Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-11 recites a process, one of the four statutory categories of patentable subject matter. Claims 12-20 recites a machine, one of the four statutory categories of patentable subject matter.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites “A method for automatically constructing knowledge graphs comprising” and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
“classifying, ... , a plurality of tables within the dataset” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement; see Spec. 46, Identifying information within text as a table or not a table.)
“classifying, ... , a plurality of hierarchal metadata of the tables” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement; see Spec. 48, Identifying attributes in the table.)
“fusing, ... , the hierarchal metadata into a knowledge graph, the knowledge graph being associated with the specific topic” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement; see Spec. 49, Adding information into a knowledge graph.)
Claim 1 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
"accessing a dataset, the dataset comprising a plurality of articles related to a specific topic” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“... using a first artificial intelligence (AI) model ...; ... using a second AI model ...; ... using a third AI model ...” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is directed to the abstract idea.
Subject Matter Eligibility Analysis Step 2B:
"accessing a dataset, the dataset comprising a plurality of articles related to a specific topic” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
“... using a first artificial intelligence (AI) model ...; ... using a second AI model ...; ... using a third AI model ...” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not recite significantly more than the abstract idea itself as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is subject-matter ineligible.
Regarding Claim 12:
The claim recites a system that performs the method as described in claim 1. Therefore, claim 12 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 12 are analyzed below.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Please see Step 2A Prong 1 analysis of claim 1
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“A system for automatically constructing knowledge graphs comprising: a computing cluster comprising a plurality of computing devices, each computing device comprising at least one processor and a memory operably coupled to the at least one processor; a database operably coupled to the computing cluster, wherein the database stores a dataset comprising a plurality of articles related to a specific topic, wherein the computing cluster is configured to” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“further comprising analyzing the dataset to parse and store content in a semi-structured format” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“further comprising preprocessing the tables to encode numerical data within the tables” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“further comprising constructing a plurality of feature vectors for each of a plurality of rows within the tables” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“further comprising clustering, ... , the tables into a plurality of sub-topics associated with the specific topic” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“... using a fourth AI model ...” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“further comprising initializing a structural hierarchy of the knowledge graph” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claims 7 and 18:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the articles are peer-reviewed articles” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h)))
Regarding Claims 8 and 19:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the specific topic is COVID-19” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h)))
Regarding Claims 9 and 20:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the first AI model is a recurrent neural network (RNN), the second AI model is a support vector machine (SVM), and the third AI model is a natural language processing (NLP) model” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h)))
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“providing a search engine comprising: providing the knowledge graph of claim 1” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of gathering and analyzing information using conventional techniques and displaying the result as identified by the court (2106.05(d) in step 2B))
“providing a user interface for interrogating the knowledge graph” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of gathering and analyzing information using conventional techniques and displaying the result as identified by the court (2106.05(d) in step 2B))
Regarding Claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“further comprising receiving a user query at the user interface and displaying search results on the user interface” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of gathering and analyzing information using conventional techniques and displaying the result as identified by the court (2106.05(d) in step 2B))
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the computing cluster is further configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the computing cluster is further configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“of feature vectors for each of a plurality of rows within the tables” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the computing cluster is further configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the computing cluster is further configured to cluster, using a fourth AI model, ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 17:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“hierarchy of the knowledge graph” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the computing cluster is further configured to ” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
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.
Claims 1-2, 5, 7-8, 10-13, 16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Hou (US20230252054A1) in view of Oelen, “Creating a Scholarly Knowledge Graph from Survey Article Tables”.
Regarding claim 1, Hou teaches:
“A method for automatically constructing knowledge graphs comprising” (abstract, [0017], Information is extracted from one or more data sources and the information can be used in a task such as knowledge graph construction.)
“accessing a dataset, the dataset comprising a plurality of articles related to a specific topic” ([0059, 0063-0065, 0092], The data source input component may receive the group of data sources. A topic of a knowledge domain may be identified from one or more data sources. A data source may include one or more documents and scientific papers. Thus, a data source may contain a plurality of articles related to the same topic.)
“classifying, using a first artificial intelligence (AI) model, a plurality of tables within the dataset” ([0066-0067, 0091, 0098], The data sources can be analyzed by an NLP component to data mine relevant information from the content of the data sources. The system may employ one or more NLP system. The task prediction component may be used for table extraction on the data.)
