DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/11/2024 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 13 is drawn to a "software" per se “A computer system for providing data from a directed graph to a language model…” and as such is non-statutory subject matter as there is no structural element mentioned in the claim. See MPEP § 2106.1V.B.1 .a. Data structures not claimed as embodied in computer readable media are descriptive material per se and are not statutory because they are not capable of causing functional change in the computer. See, e.g., Warmerdam, 33 F.3d at 1361, 31 USPQ2d at 1760 (claim to a data structure per se held nonstatutory). Such claimed data structures do not define any structural and functional interrelationships between the data structure and other claimed aspects of the invention, which permit the data structure's functionality to be realized. In contrast, a claimed computer readable medium encoded with a data structure defines structural and functional interrelationships between the data structure and the computer software and hardware components which permit the data structure's functionality to be realized, and is thus statutory. Similarly, computer programs claimed as computer listings per se, i.e., the descriptions or expressions of the programs are not physical "things." They are neither computer components nonstatutory processes, as they are not "acts" being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer, which permit the computer program's functionality to be realized.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 13 and 14, relate to the statutory category of method/process and machine/apparatus. The independent claims recite “defin(ing) a plurality of conditions and a plurality of patterns, wherein each of the conditions has at least one corresponding pattern; receiv(ing) a subset of the directed graph…, wherein the subset of the directed graph includes a plurality of statements, wherein each of the statements includes a subject, an object and a predicate relating the subject to the object; for each of the statements in the subset of the directed graph, perform(ing) the following: when one of the conditions matches a respective statement and the pattern corresponding to the condition can be applied to the respective statement, computing a string from the respective statement using the pattern; and provid(ing) the computed strings as input to the language model”.
The limitations of claims 1, 13, and 14 of “defining…”, “receiving…”, “performing..” and “providing…” as drafted covers mental activity. More specifically, for claim 1, a human given unorganized/unstructured data, and using the unorganized/unstructured data to graphically create organized/structured data by determining if the path that is followed by the using the current text, the previous text and the subsequent text which leads to creating a sentence. The created sentence is compared to a list of statements (data structure of a sentence) which contain a subject, verb (predicate), and object. If the created query matches the listed statements, it is then used as an input to a language model. The human can from previous experience create a query that increases the likelihood that it is accurate.
This judicial exception is not integrated into a practical application. In particular, claim 13 recites the additional element of “database”. Claim 14 recites the additional element of “processor”. Claims 1, 13, and 14 recite the additional element “language model”. These additional elements are recited generally in the specification. For example, in paragraphs [0203]-[0204] of the as published application, there is a description of using a general purpose computing system. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer as a general computing device is noted. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claims 2 and 15, the claims relate to determining whether the unorganized/unstructured text is related to the statements which contain the data structure of a sentence and contain a subject, verb (predicate), and object and how closely the text is related. The claims relate to a mental activity of given random words to create a complete sentence. No additional elements are present.
With respect to claims 3 and 16, the claims relate to being given random words, using previous and subsequent words to create a complete sentence. The claims relate to a mental activity of given random words to create a complete sentence. No additional elements are present.
With respect to claims 4 and 17, the claims relate to the determining if the created sentence contains a verb, additional text and is in a particular language. The claims relate to a mental activity of given random words to create a complete sentence. No additional elements are present.
With respect to claims 5 and 18, the claims relate to if the path followed by the random words can be applied to a particular sentence. If not, then a different path is chosen. If the path can be applied, then a sentence is created. The claims relate to a mental activity of given random words to create a complete sentence. No additional elements are present.
With respect to claims 6 and 19, the claims relate to determining several different paths that lead to the same sentence based on what the topic/subject the sentences are related to. The claims relate to a mental activity of given random words to create a complete sentence. No additional elements are present.
With respect to claims 7, 8, 9, and 20, the claims relate to defining the content of the graph and what it comprises such as edge and node, etc.. The claims relate to making sure that the words follows the data structure for a sentence. No additional elements are present.
