DETAILED ACTION
This action is in response to the Applicant Response filed 03 March 2026 for application 18/023,623 filed 27 February 2023.
Claim(s) 1-10 is/are currently amended.
Claim(s) 11 is/are new.
Claim(s) 1-11 is/are pending.
Claim(s) 1-11 is/are rejected.
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 .
Response to Arguments
Applicant's arguments regarding the objections to the claims have been fully considered and, in light of the amendments to the claims, are persuasive. However, in light of the amendments to the claims, new claim objections have arisen, as noted below.
Applicant's arguments regarding the 35 U.S.C. 112(b) rejection(s) of claim(s) 2-9 have been fully considered and, in light of the amendments to the claims, are partially persuasive. However, the 35 U.S.C. 112(b) rejections of claims 2-8 are maintained.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 101 rejection of the claims below.
Applicant’s arguments regarding the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims below.
Claim Objections
Claim(s) 1-11 is/are objected to because of the following informalities:
Claim 1, line 20, one or more words consecutive words should read “one or more consecutive words”
Claim 7, line 12, the one or more mathematical concepts should read “the one or more
Claim 9, lines 4-5, at least Superscript, subscript invariant string matching should read “at least superscript or subscript invariant string matching”
Claims 2-11 are objected to due to their dependence, either directly or indirectly, on claims 1, 7, 9
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the knowledge base included in the system while failing to provide a proper antecedent basis for “the knowledge base.” It is suggested that the phrase be amended to recite “the knowledge database.” Correction or clarification is required.
Claim 7 recites the machine-readable format while failing to provide a proper antecedent basis for the term. It is suggested that the claim be amended to indirectly depend from claim 3 by amending claim 6 to depend from claim 3, 4, or 5. Correction or clarification is required.
Claims 3-8 are rejected under 35 U.S.C. 112(b) due to their dependence, either directly or indirectly, on claims 2, 7.
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.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents.
The limitation of extracting ... one or more mathematical formulae from the one or more technical documents, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of identifying ... one or more concepts and one or more variables associated with each concept from the extracted one or more mathematical formulae, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determining ... interdependencies between the identified one or more variables in the extracted one or more mathematical formulae for linking the identified one or more variables, based on the identified one or more variables and the one or more concepts associated with the one or more variables, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating a knowledge graph subject to the generated knowledge graph being configured to be stored in a graph database disposed within a computing device, wherein the knowledge graph includes: (i) a linking of the one or more variables with the associated one or more concepts and (ii) a respective mathematical formula of the one or more mathematical formulae configured to calculate each variable that is linked to an associated concept, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – knowledge database, knowledge management module, one or more processing units, graph database, computing device. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – knowledge graph. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites storing the generated knowledge graph, as configured, in the graph database; searching, using a graph query, the stored knowledge graph, to find at least one mathematical formula associated with a specified concept, which is simply storing and retrieving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites wherein each concept is one or more words consecutive words, and wherein each mathematical formula is an equation having a form of A = B, wherein A is a variable appearing on the left hand side of the mathematical formula, and wherein B is a mathematical expression which is simply additional information regarding the concepts and formulae, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
knowledge database, knowledge management module, one or more processing units, graph database, computing device amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
storing and retrieving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of storing and retrieving information in memory (MPEP 2016.05(d))
knowledge graph amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the concepts and formulae do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents.
The limitation of ... performs the function of extracting the one or more mathematical formulae, identifying the one or more variables associated with the one or more concepts and determining interdependencies between the identified one or more variables so as to create one or more entities that are interconnected to each other in a graph-based data model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of wherein, by creating the graph-based data model including the one or more entities representing the one or more mathematical formulae, ... converts the one or more mathematical formulae into searchable objects, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – knowledge base, system. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – graph-based data model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites stores the searchable objects in the knowledge base included in a system, which is simply storing data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites wherein the one or more entities include at least one or more of: a concept entity to capture information related to the identified one or more concepts; a variable entity to capture information related to each of the identified one or more variables associated with the one or more concepts; a formula entity to capture information related to each of the extracted one or more mathematical formulae; a first relationship entity to capture information related to interconnection between the identified one or more concepts and the identified one or more variables associated with the one or more concepts, and a second relationship entity to identify interconnection between the formula entity with one or more variable entities with respect to the respective mathematical formula which is simply additional information regarding the entities, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
knowledge base, system amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
storing data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of storing and retrieving information in memory (MPEP 2016.05(d))
graph-based data model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the entities do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents.