“classifying, using a second AI model, a plurality of ” ([0066-0067, 0091, 0098], The task prediction component automatically performs meta-data analysis on the data. Components of the proposed system may employ one or more NLP models. Therefore, it is implied that the meta-data analysis may be performed by a second model.)
“fusing, using a third AI model, the ” ([0076-0081, 0085, 0091-0096], In one embodiment, the input data may be AI papers that are received by the system and are related to a particular topic. A knowledge graph is built on the data extracted from the input data of the AI papers. The machine learning component may implement one or more machine learning models. It is implied that the knowledge graph construction component may implement a third model to build the knowledge graph.)
Hou does not explicitly disclose an implementation of “a plurality of hierarchal metadata of the tables”. Hou (par. 98) discloses a process of table extraction and meta-data analysis, but does not provide details on these processes. Oelen is provided as an additional reference in combination of Hou to disclose the claimed invention with additional clarity. Oelen discloses in the same field of endeavor:
“classifying, using a second AI model, a plurality of hierarchal metadata of the tables” ([pg. 7, Section 3.2, par. 1; pg. 7, Section 3.3, par. 1; pg. 8, Section 3.4, par. 1-2; pg. 9, Figure 3], Tabula is used to perform table extraction from collected PDF files. The table contains references from a plurality of papers. GROBID (second AI model) is used to extract and process the references from the PDF articles. The citation is parsed and added to the table as additional columns for each of the metadata. Oelen teaches a plurality of hierarchal metadata because each row in the table represents a review paper and the columns may consist of title, author, publication date that describes the reference of each paper.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “a plurality of hierarchal metadata of the tables” from Oelen into the teaching of Hou. Doing so can improve the construction of a knowledge graph by implementing curated and high-quality tabular information from survey articles (Oelen, abstract).
Regarding claim 12:
Claim 12 recites a system that performs the same process as described in Claim 1. Therefore claim 12 is rejected under the same reasons mention for claim 1. The additional elements of claim 12 is addressed below by Hou:
“a computing cluster comprising a plurality of computing devices, each computing device comprising at least one processor and a memory operably coupled to the at least one processor” ([0047, 0053], A cloud computing environment comprises one or more cloud computing nodes, which consists of at least one processor and a memory.)
“a database operably coupled to the computing cluster, wherein the database stores a dataset comprising a plurality of articles related to a specific topic, wherein the computing cluster is configured to” ([0069, 0073-0074], The system may store and retrieve data from the database to perform the advanced knowledge analysis.)
Regarding claims 2 and 13, Hou in view of Oelen teaches:
“further comprising analyzing the dataset to parse and store content in a semi-structured format” ([Oelen, pg. 7, Section 3.2, par. 1; pg. 4, Table 1], The tables from PDF files are extracted and stored in a set of CSV files. Table 1 also discloses that the input file is a PDF and the output file can be JSON.)
Regarding claims 5 and 16, Hou teaches:
“further comprising clustering, using a fourth AI model, the tables into a plurality of sub-topics associated with the specific topic” ([0061, 0067-0068, 0074], In some embodiment, the data may come from tables in the data source. An AI model can be used to determine subtopics. The processing analytics component can identify a list of subtopics associated with the topic. It is implied the processing analytics component uses an AI model to determine the subtopics.)
Regarding claims 7 and 18, Hou in view of Oelen teaches:
“wherein the articles are peer-reviewed articles” ([Oelen, pg. 5-6, Section 3.1, par. 2; pg. 6, Table 2], Survey papers are collected from ACM Digital Library. Articles from ACM are peer-reviewed.)
Regarding claims 8 and 19, Hou in view of Oelen teaches:
“wherein the specific topic is COVID-19” ([Oelen, pg. 5-6, Section 3.1, par. 1-2; pg. 9, Figure 3], Survey papers are collected from a diverse range of domains. In Figure 3, a knowledge graph is constructed for the topic of COVID-19.)
Regarding claim 10, Hou teaches:
“A method for providing a search engine comprising: providing the knowledge graph of claim 1; and providing a user interface for interrogating the knowledge graph” ([0072, 0099-0100, Figure 5], The advanced knowledge analysis and summarization system may include a user interface for mining and navigating the data. A user may interact with the user interface to query information from the system. The system may process the input query using the knowledge graph.)
