Computer implemented method for the automated analysis or use of data
A computer implemented method for the automated analysis or use of data, comprising: (a) storing or accessing in a non-transitory storage medium, a structured, machine-readable representation of data that conforms to a machine-readable language defined by a syntax, including an unambiguous syntax that further comprises nesting of structured, machine-readable representations of data, (b) automatically processing the representation to learn, in which the learning is different to statistical machine-learning and takes place from a chain of reasoning steps, in which semantic nodes and passages generated from the chain, are stored and utilised, and (c) storing a result of the learning in the structured, machine-readable representation. The data conforming to the machine-readable language comprises semantic nodes representing an entity and is represented by an identifier and passages that are a combination of semantic nodes. Machine-readable meaning comes from choice of semantic nodes and the way they are combined and ordered as passages.
1 . A computer implemented method for the automated analysis or use of data, comprising the steps of:
(a) storing in a non-transitory storage medium, or accessing in a non-transitory storage medium, a structured, machine-readable representation of data that conforms to a machine-readable processable language defined by a syntax, wherein the syntax for the machine-readable processable language is an unambiguous syntax comprising nesting of structured, machine-readable representations of data; in which the structured, machine-readable representation of data that conforms to the machine-readable processable language comprises semantic nodes and passages; and in which a semantic node represents an entity and is represented by an identifier; and a passage is either (i) a semantic node or (ii) a combination of semantic nodes; and in which machine-readable meaning comes from choice of semantic nodes and the way the semantic nodes are combined and ordered as passages; wherein the structured machine-readable representation of data includes passages that represent reasoning statements, wherein the passages that represent the reasoning statements are represented in the processable language to represent semantics of reasoning steps;
(b) automatically processing the structured, machine-readable representation to learn, in which the learning is different to statistical machine-learning, wherein the learning takes place using at least a plurality of the reasoning steps, in which semantic nodes and passages that are generated using at least the plurality of the reasoning steps, are stored and utilised, and
(c) storing a result of the learning in the structured, machine-readable representation.
2 . The method of claim 1 in which the syntax is a simple unambiguous syntax comprising nesting of structured, machine-readable representations of data to any arbitrary depth.
3 . The method of claim 1 in which the syntax is a simple unambiguous syntax in which structured, machine-readable representations of data can only be combined in nested combinations.
4 . The method of claim 1 in which the syntax allows for expressions to be nested indefinitely to define a concept, coupled with contextual information about the concept, as a hierarchy of semantic structured, machine-readable representations of data.
5 . The method of claim 1 in which combinations of structured, machine-readable representations of data can contain any finite numbers of structured, machine-readable representations of data creating any level of nesting.
6 . The method of claim 1 in which structured, machine-readable representations of data are semantic nodes or passages.
7 . The method of claim 1 in which the machine-readable processable language is a universal language for which subject matter expressible in natural language is expressible in the structured, machine-readable representation of data or a combination of structured, machine-readable representations of data.
8 . The method of claim 7 in which a structured, machine-readable representation of data represents a specific entity, and once generated, identifies uniquely that specific entity, in the universal language.
9 . The method of claim 8 in which the specific entity is a word or a concept.
10 . The method of claim 1 in which an ordered or partially ordered collection of structured, machine-readable representations of data captures a specific meaning or semantic content.
11 . The method of claim 1 in which meaning of a structured, machine-readable representation of data comes from statements written in the machine-readable processable language.
12 . The method of claim 1 in which meaning of a structured, machine-readable representation of data comes from other structured, machine-readable representations of data that represents speech input about the structured, machine-readable representation of data.
13 . The method of claim 1 in which a structured, machine-readable representation of data that represents an entity encodes semantic meaning of that entity through links to structured, machine-readable representations of data of related words, concepts, other terms, or logical processes.
14 . The method of claim 1 in which combining structured, machine-readable representations of data generates a new word, concept, or other term with a new meaning or semantic content in the machine-readable processable language.