The limitation of ... identify one or more formulae regions in the one or more technical documents, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of convert the one or more formulae regions into machine readable format, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – machine learning model, neural network. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein for extracting the one or more mathematical formulae from the one or more technical documents, the knowledge management module is executable by the one or more processing units to implement a machine learning model that uses a neural network ... which is simply applying a model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
The claim recites wherein the machine learning model trains the neural network on a set of annotated images of the one or more technical documents to identify both block formulae and inline formulae which is simply generic training to perform the abstract idea of data identification and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites wherein the one or more formulae regions are regions in the one or more technical documents that contain the one or more mathematical formulae, and
wherein the one or more formulae regions include at least one of an inline formula and a block formula, and wherein the inline formulae refer to the one or more mathematical formulae or the one or more variables that are part of natural language text lines in the one or more technical documents, and the block formulae refer to the one or more mathematical formulae that are separately written in blocks including between paragraphs of text which is simply additional information regarding the formulae regions, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
applying a model and generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
machine learning model, neural network amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the formulae regions do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents.
The limitation of convert a machine-readable format of the one or more formulae regions into a mathematical vector representation using flags, wherein each word in the one or more formulae regions is represented in the mathematical vector representation by an aggregation of three components including: a type flag for flagging a concept to each word in the one or more formulae regions; a variable flag for flagging a variable to each word in the one or more formulae regions; and a word embedding of constituent words in the one or more formulae regions, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of ... classify an edge between words in the one or more concepts indicating whether the edge relates the two words together or not, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – classification model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein a classification model is further implemented by the knowledge management module ... which is simply applying a model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
classification model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents.
The limitation of identify all variables occurring inside each of the one or more formulae regions which is in the machine readable format, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of use the identified one or more concepts to identify relations between the variables, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of input the identified variables and the one or more concepts to a string-matching module that links the identified variables with the identified one or more concepts and in turn with the extracted one or more mathematical formulae, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – string-matching module. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
string-matching module amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents. The Step 2A Prong One Analysis for claim 5 is applicable here since claim 6 carries out the method of claim 5 but for the recitation of additional element(s) of communicating with a client device communicating via a network for the client device to search through the knowledge database and to obtain the one or more mathematical formulae, related to the one or more concepts, stored in the knowledge database; providing the graph-based data model to the client device for obtaining the one or more mathematical formulae related to the one or more concepts, stored in the knowledge database; visually representing the graph-based data model at a Graphical User Interface of a system or the client device; and wherein the knowledge management module receives the one or more technical documents from at least one of the client device communicating with the knowledge management module via the network, a web source, a node residing on the network, or a system in the network, individually or in any combination.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – client device, network, Graphical User Interface, system, web source, node residing on the network, system in the network. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites communicating with a client device communicating via a network for the client device to search through the knowledge database and to obtain the one or more mathematical formulae, related to the one or more concepts, stored in the knowledge database; providing the graph-based data model to the client device for obtaining the one or more mathematical formulae related to the one or more concepts, stored in the knowledge database; visually representing the graph-based data model at a Graphical User Interface of a system or the client device; and wherein the knowledge management module receives the one or more technical documents from at least one of the client device communicating with the knowledge management module via the network, a web source, a node residing on the network, or a system in the network, individually or in any combination, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
client device, network, Graphical User Interface, system, web source, node residing on the network, system in the network amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents.
