputting data to a LLM to obtain an output and filtering the data to generate pre-training data; and Bai teaches the use of a data evaluation template to obtain a data evaluation result, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the use of a data evaluation template to obtain a data evaluation result as taught by Bai into the combination of Hsieh, Wang, and Zelikman’s method, with a reasonable expectation of success, to teach inputting the first sample question, the first sample solving step, the first sample answer and a data evaluation template into the large language model to obtain a data evaluation result output by the large language model; and in the case where the data evaluation result is determined to meet a preset requirement, taking the first sample question, the first sample solving step and the first sample answer as the pre-training data. This combination would have been motivated by the desire to improve the accuracy of responses and allow the model to critique its own responses (Bai [Sec. 3]).
Regarding claim 11, claim 11 is a system claim that is substantially the same as the method of claim 2. Therefore, claim 11 is rejected for the same reasons as claim 2.
Regarding claim 19, claim 19 is a computer-readable storage medium claim that is
substantially the same as the method of claim 2. Therefore, claim 19 is rejected for the same reasons as claim 2.
Claims 3, 12, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hsieh, in view of Wang, in view of Zelikman, in view of Bai, as applied in claim 2, and further in view of Wang et al. ("SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS", Google, arXiv) (Year: 2023), hereafter X-Wang.
Regarding dependent claim 3, the combination of Hsieh, Wang, Zelikman, and Bai teaches the claimed invention as claimed in claim 2, including inputting sample questions to a LLM to obtain an output (Zelikman [Sec. 3 & Alg. 1] discusses inputting data to a LLM and there is a step where the output is evaluated).
The combination of Hsieh, Wang, Zelikman, and Bai does not explicitly teach inputting the first sample question into a data generation model to obtain a candidate solving step and/or a candidate answer output by the data generation model; and in the case where the candidate solving step is determined to be similar to the first sample solving step and/or the candidate answer is determined to be similar to the first sample answer, taking the first sample question, the first sample solving step and the first sample answer as the pre-training data.
However, in a similar field, X-Wang teaches a method for a data generation model to obtain an answer ([Sec. 1 & Fig. 1] discusses inputting a sample question into a data generation model to obtain an answer output); and determine its similarity to samples in order to aggregate the answer as training data ([Sec. 1 & Fig. 1] discusses taking solving steps that are determined to be similar and aggregating them as training data).
Because the combination of Hsieh, Wang, Zelikman, and Bai teaches inputting sample questions to a model to obtain an answer output; and X-Wang teaches inputting sample questions to a data generation model to obtain an answer output, then determining its similarity to other sample answers to aggregate training data, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate inputting sample questions to a data generation model to obtain an answer output, then determining its similarity to other sample answers to aggregate training data as taught by X-Wang into the combination of Hsieh, Wang, Zelikman, and Bai’s method, with a reasonable expectation of success, to teach inputting the first sample question into a data generation model to obtain a candidate solving step and/or a candidate answer output by the data generation model; and in the case where the candidate solving step is determined to be similar to the first sample solving step and/or the candidate answer is determined to be similar to the first sample answer, taking the first sample question, the first sample solving step and the first sample answer as the pre-training data. This combination would have been motivated by the desire to implement consistency in order to boost performance of chain-of-thought (X-Wang [Abstract]).
Regarding claim 12, claim 12 is a system claim that is substantially the same as the method of claim 3. Therefore, claim 12 is rejected for the same reasons as claim 3.
Regarding claim 20, claim 20 is a computer-readable storage medium claim that is
substantially the same as the method of claim 3. Therefore, claim 20 is rejected for the same reasons as claim 3.
Claims 4-6 & 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hsieh, in view of Wang, in view of Zelikman, as applied in claim 1, and further in view of Zhang et al. ("AUTOMATIC CHAIN OF THOUGHT PROMPTING IN LARGE LANGUAGE MODELS", Shanghai Jiao Tong University, Amazon, arXiv) (Year: 2022), hereafter Zhang.