With respect to claim 10, the claim relates to determining if the listed statements need to be in a particular order and create new sentences which follow the order. The claim relates to a mental activity of creating statements that follow an order of operation. No additional elements are present.
With respect to claim 11, the claim relates to grouping the different paths depending on if a certain words are included in the path. New paths are created based on the previous and subsequent words. The claim relates to a mental activity of given random words to create a complete sentence. No additional elements are present.
With respect to claim 12, the claim relates to determining the probability of if the created sentences match the listed statements. The claim relates to determining whether the created sentence will closely resemble the listed statement or not. No additional elements are present.
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 5, 6, 18, and 19 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. Claims 5 and 18 recite “wherein defining the plurality of conditions and the plurality of patterns may further comprise defining at least three conditions and at least three patterns”. It is unclear whether the limitation of “may further” requires all of the elements to be present or not. It is currently being interpreted by the examiner that all of the elements are required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 7, 10, 12-15, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipate by Oltramari et al. (US2021/0303990).
Regarding Claim 1, Oltramari et al discloses a computer-implemented method for providing data from a directed graph to a language model, the method comprising: defining a plurality of conditions and a plurality of patterns, wherein each of the conditions has at least one corresponding pattern (Fig. 5) (The nodes of the layers 60, 62, 64, 66 may be coupled to nodes of subsequent or previous layers. And each of the nodes j27 to j30 of the output layer 62 may execute an activation function—e.g., a function that contributes to whether the respective nodes should be activated to provide an output of the language model 44 (e.g., based on its relevance to the answer to the query)) (page 4, paragraph [0041]) and ( Non-limiting examples of structured data include a knowledge graph (e.g., having a plurality of nodes (each node defining a different subject matter domain), wherein some of the nodes are interconnected by at least one relation), a data array (an array of elements in a specific order), metadata (e.g., having a resource name, a resource description, a unique identifier, an author, and the like), a linked list (a linear collection of nodes of any type, wherein the nodes have a value and also may point to another node in the list), a tuple (an aggregate data structure), and an object (a structure that has fields and methods which operate on the data within the fields)) (pages 2 and 3, paragraph [0029]) (It is being interpreted by the examiner that the nodes define the conditions that have to be met to create a knowledge graph.); receiving a subset of the directed graph (Returning to FIG. 4, in at least one example, dialogue computer 10 also organizes the unstructured data 40 into a knowledge graph feature set and provides it (e.g., uploads it) to a knowledge graph 46) (page 4, paragraph [0042]), wherein the subset of the directed graph includes a plurality of statements (The knowledge graph feature set may comprise one or more of: a declarative commonsense knowledge type, a taxonomic knowledge type, a relational knowledge type, a procedural knowledge type, a sentiment knowledge type, a metaphorical knowledge type, or any other suitable type) (page 4, paragraph [0042]), wherein each of the statements includes a subject, an object and a predicate relating the subject to the object (According to an example, a triple may comprise a subject element, a relationship element, and an object element) (pages 4 and 5, paragraph [0043]); for each of the statements in the subset of the directed graph, performing the following: when one of the conditions matches a respective statement and the pattern corresponding to the condition can be applied to the respective statement, computing a string from the respective statement using the pattern (The various knowledge types of the knowledge graph 46 may be comprised of triples which are interconnected to form data structure) (pages 4 and 5, paragraph [0043]) and (Non-limiting examples of structured data include a knowledge graph (e.g., having a plurality of nodes (each node defining a different subject matter domain), wherein some of the nodes are interconnected by at least one relation)) (pages 2 and 3, paragraph [0029]); and providing the computed strings as input to the language model (the structured data 42 comprises at least a question-and-answer (Q&A) pair feature set. The Q&A pair feature set may be provided to a language model 44 stored in non-volatile memory 34 and executed using processor(s) 30 of dialogue computer 10) (page 4, paragraph [0040]).