The limitation of conversion of a machine-readable format of the one or more formulae regions into a mathematical vector representation using flags, wherein each word in the one or more formulae regions is represented in the mathematical vector representation by an aggregation of three components including: a type flag for flagging a concept to each word in the one or more formulae regions; a variable flag for flagging a variable to each word in the one or more formulae regions; and a word embedding of constituent words in the one or more formulae regions, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of ... classify an edge between two words with variable tags to identify the one or more variables related to the one or more mathematical concepts, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – classification model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites a classification model is implemented by the knowledge management module ... which is simply applying a model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
classification model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents. The Step 2A Prong One Analysis for claim 7 is applicable here since claim 8 carries out the method of claim 7 but for the recitation of additional element(s) of wherein to identify the one or more concepts, present in the one or more formulae regions, a list of keywords as potential concepts is used by the knowledge management module, and wherein the classification model is a Convolutional Neural Network classifier.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein to identify the one or more concepts, present in the one or more formulae regions, a list of keywords as potential concepts is used by the knowledge management module, and wherein the classification model is a Convolutional Neural Network classifier which is simply additional information regarding the mathematical concepts and the classification model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – Convolutional Neural Network classifier. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
Convolutional Neural Network classifier amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the mathematical concepts and the classification model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents.
The limitation of ... apply one or more heuristic approaches, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the one or more heuristic approaches include at least Superscript, subscript invariant string matching, which is simply heuristics and the variables recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
additional information regarding the heuristics and the variables do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a system with a processing unit, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for building a knowledge base for mathematical formulae present in one or more technical documents. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 10 carries out the method of claim 1 but for the recitation of additional element(s) of a system for building a knowledge base for the one or more mathematical formulae present in one or more technical documents, comprising: one or more processing units; a memory coupled to the one or more processing units for execution of one or more machine-readable instructions; and a knowledge management module stored in the memory, and wherein, upon execution of the one or more machine-readable instructions, by the one or more processing units, causes the knowledge management module to perform the method steps of claim 1.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – system, one or more processing units, memory, one or more machine-readable instructions, knowledge management module. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – knowledge base. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of:
system, one or more processing units, memory, one or more machine-readable instructions, knowledge management module amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
knowledge base amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for building a knowledge database for mathematical formulae present in one or more technical documents. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 11 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the knowledge graph comprises multiple nodes and edges between adjacent nodes of the multiple nodes, and wherein the multiple nodes consist of one or more concept nodes, one or more variable nodes, and one or more formula nodes, wherein each concept node represents one concept of the one or more concepts and is directly connected at least one formula node of the one or more formula nodes, wherein each formula node represents one mathematical formula of the one or more mathematical formulae, and wherein each variable node represents a variable appearing on the left hand side of a mathematical formula represented by a formula node to which said each variable node is directly connected.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the graph and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: 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 element(s) of additional information regarding the graph do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (Fundamental Study on Structural Understanding of Mathematical Expressions, hereinafter referred to as “Chen”).
