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.
Claim1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1 recites “receiving a mathematical question described in a natural language”, “decomposing the mathematical question into multiple sentences, wherein the multiple sentences include one question sentence”, “determining whether each of the sentences is a formula sentence based on whether it includes a noun subject”, “converting each of the formula sentences into a corresponding formula, respectively, wherein each of the formulas includes one or more variables and one or more relations of the one or more variables, respectively”, “establishing a formula relationship map according to the one or more relations of the one or more variables included each of the formulas”, and “calculating, sequentially, values of the one or more variables according to the formula relationship map until a value of the variable included in the formula of the question sentence is calculated as an answer”.
The limitation of receiving a mathematical question in natural language, as drafted, is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a server”, “a processor”, “a user computer”, and “a non-volatile memory”, nothing in the claim precludes the step from practically being performed in the mind. For example, “receiving” in the context of this claim encompasses receiving a mathematical question, which a human can do by in the mind or with a pen and paper. Next, the limitation of breaking down mathematical questions into sentences, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “decomposing” in the context of this claim encompasses breaking down a mathematical question into sentences, which a human can do in the mind or with a pen and paper. Next, the limitation of determining a formula sentence based on the presence of a noun, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “determining” in the context of this claim encompasses categorizing sentences, which a human can do in the mind or with a pen and paper. Next, the limitation of converting sentences into formulas, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “converting” in the context of this claim encompasses converting sentences into mathematical equations, which a human can do in the mind or with a pen and paper. Next, the limitation of establishing a formula relationship map, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “establishing” in the context of this claim encompasses mapping formula relationships, which a human can do in the mind or with a pen and paper. Lastly, the limitation of calculating values based on a formula map, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the elements listed above, nothing in the claim precludes the step from practically being performed in the mind. For example, “calculating” in the context of this claim encompasses performing calculations which a human can do in the mind or with a pen and paper.
The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements, using “a server”, “a processor”, “a user computer”, and “a non-volatile memory” to perform the recited limitations. These elements in these steps are recited at a high-level of generality such that is amounts no more than mere instructions to apply the exception using generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “a server”, “a processor”, “a user computer”, and “a non-volatile memory” to perform the recited limitations amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Dependent claims 2-19 are also rejected for the same reasons provided in independent claim 1 above. The dependent claim, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claim is directed to an abstract idea without significantly more.
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, 3-10, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. US 9424251 B2 (hereinafter Park) in view of Fuka (US 9858828 B1).
Regarding independent claim 1, Park teaches a method of machine solving mathematical question, wherein the method is applicable to a server, wherein the method comprising:
receiving a mathematical question described in a natural language (FIG. 7, S710, [Column 16, line 15-17] “an information input process for receiving a composite sentence including at least one of a natural word and a mathematical equation (S710)”);
decomposing the mathematical question into multiple sentences, wherein the multiple sentences include one question sentence (S720 [Column 5, line 57-59] “the semantic parsing unit 120 may convert the mathematical contents into a logical combination of a simple sentence to generate the semantic information”);
determining whether each of the sentences is a formula sentence based on whether it includes a noun subject ([Column 16, line 17-23] “a semantic parsing process for configuring the natural word and the mathematical equation, respectively, from a composite sentence and parsing each configuration information configuring the divided natural words and mathematical equations to generate the semantic information and generate the natural language token and the mathematical equation token (S720)”);
establishing a formula relationship map according to the one or more relations of the one or more variables included each of the formulas ([Column 5, line 27-31] “the semantic parsing unit 120 may convert a mathematical equation into a tree type, perform a traversal process on the mathematical equation converted into the tree type, and perform tokenization on the mathematical equation suffering from the traversal process”, examiner interprets the tree as the relationship map); and
calculating, sequentially, values of the one or more variables according to the formula relationship map until a value of the variable included in the formula of the question sentence is calculated as an answer (FIG. 7, S770, [Column 16, line 46-47] “outcome providing process (S770) corresponds to the operation of the outcome providing unit 170”).
Park fails to teach converting each of the formula sentences into a corresponding formula, respectively, wherein each of the formulas includes one or more variables and one or more relations of the one or more variables, respectively;
However, Fuka teaches converting each of the formula sentences into a corresponding formula, respectively, wherein each of the formulas includes one or more variables and one or more relations of the one or more variables, respectively (FIG. 6, 608);
Park in view of Fuka are considered to be analogous to the claimed invention because both are the same field of education software and services. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of extracting a semantic distance of Park with the technique of converting formula sentences into a corresponding formula by Fuka in order to improve a system, method, and computer program product for the assessment of students through online systems for self-practice, homework, quizzes, exams and certification tests, useful for educational testing, professional certification exams, and student practice systems taught (see Fuka [Column 1, line 42-47]).
