Abstract: Methods and systems are disclosed for encoding and decoding data from genetic traits. In one embodiment, the invention provides a method of encoding data from genetic traits. The method comprises encoding genetic traits information, including using quantum dot wavelengths to identify distinct genetic traits, and using numbers of the quantum dots to represent probabilities associated with the traits. In an embodiment, the invention provides a genetic characteristics decoding system for decoding genetic information encoded using quantum dots in a carrier. The decoding system comprises a light source for charging the quantum dots in the carrier; a scanner for scanning the carrier to retrieve information from the charged quantum dots; and a processing system for processing the retrieved information to determine quantum dot wavelengths to identify distinct genetic traits, and to determine numbers of the quantum dots to identify probabilities associated with the genetic traits.

Abstract: A computer-implemented method controls a self-driving vehicle. One or more processors, based on a set of sensor readings from one or more passenger sensors within a self-driving vehicle (SDV), determine an identity of a current passenger in the SDV. The processor(s) establish a destination for the current passenger in the SDV based on the identity of the current passenger in the SDV, and receive computer executable instructions directing the SDV to travel to the destination for the current passenger in the SDV. The processor(s) then execute the computer executable instructions to cause the SDV to travel to the destination.

Abstract: An approach is provided for authenticating a payment card. Information is read from the payment card being used for a purchase. The information includes an identifier and data on a chip, in braille cells, and in markings in the payment card. A hash read from the chip, the identifier, and security codes derived from the braille cells and the markings are sent to a payment system. A hash of an (n+1)-th block of a blockchain is received and recorded in the chip in response to validations of the hash as matching a hash of an n-th block of the blockchain, the identifier, and the first and second security codes, and a generation of the hash of the (n+1)-th block. Data about the purchase and the hash of the (n+1)-th block are sent to the payment system.

Abstract: Systems, methods and platforms for generating and improving conversational dialogs between a computer system and a user to create more intelligent, realistic human-like conversations. The systems, methods and platforms achieve a realistic human conversation by identify parameters such as the user's culture, age and known interests, identify a corpus that contains the knowledge appropriate for achieving conversational goals to a perceived topic of interest and generate expressions into dialog that may be extracted from human generated sources. The system appropriately matches the targeted user's parameters and topic based on the input of the user initiating the dialog interaction. The system may analyze and/or record the emotion of the user, cognitive context, gestures and the user's expressions, which may be performed using one or more recording systems to increase the accuracy of topic identification and the appropriate dialog responses.

Abstract: Data indicating current driving characteristics of a driver driving a vehicle can be received. A driving profile for the driver can be determined and categorized into a driving pattern category. A plurality of candidate travel routes for the driver to reach an intended destination can be identified. For each of the candidate travel routes a plurality of route segments can be identified. Based on the driving pattern category, a probability that the current driving characteristics of the driver will cause a traffic accident can be determined for each of the route segments. A route segment for which the probability that the current driving characteristics of the driver will cause the traffic accident is lowest can be determined and a notification can be communicated to the driver or vehicle indicating that route segment.

Abstract: Method of collision avoidance which includes: sensing parameters by a first vehicle with respect to relative movements of the first vehicle and a second vehicle; responsive to determining by the first vehicle, based on the sensed parameters, of an impending collision with the second vehicle, communicating, by the first vehicle, a query to the second vehicle as to if the second vehicle can perform a corrective action to avoid a collision with the first vehicle; and performing, by the first vehicle and independent of any corrective action performed by the second vehicle, a corrective action to avoid a collision with the second vehicle.

Abstract: A method and system for cognitively controlling a mobile having mobile elements controllable by a mobile stimulation pattern. Sensor data is captured from sensors detecting reactions of a baby in proximity to the mobile to a first mobile stimulation pattern and audio/video data is captured from a video camera recording reactions of the baby in proximity to the mobile. The sensor data and the audio/video data are analyzed to determine the behavior of the baby in response to the first mobile stimulation pattern and the determined behavior is compared with expected behaviors of the baby using a machine learning algorithm to learn the baby's reactions to the first mobile stimulation pattern. A second mobile stimulation pattern is selected based on the learning of the baby's reactions to the first mobile stimulation pattern and the mobile is controlled based on the second mobile stimulation pattern.