Regarding claim 1 (Currently Amended), Chen teaches a method for building a knowledge database for mathematical formulae present in one or more technical documents (Chen, section 1 – teaches setting up a database of mathematical formulae from scientific papers), said method comprising:
extracting, by a knowledge management module executable by one or more processing units (Chen, section 1 – teaches generating a database of formulae on a computer; Chen, section 4 - teaches using a computer), one or more mathematical formulae from the one or more technical documents (Chen, section 2 – teaches extracting mathematical formulae from the document; see also Chen, Figures 1-2);
identifying, by the knowledge management module, one or more concepts and one or more variables associated with each concept from the extracted one or more mathematical formulae (Chen, section 2 - teaches character recognition which identifies operators and variables wherein the operators can include concepts such as natural logarithm, sin functions and fractions; see also Chen, Figures 1-2; Chen sections 3-4 – teaches concepts such as integration and trigonometric functions);
determining, by the knowledge management module, interdependencies between the identified one or more variables in the extracted one or more mathematical formulae for linking the identified one or more variables, based on the identified one or more variables and the one or more concepts associated with the one or more variables (Chen, section 2 – teaches determining a layout tree and a semantic tree for the formula which identifies the interdependencies between variables and concepts of the formulae; see also Chen, Figures 1-2);
generating a knowledge graph (Chen, section 2 – teaches determining a layout tree and a semantic tree for the formula which identifies the interdependencies between variables and concepts of the formulae; see also Chen, Figures 1-2) subject to the generated knowledge graph being configured to be stored in a graph database disposed within a computing device (Chen, section 3 – configuring the knowledge graph for storage in a computer database), wherein the knowledge graph includes: (i) a linking of the one or more variables with the associated one or more concepts (Chen, section 2 – teaches determining a layout tree and a semantic tree for the formula which identifies the interdependencies between variables and concepts of the formulae; see also Chen, Figures 1-2) and (ii) a respective mathematical formula of the one or more mathematical formulae configured to calculate each variable that is linked to an associated concept (Chen, section 2 – teaches determining a layout tree and a semantic tree for the formula which identifies the interdependencies between variables and concepts of the formulae; see also Chen, Figures 1-2);
storing the generated knowledge graph, as configured, in the graph database (Chen, section 1 – teaches a database of mathematical expressions for search by students and researchers [searching the database requires storage in the database]; see also Chen, section 3); and
searching, using a graph query, the stored knowledge graph, to find at least one mathematical formula associated with a specified concept (Chen, section 1 – teaches a database of mathematical expressions for search by students and researchers),
wherein each concept is one or more words consecutive words (Chen, section 2 - teaches character recognition which identifies operators and variables wherein the operators can include concepts such as natural logarithm, sin functions and fractions; see also Chen, Figures 1-2; Chen sections 3-4 – teaches concepts such as integration and trigonometric functions), and
wherein each mathematical formula is an equation having a form of A = B, wherein A is a variable appearing on the left hand side of the mathematical formula, and wherein B is a mathematical expression (Chen, section 4 – teaches A=B format; see also Chen Figure 5).
Regarding claim 2 (Currently Amended), Chen teaches all of the limitations of the method of claim 1 as noted above. Chen further teaches
wherein the knowledge management module executable by one or more processing units performs the function of extracting the one or more mathematical formulae (Chen, section 1 – teaches generating a database of formulae extracted on a computer; Chen, section 4 - teaches using a computer), identifying the one or more variables associated with the one or more concepts and determining interdependencies between the identified one or more variables so as to create one or more entities that are interconnected to each other in a graph-based data model (Chen, section 2 – teaches determining a layout tree and a semantic tree for the formula which identifies the interdependencies between variables and concepts of the formulae; see also Chen, Figures 1-2),
wherein the one or more entities include at least one or more of:
a concept entity to capture information related to the identified one or more concepts (Chen, section 2, Figure 2 – teaches the entities represented as nodes in a graph symbolizing variables and operators, including natural log and trig functions [concepts]);
a variable entity to capture information related to each of the identified one or more variables associated with the one or more concepts (Chen, section 2, Figure 2 – teaches the entities represented as nodes in a graph symbolizing variables and operators);
a formula entity to capture information related to each of the extracted one or more mathematical formulae (Chen, section 2, Figure 2 – teaches the entities represented as nodes in a graph symbolizing variables and operators [concepts] identified in a formula);
a first relationship entity to capture information related to interconnection between the identified one or more concepts and the identified one or more variables associated with the one or more concepts (Chen, section 2, Figure 2 – teaches the entities represented as nodes in a graph symbolizing variables and operators [concepts] and edges showing the relationships of variables and operators), and
a second relationship entity to identify interconnection between the formula entity with one or more variable entities with respect to the respective mathematical formula (Chen, section 2, Figure 2 – teaches the entities represented as nodes in a graph symbolizing variables and operators [concepts] and edges showing the relationships of variables and operators), and
wherein, by creating the graph-based data model including the one or more entities representing the one or more mathematical formulae, the knowledge management module converts the one or more mathematical formulae into searchable objects and stores the searchable objects in the knowledge base included in a system (Chen, section 1 – teaches allowing searching of a database of mathematical expressions; Chen, section 2 – teaches translating to source code and script code; see also Chen, Figure 1).
