Computer implemented method for the automated analysis or use of data
There is provided a computer implemented method for automated analysis or use of data, comprising the steps of: (a) storing in a memory store a structured, machine-readable representation of data that conforms to a machine-readable language; in which the data includes personal health or medical data; (b) automatically processing the structured representation of the data to analyse the personal health or medical data; in which the method includes the steps of (c) the machine-readable language representing a question in a memory in the structured, machine-readable representation of data; and (d) automatically generating a response to the question, using the following steps: (i) matching the question with the structured, machine-readable representations of data previously stored in the memory store; (ii) fetching and executing one or more computation units, wherein the computation units represent computational capabilities relevant to answering the question; (iii) fetching and execution of one or more reasoning passages.
1. A computer implemented method for automated analysis or use of data, comprising the steps of:
(a) storing or accessing in a memory store a structured, machine-readable representation of data that conforms to a machine-readable language; in which the data includes personal health or medical data;
(b) automatically processing the structured representation to analyse the personal health or medical data;
in which the method includes the step of
(c) the machine-readable language representing a question in a memory in the structured, machine-readable representation of data; and the method further includes the step of
(d) automatically generating a response to the question, using the following steps:
(i) matching the question with the structured, machine-readable representations of data previously stored in the memory store;
(ii) fetching and executing one or more computation units, wherein the computation units represent computational capabilities relevant to answering the question;
(iii) fetching and execution of one or more reasoning passages, which are structured, machine-readable representations of data that represent semantics of potentially applicable reasoning steps relevant to answering the question; and
in which the representation of the question, the structured, machine-readable representations of data previously stored in the memory store, the computation units and the reasoning passages are all represented in the same machine-readable language;
in which the machine-readable language used to represent the question, the structured, machine-readable representations of data previously stored in the memory store, the computation units and the reasoning passages uses semantic nodes and passages to represent the question, the structured, machine-readable representations of data previously stored in the memory store, the computation units and the reasoning passages; and in which each semantic node represents a respective entity which is represented by a respective identifier; and each passage is either (i) a semantic node or (ii) a combination of semantic nodes; and where machine-readable meaning comes from choice of semantic nodes and the way they are combined and ordered as passages;
in which the machine-readable language: (I) uses a syntax that is a single shared syntax that applies to passages that represent factual statements, query statements and reasoning statements; (II) uses a syntax that is an unambiguous syntax comprising nesting of structured representations of data; and (III) uses an identifier 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; and (IV) is scalable.
2. The method of claim 1 in which a broad set of health data for one or more people is managed and where at least some of the health data is represented in a structured machine-readable form that encodes meaning stored within one or more data stores.
3. The method of claim 1 in which the health data includes nutrition data about food or drink that has been consumed by at least one of one or more people.
4. The method of claim 3 in which the nutrition data includes data that represents uncertainty about volume or nutritional information or composition of what was consumed.
5. The method of claim 1 in which the health data comprises data about results of blood tests or measurements or body composition or activity information or genetic data or microbiome data or bowel motion events or sleep data or workout data or activity data or symptoms of diseases or human moods or menstruation or ingestion of medication or medical conditions or data from any wearable device.
6. The method of claim 1 including the step of conversing with one or more users via text.
7. The method of claim 6 including the step of providing for other people to converse with the one or more users and to view relevant health data.
8. The method of claim 1 including the step of creating graphs of specific types of health data wherein users can see how different data correlates.
9. The method of claim 1 including the step of analysing health data to uncover insights relevant to a specific user's health.
10. The method of claim 1 including the step of analysing potential dietary intolerances or behaviours that influence sleep.
11. The method of claim 1 including the step of combining elements of the health data to calculate an additional item of health data not already present in the health data.
12. The method of claim 11 in which the additional item of health data is an estimate of caffeine present in a user's body at a particular time.
13. The method of claim 1 in which the structured representation of data further includes a representation of a spoken, written or graphical user interface (GUI) instruction provided by a human to a human/machine interface.
14. The method of claim 1 , in which representations of personal health or medical data are automatically translated into the machine readable language by a machine learning system that generates the semantic nodes or passages that are the representations of personal health or medical data.