Regarding dependent claim 4, the combination of Hsieh, Wang, and Zelikman teaches the claimed invention as claimed in claim 1, including:
acquiring a second sample question (Hsieh [Sec. 3.1] discusses drawing a sample question from a dataset);
carrying out supervised fine tuning on the step planning model obtained by the pre-training according to the second sample question and the second sample solving step (Hsieh [Sec. 3.2 & Appendix A.1] discusses fine-tuning the model using the sample question and sample solving step for each iteration and thus, the second sample question and second sample solving step);
and acquiring the question solving model according to the large language model obtained by the pre-training and the step planning model obtained by the supervised fine tuning (Hsieh [Sec. 4.2 & Fig. 2] discusses experiments in which the training data, tuned step planning model and trained LLM are acquired together to perform as one system pipeline).
The combination of Hsieh, Wang, and Zelikman does not explicitly teach determining a question type of the second sample question; acquiring a solving step corresponding to the question type as a second sample solving step of the second sample question.
However, in a similar field of endeavor, Zhang teaches a method for determining a question type ([Sec. 4.1 & Fig. 4] discusses determining a question type of the sample question); and further providing a solving step associated with the question type ([Sec. 4 & Fig. 4] discusses acquiring a solving step based on the question type of the sample question).
Because the combination of Hsieh, Wang, and Zelikman teaches acquiring a second sample question, fine tuning the model using the second sample question and solving step, and acquiring the model using the trained LLM and fine-tuned step planning model; and Zhang teaches determining a question type and further providing a solving step associated with the question type, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate determining a question type and further providing a solving step associated with the question type as taught by Zhang into the combination of Hsieh, Wang, and Zelikman’s method, with a reasonable expectation of success, to teach acquiring a second sample question, and determining a question type of the second sample question; acquiring a solving step corresponding to the question type as a second sample solving step of the second sample question; carrying out supervised fine tuning on the step planning model obtained by the pre-training according to the second sample question and the second sample solving step; and acquiring the question solving model according to the large language model obtained by the pre-training and the step planning model obtained by the supervised fine tuning. This combination would have been motivated by the desire to mitigate misleading responses and provide context-aware responses to questions (Zhang [Sec. 4]).
Regarding dependent claim 5, the combination of Hsieh, Wang, and Zelikman teaches the claimed invention as claimed in claim 1, including:
acquiring a second sample question (Hsieh [Sec. 3.1] discusses drawing a sample question from a dataset);
determining a solving step type of the second sample solving step, and acquiring an answer corresponding to the solving step type as a second sample answer of the second sample question (Wang [Sec. 2.2 & Fig. 3] discusses determining a solving step type for the sample solving step and acquiring an answer corresponding to the solving step type);
performing supervised fine tuning on the large language model obtained by pre-training according to the second sample question, the second sample solving step and the second sample answer (Hsieh [Sec. 3.2, Eq. 4, & Appendix A.1] discusses fine-tuning the model using the sample question, sample answer, and sample solving step for each iteration and thus, the second sample question, second sample answer and second sample solving step);
and acquiring the question solving model according to the step planning model obtained by the pre-training and the large language model obtained by the supervised fine tuning (Hsieh [Sec. 4.2 & Fig. 2] discusses experiments in which the training data, trained step planning model and tuned LLM are acquired together to perform as one system pipeline).
The combination of Hsieh, Wang, and Zelikman does not explicitly teach determining a question type of the second sample question; acquiring a solving step corresponding to the question type as a second sample solving step of the second sample question.
However, in a similar field of endeavor, Zhang teaches a method for determining a question type ([Sec. 4.1 & Fig. 4] discusses determining a question type of the sample question); and further providing a solving step associated with the question type ([Sec. 4 & Fig. 4] discusses acquiring a solving step based on the question type of the sample question).
Because the combination of Hsieh, Wang, and Zelikman teaches acquiring a second sample question, determining a solving step type and acquiring an associated answer corresponding to the solving step type, fine tuning the LLM using the second sample question, the second sample solving step and the second sample answer, and acquiring the model using the tuned LLM and trained step planning model; and Zhang teaches determining a question type and further providing a solving step associated with the question type, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate determining a question type and further providing a solving step associated with the question type as taught by Zhang into the combination of Hsieh, Wang, and Zelikman’s method, with a reasonable expectation of success, to teach acquiring a second sample question, and determining a question type of the second sample question; acquiring a solving step corresponding to the question type as a second sample solving step of the second sample question; determining a solving step type of the second sample solving step, and acquiring an answer corresponding to the solving step type as a second sample answer of the second sample question; performing supervised fine tuning on the large language model obtained by pre-training according to the second sample question, the second sample solving step and the second sample answer; and acquiring the question solving model according to the step planning model obtained by the pre-training and the large language model obtained by the supervised fine tuning. This combination would have been motivated by the desire to mitigate misleading responses and provide context-aware responses to questions (Zhang [Sec. 4]).