Regarding claim 9 (Currently Amended), Chen teaches all of the limitations of the method of claim 1 as noted above. Chen further teaches
wherein the knowledge management module is further configured to apply one or more heuristic approaches (Chen, sections 2-3 – teaches using rules [heuristics] which improve the accuracy of the formula recognition),
wherein the one or more heuristic approaches include at least Superscript, subscript invariant string matching (Chen, section 3 – teaches rules involving superscript and subscript and string matching).
Regarding claim 10 (Currently Amended), it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Chen further teaches a system for building a knowledge base for the one or more mathematical formulae present in one or more technical documents (Chen, section 1 – teaches setting up a database of mathematical formulae from scientific papers; see also Chen, Figure 1), comprising:
one or more processing units (Chen, section 1 – teaches generating a database of formulae on a computer; Chen, section 4 - teaches using a computer);
a memory coupled to the one or more processing units for execution of one or more machine-readable instructions (Chen, section 1 – teaches generating a database of formulae on a computer; Chen, section 4 - teaches using a computer); and
a knowledge management module stored in the memory, and wherein, upon execution of the one or more machine-readable instructions, by the one or more processing units, causes the knowledge management module to perform the method steps of claim 1 (Chen, section 1 – teaches generating a database of formulae on a computer; Chen, section 4 - teaches using a computer).
Claim(s) 3-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Lin et al. (Mathematical Formula Identification and Performance Evaluation in PDF Documents, hereinafter referred to as “Lin”).
Regarding claim 3 (Currently Amended), Chen teaches all of the limitations of the method of claim 2 as noted above. However, Chen does not explicitly teach wherein for extracting the one or more mathematical formulae from the one or more technical documents, the knowledge management module is executable by the one or more processing units to implement a machine learning model that uses a neural network to identify one or more formulae regions in the one or more technical documents, wherein the one or more formulae regions are regions in the one or more technical documents that contain the one or more mathematical formulae, and wherein the one or more formulae regions include at least one of an inline formula and a block formula, and wherein the inline formulae refer to the one or more mathematical formulae or the one or more variables that are part of natural language text lines in the one or more technical documents, and the block formulae refer to the one or more mathematical formulae that are separately written in blocks including between paragraphs of text; convert the one or more formulae regions into machine readable format; and wherein the machine learning model trains the neural network on a set of annotated images of the one or more technical documents to identify both block formulae and inline formulae.
Lin teaches
wherein for extracting the one or more mathematical formulae from the one or more technical documents (Lin, section 3.2 teaches extracting mathematical formulas from PDF documents), the knowledge management module is executable by the one or more processing units (Lin, section 4.1 - teaches using software datasets; Lin, Acknowledgements - teaches using open source software) to implement a machine learning model that uses a neural network to identify one or more formulae regions in the one or more technical documents (Lin, section 3.3 – teaches using machine learning, including MLP [neural network] to identify text lines [formula regions] as being isolated [block] or embedded [inline]),
wherein the one or more formulae regions are regions in the one or more technical documents that contain the one or more mathematical formulae (Lin, section 3.3 – teaches using machine learning, including MLP [neural network] to identify text lines [formula regions] as being isolated [block] or embedded [inline]), and
wherein the one or more formulae regions include at least one of an inline formula and a block formula (Lin, section 3.3 – teaches using machine learning, including MLP [neural network] to identify text lines [formula regions] as being isolated [block] or embedded [inline]), and
wherein the inline formulae refer to the one or more mathematical formulae or the one or more variables that are part of natural language text lines in the one or more technical documents (Lin, section 3.1 – teaches embedded formulae are embedded in lines of text), and the block formulae refer to the one or more mathematical formulae that are separately written in blocks including between paragraphs of text (Lin, section 3.1 – teaches isolated formulae comprise the entire text line);
convert the one or more formulae regions into machine readable format (Lin, section 3.3.2 – teaches converting text lines into feature vectors for input into isolated formula classifier; Lin, section 3.4.2 – teaches converting text lines into feature vectors for input into embedded formula classifier); and
