A method for performing de-identified location analytics is provided. The method may include generating a de-identified image by removing the identifiable characteristics from a building arrangement image; generating a binary threshold image by processing the de-identified image using a threshold process; generating a segmentation image by removing any segmentation objects of the binary threshold image with an area less than a defined pixel area; extracting room definitions from the segmentation image, wherein each room definition comprises a series of pixels corresponding to an outline of one of the rooms of the building arrangement image; generating a pixilation table, the pixilation table comprising room definition entries corresponding to the room definitions of the segmentation image, wherein each pixel of the segmentation image is mapped to the room definition entry corresponding to the room definition of the outline surrounding the pixel; and assigning a label to each room definition entry.

A method for performing de-identified location analytics is provided. The method may include generating a de-identified image by removing the identifiable characteristics from a building arrangement image; generating a binary threshold image by processing the de-identified image using a threshold process; generating a segmentation image by removing any segmentation objects of the binary threshold image with an area less than a defined pixel area; extracting room definitions from the segmentation image, wherein each room definition comprises a series of pixels corresponding to an outline of one of the rooms of the building arrangement image; generating a pixilation table, the pixilation table comprising room definition entries corresponding to the room definitions of the segmentation image, wherein each pixel of the segmentation image is mapped to the room definition entry corresponding to the room definition of the outline surrounding the pixel; and assigning a label to each room definition entry.

Computer-readable media, methods, and systems are disclosed for converting a diagram into a digital diagram format. A diagram is received as an image file and a plurality of recognition stages produce a final diagram based on predicted information from one or more of the recognition stages. The plurality of recognition stages including a shape detection stage for detecting a plurality of shapes and at least one arrow detection stage in which arrows are detected as relations between pairs of shapes. The final diagram is generated based on the predicted information and is converted into the digital diagram format compatible with a diagram modeling language.

There are provided an information processing apparatus, an information processing method, and a program with which structural elements in a structural formula can be identified from an image showing the structural formula and the results of identification can be used in a compound search performed later on.

An information processing apparatus includes a processor, and the processor is configured to identify, on the basis of feature values of respective regions in a subject image showing a structural formula of each subject compound among subject compounds, structural elements shown by the respective regions among structural elements in the structural formula of the subject compound, by using an identification model, and store element information about the identified structural elements in the structural formula of each subject compound in association with the subject compound The identification model is a model created through machine learning using a learning image showing one structural element in a structural formula of a compound.

Artificial Intelligence systems quantify requirements for construction of a building by receiving two-dimensional representations (e.g., physical or electronic documents) and mimicking the perception, learning, problem-solving, and decision-making formerly performed by human workers. AI analysis may be repeated for multiple two-dimensional representations, each two-dimensional reference including a change to a design of a building to be constructed. The AI processes denote and track changes made in the sequence of two-dimensional references, and extrapolate changes to materials and labor that relate to the changes in design of the building to be constructed.

Artificial Intelligence systems quantify requirements for construction of a building by receiving two-dimensional representations (e.g., physical or electronic documents) and mimicking the perception, learning, problem-solving, and decision-making formerly performed by human workers. AI analysis may be repeated for multiple two-dimensional representations, each two-dimensional reference including a change to a design of a building to be constructed. The AI processes denote and track changes made in the sequence of two-dimensional references, and extrapolate changes to materials and labor that relate to the changes in design of the building to be constructed.

An image object labeling method and system are disclosed. The method is executed by a processor coupled to a memory and includes providing an image file; detecting at least one first object on the image file and generating at least one graphic block and attribute thereof; performing a binarization process to present a first graphic feature on a region containing the graphic block in the image file and a second graphic feature on the rest region; combining the results of detecting and the binarization process, and filtering the image file through several gradually reduced masks to show several separated graphic components until number of the separated graphic components and the at least one graphic block are the same; and assigning a label to the separated graphic components according to the attribute, alternatively, outputting a message, receiving a command for adjusting the graphic block and attribute thereof, as the labels.

Computer-readable media, methods, and systems are disclosed for converting a diagram into a digital diagram format. A diagram is received as an image file and a plurality of recognition stages produce a final diagram based on predicted information from one or more of the recognition stages. The plurality of recognition stages including a shape detection stage for detecting a plurality of shapes and at least one arrow detection stage in which arrows are detected as relations between pairs of shapes. The final diagram is generated based on the predicted information and is converted into the digital diagram format compatible with a diagram modeling language.

Provided is a method of estimating a relationship between objects through machine learning, the method comprising receiving a scan image, recognizing a starting point of a relationship line and an object code in the scan image, determining whether a relationship line is present between the recognized starting point and the recognized object code, and, based on determining that the relationship line is present, transmitting a combination of the starting point and the object code connected with the relationship line to a database.

An engineering support system that supports engineering of a process control system, includes: a storage; and a processor connected to the storage and that: transforms design drawings into semantic models and outputs the semantic models to the storage; and generates a combined semantic model by combining the semantic models based on a degree of similarity among the semantic models and outputs the combined semantic model to the storage, wherein each of the semantic models is expressed by first information indicating elements included in the design drawings and second information indicating a relationship between the elements.