US 12,175,200 B2
Computer implemented method for the automated analysis or use of data
William Tunstall-Pedoe, Cambridgeshire (GB); Finlay Curran, Cambridgeshire (GB); Harry Roscoe, Cambridgeshire (GB); and Robert Heywood, Cambridgeshire (GB)
Assigned to UNLIKELY ARTIFICIAL INTELLIGENCE LIMITED, Cambridgeshire (GB)
Filed by UNLIKELY ARTIFICIAL INTELLIGENCE LIMITED, Cambridgeshire (GB)
Filed on Dec. 25, 2022, as Appl. No. 18/088,580.
Application 18/088,580 is a continuation of application No. 18/001,368, previously published as PCT/GB2021/052196, filed on Aug. 24, 2021.
Claims priority of application No. 2013207 (GB), filed on Aug. 24, 2020; application No. 2014876 (GB), filed on Sep. 21, 2020; and application No. 2020164 (GB), filed on Dec. 18, 2020.
Prior Publication US 2023/0129464 A1, Apr. 27, 2023
Int. Cl. G06F 40/40 (2020.01); G06F 16/242 (2019.01); G06F 16/31 (2019.01); G06F 16/332 (2019.01); G06F 16/951 (2019.01); G06F 40/123 (2020.01); G06F 40/126 (2020.01); G06F 40/20 (2020.01); G06F 40/205 (2020.01); G06F 40/211 (2020.01); G06F 40/226 (2020.01); G06F 40/242 (2020.01); G06F 40/279 (2020.01); G06F 40/30 (2020.01); G06F 40/35 (2020.01); G06F 40/45 (2020.01); G06F 40/47 (2020.01); G06F 40/58 (2020.01); G06N 3/0442 (2023.01); G06N 3/0455 (2023.01); G06N 3/0499 (2023.01); G06N 3/08 (2023.01); G06N 5/02 (2023.01); G06Q 10/1053 (2023.01); G06Q 30/0251 (2023.01); G06Q 30/0601 (2023.01); G10L 15/16 (2006.01); G10L 15/18 (2013.01); G10L 15/22 (2006.01); G10L 15/26 (2006.01); G10L 25/63 (2013.01); G16H 10/60 (2018.01); H04L 51/02 (2022.01); G06N 3/091 (2023.01); G10L 15/08 (2006.01)
CPC G06F 40/35 (2020.01) [G06F 16/243 (2019.01); G06F 16/322 (2019.01); G06F 16/3329 (2019.01); G06F 16/951 (2019.01); G06F 40/123 (2020.01); G06F 40/126 (2020.01); G06F 40/20 (2020.01); G06F 40/205 (2020.01); G06F 40/211 (2020.01); G06F 40/226 (2020.01); G06F 40/242 (2020.01); G06F 40/279 (2020.01); G06F 40/30 (2020.01); G06F 40/45 (2020.01); G06F 40/47 (2020.01); G06F 40/58 (2020.01); G06N 3/0442 (2023.01); G06N 3/0455 (2023.01); G06N 3/0499 (2023.01); G06N 3/08 (2013.01); G06N 5/02 (2013.01); G06Q 10/1053 (2013.01); G06Q 30/0255 (2013.01); G06Q 30/0257 (2013.01); G06Q 30/0631 (2013.01); G10L 15/16 (2013.01); G10L 15/1815 (2013.01); G10L 15/22 (2013.01); G10L 15/26 (2013.01); G10L 25/63 (2013.01); G16H 10/60 (2018.01); H04L 51/02 (2013.01); G06N 3/091 (2023.01); G10L 2015/088 (2013.01)] 28 Claims
OG exemplary drawing
 
1. A computer implemented method for translating between a first natural language and a second natural language, 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;
(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 first translating that word or sequence of words expressed in the first natural language into the machine readable language, and then translating from the machine readable language into the second natural language, wherein the machine readable language is one in which:
(I) syntax for the machine-readable language is a substantially unambiguous syntax comprising nesting of structured representations of data;
(II) each structured representation of data includes an identifier selected from an address space that is sufficiently large to select a new identifier with negligible risk of selecting a previously allocated identifier;
in which the structured, machine-readable representation of data that conforms to the machine-readable language comprises semantic nodes and passages; and in which each semantic node represents an entity and is itself represented by an identifier; and each passage is either a semantic node or a combination of semantic nodes; and at least some of the passages include combinations of semantic nodes; and where machine-readable meaning comes from choice of semantic nodes and a way they are combined and ordered as passages; the method including:
(i) identifying a structured, machine-readable representation of data comprising semantic nodes and/or passages that conform to the machine-readable language, which represents semantics of the word or sequence of words in the first natural language;
(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; and
(iii) outputting the retrieved word or sequence of words in the second natural language that corresponds in meaning to the identified structured, machine-readable representation of data.