wherein the machine learning model trains the neural network on a set of annotated images of the one or more technical documents to identify both block formulae and inline formulae (Lin, section 3.3.3.2 – teaches training on imbalanced dataset; see also Lin, section 4.1).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Chen with the teachings of Lin in order to correctly identify the formulas in documents in the field of formula identification in technical documents (Lin, Abstract – “An important initial step of mathematical formula recognition is to correctly identify the location of formulae within documents. Previous work in this area has traditionally focused on image-based documents; however, given the prevalence and popularity of the PDF format for dissemination, alternatives to image-based approaches are increasingly being explored. In this paper, we investigate the use of both machine learning techniques and heuristic rules to locate the boundaries of both isolated and embedded formulae within documents, based upon data extracted directly from PDF files. We propose four new features along with preprocessing and post-processing techniques for isolated formula identification. Furthermore, we compare, analyse and extensively tune nine state-of-the-art learning algorithms for a comprehensive evaluation of our proposed methods. The evaluation is carried out over a ground-truth dataset, which we have made publicly available, together with an application adaptable fine-grained evaluation metric. Our experimental results demonstrate that the overall accuracies of isolated and embedded formula identification are increased by 11.52 and 10.65%, compared with our previously proposed formula identification approach.”).
Regarding claim 4 (Currently Amended), Chen in view of Lin teaches all of the limitations of the method of claim 3 as noted above. Lin further teaches wherein for the identifying of the one or more concepts and the one or more variables associated with the one or more concepts (Lin, section 3.4 – teaches identifying embedded mathematical formulae), the knowledge management module is executable by the one or more processing units (Lin, section 4.1 - teaches using software datasets; Lin, Acknowledgements - teaches using open source software) in the method to:
convert a machine-readable format of the one or more formulae regions into a mathematical vector representation (Lin, section 3.4.2 – teaches converting text lines into feature vectors for input into embedded formula classifier) using flags (Lin, section 3.4.2 – teaches feature vector including 12 feature elements [flags]; see also Lin, Table 4),
wherein each word in the one or more formulae regions is represented in the mathematical vector representation by an aggregation of three components (Lin, section 3.4 – teaches generating features for each word; Lin, section 3.4.2 – teaches feature vector including 12 feature elements [flags]; see also Lin, Table 4) including:
a type flag for flagging a concept to each word in the one or more formulae regions (Lin, section 3.4.2 – teaches a flag for math entities and for leftmost and rightmost symbol types);
a variable flag for flagging a variable to each word in the one or more formulae regions (Lin, section 3.4.2 – teaches a flag for math entities including Greek letters and for leftmost and rightmost symbol types including operand domains); and
a word embedding of constituent words in the one or more formulae regions (Lin, section 3.4.2 – teaches symbols of previous and subsequent words), and
wherein a classification model is further implemented by the knowledge management module to classify an edge between words in the one or more concepts indicating whether the edge relates the two words together or not (Lin, section 3.4 – teaches machine learning to identify embedded formula fragments and merge regions of successive embedded formula fragments).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Chen and Lin in order to generate feature vectors to correctly identify the formulas in documents (Lin, Abstract).
Regarding claim 5 (Currently Amended), Chen in view of Lin teaches all of the limitations of the method of claim 3 as noted above. Lin further teaches wherein for determining the interdependencies between the identified one or more variables in the extracted one or more mathematical formulae (Lin, section 3.4 – teaches machine learning to identify embedded formula fragments and merge regions of successive embedded formula fragments), the knowledge management module is executable by the one or more processing units (Lin, section 4.1 - teaches using software datasets; Lin, Acknowledgements - teaches using open source software) in the method to:
identify all variables occurring inside each of the one or more formulae regions which is in the machine readable format (Lin, section 3.4 – teaches identifying embedded formulas [including variables] from vector representations [machine readable format] for each text line [formula region]);
use the identified one or more concepts to identify relations between the variables (Lin, section 3.4 – teaches machine learning to identify embedded formula fragments and merge regions of successive embedded formula fragments; see also Lin, section 3.4.2 – teaches relationships between words);
input the identified variables and the one or more concepts to a string-matching module that links the identified variables with the identified one or more concepts (Lin, section 3.4.2 – teaches string matching mathematical entities with the mathematical entities dictionary) and in turn with the extracted one or more mathematical formulae (Lin, section 3.4 – teaches machine learning to identify embedded formula fragments and merge regions of successive embedded formula fragments based on the vector representations).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Chen and Lin in order to identify interdependencies to correctly identify the formulas in documents (Lin, Abstract).
