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 .
DETAILED ACTION
This office action is in response to correspondence 09/04/25 regarding application 18/470,194, in which claims 1, 15, and 16 were amended and new claims 17-20 were added. Claims 1-20 are pending in the application and have been considered.
Response to Arguments
The examiner respectfully disagrees with Applicant on page 12 that no new matter is added via the amendments and newly added claims. Applicant alleges that the amendments and new claims find support at paragraphs [0006], [0061], [0069], and [0087] of the original specification, but the examiner was unable to locate support in these sections for new claims 19 and 20 in particular. See below 112(a) rejections of new claims 19 and 20 as containing new matter.
Applicant argues on pages 12-13 that the amended independent claims overcome the 35 U.S.C. 101 rejections as being directed to an abstract idea without significantly more, allegedly because the claims now recite using both semantic and syntactic parsing in the combined lexical representation, which provides a specific technical solution to a problem in computerized natural language processing which improves accuracy of word extraction.
In response, it is unclear why the claims as amended provide a specific technical solution to a problem in computerized natural language processing which improves accuracy of word extraction, or why the combined lexical representation is unique to computer processing of natural language. Consider the sentences “I ate the sushi with chopsticks” versus “I ate the sushi with wasabi”. For example, given the sentence “I ate the sushi with chopsticks”, a human could draw both semantic and syntactic graphs with a pen and paper, align them by drawing lines, consider that based the syntax, “sushi” and “chopsticks” are both candidates for what “I ate”, but further consider based on the aligned and combined graphs that “chopsticks” does not make semantic sense as a candidate for what “I ate” since chopsticks are typically made of wood and not edible. Thus by leveraging the combined semantic and syntactic representations, a human could improve accuracy of “extracting” which word from the sentence answers a query by writing down the correct word as an answer on a sheet of paper.
Regarding the 35 U.S.C. 103 rejections based on Subramanya and Hasan, Applicant argues on pages 13-14 that while Subramanya discloses linking graphs, it does not teach the specific process of aligning and combining nodes and edges. The examiner finds this argument persuasive with regard to alignment of edges, since the Fig. 2 of Subramanya aligning and combining of nodes (e.g. “Miles Davis”) combining of edges (e.g. since “Miles Davis” is linked, the edges “gender” and “album” are combined with the dependency graph edges 270 from the text graph). However, the newly discovered reference to Chen et al. (“Unsupervised AMR-Dependency Parse Alignment”. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 558–567, Valencia, Spain, April 3-7, 2017), which discloses combining and aligning nodes and edges of semantic and syntactic graphs. A new grounds for rejection based in part on Chen is made in response to Applicant’s amendments.
The examiner respectfully disagrees with Applicant’s assertion on page 14 that “Indeed, the alleged combination of the references does not even recognize the problems addressed by the claims”. Subramanya discloses extracting an entity answer from the linked graph, by following the path represented by the features, Col 13 lines 31-54, for example the linked graph in Fig. 7, which is plainly a combined syntactic-semantic lexical representation generated based on a crawled “target”, document, Col 5 lines 42-51.