Regarding dependent claim 6, the combination of Hsieh, Wang, and Zelikman teaches the claimed invention as claimed in claim 1, including:
acquiring a second sample question (Hsieh [Sec. 3.1] discusses drawing a sample question from a dataset);
determining a solving step type of the second sample solving step, and acquiring an answer corresponding to the solving step type as a second sample answer of the second sample question (Wang [Sec. 2.2 & Fig. 3] discusses determining a solving step type for the sample solving step and acquiring an answer corresponding to the solving step type);
carrying out supervised fine tuning on the step planning model obtained by the pre-training according to the second sample question and the second sample solving step (Hsieh [Sec. 3.2, Eq. 4, & Appendix A.1] discusses fine-tuning the model using the sample question, sample answer, and sample solving step for each iteration and thus, the second sample question, second sample answer and second sample solving step);
performing supervised fine tuning on the large language model obtained by pre-training according to the second sample question, the second sample solving step and the second sample answer (Hsieh [Sec. 3.2 & Appendix A.1] discusses fine-tuning the model using the sample question and sample solving step for each iteration and thus, the second sample question and second sample solving step);
and acquiring the question solving model according to the step planning model and the large language model obtained by the supervised fine tuning (Hsieh [Sec. 4.2 & Fig. 2] discusses experiments in which the training data, trained step planning model and tuned LLM are acquired together to perform as one system pipeline).
The combination of Hsieh, Wang, and Zelikman does not explicitly teach determining a question type of the second sample question; acquiring a solving step corresponding to the question type as a second sample solving step of the second sample question.
However, in a similar field of endeavor, Zhang teaches a method for determining a question type ([Sec. 4.1 & Fig. 4] discusses determining a question type of the sample question); and further providing a solving step associated with the question type ([Sec. 4 & Fig. 4] discusses acquiring a solving step based on the question type of the sample question).
Because the combination of Hsieh, Wang, and Zelikman teaches acquiring a second sample question, determining a solving step type and acquiring an associated answer corresponding to the solving step type, fine tuning the step planning model using the second sample question and the second sample solving step, fine tuning the LLM using the second sample question, second sample answer, and the second sample solving step, and acquiring the model using the tuned LLM and tuned step planning model; and Zhang teaches determining a question type and further providing a solving step associated with the question type, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate determining a question type and further providing a solving step associated with the question type as taught by Zhang into the combination of Hsieh, Wang, and Zelikman’s method, with a reasonable expectation of success, to teach acquiring a second sample question, and determining a question type of the second sample question; acquiring a solving step corresponding to the question type as a second sample solving step of the second sample question; determining a solving step type of the second sample solving step, and acquiring an answer corresponding to the solving step type as a second sample answer of the second sample question; carrying out supervised fine tuning on the step planning model obtained by the pre-training according to the second sample question and the second sample solving step; and performing supervised fine tuning on the large language model obtained by pre-training according to the second sample question, the second sample solving step and the second sample answer; and acquiring the question solving model according to the step planning model and the large language model obtained by the supervised fine tuning. This combination would have been motivated by the desire to mitigate misleading responses and provide context-aware responses to questions (Zhang [Sec. 4]).
Regarding claims 13-15, claims 13-15 are system claims that are substantially the same as the method of claims 4-6, respectively. Therefore, claims 13-15 are rejected for the same reasons as claims 4-6, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wei et al. ("FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS", Google Research, arXiv) (Year: 2022) ([Abstract] We take a 137B parameter pretrained language model and instruction tune it on over 60 NLP datasets verbalized via natural language instruction templates. We evaluate this instruction-tuned model, which we call FLAN, on unseen task types. FLAN substantially improves the performance of its unmodified counterpart and surpasses zero-shot 175B GPT-3 on 20 of 25 datasets that we evaluate. FLAN even outperforms few-shot GPT-3 by a large margin on ANLI, RTE, BoolQ, AI2-ARC, OpenbookQA, and StoryCloze. Ablation studies reveal that number of finetuning datasets, model scale, and natural language instructions are key to the success of instruction tuning).
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/RILEY S ACOSTA/Examiner, Art Unit 2143 /JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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