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Lin and further in view of Zanibbi et al. (Math Search for the Masses: Multimodal Search Interfaces and Appearance-Based Retrieval, hereinafter referred to as “Zanibbi”).
Regarding claim 6 (Currently Amended), Chen in view of Lin teaches all of the limitations of the method of claim 2 [Interpreted as depending from claims 3, 4, or 5 in light of the 35 U.S.C. 112(b) rejection of claim 7] as noted above.
Chen further teaches
communicating with a client device communicating via a network for the client device to search through the knowledge database and to obtain the one or more mathematical formulae, related to the one or more concepts, stored in the knowledge database (Chen, section 1 – teaches a database of mathematical expressions for search by students and researchers [client device]);
wherein the knowledge management module receives the one or more technical documents from at least one of the client device communicating with the knowledge management module via the network, a web source, a node residing on the network, or a system in the network, individually or in any combination (Chen, section 1 – teaches scanning the documents into the computer).
Lin further teaches
wherein the knowledge management module receives the one or more technical documents from at least one of the client device communicating with the knowledge management module via the network, a web source, a node residing on the network, or a system in the network, individually or in any combination (Lin, section 4.1 – teaches acquiring documents by crawling CiteSeerX site).
While Chen in view of Lin teaches document acquisition and reuse of the extracted formulae by other users, Chen in view of Lin does not explicitly teach provide the graph-based data model to the client device for obtaining one or more mathematical formulae related to a mathematical concept, stored in the knowledge database; visually represent the graph-based data model at a Graphical User Interface of a system or the client device.
Zanibbi teaches
communicating with a client device communicating via a network for the client device to search through the knowledge database and to obtain the one or more mathematical formulae, related to the one or more concepts, stored in the knowledge database (Zanibbi, section 4 – teaches users interacting with search engines to search for mathematical formulae);
providing the graph-based data model (Zanibbi, section 1 - teaches presenting spatial layout of mathematical formulae for visualization; Zanibbi, section 3.2 - teaches displaying the symbol layout tree; see also Zanibbi, Fig. 1, section 2) to the client device for obtaining the one or more mathematical formulae related to the one or more concepts, stored in the knowledge database (Zanibbi, section 3 - teaches a graphical user interface for mathematical equation recognition and parsing; Zanibbi, section 4 - teaches search engines [databases] used for searching equations; see also Zanibbi, Figs. 2-5);
visually representing the graph-based data model (Zanibbi, section 1 - teaches presenting spatial layout of mathematical formulae for visualization; Zanibbi, section 3.2 - teaches displaying the symbol layout tree; see also Zanibbi, Fig. 1, section 2) at a Graphical User Interface of a system or the client device (Zanibbi, section 3 - teaches a graphical user interface for mathematical equation recognition and parsing; see also Zanibbi, Figs. 2-5); and
wherein the knowledge management module receives the one or more technical documents from at least one of the client device communicating with the knowledge management module via the network, a web source, a node residing on the network, or a system in the network, individually or in any combination (Zanibbi, section 4 – teaches users interacting with search engines to search for mathematical formulae).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Chen in view of Lin with the teachings of Zanibbi in order to create a searchable interface for mathematical formulae in the field of formula identification in technical documents (Zanibbi, Abstract – “We summarize math search engines and search interfaces produced by the Document and Pattern Recognition Lab in recent years, and in particular the min math search interface and the Tangent search engine. Source code for both systems are publicly available. "The Masses" refers to our emphasis on creating systems for mathematical non-experts, who may be looking to define unfamiliar notation, or browse documents based on the visual appearance of formulae rather than their mathematical semantics.”).