On page 15, Applicant argues that the Subramanya and Hasan references are unrelated and would not have been combined without impermissible hindsight. The examiner respectfully disagrees, and maintains that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Subramanya by including a predetermined target search document in order to facilitate the user to utilize document information in a more useful manner, as suggested by Hasan (Col 1 lines 44-46). Doing so would have led to predictable results of reducing user effort and time in manually accessing information, as suggested by Hasan (Col 1 lines 33-35). The references cited are analogous art in the same field of natural language processing. In response to Applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
On page 15, regarding dependent claim 5, Applicant argues the combination of Subramanya, Hasan, and Tung fails to teach or suggest "calculates a normalized edit distance between nodes in the first lexical representation and nodes in the second lexical representation" and "identifies nodes that satisfy a predetermined normalized edit distance criterion as the shared nodes", allegedly because Tung’s edit distance calculation is not used for identifying shared nodes between different lexical representations during a combination process. In response, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Subramanya discloses wherein, in generation of the first combined lexical representation, the lexical representation generation unit: calculates a normalized distance between nodes in the first lexical representation and nodes in the second lexical representation (determining the length of paths that exist between a given entity and another entity, the paths between the data graph and text graph excluding mention edges, i.e. normalized distance of 4 or less, Col 12 lines 1-16); and identifies nodes that satisfy a predetermined normalized distance criterion as the shared nodes (those entities having linking paths between the text graph and the data graph with an adjusted, i.e. normalized, length of 4 or less, after the mention edges are excluded, Col 12 lines 1-16). Subramanya in view of Hasan and Chen does not specifically mention an edit distance. Tung discloses an edit distance (traversal path edit distance between entity nodes, [0040]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Subramanya, Hasan, and Chen by utilizing an edit distance in order to improve the entities in the knowledge graph, predictably assisting users in knowing which queries are more efficient, as suggested by Tung ([0039-0040]).
Applicant’s arguments on page 16 regarding claims 2-6 as well as new claims 17-20 are similar to those addressed above, and are not persuasive for similar reasons.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 19 and 20 rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contain new subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Claim 19 recites: “…the processor comprises specialized natural language processing circuitry configured with hardware acceleration for graph traversal operations; the lexical representation generation unit is implemented using dedicated semantic analysis hardware that automatically performs Abstract Meaning Representation parsing to generate the first lexical representation with semantic role labels and predicate-argument structures that cannot be mentally processed; the lexical representation generation unit is further implemented using dedicated syntactic analysis hardware that automatically performs dependency parsing to generate the second lexical representation with part-of-speech tags and syntactic dependency relationships; and the word extraction unit automatically optimizes extraction performance by dynamically weighting semantic versus syntactic information based on real-time parsing technique performance metrics stored in the memory” which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Claim 20 recites: “…the memory stores machine-executable graph processing algorithms that automatically calculate normalized edit distances between nodes using mathematical similarity functions that require computational processing beyond human mental capabilities; the lexical representation generation unit automatically identifies shared nodes by executing computerized similarity threshold comparisons that process multiple dimensional feature vectors representing linguistic attributes; the device automatically generates confidence scores for extracted information based on computational analysis of multiple graph matching criteria; the processor executes real-time performance monitoring algorithms that automatically adjust extraction parameters based on statistical analysis of extraction accuracy metrics; the device comprises specialized graph database circuitry configured to store and process the first combined lexical representation and second combined lexical representation using optimized graph traversal algorithms; the lexical representation generation unit automatically performs computerized node alignment operations using mathematical vector space calculations that map semantic and syntactic features into unified coordinate systems; the query representation generation unit automatically generates subgraph extraction patterns using computational pattern recognition algorithms that identify recurring structural motifs in the combined lexical representations; and the word extraction unit executes automated graph matching algorithms that perform simultaneous multi-dimensional comparisons of node attributes and edge relationships using computational resources that exceed human cognitive processing capabilities” which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Each of these claims are directed to an abstract idea without significantly more.
Illustratively, independent method claim 16 recites “a step of acquiring training data that includes sentences in which target extraction words are specified; a step of generating a first lexical representation by processing the training data with a first parsing technique; a step of generating a second lexical representation by processing the training data with a second parsing technique; a step of generating a first combined lexical representation by combining the first lexical representation and the second lexical representation; a step of generating, based on the first combined lexical representation, an extraction query representation that indicates a query for extracting the target extraction words from a predetermined target search document; and a step of extracting, by using the extraction query representation, extraction information that indicates information about the target extraction words from a second combined lexical representation generated based on the target search document.”
The limitation of acquiring training data that includes sentences in which target extraction words are specified, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “acquiring training data that includes sentences in which target extraction words are specified” in the context of this claim encompasses the user manually acquiring a sheet of paper with training data that includes sentences in which target extraction words are specified printed thereon.
