A support apparatus includes a generation apparatus and an analysis apparatus. The generation apparatus executes (a-1) to (a-5) with I=1 to n, and generates pieces of process information. The generation apparatus extracts material words from a document i in (a-1), extracts a treatment word i from the document i in (a-2), extracts a synthesis condition i from the document i in (a-3), extracts a characteristic value i related to a target material from the document i in (a-4), and associates the material words, the treatment word i, the synthesis condition i, and the characteristic value i with each other to generate process information i in (a-5). The analysis apparatus includes a combiner that generates composite process information including a common part common to the pieces of process information and different parts different among the pieces of process information, and an outputter that outputs the composite process information.

A handwriting data generation apparatus includes a memory containing processor-executable instructions and a processor coupled to the memory. The processor is configured to perform, when loaded with the processor-executable instructions, associating tactile feedback with at least part of stroke data generated according to handwriting input, and generating digital ink including haptics data indicating the stroke data and the tactile feedback.

Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.

Aspects of the disclosure provide for automatically generating labels for sensor data. For instance, first sensor data for a first vehicle may be identified. This first sensor data may have been captured by a first sensor of the vehicle at a first location during a first point in time and may be associated with a first label for an object. Second sensor data for a vehicle may be identified. The second sensor data may have been captured by a second sensor of the vehicle at a second location at a second point in time outside of the first point in time. The second location is different from the first location. The object is a static object may be determined. Based on the determination that the object is a static object, the first label may be used to automatically generate a second label for the second sensor data.

A learning device estimating apparatus aims at a learning device as an attack target, and comprises a recording part, an inquiring part, a capturing part and a learning part. A predetermined plurality of pieces of observation data are recorded. The inquiring part inquires of the attack target learning device for each of the pieces of observation data recorded in the recording part to acquire label data and records the acquired label data to the recording part in association with observation data. The capturing part inputs the observation data and the label data associated with the observation data that have been recorded to the recording part, to the learning part. The learning part is characterized by using an activation function that outputs a predetermined ambiguous value in a process for determining a classification prediction result, and the learning part performs learning using the inputted observation data and label data.

Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.

A system and method for field extraction including determining a key position of a key in an electronic file, isolating candidate key values based on a distance from the key position, selecting a key value from the candidate key values based on an output of a trained neural network, and extracting the key and the key value from the electronic file, regardless of a key-value structure.

In some examples, deterring a third party from copying a product or process may include scanning code to generate scanned code. Text in the scanned code may be read and/or at least one image in the scanned code may be recognized. The text and/or the at least one image may be analyzed to determine whether the code includes a specified trademark. Based on a determination that the code includes the specified trademark, an indication of authenticity of the code may be generated.

Computer-implemented methods, computer program products, and computer systems for improving intent determination in a messaging dialog manager system. The computer-implemented method for improving intent determination in a messaging dialog manager system may include one or more processors configured for receiving first agent entry data corresponding to a first agent communicating in a messaging dialog interface, determining that the first agent entry data expects a response that is within a first response domain, determining that a first user entry entered in the messaging dialog interface is not within the first response domain. Further, the computer-implemented may include identifying a second agent configured with a second response domain that includes the first user entry and transmitting the first user entry to the second agent to facilitate a seamless transition of an established communicational flow between the first agent and a first user.

An exposure monitoring system is disclosed. The system comprises: at least one camera to observe a monitoring area; a plurality of beacons each comprising at least one light emitting element and a controller to provide an actuation sequence to the light emitting element; and a video analytics system. The video analytics system comprises a processor configured to receive video data captured by the at least one camera, analyse the video for the presence of light emissions from the beacons, decode the actuation sequence of the light emissions from any detected beacon to provide an identification for the beacon, compile a location history for each beacon, over time, detected within the surveillance area, and determine an exposure risk associated with each beacon. A method of exposure monitoring is also disclosed.