Regarding claim 7 (Currently Amended), Chen in view of Lin and further in view of Zanibbi teaches all of the limitations of the method of claim 6 as noted above. Lin further teaches
wherein for the identifying of a one or more concepts one or more and the one or more variables associated with the one or more concepts (Lin, section 3.4 – teaches identifying embedded mathematical formulae), the knowledge management module is configured to perform (Lin, section 4.1 - teaches using software datasets; Lin, Acknowledgements - teaches using open source software):
conversion of a machine-readable format of the one or more formulae regions into a mathematical vector representation (Lin, section 3.4.2 – teaches converting text lines into feature vectors for input into embedded formula classifier) using flags (Lin, section 3.4.2 – teaches feature vector including 12 feature elements [flags]; see also Lin, Table 4),
wherein each word in the one or more formulae regions is represented in the mathematical vector representation by an aggregation of three components (Lin, section 3.4 – teaches generating features for each word; Lin, section 3.4.2 – teaches feature vector including 12 feature elements [flags]; see also Lin, Table 4) including:
a type flag for flagging a concept to each word in the one or more formulae regions (Lin, section 3.4.2 – teaches a flag for math entities and for leftmost and rightmost symbol types);
a variable flag for flagging a variable to each word in the one or more formulae regions (Lin, section 3.4.2 – teaches a flag for math entities including Greek letters and for leftmost and rightmost symbol types including operand domains); and
a word embedding of constituent words in the one or more formulae regions (Lin, section 3.4.2 – teaches symbols of previous and subsequent words), and
a classification model is implemented by the knowledge management module to classify an edge between two words with variable tags to identify the one or more variables related to the one or more mathematical concepts (Lin, section 3.4 – teaches machine learning to identify embedded formula fragments and merge regions of successive embedded formula fragments).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Chen, Lin and Zanibbi in order to generate feature vectors to correctly identify the formulas in documents (Lin, Abstract).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Lin, further in view of Zanibbi and further in view of Mahdavi et al. (LPGA: Line-of-Sight Parsing with Graph-based Attention for Math Formula Recognition, hereinafter referred to as “Mahdavi”).
Regarding claim 8 (Currently Amended), Chen in view of Lin and further in view of Zanibbi teaches all of the limitations of the method of claim 7 as noted above. Lin further teaches wherein to identify the one or more concepts, present in the one or more formulae regions, a list of keywords as potential concepts is used by the knowledge management module (Lin, section 3.4.2 – teaches a math entity dictionary to identify math entities).
However, Chen in view of Lin and further in view of Zanibbi does not explicitly teach wherein the classification model is a Convolutional Neural Network classifier.
Mahdavi teaches wherein the classification model is a Convolutional Neural Network classifier (Mahdavi, section III – teaches using a CNN for edge classification).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Chen in view of Lin and further in view of Zanibbi with the teachings of Mahdavi in order to reduce the search space for formula structure interpretations and to guide classification in the field of formula identification in technical documents (Mahdavi, Abstract – “We present a model for recognizing typeset math formula images from connected components or symbols. In our approach, connected components are used to construct a line-of-sight (LOS) graph. The graph is used both to reduce the search space for formula structure interpretations, and to guide a classification attention model using separate channels for inputs and their local visual context. For classification, we used visual densities with Random Forests for initial development, and then converted this to a Convolutional Neural Network (CNN) with a second branch to capture context for each input image. Formula structure is extracted as a directed spanning tree from a weighted LOS graph using Edmonds’ algorithm. We obtain strong results for formulas without grids or matrices in the InftyCDB-2 dataset (90.89% from components, 93.5% from symbols). Using tools from the CROHME handwritten formula recognition competitions, we were able to compile all symbol and structure recognition errors for analysis. Our data and source code are publicly available.”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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