Regarding Claim 2, Oltramari et al discloses the method, wherein each of the conditions includes at least three condition variables, wherein each of the condition variables corresponds to a different component of a statement (when the HMI 14a receives a query (e.g., question Q) from a user and the query is delivered to the language model 44, the query may be received at the input layer 60 and pass through neural network—being evaluated using a data-oriented language model. However, once the dialogue computer 10 injects one or more triples into the output layer 62 of the neural network, the answer generated by language model 44 may be influenced by a knowledge-oriented approach) (page 5, paragraph [0045]); wherein a first one of the condition variables matches the subject, a second one of the condition variables matches the predicate and a third one of the condition variables matches the object (According to an example, a triple may comprise a subject element, a relationship element, and an object element) (pages 4 and 5, paragraph [0043]); wherein at least one of the condition variables is bound to a value ( Each of the subject, relationship, and object elements may be assigned a value; e.g., each triple may be represented as a mathematical vector) (pages 2 and 3, paragraph [0043]); wherein each of the condition variables may specify an instance of a class or a literal (Non-limiting examples of structured data include a knowledge graph (e.g., having a plurality of nodes (each node defining a different subject matter domain), wherein some of the nodes are interconnected by at least one relation)) (pages 2 and 3, paragraph [0029]).
Regarding Claim 7, Oltramari et al discloses the method, the subset of the directed graph is the entire directed graph (Fig. 4, knowledge graph 46) (in at least one example, dialogue computer 10 also organizes the unstructured data 40 into a knowledge graph feature set and provides it (e.g., uploads it) to a knowledge graph 46) (page 4, paragraph [0042]), or wherein the subset of the directed graph is determined by means of a query of the directed graph; wherein receiving the subset of the directed graph may include receiving a reference to the subset and retrieving the subset using the reference. (Since there is an “or” statement after the first limitation, the other limitations are not being mapped.)
Regarding Claim 10, Oltramari et al discloses the method, further comprising: receiving one or more rules corresponding to the subset of the directed graph (a procedural knowledge type (scope comprising prescriptive knowledge, a.k.a., order of operations) (page 3, paragraph [0030]); materializing the subset of the directed graph by applying the rules to the plurality of statements to compute additional statements (e.g., “one needs an oven before baking cakes,” “the electricity should be disconnected while the switch is being repaired,” etc.) (page 3, paragraph [0030]).
Regarding Claim 12, Oltramari et al discloses the method, wherein the language model is a probability distribution over sequences of words (In block 760, computer 10—via language model 44—may calculate a probability value for at least some of the output nodes of the output layer 62) (page 6, paragraph [0064]); wherein the language model is a large language model (the language model 44 may be a neural network (e.g., and in some cases, while not required, a deep neural network)) (page 4, paragraph [0041]).
Regarding Claim 13, Oltramari et al discloses a computer system for providing data from a directed graph to a language model, comprising: a database storing a directed graph (dialogue computer 10 also organizes the unstructured data 40 into a knowledge graph feature set and provides it (e.g., uploads it) to a knowledge graph 46. The knowledge graph feature set may comprise one or more of: a declarative commonsense knowledge type, a taxonomic knowledge type, a relational knowledge type, a procedural knowledge type, a sentiment knowledge type, a metaphorical knowledge type, or any other suitable type. According to one embodiment, the knowledge graph 46 may form part of the programming of dialogue computer 10 (e.g., the computer 10 may host and manage the knowledge graph 46)) (page 4, paragraph [0042]); a software service configured to: define a plurality of conditions and a plurality of patterns, wherein each of the conditions has at least one corresponding pattern (Fig. 5) (The nodes of the layers 60, 62, 64, 66 may be coupled to nodes