15 . The method of claim 1 in which the machine-readable processable language is understandable to human users where it corresponds to an equivalent statement in natural language.
16 . The method of claim 1 in which a semantic node is a structured, machine-readable representation of data that, once defined, has an identifier or ID so it can be referred to within the machine-readable processable language, and the identifier is selected from an address space to select a new identifier with negligible risk of selecting a previously allocated identifier.
17 . The method of claim 16 , wherein the identifier is a 128-bit version 4 Universally Unique IDentifier (UUID) (RFC 4122) with hyphenated lower-case syntax.
18 . The method of claim 16 , wherein the identifier is a string.
19 . The method of claim 18 , wherein the string is a Unicode string.
20 . The method of claim 1 in which the machine-readable processable language is scalable since any natural language word, or concept, can be represented by a structured, machine-readable representation of data.
21 . The method of claim 1 in which the machine-readable processable language is scalable since there are no restrictions on creating a structured, machine-readable representation of data or related identifier.
22 . The method of claim 1 including processing the semantic nodes and passages to learn new information.
23 . The method of claim 1 including learning new information from input provided by human users, in which natural language provided by the human users in spoken or written form is translated into semantic nodes and passages and then new information represented by these semantic nodes and passages is stored and used in step (b).
24 . The method of claim 1 wherein a machine learning system is used to analyse document and non-document data and create passages from that data.
25 . The method of claim 1 wherein a machine learning system is used to generate semantic nodes or passages.
26 . The method of claim 25 wherein the machine learning system is a neural network system.
27 . The method of claim 26 wherein the neural network system is a deep learning system.
28 . A computer-based system configured to analyse data, the system being configured to:
(a) store in a non-transitory storage medium, or access in a non-transitory storage medium, a structured, machine-readable representation of data that conforms to a machine-readable processable language, wherein the language has a syntax in which the syntax for the machine-readable processable language is an unambiguous syntax comprising nesting of structured, machine-readable representations of data; in which the structured, machine-readable representation of data that conforms to the machine-readable processable language comprises semantic nodes and passages; and in which a semantic node represents an entity and is represented by an identifier; and a passage is either (i) a semantic node or (ii) a combination of semantic nodes; and in which machine-readable meaning comes from choice of semantic nodes and the way the semantic nodes are combined and ordered as passages; wherein the structured machine-readable representation of data includes passages that represent reasoning statements, wherein the passages that represent the reasoning statements are represented in the processable language to represent semantics of reasoning steps;
(b) automatically process the structured, machine-readable representation to learn, in which the learning is different to statistical machine-learning, wherein the learning takes place using at least a plurality of the reasoning steps, in which semantic nodes and passages that are generated using at least the plurality of the reasoning steps, are stored and utilised; and
(c) store a result of the learning in the structured, machine-readable representation.
29 . A computer program product embodied on a first non-transitory storage medium, the computer program product executable on a processor to:
(a) access in a second non-transitory storage medium a structured, machine-readable representation of data that conforms to a machine-readable processable language, wherein the processable language has a syntax in which the syntax for the machine-readable processable language is an unambiguous syntax comprising nesting of structured, machine-readable representations of data; in which the structured, machine-readable representation of data that conforms to the machine-readable processable language comprises semantic nodes and passages; and in which a semantic node represents an entity and is represented by an identifier; and a passage is either (i) a semantic node or (ii) a combination of semantic nodes; and in which machine-readable meaning comes from choice of semantic nodes and the way the semantic nodes are combined and ordered as passages; wherein the structured machine-readable representation of data includes passages that represent reasoning statements, wherein the passages that represent the reasoning statements are represented in the processable language to represent semantics of reasoning steps;
(b) automatically process the structured machine-readable representation of data that conforms to the machine-readable processable language, to learn, in which the learning is different to statistical machine-learning, wherein the learning takes place using at least a plurality of the reasoning steps, in which semantic nodes and passages that are generated using at least the plurality of the reasoning steps, are stored and utilised, and
(c) store a result of the learning in the structured, machine-readable representation.