Regarding claim 3, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 3 depends.
Additionally, Fuka teaches generating a corresponding question sentence and its answer according to the variable and its value included in each of the formulas and (FIG. 6, 610);
interacting with a student via an input device and an output device of the server according to the corresponding question sentences and the answers (FIG. 4, 408, [Column 4, line 10-13] “presenting the personal lesson plan to the student on a client device communicatively connected to the computer over a network connection”).
Regarding claim 4, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 4 depends.
Additionally, Fuka teaches after the receiving, performing image and character recognition to a graph of the mathematical question to generate one or more sentences for describing the graph in the natural language ([Column 15, line 16-24] “While part of this problem can be address using an Optical Character Recognition (OCR) engine, the higher-level of understanding of the question text requires separating math equations, chemical symbols, charts and graphs, and other question elements from the basis textual part of the question. The expert agent 422 can be configured to take the output of an OCR engine, and identify the components of the question, saving the DQT author from having to specify each portion of the question”).
Regarding claim 5, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 5 depends.
Additionally, Park teaches further comprises: before the converting, expanding a simplified one of the formula sentences to a plurality of formula sentences ([Column 3, line 39-44] ““Find the solution”, “Answer”, “Calculate”, “What is the value of”, and the like unitedly uses the action from the similarity between the following equation as solve. In addition to “solve”, there may be several actions such as Evaluate, Integrate, Differentiate, Factorize, and Expand”).
Regarding claim 6, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 6 depends.
Additionally, Park teaches further comprises: before the converting, organizing one of the formula sentences into a standard structure, which includes a subject, a verb, and an object ([Column 16, line 16-18] “receiving a composite sentence including at least one of a natural word and a mathematical equation (S710), a semantic parsing process for configuring the natural word and the mathematical equation”).
Regarding claim 7, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 7 depends.
Additionally, Park teaches further comprises: before the converting, refilling an omitted part in one of the formula sentences ([Column 17, line 16-18] “Operations may be added, replaced, changed order, and/or eliminated as appropriate, in accordance with the spirit and scope of embodiments of the disclosure”).
Regarding claim 8, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 8 depends.
Additionally, Park teaches further comprises: before the converting, inferencing a noun referred by a demonstrative pronoun in one of the formula sentences ([Column 26, line 32-36] “Based on these rules, the ESS module 1208 can make an inference of what is drawn and determine whether the student's drawing represents the function given in the question and hence whether the student has correctly answered the question asked”).
Regarding claim 9, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 9 depends.
Additionally, Park teaches wherein the converting further comprises: looking up one of sentence types corresponding to one of the formula sentences in a database, wherein the database includes information of the sentence types (FIG. 4, Left Column (Type));
and composing the corresponding formula based on a quantity and a variable in the one of the formula sentences according to a formula pattern corresponding to the one of the sentence types corresponding to the one of the formula sentences (FIG. 4, Middle Column (Expression)).
Regarding claim 10, Park in view of Fuka teaches all of the limitations of claim 9, upon which claim 10 depends.
Additionally, Park teaches wherein the converting further comprises: composing the corresponding formula based on the quantity and the variable implicitly denoted in the one of the formula sentences according to an entailment formula pattern corresponding to the one of the sentence types corresponding to the one of the formula sentences (FIG. 8, [Column 8, line 4-5] “FIG. 8 is a diagram of Boolean values set for each semantic element for indexed mathematical sentences” examiner interprets semantic elements as the formula pattern).
Regarding claim 15, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 15 depends.
Additionally, Park teaches finding out one or more peripheral formulas of the formula relationship map, wherein the value of the variable corresponding to the peripheral formula is known ([Column 5, line 27-31] “the semantic parsing unit 120 may convert a mathematical equation into a tree type, perform a traversal process on the mathematical equation converted into the tree type, and perform tokenization on the mathematical equation suffering from the traversal process”); and
recursively calculating the value of the variable of the formula adjacent to the formula including the calculated value of the variable ([Column 6, line 39-42] “the terms “Find the root of”, “Find the solution”, “Answer”, “Calculate”, “What is the value of”, and the like unitedly uses the action from the similarity between the following equation as solve”).