Similarly, the limitation of “generating a first lexical representation having a graph structure by processing the training data with a first parsing technique that extracts semantic information about relationships between words”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “generating a first lexical representation having a graph structure by processing the training data with a first parsing technique that extracts semantic information about relationships between words” in the context of this claim encompasses the user mentally processing the training data with a first parsing technique and drawing a a first lexical representation in graph form that represents semantic content on a sheet of paper.
Similarly, the limitation of “generating a second lexical representation having a graph structure by processing the training data with a second parsing technique that extracts syntactic information about relationships between words”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “generating a second lexical representation having a graph structure by processing the training data with a second parsing technique that extracts syntactic information about relationships between words” in the context of this claim encompasses the user mentally processing the training data with a second parsing technique and drawing a second lexical representation in graph form that represents syntactic content on the sheet of paper.
Similarly, the limitation of “generating a first combined lexical representation by aligning and combining the nodes and edges of the first lexical representation and the second lexical representation to create a unified graph structure that integrates both the semantic information and syntactic information”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “generating a first combined lexical representation by aligning and combining the nodes and edges of the first lexical representation and the second lexical representation to create a unified graph structure that integrates both the semantic information and syntactic information” in the context of this claim encompasses the user drawing lines linking the nodes and edges of the semantic graph and the syntactic graph on the sheet of paper to create a unified graph structure that integrates both the semantic information and syntactic information.
Similarly, the limitation of “generating, based on the first combined lexical representation, an extraction query representation that indicates a query for extracting the target extraction words from a predetermined target search document”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “generating, based on the first combined lexical representation, an extraction query representation that indicates a query for extracting the target extraction words from a predetermined target search document” in the context of this claim encompasses the user mentally thinking of and drawing, based on the first combined lexical representation, an extraction query representation that indicates a query for extracting the target extraction words from a predetermined target search document, such as a paper document.
Similarly, the limitation of “extracting, by using the extraction query representation, extraction information that indicates information about the target extraction words from a second combined lexical representation generated based on the target search document, wherein the extraction improves accuracy by leveraging both the semantic information and syntactic information in the unified graph structure”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “extracting, by using the extraction query representation, extraction information that indicates information about the target extraction words from a second combined lexical representation generated based on the target search document, wherein the extraction improves accuracy by leveraging both the semantic information and syntactic information in the unified graph structure” in the context of this claim encompasses the user mentally identifying, by using the extraction query representation, extraction information that indicates information about the target extraction words from a second combined lexical representation generated based on the target search document, and writing down the extraction information on the sheet of paper, while mentally leveraging both the semantic information and syntactic information in the unified graph structure for more accurate extraction.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim does not recite any additional elements that would integrate the abstract idea into a practical application by imposing any meaningful limits on practicing the abstract idea. The claim is not patent eligible.
Specifically with respect to Step 2A, Prong Two, of the Alice/Mayo test, the judicial exception is not integrated into a practical application. Claim 1 does not recite any limitations that are not mental steps.
Specifically with respect to Step 2B of the Alice/Mayo test, “the claim as a whole does not amount to significantly more than the exception itself (there is no inventive concept in the claim)”. MPEP 2106.05 Il. There are no limitations in claim 1 outside of the judicial exception. As a whole, there does not appear to contain any inventive concept. As discussed above, claim 1 is a mental process that pertains to the mental process of extracting information from a document, which can be performed entirely by a human with physical aids.
Claim 1 is directed to a device that corresponds to the method of claim 16 and is therefore rejected for the same reasons set for the above with respect to claim 16. While claim 1 recites generic computer components (processor, memory, instructions), such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing generic instructions, and generic memory) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of using generic computer components amount 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. Claim 1 is not patent eligible.
Claim 15 is directed to a system that corresponds to the device of claim 1 and is therefore rejected for the same reasons set for the above with respect to claim 1. While claim 15 also recites generic computer components (user terminal, communication network, processor, memory, instructions), such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing generic instructions, and generic memory, generic user terminal, and generic communication network) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of using generic computer components amount 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. Claim 15 is not patent eligible.
