DETAILED ACTION
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 .
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-23 are drawn to a method and a non-transitory computer-readable media, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claim 1 recites obtaining data input; identifying a language and a subject associated with the data input; determining that the data input is an eligible test input based on the language being part of a predefined set of languages and the subject being part of a predefined set of subjects; determining that the test input is a non-practice-test-type input using a classification model; executing an inference stage of the classification model over non-practice-test-type input to extract one or more concepts; executing an embedding model over each one of the one or more concepts to output a corresponding concept word embedding; accessing a plurality of records corresponding to questions having associated answers, wherein each question includes an associated question word embedding; measuring a concept similarity between the concept word embedding for each one of the one or more concepts and the question word embedding for each question; assessing a matching of one or more questions for at least one of the one or more concepts, based on the concept similarity being above a selected first threshold; in response to determining the matching, selecting the one or more questions and corresponding one or more answers to the one or more questions; in response to determining an absence of the matching, executing a first inference stage of a generative model over the one or more concepts to output the one or more questions, and executing a second inference stage of the generative model over the one or more questions to output the one or more answers; and presenting a practice test comprising displaying a question from the one or more questions and displaying an answer from the one or more answers.
Independent claim 12 recites obtaining data input; identifying a language and a subject associated with the data input; determining that the data input is an eligible test input based on the language being part of a predefined set of languages and the subject being part of a predefined set of subjects; determining that the test input is a non-practice-test-type input using a classification model; executing an inference stage of the classification model over non-practice-test-type input to extract one or more concepts; executing an embedding model over each one of the one or more concepts to output a corresponding concept word embedding; accessing a plurality of records corresponding to questions having associated answers, wherein each question includes an associated question word embedding; measuring a concept similarity between the concept word embedding for each one of the one or more concepts and the question word embedding for each question; assessing a matching of one or more questions for at least one of the one or more concepts, based on the concept similarity being above a selected first threshold; in response to determining the matching, selecting the one or more questions and corresponding one or more answers to the one or more questions; in response to determining an absence of the matching, executing a first inference stage of a generative model over the one or more concepts to output the one or more questions, and executing a second inference stage of the generative model over the one or more questions to output the one or more answers; and presenting a practice test displaying a question from the one or more questions and displaying an answer from the one or more answers.
The respective dependent claims 2-11 and 13-23, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
Said above recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that the invention is to “utilize[ing] text segmentation and contextual embedding analysis to determine questions and answers from provided content automatically” (see: specification paragraph 28). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, such as by teaching and following rules or instructions, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a problem where “creating customized assessments and practice tests remains a challenge” because “[c]urrent systems…often rely[] on static question banks or requir[e] significant manual effort to design tests that align with specific user-provided study materials”, which “results in a lack of personalized and contextualized test preparation resources for users” (see: specification paragraph 5). Further, conventional methods for “generating practice tests typically involves educators or content creators manually selecting or writing questions and answers”, which can be “time-consuming and may not adequately reflect the specific focus of a learner's study material” and “students may practice questions that are either too generic or irrelevant to their particular needs” (see: specification paragraph 6). The result is “a pressing need for improved techniques in creating customized practice tests” (see: specification paragraph 8). The above recited limitations address these concerns “by automating the extraction, generation, and validation of practice test questions, ensuring that the content is aligned with the source material and tailored to the learning objectives” (see: specification paragraph 27). This is achieved by “utiliz[ing] text segmentation and contextual embedding analysis to determine questions and answers from provided content automatically” such that “questions and answers are then stored in a question/answer (Q/A) bank database and can be used for practice tests” (see: specification paragraph 28). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including an “using a server computer…from a client computer…a question-answer database containing…in the question-answer database…in the question-answer database…one or more digital cards, each of the one or more digital cards including a first graphical user element…a second graphical user element…” (claim 1), “by querying a large language model…using the large language model as an eligibility model…” (claim 4), “wherein the segmentation model is a rule-based model trained to extract text data from the data input and identify the one or more sections by detecting specific patterns within the text data…” (claim 5), “wherein the segmentation model is trained to identify logical boundaries between the one or more sections using syntactic and semantic feature extraction” (claim 6), “wherein the uncertainty estimation comprises at least one of Monte Carlo Dropout or Bayesian Neural Networks” (claim 7), “via an attention model…wherein the attention model is trained to assign higher weights to the individual words or the phrases that are contextually significant…” (claim 10), “accessing the question-answer database…” (claim 11), and “One or more non-transitory computer-readable media storing one or more sequences of instructions which, when executed using a server computer, cause the server computer to execute:…from a client computer…a question-answer database containing…in the question-answer database…in the question-answer database…comprising one or more digital cards, each of the one or more digital cards including a first graphical user element…a second graphical user element…” (claim 12), “sequences of instructions which when executed by the server computer cause…” (claims 13-15 and 18-22), “by querying a large language model…using the large language model as an eligibility model…” (claim 15), “wherein the segmentation model is a rule-based model trained to extract text data from the data input and identify the one or more sections by detecting specific patterns within the text data…” (claim 16), “wherein the segmentation model is trained to identify logical boundaries between the one or more sections using syntactic and semantic feature extraction…” (claim 17), “wherein the uncertainty estimation comprises at least one of Monte Carlo Dropout or Bayesian Neural Networks” (claim 18), “via an attention model…wherein the attention model is trained to assign higher weights to the individual words or the phrases that are contextually significant…” (claim 21), “accessing the question-answer database…” (claim 22), and “wherein the classification model is an Information Maximizing Generative Adversarial Network (Info-GAN) comprising a generator, a discriminator, and an additional auxiliary network…wherein the generator creates…wherein the discriminator differentiates…wherein the auxiliary network predicts…” (claim 23), which are additional elements that are claimed at a high level of generality (e.g., the “server” is configured through no more than a statement than that functions are performed “using” said server; the “client computer” is configured through no more than a statement than that input data is received “from” said client computer; the “question-answer database” is configured through no more than a statement than that it is “containing” information and otherwise storing information “in” said database for “accessing”; the “one or more digital cards” are configured through no more than a statement than that are “including” a first and second “graphical user element”; the “segmentation model” is configured through no more than a statement than that it has been “trained” to perform the desired functions; the “Monte Carlo Dropout or Bayesian Neural Networks” are implemented through no more than a statement than that the uncertainty estimation “comprises” them; the “attention model” is configured through no more than a statement than that it has been “trained” to perform the desired functions such that said functions can be performed “via” said attention model; the “[o]ne or more non-transitory computer-readable media” is configured through no more than a statement than that it is “storing” one or more sequences of instructions; the “server computer” is configured through no more than a statement than “using” said server computer may “cause” it “to execute” said sequences of instructions; the “large language model” is configured through no more than a statement than that querying may be performed “using” said large language model; the “an Information Maximizing Generative Adversarial Network (Info-GAN)” is configured through no more than a statement than that “is” the classification model and performs desired functions) such that they amount to no more than mere instruction to apply the exception using generic computer elements. See: MPEP 2106.05(f).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer elements. Accordingly, even in combination, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Accordingly, the claims are directed to an abstract idea(s) (Step 2A Prong Two: NO).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea(s) into a practical application, using the additional elements to perform the abstract idea(s) amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Viewing the claimed limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment. Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea(s) (Step 2B: NO).
Dependent claim(s) 2-11 and 13-23, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because they fail to establish that the claim(s) is/are not directed to an abstract idea(s) without significantly more. These claims fail to remedy the deficiencies of their parent claims above and are therefore rejected for at least the same rationale as applied to their parent claims above and incorporated herein.