Regarding claim 16, Park in view of Fuka teaches all of the limitations of claim 15, upon which claim 16 depends.
Additionally, Park teaches after the recursively calculating, when at least one value of the variables included in the formula relationship map is unknown, performing a step of variable elimination to solve simultaneous equations to calculate all values of the variables included in the formula relationship map ([Column 13, line 7-13] “when the Boolean values are set to each semantic element of each mathematical sentence for a polynomial, a function, a factor, a solve solving the problem, an evaluate solving a value, the number of variables, a degree, and the like, in the mathematical sentence, all the mathematical sentences may be represented by the Boolean vector representing the semantic elements as illustrated in FIG. 8”).
Regarding claim 17, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 17 depends.
Additionally, Park teaches a server of machine solving mathematical question, comprising a processor configured for executing multiple instructions stored in a non-volatile memory to fulfill the method recited in claim 1 ([Column 2, line 11-19] “at least one embodiment of the present disclosure provides a non-transitory computer-readable recording medium storing therein a program including computer-executable instructions which, when executed by a processor, cause the processor to perform each process of a method for extracting a semantic distance from a mathematical sentence and classifying the mathematical sentence by the semantic distance”)
Regarding claim 18, Park in view of Fuka teaches all of the limitations of claim 17, upon which claim 18 depends.
Additionally, Fuka teaches an input device for receiving the mathematical question; and an output device for outputting the answer (FIG. 1, 128).
Regarding claim 19, Park in view of Fuka teaches all of the limitations of claim 17, upon which claim 19 depends.
Additionally, Fuka teaches a user computer, configured for transmitting the mathematical question to the server via a network and for receiving the answer from the server via the network (FIG. 1, 14).
Claims 2 and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Fuka as shown in claim 1, in further view of Wang et al. US 20230342348 A1 (hereinafter Wang).
Regarding claim 2, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 2 depends.
Park in view of Fuka fails to teach determining whether each of the formulas is satisfied according to the value of the variables included in the formula relationship map; and determining the answer is wrong when at least one of the formulas is no satisfied.
However, Wang teaches determining whether each of the formulas is satisfied according to the value of the variables included in the formula relationship map; and determining the answer is wrong when at least one of the formulas is no satisfied ([0113] “comparing the neural network output with the associated target with a loss function, computing the gradient of the loss function with respect to the edge 804 values, and updating the edge 804 values with a step guided by the gradient, is repeated until a termination criterion is reached”).
Park in view of Fuka in view of Wang are considered to be analogous to the claimed invention because all are the same field of education software and services. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of extracting educational content of Park in view of Fuka with the technique of having formulas satisfied according to values in a relationship map by Wang in order to process formulae, which includes encoding a formula and generating one or more second formula candidates (see Wang [0006]).
Regarding claim 11, Park in view of Fuka teaches all of the limitations of claim 9, upon which claim 11 depends.
Park in view of Fuka fails to teach classifying the one of the formula sentences according to a trained neural network model for looking up the one of the sentence types corresponding to the one of the formula sentences.
However, Wang teaches classifying the one of the formula sentences according to a trained neural network model for looking up the one of the sentence types corresponding to the one of the formula sentences ([0075] “Step 101 includes training, with a server, a model by using a machine learning algorithm with a data set that includes a plurality of formulae. A machine learning algorithm is used to train a model on a dataset that includes multiple formulae”).
Park in view of Fuka in view of Wang are considered to be analogous to the claimed invention because all are the same field of education software and services. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of extracting educational content of Park in view of Fuka with the technique of training a neural network by classifying formula sentences by Wang in order to process formulae, which includes encoding a formula and generating one or more second formula candidates (see Wang [0006]).
Regarding claim 12, Park in view of Fuka teaches all of the limitations of claim 1, upon which claim 12 depends.
Park in view of Fuka fails to teach wherein the converting further comprises: inferencing semantically one of the formula sentences according to a knowledge map; and composing the corresponding formula based on a quantity included in the one of the formula sentences and a hidden variable inferenced semantically.
However, Wang teaches wherein the converting further comprises: inferencing semantically one of the formula sentences according to a knowledge map ([0030] “a new method for processing mathematical formula representations using tree embeddings. By representing each symbolic formula (such as math equation) as an operator tree, one can explicitly capture its inherent structural and semantic properties”); and composing the corresponding formula based on a quantity included in the one of the formula sentences and a hidden variable inferenced semantically (FIG. 2, [0076] “the model may be trained to predict an embedding vector for each node in a given formula tree”; [0118] “Each hidden layer applies a non-linear transformation to the input features, which allows the model to learn complex patterns and relationships between different parts of the formula tree.”).