Dependent claims 2-14, 17, and 18 depend from claim 1, do not remedy any of the deficiencies of claim 1, and therefore are rejected on the same grounds as claim 1 above.
Generally, claims 2-14, 17, and 18 merely recite additional steps for a mental process of extracting information from a document, all of which could be performed mentally or by writing down relationships with a pen and paper, and do not amount to anything more than substantially the same abstract idea as explained with respect to claim 1.
Specifically:
Claim 2 recites “the first parsing technique is an Abstract Meaning Representation technique; and the second parsing technique is a Dependency Parsing technique” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claim 1. For example, the parsing techniques could be applied mentally.
Claim 3 recites “the first lexical representation, the second lexical representation, the first combined lexical representation, the second combined lexical representation, and the extraction query representation have a graph structure in which words are represented as nodes, and relationships between words are represented as edges” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claim 1. For example, the graph components could be drawn with a pen and paper.
Claim 4 recites “each node in the first lexical representation is associated with first node information and first edge information from the first parsing technique; each node in the second lexical representation is associated with second node information and second edge information from the second parsing technique; and the lexical representation generation unit: compares a first node in the first lexical representation with a second node in the second lexical representation, and identifies nodes shared between the first lexical representation and the second lexical representation as shared nodes; and generates the first combined lexical representation by mapping the first node information and the first edge information associated with a first shared node among the shared nodes to a second shared node among the shared nodes in the second lexical representation” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 5 recites “wherein, in generation of the first combined lexical representation, the lexical representation generation unit: calculates a normalized edit distance between nodes in the first lexical representation and nodes in the second lexical representation; and identifies nodes that satisfy a predetermined normalized edit distance criterion as the shared nodes” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 6 recites “: the query representation generation unit generates a subgraph representing a portion of the first combined lexical representation as the extraction query representation” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 7 recites “, wherein the lexical representation generation unit: generates a third lexical representation by processing the target search document using the first parsing technique, generates a fourth lexical representation by processing the target search document using the second parsing technique, and generates the second combined lexical representation by combining the third lexical representation and the fourth lexical representation; and each node in the second combined lexical representation is associated with third node information and third edge information from the first parsing technique and fourth node information and fourth edge information from the second parsing technique” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 8 recites “, wherein the word extraction unit: compares the extraction query representation with the second combined lexical representation; and extracts, in a case that it is determined that either one of the first node information or the second node information of each node in the extraction query representation satisfies a matching condition with respect to either one of the third node information or the fourth node information of a first node in the second combined lexical representation, and either one of the first edge information or the second edge information of each node in the extraction query representation satisfies a matching condition with respect to either one of the third edge information or the fourth edge information of the first node in the second combined lexical representation, the third node information, the fourth node information, the third edge information, and the fourth edge information of the first node from the second combined lexical representation as the extraction information” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 9 recites “, wherein the word extraction unit: compares the extraction query representation with the second combined lexical representation; and extracts, in a case that it is determined that both of the first node information and the second node information of each node in the extraction query representation satisfies a matching condition with respect to the third node information and the fourth node information of a first node in the second combined lexical representation, and both of the first edge information and the second edge information of each node in the extraction query representation satisfy a matching condition with respect to the third edge information and the fourth edge information of the first node in the second combined lexical representation, the third node information, the fourth node information, the third edge information, and the fourth edge information of the first node from the second combined lexical representation as the extraction information” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 10 recites “: in a case that a performance of the first parsing technique satisfies a first performance criterion, the word extraction unit: compares the extraction query representation with the second combined lexical representation, and extracts, in a case that it is determined that the first node information of each node in the extraction query representation satisfies a matching condition with respect to the third node information of a first node in the second combined lexical representation and the first edge information of each node in the extraction query representation satisfies a matching condition with respect to the third edge information of the first node in the second combined lexical representation, the third node information and the third edge information of the first node from the second combined lexical representation as the extraction information; and in a case that a performance of the second parsing technique satisfies a second performance criterion, the word extraction unit: extracts, in a case that it is determined that the second node information of each node in the extraction query representation satisfies a matching condition with respect to the fourth node information of the