15. The method of claim 14 in which the machine learning system has been trained on training data comprising natural language and a corresponding structured machine-readable representation.
16. The method of claim 1 in which the machine-readable language uses a single syntactical item, which are parentheses or brackets, to disambiguate meaning of structured representations of data.
17. The method of claim 1 in which the machine-readable language comprises a plurality of identifiers or IDs 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.
18. 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.
19. The method of claim 1 in which the machine-readable language (i) uses a single syntactical item to disambiguate meaning of structured representations of data; and (ii) is scalable since there are no restrictions on which users can create a structured representations of data or related identifier.
20. The method of claim 1 which includes the step of learning new information and representing the new information in a structured, machine-readable representation of data that conforms to the machine-readable language.
21. The method of claim 1 which includes the step of (i) receiving a word or sequence of words in a natural language; and (ii) automatically translating that word or sequence of words into the machine-readable language by identifying or generating structured machine-readable representations that semantically represent meaning of the word or sequence of words in the machine-readable language.
22. The method of claim 1 which 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, so that a user is able to use a shared identifier for the entity.
23. The method of claim 1 in which the method includes translating between a first natural language and a second natural language, by: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language; (b) receiving a word or sequence of words in the first natural language to be translated into the second natural language; (c) automatically translating that word or sequence of words expressed in the first natural language into the second natural language by (i) identifying a structured, machine-readable representation of data that represents semantics of the word or sequence of words in the first natural language and (ii) retrieving a word or sequence of words in the second natural language that corresponds in meaning to the identified structured, machine-readable representation of data.
24. The method of claim 1 which includes the step of automatically and autonomously processing detected audio or text into the structured representation of data whenever audio or text is detected or received.
25. The method of claim 1 which enables automatically selecting, deciding on or executing actions, and in which the structured representation of data includes one or more tenets, statements or other rules defining the objectives or motives, also represented using the structured representation of data; and the method further includes the steps of (i) analysing a potential action to determine whether executing the action would optimize or otherwise affect achievement or realization of those tenets, statements or other rules; (ii) automatically selecting, deciding on or executing actions only if they optimize or otherwise positively affect the achievement or realization of those tenets, statements or other rules.
26. Computer-based system configured to analyse data, the system being configured to:
(a) store in a memory store, or access in a memory store, a structured, machine-readable representation of data that conforms to a machine-readable language; in which the data includes personal health or medical data;
(b) automatically process the structured representation of the data to analyse the personal health or medical data;
(c) represent in the machine-readable language a question in a memory in the structured, machine-readable representation of data;
(d) automatically generate a response to the question, including: (i) matching the question with the structured, machine-readable representations of data previously stored in the memory store; (ii) fetching and executing one or more computation units, wherein the computation units represent computational capabilities relevant to answering the question; (iii) fetching and execution of one or more reasoning passages, which are structured, machine-readable representations of data that represent semantics of potentially applicable reasoning steps relevant to answering the question; and in which the representation of the question, the structured, machine-readable representations of data previously stored in the memory store, the computation units and the reasoning passages are all represented in the same machine-readable language;
in which the machine-readable language used to represent the question, the structured, machine-readable representations of data previously stored in the memory store, the computation units and the reasoning passages uses semantic nodes and passages to represent the question, the structured, machine-readable representations of data previously stored in the memory store, the computation units and the reasoning passages; and in which each semantic node represents a respective entity which is represented by a respective identifier; and each passage is either (i) a semantic node or (ii) a combination of semantic nodes; and where machine-readable meaning comes from choice of semantic nodes and the way they are combined and ordered as passages;
in which the machine-readable language: (I) uses a syntax that is a single shared syntax that applies to passages that represent factual statements, query statements and reasoning statements; (II) uses a syntax that is an unambiguous syntax comprising nesting of structured representations of data; and (III) uses an identifier 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; and (IV) is scalable.
27. The method of claim 15 in which the corresponding structured machine-readable representation is a machine-readable language comprising semantic nodes and passages.
28. The method of claim 17 in which the plurality of identifiers or IDs are selected from an address space which is Universal Unique Identifier (UUID) or Unicode.