of subsequent or previous layers. And each of the nodes j27 to j30 of the output layer 62 may execute an activation function—e.g., a function that contributes to whether the respective nodes should be activated to provide an output of the language model 44 (e.g., based on its relevance to the answer to the query)) (page 4, paragraph [0041]) and ( Non-limiting examples of structured data include a knowledge graph (e.g., having a plurality of nodes (each node defining a different subject matter domain), wherein some of the nodes are interconnected by at least one relation), a data array (an array of elements in a specific order), metadata (e.g., having a resource name, a resource description, a unique identifier, an author, and the like), a linked list (a linear collection of nodes of any type, wherein the nodes have a value and also may point to another node in the list), a tuple (an aggregate data structure), and an object (a structure that has fields and methods which operate on the data within the fields)) (pages 2 and 3, paragraph [0029])(It is being interpreted by the examiner that the nodes define the conditions that have to be met to create a knowledge graph.); receive a subset of the directed graph from the database (Returning to FIG. 4, in at least one example, dialogue computer 10 also organizes the unstructured data 40 into a knowledge graph feature set and provides it (e.g., uploads it) to a knowledge graph 46) (page 4, paragraph [0042]), wherein the subset of the directed graph includes a plurality of statements (The knowledge graph feature set may comprise one or more of: a declarative commonsense knowledge type, a taxonomic knowledge type, a relational knowledge type, a procedural knowledge type, a sentiment knowledge type, a metaphorical knowledge type, or any other suitable type) (page 4, paragraph [0042]), wherein each of the statements includes a subject, an object and a predicate relating the subject to the object (According to an example, a triple may comprise a subject element, a relationship element, and an object element) (pages 4 and 5, paragraph [0043]); for each of the statements in the subset of the directed graph, perform the following: when one of the conditions matches a respective statement and the pattern corresponding to the condition can be applied to the respective statement, compute a string from the respective statement using the pattern (The various knowledge types of the knowledge graph 46 may be comprised of triples which are interconnected to form data structure) (pages 4 and 5, paragraph [0043]) and (Non-limiting examples of structured data include a knowledge graph (e.g., having a plurality of nodes (each node defining a different subject matter domain), wherein some of the nodes are interconnected by at least one relation)) (pages 2 and 3, paragraph [0029]); provide the computed strings as input to the language model (the structured data 42 comprises at least a question-and-answer (Q&A) pair feature set. The Q&A pair feature set may be provided to a language model 44 stored in non-volatile memory 34 and executed using processor(s) 30 of dialogue computer 10) (page 4, paragraph [0040]).
Claim 14 is rejected for the same reason as claim 1.
Claim 15 is rejected for the same reason as claim 2.
Regarding Claim 20, Oltramari et al discloses the medium, wherein the subset of the directed graph is the entire directed graph (Fig. 4, knowledge graph 46) (in at least one example, dialogue computer 10 also organizes the unstructured data 40 into a knowledge graph feature set and provides it (e.g., uploads it) to a knowledge graph 46) (page 4, paragraph [0042]), or wherein the subset of the directed graph is determined by means of a query of the directed graph; wherein receiving the subset of the directed graph may include receiving a reference to the subset and retrieving the subset using the reference; wherein the subset of the directed graph includes a plurality of nodes connected by edges; wherein the subject corresponds to a node, the object corresponds to a node and an edge for the predicate connects the subject to the object; wherein the nodes have corresponding classes, such that each of the nodes has a corresponding class; wherein the classes may be organized in a schema, wherein the schema may be defined using the resource description framework or the web ontology language. (Since there is an “or” statement after the first limitation, the other limitations are not being mapped.)
