Computer implemented method for the automated analysis or use of data
A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.
1. A computer implemented method for the automated analysis or use of data, comprising the steps of:
(a) storing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language, where the structured, machine-readable representation of data comprises a plurality of identifiers which are selected from an address space that is sufficiently large to enable users to select a new identifier with negligible risk of selecting a previously allocated identifier;
(b) automatically processing the structured, machine-readable representation of data for one or more of the following: to derive facts or relationships, to reason, to learn, to translate, to answer questions, to process natural language content, to enable man-machine interaction, to represent and to police rules or tenets, to enable one or more vertical applications;
wherein the identifiers include a “shared ID”, a “private ID” or “local ID,” and a “public ID”;
in which the “shared ID” is an identifier usable by more than one user; the “private ID” or “local ID” is similarly an identifier usable by only one user and is not published or exposed to other users; and the “public ID” is an identifier usable by a user, in universal language (UL), that can be seen by every user.
2. The method of claim 1 in which the structured, machine-readable representation of data that conforms to the machine-readable language comprises semantic nodes and passages; and in which a semantic node represents an entity and is itself represented by an identifier; and a passage is either (i) a semantic node or (ii) a combination of semantic nodes; and where machine-readable meaning comes from the choice of semantic nodes and the way they are combined and ordered as passages.
3. The method of claim 2 in which a semantic node once defined has an identifier or ID.
4. The method of claim 1 in which the new identifier is selected from an address space that is sufficiently large to enable client entities to select new identifiers independently of other client entities without duplication.
5. The method of claim 1 in which the new identifier is a Universally Unique Identifier (UUID).
6. The method of claim 5 in which the new identifier is a 128-bit version 4 UUID (RFC 4122) with hyphenated lower-case syntax.
7. The method of claim 5 in which the new identifier is a 128-bit version 4 UUID (RFC 4122).
8. The method of claim 1 in which the new identifier is a string, such as a Unicode string.
9. The method of claim 1 in which a string can denote itself as a structured, machine-readable representation of data and its meaning is strictly the string itself only, and any natural language meaning contained within the string is not part of the meaning of the string.
10. The method of claim 1 in which a string is represented by an ID as an additional identifier.
11. The method of claim 1 in which a string is represented as a UUID or other numerical ID and a separate passage links the string to that numerical ID to provide its meaning.
12. The method of claim 1 in which two identical strings used as structured, machine-readable representations of data have universal meaning as that string.
13. The method of claim 1 including the step of receiving from a user a structured, machine-readable representation of data with its own local meaning including receiving a selection of an unused identifier.
14. The method of claim 1 including the step of receiving from a user a selection of an identifier for a semantic node even if another identifier is already used for the semantic node.
15. The method of claim 1 including the step of receiving from a user a definition of a meaning of a combination of structured, machine-readable representations of data.
16. The method of claim 1 in which the non-transitory computer-readable medium includes multiple different structured, machine-readable representations of data for the same specific word, concept, or other thing.
17. The method of claim 1 in which meaning that comes from combining shared structured, machine-readable representations of data is universal.
18. The method of claim 1 in which the non-transitory computer-readable medium includes a dictionary, in which each sense of each word in the dictionary is represented by a structured, machine-readable representation of data.
19. The method of claim 1 in which a semantic node is a structured, machine-readable representation of data that, once defined, has an identifier so it can be referred to within the machine-readable language.
20. The method of claim 1 in which a passage is a combination of semantic nodes expressing meaning, and is the sole nesting construct.
21. The method of claim 1 in which semantic nodes in infinite classes can be represented as a combination of a plurality of other nodes.
22. The method of claim 1 in which the method includes the step of providing a service operable to receive a description of an entity and return one or more identifiers for structured, machine-readable representations of data corresponding to the entity, which includes a shared identifier for the entity.
23. The method of claim 22 in which the service probabilistically compares the description, which is a description of a proposed semantic node or passages with available information about existing entities to determine if there is a match and optionally returns a new identifier if no match is found.
24. The method of claim 1 in which the machine-readable language is scalable since any natural language word, concept, or other thing can be represented by a structured, machine-readable representation of data.
25. The method of claim 1 in which the machine-readable language is scalable since there are no restrictions on which users can create a structured, machine-readable representation of data or related identifier.
26. Computer-based system configured to analyse data, the computer-based system being configured to:
(a) store in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language, where the structured, machine-readable representation of data comprises a plurality of identifiers which are selected from an address space that is sufficiently large to enable client entities to select a new identifier with negligible risk of selecting a previously allocated identifier;
(b) automatically process the structured, machine-readable representation of data for one or more of the following: to derive facts or relationships, to reason, to learn, to translate, to answer questions, to process natural language content, to enable man-machine interaction, to represent and to police rules or tenets, to enable one or more vertical applications;
wherein the identifiers include a “shared ID”, a “private ID” or “local ID,” and a “public ID”;
in which the “shared ID” is an identifier usable by more than one user; the “private ID” or “local ID” is similarly an identifier usable by only one user and is not published or exposed to other users; and the “public ID” is an identifier usable by a user, in universal language (UL), that can be seen by every user.
27. A computer implemented method for the automated analysis or use of data, comprising the steps of:
(a) storing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language, where the structured, machine-readable representation of data comprises a plurality of identifiers which are selected from an address space that is sufficiently large to enable users to select a new identifier with negligible risk of selecting a previously allocated identifier;
(b) automatically processing the structured, machine-readable representation of data for one or more of the following: to derive facts or relationships, to reason, to learn, to translate, to answer questions, to process natural language content, to enable man-machine interaction, to represent and to police rules or tenets, to enable one or more vertical applications;
in which the method includes the step of providing a service operable to receive a description of an entity and return one or more identifiers for structured, machine-readable representations of data corresponding to the entity, which includes a shared identifier for the entity;
in which the service probabilistically compares the description, which is a description of a proposed semantic node or passages, with available information about existing entities to determine if there is a match.
28. The method of claim 27 , in which the service returns a new identifier if no match is found.
29. A computer implemented method for the automated analysis or use of data, comprising the steps of:
(a) storing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language, where the structured, machine-readable representation of data comprises a plurality of identifiers which are selected from an address space that is sufficiently large to enable users to select a new identifier with negligible risk of selecting a previously allocated identifier;
(b) automatically processing the structured, machine-readable representation of data for one or more of the following: to derive facts or relationships, to reason, to learn, to translate, to answer questions, to process natural language content, to enable man-machine interaction, to represent and to police rules or tenets, to enable one or more vertical applications;
in which the new identifier is a 128-bit version 4 UUID (RFC 4122).
30. The method of claim 29 , in which the new identifier is a 128-bit version 4 UUID (RFC 4122) with hyphenated lower-case syntax.