Park in view of Fuka in view of Wang are considered to be analogous to the claimed invention because all are the same field of education software and services. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the techniques of extracting educational content of Park in view of Fuka with the technique of inferencing formula sentences according to knowledge map by Wang in order to process formulae, which includes encoding a formula and generating one or more second formula candidates (see Wang [0006]).
Regarding claim 13, Park in view of Fuka in view of Wang teaches all of the limitations of claim 12, upon which claim 13 depends.
Additionally, Wang teaches classifying the one of the formula sentences according to a trained neural network model for finding out a question type of the one of the formula sentences to get an implicit formula and the hidden variable corresponding to the question type ([0075] “A machine learning algorithm is used to train a model on a dataset that includes multiple formulae. The goal of the training process is to learn how to encode formulae into vector representations that capture their semantic meaning. the trained model captures both local and global dependencies between nodes in the tree format for accurate encoding of formulas”; [0048] “This vector representation captures both semantic and structural information about the formula and can be used for various downstream tasks such as retrieval or classification.”; [0028] “mathematical expressions often involve a large number of variables and constants that must be defined and referenced throughout the equation, adding to the complexity”).
Regarding claim 14, Park in view of Fuka in view of Wang teaches all of the limitations of claim 1, upon which claim 14 depends.
Additionally, Wang teaches wherein the establishing further comprises: unifying all of the variables with same essence to reduce a quantity of variables ([0114] “the nodes 802 represent variables, math operators and numbers.”); and
connecting nodes of the formula relationship map with an edge referred to a common one of the variables, wherein the connected nodes are corresponding to the formula sentences including the common one of the variables, respectively ([0055] “Each edge between nodes represents a relationship between the operator and operand. In some embodiments, a leaf node is a node in the generated formula tree that has no child nodes. Specifically, a leaf node represents a basic building block of the formula, such as a variable or constant”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Amare (US 20130042197 A1) teaches a chemistry and physics calculator for helping a user to solve chemistry and physics problems. The calculator does more than calculating numbers or solving equations; rather, the calculator includes an adaptable menu and sub-menu system that helps a user analyze a problem, determine the type of the problem, and helps a user choose equations that are needed to solve the problem. The calculator includes at least some of these topics: balancing equations, stoichiometry, gas laws, equilibrium, dimension analysis, electrochemistry, electricity, Newton laws, thermodynamics, properties of matter, mirrors and lenses, Ohm's law, and Kirchhoff's Law. Additionally, the calculator prompts users to input units for variables, performs unit analysis, and displays results with units. The invention can be implemented as a handheld calculator, as a computer program, or as a program for a handheld device such as a smart phone.
Crouse et al. (US 20170084197 A1) teaches systems and methods of automatically distilling concepts from math problems and dynamically constructing and testing the creation of math problems from a collection of math concepts comprising: providing a user interface to a user; receiving as input: a math problem; one or more math concepts; and/or a user data packet; extracting and compiling a concept cloud of one or more CLIs that comprise the mathematical concepts embodied in the input, describe the operation of the one or more math concepts, or relate to the UDP, respectively; generating one or more math problem building blocks from the concept cloud CLIs; applying a mathematical rules engine to the one or more math problem building blocks to build one or more additional math problems; and returning to the user, through the user interface, the one or more additional math problems built from the CLIs that define the concept cloud extracted from the input.
Mungi et al. (US 20170161261 A1) teaches a method providing an answer to an input question containing at least one time-sensitive word or at least one time-sensitive phrase using natural language processing (NLP) is provided. The method may include receiving the input question. The method may also include performing natural language processing (NLP) analysis on the input question to extract a required value phrase. The method may further include forming at least one mathematical equation based on the extracted required value phrase. Additionally, the method may include forming at least one interim question based on the extracted required value phrase. The method may further include solving the at least one formed mathematical equation and the at least one formed interim question. The method may also include narrating the answer to the input question in natural language based on the solved at least one interim question or the solved at least one mathematical equation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEESHAN SHAIKH whose telephone number is (703)756-1730. The examiner can normally be reached Monday-Friday 7:30AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZEESHAN MAHMOOD SHAIKH/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
Prosecution Insights