first node in the second combined lexical representation and the second edge information of each node in the extraction query representation satisfies a matching condition with respect to the fourth edge information of the first node in the second combined lexical representation, the fourth node information and the fourth edge information of the first node from the second combined lexical representation as the extraction information” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 11 recites “, wherein in a case that matching necessity information is received that defines a matching necessity criterion defining nodes and edges that need to match and nodes and edges that do not need to match between the extraction query representation and the second combined lexical representation, the word extraction unit: compares the extraction query representation with the second combined lexical representation; and identifies a first node in the second combined lexical representation that satisfies the matching necessity criterion; and extracts node information and edge information of the first node from the second combined lexical representation as the extraction information” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 12 recites “, wherein in a case that fifth node information is received that indicates related terms of a second node in the extraction query representation, the word extraction unit: compares the extraction query representation with the second combined lexical representation; and extracts, in a case that the fifth node information of the second node in the extraction query representation satisfies a matching condition with respect to either one of the third node information or the fourth node information of a first node in the second combined lexical representation, the third node information, the fourth node information, the third edge information and the fourth edge information of the first node from the second combined lexical representation as the extraction information” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 13 recites “, wherein in a case that lexical attribute information is received that indicates a predetermined lexical attribute, the word extraction unit: compares the extraction query representation with the second combined lexical representation; identifies a first node in the second combined lexical representation for which the predetermined lexical attribute matches between the extraction query representation and the second combined lexical representation; and extracts node information and edge information of the first node from the second combined lexical representation as the extraction information” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 14 recites “, wherein the lexical attribute includes one or more selected from the group consisting of a lemma, a part-of-speech, and a consonant” which merely adds details to the claimed natural language processing that could be performed mentally or with a pen and paper to similar reasons to those explained above with respect to claims 1-3 above.
Claim 17 recites “…wherein the word extraction unit: compares the extraction query representation with the second combined lexical representation; and extracts, in a case that it is determined that both of the first node information and the second node information of each node in the extraction query representation satisfies a matching condition with respect to a third node information and a fourth node information of a first node in the second combined lexical representation, and both of the first edge information and the second edge information of each node in the extraction query representation satisfy a matching condition with respect to a third edge information and the fourth edge information of the first node in the second combined lexical representation, the third node information, the fourth node information, the third edge information, and the fourth edge information of the first node from the second combined lexical representation as the extraction information.” which could be performed by mentally comparing the extraction query representation with the second combined lexical representation; extracting, by copying words with a pen and paper, in a case that it is mentally determined that both of the first node information and the second node information of each node in the extraction query representation satisfies a matching condition with respect to a third node information and a fourth node information of a first node in the second combined lexical representation, and both of the first edge information and the second edge information of each node in the extraction query representation satisfy a matching condition with respect to a third edge information and the fourth edge information of the first node in the second combined lexical representation, the third node information, the fourth node information, the third edge information, and the fourth edge information of the first node from the second combined lexical representation as the extraction information.
Claim 18 recites: “…wherein, in a case that a performance of the first parsing technique satisfies a first performance criterion, the word extraction unit: compares the extraction query representation with the second combined lexical representation; and extracts, in a case that it is determined that the first node information of each node in the extraction query representation satisfies a matching condition with respect to a third node information of a first node in the second combined lexical representation and the first edge information of each node in the extraction query representation satisfies a matching condition with respect to a third edge information of the first node in the second combined lexical representation, the third node information and the third edge information of the first node from the second combined lexical representation as the extraction information.” which could be performed by, in a case where it is mentally determined that a performance of the first parsing technique satisfies a first performance criterion: mentally comparing the extraction query representation with the second combined lexical representation; and extracting, by copying down words with a pen and paper, in a case that it is mentally determined that the first node information of each node in the extraction query representation satisfies a matching condition with respect to a third node information of a first node in the second combined lexical representation and the first edge information of each node in the extraction query representation satisfies a matching condition with respect to a third edge information of the first node in the second combined lexical representation, the third node information and the third edge information of the first node from the second combined lexical representation as the extraction information.