Novelty of Claims
As per claims 1-23, the closest prior art of record, U.S. Patent Application Publication 2023/0080674 to Attali and U.S. Patent Application Publication 2021/0358317 to Faldu, neither alone nor in combination teach the claimed invention. For example, Attali teaches in paragraph 274: “Each element (or example) in the form of one or more parameters, variables, characteristics, or "features" of the set of training data is associated with a label or annotation that defines how the element should be classified by the trained model. A machine learning model can predict or infer an outcome based on the training data and labels and be used as part of a decision process. When trained, the model will operate on a new element of input data to generate the correct label or classification as an output.” And in paragraph 141: “Generate a set of possible answers to the item using the Instructions and Examples defined in step 1 and the Source Passage as the Conditioning for the Transformer-Based Language Model.” But the closest prior art of record does not anticipate or otherwise render the combination of limitations of the independent claims obvious, particularly, with regard to claim 1, the combination of limitations including “executing an inference stage of the classification model over non-practice-test-type input to extract one or more concepts; executing an embedding model over each one of the one or more concepts to output a corresponding concept word embedding; accessing a question-answer database containing a plurality of records corresponding to questions having associated answers, wherein each question in the question-answer database includes an associated question word embedding; measuring a concept similarity between the concept word embedding for each one of the one or more concepts and the question word embedding for each question; assessing a matching of one or more questions in the question-answer database for at least one of the one or more concepts, based on the concept similarity being above a selected first threshold; in response to determining the matching, selecting the one or more questions and corresponding one or more answers to the one or more questions; in response to determining an absence of the matching, executing a first inference stage of a generative model over the one or more concepts to output the one or more questions, and executing a second inference stage of the generative model over the one or more questions to output the one or more answers”, and similarly for the corresponding limitations of claim 12. Rejection of the claimed combination of limitations would require an unreasonable combination of the available prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found on the attached PTO-892 form, including:
CN 113764112 A (“good doctor online collecting medical question data, as TextCNN text classification model of the data set, such as the collected data set a total of hundred thousand, wherein the medical related question sentence marking sign is 1, and medical irrelevant oral sentence marking sign is 0, and according to the proportion of 7: 3 the data set is divided into a training set and a test set; converting the training set data into the input of TextCNN model training after the word embedding set data, namely the first layer of the neural network model;” and “Optionally, it can adopt python ahocoratek packet to realize the matching of the character string, which is substantially combined with the trie algorithm and Aho-Corasick automaton, the matching of the multi-mode string by the KMP algorithm, in addition; The forward or reverse maximum matching algorithm can also be used.”);
US 2026/0111766 (abstract: “A query including a question and a proposed answer is accessed for evaluation using machine learning. A corpus is accessed, and a set of hyperedges is generated based on the corpus of information, where each hyperedge links a set of related concepts from the corpus. Based on the question, a context for the question is identified, where the context comprises a set of relevant hyperedges. A set of candidate answers is generated based on processing the question and the context using one or more language models. A set of regression scores is generated based on processing the question, the context, and the proposed answer using one or more regression models. The proposed answer is evaluated based on the candidate answers and the regression scores. Based on the evaluation, an indication that the proposed answer is not valid is output.”);
US 2014/0358922 (abstract: “Mechanisms for selecting a pipeline of a question and answer (QA) system to process an input question are provided. An input question is received and analyzed to identify at least one feature of the input question. Clustering of the input question, with one or more previously generated clusters of questions, is performed based on the at least one feature of the input question. Based on results of the clustering, a matching cluster, of the one or more previously generated clusters, is identified with which the input question is associated. A QA system pipeline associated with the matching cluster is identified and the input question is processed using the identified QA system pipeline to generate one or more candidate answers for the input question. Each cluster in the one or more previously generated clusters has an associated QA system pipeline”);
US 9,720,981 (abstract: “A mechanism is provided in a data processing system for question answering using multi-instance learning. The mechanism trains an answer ranking multi-instance learned model using a ground truth question and answer-key pairs set. When used for answering questions, the mechanism receives an input question from a user and generates one or more candidate answers to the input question. Each of the one or more candidate answers has an associated set of supporting passages. The mechanism determines a confidence value for each of the one or more candidate answers using an answer ranking multi-instance learned model based on the sets of supporting passages. The mechanism ranks the one or more candidate answers by confidence value to form a ranked set of answers, classifies supporting passages to identify the ones which truly support the answer, and presents a final answer from the ranked set of answers, the confidence value for the final answer, and supporting evidence for the final answer to the user”); and
US 11,914,967 (Fig. 5, ele. 504: “Execute (I) a first trained model, based on the passage and the question and in response to determining that the question, passage, or a combination thereof include more than a first threshold number of out of vocabulary (OOV) words, relative to a general purpose language model, to generate a first answer to the question…”).
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/ROBERT A SOREY/ Primary Examiner, Art Unit 3682
