DETAILED ACTION
This communication is in response to the Application filed on 05/08/2024. Claims 1-5 are pending and have been examined. Claims 1, 4, and 5 are independent. This Application was published as U.S. Pub. No. 2025/0013825.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/08/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
This application is a 371of PCT/JP2021/041807 submitted on 11/12/2021.
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 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Regarding Claims 1, 4, and 5,
Claim 1 recites an information processing apparatus which falls under the statutory category of machine. Claims 4 and 5 recite an information processing method and a non-transitory computer-readable recording medium, which fall under the statutory category of process and manufacture, respectively (Step 1: Yes).
Claims recites limitations “(a) receive an analysis target sentence…”, “(b) generate a generation sentence…and a classification type…”, and “(c) output the generation sentence…”. Except for the recitation of generic computer components (i.e., processing circuitry, a non-transitory computer-readable recording medium, and machine learning model), limitation (b) can be performed in the human mind or with pen and paper. The claims, under their broadest reasonable interpretation, cover the concept of receiving text (e.g., sentence or documents) and extracting key phrases in specific area and composing the questions related key phrases (or target answer) with words not used in the input text (see MPEP 2106.04(a)(2) III.
Under its broadest reasonable interpretation when read in light of the specification, the actions recited in limitation (b) encompass mental processes practically performed in the human mind. . According, the claim recites an abstract idea (Step 2A, Prong one).
The judicial exception is not integrated into a practical application. In particular, limitations recite an additional elements of processing circuitry, a non-transitory computer-readable recording medium, and a machine learning model, but they are recited at a high level of generality (i.e., machine learning model and processing circuitry are combination of hardware and software of a generic computing device or generic computer components performing a generic computer functions such as processing and storing data from given input) such that it amounts to no more than mere instructions to apply to the exception using a generic computer component.
Claims recite additional limitations (a) and (c). The additional limitations are recited at a high level of generality, and amounts to mere data gathering and output, which is a form of insignificant extra-solution activity. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the claim is therefore directed to the judicial exception. (Step 2A: YES).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not include subject matter that could not be performed by a human, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the generic computing elements to perform the claimed elements amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As noted previously, the claim as a whole merely describes how to generally linking the use of the aforementioned concept to a particular technological environment or field of use. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is not patent eligible. (Step 2B: NO).
Regarding Dependent Claims 2 and 3,
Claims 2 and 3 are dependent on supra Claim 1 and includes all the limitations of the claim and further limits the elements of Claim 1. Therefore, the dependent claims recite the same abstract idea.
No additional elements beyond the use of generic computing elements are claimed, therefore the judicial exception is not integrated into a practical application nor are the claim elements sufficient to amount to significantly more than the judicial exception. Therefore, claims are 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.
Claims 1, 4, and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al., (US Pub No. 2019/0043379, hereinafter, Yuan) in view of Yuan et al., ("Machine comprehension by text-to-text neural question generation" Proceedings of the 2nd Workshop on Representation Learning for NLP, 2017, hereinafter, Yuan2)
Regarding Claim 1,
Yuan discloses an information processing apparatus comprising: processing circuitry configured to (Yuan, Fig.12, par [084-091], "…a computing device 1200...a neural entity selector 1224, a pointer network 1226, a question generator 1228..."; par [025], "…end-to-end trainable neural models for automatic question generation..."):
receive an analysis target sentence as an input (Yuan, par [025], "…a system to identify interesting entities or events (key phrases) within a passage or Document...") and generate a generation sentence, the generation sentence being a sentence regarding content of the analysis target sentence (Yuan, par [039], "…a neural model trained from scratch to extract all answer key phrases in a particular document..."; Question Generation, paras [051-070], "…The model takes a document D10 and an answer A12 as inputs, and outputs a question Q...").
output the generation sentence and the classification type generated (Yuan, Figs. 5 and 6, paras [039-049], Pointer Networks, "…a neural model trained from scratch to extract all answer key phrases in a particular document. This model is parameterized as a pointer network to point sequentially to start and end location of all key phrase answers..."; Fig.11, paras [051-069], "…output a quest Q..."; par [068], "…the decoder 1100 operates as a recurrent neural network-based decoder employing a pointer-softmax mechanism. At each generation step, the decoder 1100 decides adaptively whether (a) to generate from a decoder vocabulary or (b) to point to a word in the source sequence ( and copy over)...")
But, Yuan does not discloses explicitly the generation sentence is a question sentence for information in which an answer is not included in the analysis target sentence.
However, Yuan2 discloses a classification type, the classification type being information indicating whether or not the generation sentence is a question sentence for information in which an answer is not included in the analysis target sentence, using a machine learning model learned in advance (Yuan2, section 3.3, "…First, we encourage the model not to generate answer words in the question. We use the soft answer-suppression constraint given in (10) with the penalty hyperparameter λs...").
Therefore, It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to generate a word of the question representation based on the minimize probability, because it would encourage the model not to generate answer words in the questions, as taught by Yuan 2 (Section 3.3).
Claim 4 is a method claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 1.
Claim 5 is a non-transitory computer-readable recording medium claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale.
Additionally,
Yuan discloses a non-transitory computer-readable recording medium storing therein an information processing program that causes a computer to execute a process (Yuan, Fig.12, par [090], "…Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, or program modules...") comprising:
…
Rationale for combination is similar to that provided for Claim 1.
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Yuan in view of Yuan2 further in view of Harrison et al., ("Neural generation of diverse questions using answer focus, contextual and linguistic features." arXiv preprint arXiv:1809.02637, 2018, hereinafter, Harrison).
Regarding Claim 2,
Yuan in view of Yuan2 discloses information processing apparatus according to claim 1, wherein the processing circuitry is further configured to
Yuan discloses a baseline model with the entity tagging (par [030], Entity Tagging Baseline) and a neural entity selection model (paras [031-032), but does not explicitly discloses the limitation, "acquire a viewpoint which is information representing a tendency of description content of the generation sentence."
However, Harrison, in the analogous field of endeavor, discloses acquire a viewpoint which is information representing a tendency of description content of the generation sentence, and receive the analysis target sentence and the viewpoint as inputs to generate the generation sentence and the classification type (Harrison, Fig.1, Table 2, section 2.1. Feature Supervision, "…A feature-rich encoding is constructed by concatenating several token level features onto the token’s word-based embedding…The Answer Signal feature guides the model in deciding which information to focus on when reading the sentence. The signal being active in some location of the sentence indicates the answer to the question being generated….").
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified a neural question generation method or system of Yuan in view of Yuan2 with the answer focus model with feature supervision of Harrison with a reasonable expectation of success to achieve efficient and accurate question generation without being domain specific and resorting to relying on syntactic parsers, which may lead to parser inaccuracy (Harrison, 1. Introduction).
Regarding Claim 3,
Yuan in view of Yuan2 further in view of Harrison discloses the information processing apparatus according to claim 2.
Harrison further discloses wherein the processing circuitry is further configured to add information of the viewpoint and output the generation sentence and the classification type (Harrison, Fig.1, section 2.1 Feature Supervision, "…The Answer Signal feature guides the model in deciding which information to focus on when reading the sentence…The Answer Signal feature is implemented as a binary feature indicating whether or not a given token is part of the answer span…Figure 1 shows a diagram depicting how the NER feature is incorporated into each token’s feature rich encoding via concatenation...").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Liu et al., ("Asking questions the human way: Scalable question-answer generation from text corpus." Proceedings of the web conference 2020, 2020, hereinafter, Liu) discloses Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation (Liu, Abstract).
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/JANGWOEN LEE/Examiner, Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656