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 3, 4, 8, 9, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Oltramari et al. in view of McCarthy et al. (US 2021/0216881)
Regarding Claim 3, Oltramari et al fails to teach the method, wherein at least one of the conditions has a plurality of corresponding patterns; wherein computing a string from the respective statement using the pattern comprises, computing a plurality of strings from the respective statement using each pattern corresponding to the condition that can be applied to the respective statement; or wherein computing a string from the respective statement using the pattern comprises, determining a random order of the patterns corresponding to the condition, and computing a string from the respective statement using a first one in the random order of the patterns that can be applied to the respective statement
McCarthy et al teaches the method, wherein at least one of the conditions has a plurality of corresponding patterns (Fig. 1, the multiple paths being followed within the knowledge graph) (The example materialized knowledge graph 100 of FIG. 1 includes various entities, concepts, numerals, and relationships of various types) (page 2, paragraph [0016]); wherein computing a string from the respective statement using the pattern comprises, computing a plurality of strings from the respective statement using each pattern corresponding to the condition that can be applied to the respective statement (Fig. 1) (The knowledge graph 100 of FIG. 1 essentially represents a collection of materialized subject-predicate-object triples following the underlying schema, where a subject may be any one of the entities (including the various concepts) represented as a collection E, a predicate may be any one of the relationships collectively referred to as R, and an object may be any one of the entities within E or any one of the numerals collectively referred to as V (standing for numerical “V”alue)) (page 2, paragraph 90018]); or wherein computing a string from the respective statement using the pattern comprises, determining a random order of the patterns corresponding to the condition, and computing a string from the respective statement using a first one in the random order of the patterns that can be applied to the respective statement. (Since there is an “or” statement after the first set of limitations, the other limitations are not being mapped.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Oltramari with the teachings of McCarthy to improve predictive expansion of knowledge graphs to further facilitate their wider spread in various applications.
Regarding Claim 4, Oltramari et al fails to teach wherein each pattern includes one or more of the following: at least one variable, wherein the variable specifies a class, an instance of a class, a literal or a predicate; text, such as one or more articles; at least one property that applies to the variable; wherein each pattern may further include a language filter; wherein the literal may specify a numeric value or text, wherein the literal may conform to a resource description framework schema class of literal values.
McCarthy et al teaches the method, wherein each pattern (Fig. 1, the multiple paths being followed within the knowledge graph) includes one or more of the following: at least one variable, wherein the variable specifies a class, an instance of a class, a literal or a predicate (Such a knowledge graph schema may function as a blueprint for expressing a set of predefined types/classes of entities, concepts, numerals, and relationships. A knowledge graph schema may be materialized or instantiated into an actual knowledge graph with specific entities, concepts, numerals, and relationships of the various types/classes as specified in the knowledge graph schema) (page 2, paragraph [0016]); text, such as one or more articles; at least one property that applies to the variable; wherein each pattern may further include a language filter (a knowledge graph schema may be constructed based on a Resource Description Framework (RDF) to provide a data model that represents the knowledge base as a collection of expressions in the form of subject-predicate-object) (page 2, paragraph [0016]); wherein the literal may specify a numeric value or text, wherein the literal may conform to a resource description framework schema class of literal values ( A knowledge graph schema may be described in various different formats. For example, a knowledge graph schema may be constructed based on a Resource Description Framework (RDF) to provide a data model that represents the knowledge base as a collection of expressions in the form of subject-predicate-object. A subject in the schema may be any one of the predefined entity or concept types/classes whereas an object in the schema may be any one of the predefined entity, concept, or numerical types/classes. A predicate in the schema may be any one of the predefined types/classes of relationship) (page 2, paragraph [0016]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Oltramari with the teachings of McCarthy to improve predictive expansion of knowledge graphs to further facilitate their wider spread in various applications.
Regarding Claim 8, Oltramari et al fails to teach the method, the subset of the directed graph includes a plurality of nodes connected by edges; wherein the subject corresponds to a node, the object corresponds to a node and an edge for the predicate connects the subject to the object; wherein the nodes have corresponding classes, such that each of the nodes has a corresponding class; wherein the classes may be organized in a schema, wherein the schema may be defined using the resource description framework or the web ontology language.