In sum, claims 2-14, 17, and 18 depend from claim 1 and further recite mental processes as explained above. None of the additional limitations recited in claims 2-14, 17, and 18 amount to anything more than the same or a similar abstract idea as recited in claim 1. Nor do any limitations in claims 2-14, 17, and 18 (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception. Claims 2-14, 17, and 18 are not patent eligible.
Eligible Claims
Claim 19 recites: “…the processor comprises specialized natural language processing circuitry configured with hardware acceleration for graph traversal operations; the lexical representation generation unit is implemented using dedicated semantic analysis hardware that automatically performs Abstract Meaning Representation parsing to generate the first lexical representation with semantic role labels and predicate-argument structures that cannot be mentally processed; the lexical representation generation unit is further implemented using dedicated syntactic analysis hardware that automatically performs dependency parsing to generate the second lexical representation with part-of-speech tags and syntactic dependency relationships; and the word extraction unit automatically optimizes extraction performance by dynamically weighting semantic versus syntactic information based on real-time parsing technique performance metrics stored in the memory” which cannot be practically performed as a mental process.
Claim 20 recites: “…the memory stores machine-executable graph processing algorithms that automatically calculate normalized edit distances between nodes using mathematical similarity functions that require computational processing beyond human mental capabilities; the lexical representation generation unit automatically identifies shared nodes by executing computerized similarity threshold comparisons that process multiple dimensional feature vectors representing linguistic attributes; the device automatically generates confidence scores for extracted information based on computational analysis of multiple graph matching criteria; the processor executes real-time performance monitoring algorithms that automatically adjust extraction parameters based on statistical analysis of extraction accuracy metrics; the device comprises specialized graph database circuitry configured to store and process the first combined lexical representation and second combined lexical representation using optimized graph traversal algorithms; the lexical representation generation unit automatically performs computerized node alignment operations using mathematical vector space calculations that map semantic and syntactic features into unified coordinate systems; the query representation generation unit automatically generates subgraph extraction patterns using computational pattern recognition algorithms that identify recurring structural motifs in the combined lexical representations; and the word extraction unit executes automated graph matching algorithms that perform simultaneous multi-dimensional comparisons of node attributes and edge relationships using computational resources that exceed human cognitive processing capabilities” which cannot be practically performed as a mental process.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Subramanya et al. (US 10810193) in view of Hasan et al. (US 11669556), in further view of Chen et al. (“Unsupervised AMR-Dependency Parse Alignment”. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 558–567, Valencia, Spain, April 3-7, 2017).
