Patent application EP18193452A (EP), filed 2018-09-10
Publication number: EP3460685A1
Title: IMPROVED SEMANTIC CLASSIFICATION OF AN ENTITY IN A BUILDING INFORMATION MODEL
Abstract: [0001]    The current invention concerns a computer-implemented method, a computer system, and a computer program product for the semantic classification of an entity in a building information model (BIM). The BIM comprises multiple target entities. Update data is obtained. For each target entity, geometric information about the target entity is obtained from the BIM. For each target entity, an initial probability distribution of semantic classification is determined based on the obtained geometric information about the target entity. Relative geometric information about the target entities is obtained from the BIM. For each target entity, an updated probability distribution of semantic classification is determined, based on the obtained relative geometric information, the initial probability distributions of all target entities, and the update data. For each target entity, a semantic classification is selected based on the updated probability distribution of the target entity.
IPC codes: G06F  17/    50            A I
G06K   9/    00            A I
Claims: number: 1
text: Computer-implemented method for the semantic classification of an entity of a building information model, the building information model comprising multiple target entities, whereby the method comprises the steps of:
number: 2
text: Computer-implemented method according to preceding claim 1, comprising the steps of:
number: 3
text: Computer-implemented method according to any one of the preceding claims, wherein relative geometric information about entities comprises for a binary relative geometric property an assessment, the assessment comprising for each pair of said entities a fulfilment value of the binary relative geometric property.
number: 4
text: Computer-implemented method according to preceding claim 3, wherein the update data comprises for the binary relative geometric property a table, the table comprising for each pair of candidate semantic classifications of the set a conditional probability, the conditional probability being the probability for a pair of entities to comprise said pair of candidate semantic classifications in case of fulfilment of the binary relative geometric property.
number: 5
text: Computer-implemented method according to preceding claim 4, comprising the steps of:
number: 6
text: Computer-implemented method according to any one of preceding claims 4 and 5, wherein relative geometric information about entities comprises for each of N binary relative geometric properties an assessment with N ≥ 2, wherein the update data comprises for each binary relative geometric property a table, wherein an initial probability distribution is a 0-th updated probability distribution, wherein iteratively for each target entity an i-th updated probability distribution of semantic classification is determined based on the assessment for the i-th binary relative geometric property, the (i-1)-th updated probability distributions of all target entities, and the table of the i-th binary relative geometric property, with 1 ≤ i ≤ N, wherein for each target entity a semantic classification is selected based on the N-th updated probability distribution of the target entity.
number: 7
text: Computer-implemented method according to any one of the preceding claims, wherein a binary relative geometric property is a binary reciprocal proximity property for a pair of entities, preferably being one of:
number: 8
text: Computer-implemented method according to any one of the preceding claims, comprising the steps of:
number: 9
text: Computer-implemented method according to preceding claim 8, wherein the single entity training data is obtained by inputting the geometric information and semantic classification of each training entity to a machine learning module.
number: 10
text: Computer-implemented method according to preceding claim 9, wherein the machine learning module comprises computer-executable instructions for machine learning based on gradient boosting.
number: 11
text: Computer-implemented method according to any one of the preceding claims, wherein an entity is a solid.
number: 12
text: Computer-implemented method according to preceding claim 11, wherein geometric information about a solid comprises at least two geometric properties of the list comprising: a number of faces; a number of edges; a number of vertices; a volume; a dimension; a maximal dimension; three maximal dimensions in mutually orthogonal directions, such as a height, a length and a width; a ratio of two maximal dimensions in mutually orthogonal directions; a ratio of a volume of the solid and a volume of a bounding box of the solid; a largest face area; a ratio of a largest face area and another face area; an inclination of a largest face with respect to a reference direction, such as a Z-direction; a largest edge length; a ratio of a largest edge length to another edge length; an inclination of a largest edge with respect to a reference direction, such as a Z-direction; a relative vertical location of the solid with respect to a total height of the building information model; a relative size of the solid with respect to a size of the building information model; a number of planar faces; a number of cylindrical faces; a number of conical faces; a number of toroidal faces; a number of horizontal faces; a number of vertical faces; a presence of an extrusion direction; in case of presence of an extrusion direction, a shape of an extrusion profile; and an inclination to a reference direction, such as a Z-direction, of a line fitted through a horizontal face of a solid.
number: 13
text: Computer-implemented method according to any one of the preceding claims, comprising the steps of:
number: 14
text: Computer system for the semantic classification of an entity of a building information model, whereby the computer system is configured for performing the computer-implemented method according to any one of the preceding claims 1 to 13.
number: 15
text: Computer program product for the semantic classification of an entity of a building information model, whereby the computer program product comprises instructions which, when the computer program product is executed by a computer, cause the computer to carry out the computer-implemented method according to any one of the preceding claims 1 to 13.
Office action: search_report, dated 2019-03-27
Prior art cited: ref_id: US2015006117A1
ref_type: patent
severity: background
ref_id: US2017061326A1
ref_type: patent
severity: background
ref_id: - MICHAEL BELSKY ET AL: "Semantic Enrichment for Building Information Modeling : Semantic enrichment for building information modeling", COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, vol. 31, no. 4, 1 April 2016 (2016-04-01), US, pages 261 - 274, XP055459954, ISSN: 1093-9687, DOI: 10.1111/mice.12128
ref_type: npl
severity: background
ref_id: US2015006117
ref_type: patent
severity: background
ref_id: US2017061326
ref_type: patent
severity: background
ref_id: - MICHAEL BELSKY ET AL, "Semantic Enrichment for Building Information Modeling : Semantic enrichment for building information modeling", COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, US, (20160401), vol. 31, no. 4, doi:10.1111/mice.12128, ISSN 1093-9687, pages 261 - 274, XP055459954 [ID] 1-15 * abstract * * page  263, column  1, paragraph  1 * * page  264, column  2, paragraph  6 *
ref_type: npl
severity: background
Final disposition: pending (2019-03-27)