McCarthy et al teaches the method, the subset of the directed graph includes a plurality of nodes connected by edges (The example knowledge graph 100 of FIG. 1 may be obtained by materializing or instantiating an underlying schema with specific entities, concepts, numerals, and relationships, where the specific entities, concepts, and numerals are represented as nodes of the knowledge graph 100 representing various subjects and objects, and the specific relationships or predicates are represented as edges of the knowledge graph 100( (page 2, paragraph [0016]); wherein the subject corresponds to a node, the object corresponds to a node and an edge for the predicate connects the subject to the object (The example knowledge graph 100 of FIG. 1 may be obtained by materializing or instantiating an underlying schema with specific entities, concepts, numerals, and relationships, where the specific entities, concepts, and numerals are represented as nodes of the knowledge graph 100 representing various subjects and objects, and the specific relationships or predicates are represented as edges of the knowledge graph 100) (page 2, paragraph [0016]); wherein the nodes have corresponding classes, such that each of the nodes has a corresponding class (The knowledge graph 100 may be established from known facts according to an underlying schema for the knowledge graph. Such a knowledge graph schema may function as a blueprint for expressing a set of predefined types/classes of entities, concepts, numerals, and relationships) (page 2, paragraph [0016]); wherein the classes may be organized in a schema (A knowledge graph schema may be materialized or instantiated into an actual knowledge graph with specific entities, concepts, numerals, and relationships of the various types/classes as specified in the knowledge graph) (page 2, paragraph [0016]), wherein the schema may be defined using the resource description framework or the web ontology language (a knowledge graph schema may be constructed based on a Resource Description Framework (RDF) to provide a data model that represents the knowledge base as a collection of expressions in the form of subject-predicate-object) (page 2, paragraph [0016]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Oltramari with the teachings of McCarthy to improve predictive expansion of knowledge graphs to further facilitate their wider spread in various applications.
Regarding Claim 9, Oltramari et al teaches the method, wherein each statement is identified by at least one uniform resource identifier (metadata (e.g., having a resource name, a resource description, a unique identifier, an author, and the like) (pages 2 and 3, paragraph [0029]; wherein at least one of the nodes (Non-limiting examples of structured data include a knowledge graph (e.g., having a plurality of nodes (each node defining a different subject matter domain), wherein some of the nodes are interconnected by at least one relation)) (pages 2 and 3, paragraph [0029])) and edges is identified by a uniform resource identifier (metadata (e.g., having a resource name, a resource description, a unique identifier, an author, and the like) (pages 2 and 3, paragraph [0029]; wherein the directed graph is a knowledge graph (Non-limiting examples of structured data include a knowledge graph (e.g., having a plurality of nodes (each node defining a different subject matter domain)) (pages 2 and 3, paragraph [0029].
Oltramari et al fails to teach the method, wherein the directed graph is represented using the resource description framework.
McCarthy et al teaches the method, wherein the directed graph is represented using the resource description framework (a knowledge graph schema may be constructed based on a Resource Description Framework (RDF) to provide a data model that represents the knowledge base as a collection of expressions in the form of subject-predicate-object) (page 2, paragraph [0016]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Oltramari with the teachings of McCarthy to improve predictive expansion of knowledge graphs to further facilitate their wider spread in various applications.
Claim 16 is rejected for the same reason as claim 3.
Claim 17 is rejected for the same reason as claim 4.
Allowable Subject Matter
Claims 5, 6, 18 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the 35 USC 101 and USC 112(b) rejections above are overcome.
Claim 11 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the 35 USC 101 rejections above are overcome.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bornea et al (US 9,323,862) discloses identifying the optimal schema to store graph data in a relational store.
Bornea et al (US 10,949,464) discloses identifying the optimal schema to store graph data in a relational store.
Bedi et al. (US 10,963,512) discloses query language interoperability in a graph language.
Bedi et al. (US 11,567,997) discloses query language interoperability in a graph language.
Bhatia et al. (US 11,997,056) discloses a language model with external knowledge base.
Hubauer (US 2022/0101153) discloses generation of completion rules for knowledge graphs.
Bedi et al. (US 2023/0169117) discloses query language interoperability in a graph language.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATWANT K SINGH whose telephone number is (571)272-7468. The examiner can normally be reached Monday thru Friday 9:00 AM to 6:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras D Shah can be reached at (571}270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SATWANT K SINGH/ Primary Examiner, Art Unit 2653