Consider claim 1, Subramanya discloses a word extraction device (computer device, Col 15 lines 27-30, for extracting e.g. Michelle Obama as the inferred answer to a query from a data graph, Col 13 lines 47-55), comprising:
a processor (one or more processors, Col 5 lines 33-34); and
a memory, wherein the memory includes processing instructions (memory storing modules executed by the processor, Col 5 lines 34-36) for causing the processor to function as:
a lexical representation generation unit for acquiring training data that includes sentences in which target extraction words are specified (e.g. “Miles Davis and John Coltrane came together in New York in the mid-50”, “Miles Davis”, “John Coltrane” and “New York” being the specified target extraction words, Fig 2, the sentence used for training process 400, Col 10 lines 34-43, Fig 4), generating a first lexical representation having a graph structure by processing the training data with a first parsing technique that extracts semantic information about relationships between words (noun-phrase extraction and coreference resolution as part of syntactic-semantic parsing, Col 6 lines 18-26, data graph representing semantic information as shown in Fig. 2), generating a second lexical representation having a graph structure by processing the training data with a second parsing technique that extracts syntactic information about relationships between words (part of speech tagging and dependency parsing, Col 6 lines 7-13, the syntactic parsing generating the dependency tree shown in Fig. 2), and generating a first combined lexical representation by combining nodes and edges of the first lexical representation and the second lexical representation to create a unified graph structure that integrates both the semantic information and syntactic information (generating linked graph, Col 6 lines 35-55, Fig 2, and providing the linked graph and examples to produce multiple weighted features that model the relationship, Fig. 4 steps 420 and 425, Col 12 lines 37-56);
a query representation generation unit for generating, based on the first combined lexical representation, an extraction query representation that indicates a query for extracting the target extraction words from a predetermined target (determining a received natural language query matches the linked graph, a set of features for mapping to the linked graph, Col 12-13 lines 57-35; for extracting an entity answer for the natural language query from the linked graph, a predetermined target, by following the path represented by the features, Col 13 lines 31-54); and
a word extraction unit for extracting, by using the extraction query representation, extraction information that indicates information about the target extraction words from a second combined lexical representation generated based on a target document, wherein the extraction improves accuracy by leveraging both the semantic information and syntactic information in the unified graph structure (the entity answer from the linked graph, by following the path represented by the features, Col 13 lines 31-54, for example the linked graph in Fig. 7, for example, a “combined lexical representation” generated based on a crawled “target”, document, Col 5 lines 42-51; the examiner notes the claim language requires extracting from the “lexical representation” generated from the document, not from the document itself).
Subramanya does not specifically mention a predetermined target search document.
Hasan discloses a predetermined target search document (in response to a search query, a document added to a list sent to the client device for presentation to the user, i.e. a predetermined target search document, and selected by a user for retrieval and entities and relationships, Col 6 lines 55-63, Col 7 lines 11-19).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Subramanya by including a predetermined target search document in order to facilitate the user to utilize document information in a more useful manner, as suggested by Hasan (Col 1 lines 44-46). Doing so would have led to predictable results of reducing user effort and time in manually accessing information, as suggested by Hasan (Col 1 lines 33-35). The references cited are analogous art in the same field of natural language processing.
Subramanya and Hasan do not specifically mention aligning nodes and edges of the first lexical representation and the second lexical representation.
Chen discloses aligning nodes and edges of the first lexical representation and the second lexical representation (alignment between a subgraph of an AMR and a dependency parse, Figure 2, page 559).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Subramanya and Hasan by aligning nodes and edges of the first lexical representation and the second lexical representation in order to leverage syntactic information, as suggested by Chen (Introduction, page 558), predictably improving parser performance, as suggested by Chen (Abstract, page 558). The references cited are analogous art in the same field of natural language processing.
Consider claim 15, Subramanya discloses a word extraction system (computer device operated as system 100, Col 15 lines 27-30, for extracting e.g. Michelle Obama as the inferred answer to a query from a data graph, Col 13 lines 47-55) comprising:
a user terminal and a word extraction device connected via a communication network (client computer with user interface connected over networks, Col 17-18 lines 60-6, extracting words as answers to queries, Col 13 lines 47-55);
wherein: the word extraction device includes:
a processor, and a memory (processor and memory, Col 17 lines 47-59);
and the memory includes: processing instructions for causing the processor to function (instructions in memory executed by the processor, Col 17 lines 47-59) as:
a lexical representation generation unit for acquiring training data that includes sentences in which target extraction words are specified (e.g. “Miles Davis and John Coltrane came together in New York in the mid-50”, “Miles Davis”, “John Coltrane” and “New York” being the specified target extraction words, Fig 2, the sentence used for training process 400, Col 10 lines 34-43, Fig 4), generating a first lexical representation having a graph structure by processing the training data with a first parsing technique that extracts semantic information about relationships between words (noun-phrase extraction and coreference resolution as part of syntactic-semantic p