Regarding claim 11, Hou teaches:
“further comprising receiving a user query at the user interface and displaying search results on the user interface” ([0072, 0099-0102, Figure 5], The system can implement a QA model to process the user query and generate an answer using the knowledge grpah.)
Claims 3-4 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hou (US20230252054A1) in view of Oelen, “Creating a Scholarly Knowledge Graph from Survey Article Tables” and Milosevic, “A Framework For Information Extraction From Tables In Biomedical Literature”.
Regarding claims 3 and 14, Hou in view of Oelen does not explicitly disclose an implementation of “further comprising preprocessing the tables to encode numerical data within the tables”. However, Milosevic discloses in the same field of endeavor:
“further comprising preprocessing the tables to encode numerical data within the tables” ([pg. 5, Section 3.3.1, par. 2-3; pg. 7, Section 3.4.5, par. 5-6], Tables can be processed by a machine learning model to define classes of tables. The tables contain variable groups such as number of patients, age, weight, and body mass index. The model uses term frequency-inverse document frequency transformation, which involves an encoding process.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “further comprising preprocessing the tables to encode numerical data within the tables” from Milosevic into the teaching of Hou in view of Oelen. Doing so can improve the data mining process of tables in literature by implementing a framework for information processing (Milosevic, abstract).
Regarding claims 4 and 15, Hou in view of Oelen and Milosevic teaches:
“further comprising constructing a plurality of feature vectors for each of a plurality of rows within the tables” ([Milosevic, pg. 3-4, Section 3.1, par. 1-2], An extraction template is used to extract variables from tables. The template stores extracted information such as variable name, value, and unit.)
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hou (US20230252054A1) in view of Oelen, “Creating a Scholarly Knowledge Graph from Survey Article Tables” and Wang, “COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation”.
Regarding claims 6 and 17, Hou in view of Oelen teaches:
“further comprising initializing a structural ” ([Hou, 0081, 0093-0096], A knowledge graph can be built from a set of input papers consisting of AI papers. One or more candidate subtopic can be provided to the knowledge graph. It is not explicitly disclosed that the knowledge graph is a structural hierarchy.)
Hou in view of Oelen does not explicitly disclose an implementation of “a structural hierarchy of the knowledge graph”. However, Wang discloses in the same field of endeavor:
“further comprising initializing a structural hierarchy of the knowledge graph” ([pg. 2-3, Section 2.1, par. 1; pg. 2, Figure 2 & 3], A knowledge graph can be built from a set of scientific literature. Figure 2 and 3 are shown as a hierarchical spherical knowledge graph.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “a structural hierarchy of the knowledge graph” from Wang into the teaching of Hou in view of Oelen. Doing so can improve the data mining process in literature by implementing a framework for extracting fine-grained multimedia knowledge elements (Wang, abstract).
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hou (US20230252054A1) in view of Oelen, “Creating a Scholarly Knowledge Graph from Survey Article Tables” and Jelodar, “Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach”.
Regarding claims 9 and 20, Hou in view of Oelen teaches:
“wherein the first AI model is a ([Hou, 0094, 0098], The task prediction component may be used for table extraction on the data. The extraction model can be a BERT model, which is a neural network.), the second AI model is a support vector machine (SVM) ([Hou, 0067, 0085], A machine learning component learns different sets of data and the machine learning component may comprise of support vector machines., and the third AI model is a natural language processing (NLP) model” ([Hou, 0067, 0085, Figure 4], Components of the system can comprise of one or more NLP models. Therefore, it is implied that the knowledge graph construction component may implement a separate NLP model to build the knowledge graph.)
Hou in view of Oelen does not explicitly disclose an implementation of “the first AI model is a recurrent neural network”. However, Jelodar discloses in the same field of endeavor:
“wherein the first AI model is a recurrent neural network ...” ([pg. 4, Section D, par. 1-2], A RNN is employed for classification of data related to COVID-19.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “the first AI model is a recurrent neural network” from Jelodar into the teaching of Hou in view of Oelen. Doing so can improve the extraction of information related to a particular topic by implementing a LSTM RNN for sentiment classification (Jelodar, abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GARY MAC whose telephone number is (703)756-1517. The examiner can normally be reached Monday - Friday 8:00 AM - 5:00 PM.
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/GARY MAC/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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