Abstract: A cognitive control of a potentially hazardous item is disclosed. A user profile can be generated for the potentially hazardous item, the user profile including a risk value and a risk threshold. A risk value can be calculated for the potentially hazardous item based on the user profile and context associated with the potentially hazardous item. The risk value can be continuously determined. The risk value can be compared to the risk threshold, and the operational state of the potentially hazardous item changed a first state to a second state, based at on a determination that the risk value exceeds the risk threshold.

Abstract: A cognitive control of a potentially hazardous item is disclosed. A user profile can be generated for the potentially hazardous item, the user profile including a risk value and a risk threshold. A risk value can be calculated for the potentially hazardous item based on the user profile and context associated with the potentially hazardous item. The risk value can be continuously determined. The risk value can be compared to the risk threshold, and the operational state of the potentially hazardous item changed a first state to a second state, based at on a determination that the risk value exceeds the risk threshold.

Abstract: Techniques for autonomously tracking and/or predicting an alert event are provided. In one example, a system can comprise a memory that stores computer executable components. The system can also comprise a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory. The computer executable components can comprise a schedule component that determines plan information for a hub of a plurality of hubs, and the hub can be coupled to a device. The computer executable components can further comprise a tracking component that identifies a deviation from the plan information by the hub. Additionally, the computer executable components can comprise a prediction component that determines a probability that the deviation will result in an alert event.

Abstract: Techniques for autonomously tracking and/or predicting an alert event are provided. In one example, a system can comprise a memory that stores computer executable components. The system can also comprise a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory. The computer executable components can comprise a schedule component that determines plan information for a hub of a plurality of hubs, and the hub can be coupled to a device. The computer executable components can further comprise a tracking component that identifies a deviation from the plan information by the hub. Additionally, the computer executable components can comprise a prediction component that determines a probability that the deviation will result in an alert event.

Abstract: Data indicating current driving characteristics of a driver driving a vehicle can be received in real time. A driving profile for the driver can be determined based on the data. The driving profile can be categorized into a driving pattern category. A plurality of candidate travel routes for the driver to reach an intended destination can be identified and, for each of the candidate travel routes, a plurality of route segments can be identified. Based on the driving pattern category, a probability that the current driving characteristics of the driver will cause a traffic accident can be determined for each of the plurality of route segments. Route segments for which the probability that the current driving characteristics of the driver will cause the traffic accident exceeds a threshold value can be determined. A notification can be communicated to the driver or vehicle indicating such probability.

Abstract: Data indicating current driving characteristics of a driver driving a vehicle can be received in real time. A driving profile for the driver can be determined based on the data. The driving profile can be categorized into a driving pattern category. A plurality of candidate travel routes for the driver to reach an intended destination can be identified and, for each of the candidate travel routes, a plurality of route segments can be identified. Based on the driving pattern category, a probability that the current driving characteristics of the driver will cause a traffic accident can be determined for each of the plurality of route segments. Route segments for which the probability that the current driving characteristics of the driver will cause the traffic accident exceeds a threshold value can be determined. A notification can be communicated to the driver or vehicle indicating such probability.

Abstract: Method of collision avoidance which includes: sensing parameters by a first vehicle with respect to its movement in a first direction; sensing a second vehicle having a movement in the first direction and separated from the first vehicle by a spaced distance; sensing parameters with respect to the movement of the second vehicle; responsive to sensing by the first vehicle of an imminent need to stop the first vehicle to avoid a collision with the second vehicle, calculating a braking distance between the first vehicle and the second vehicle; determining that the braking distance is greater than the spaced distance; communicating by the first vehicle with the second vehicle; querying the second vehicle as to if the second vehicle can take a corrective action to avoid a collision with the first vehicle; and braking the first vehicle independent of any corrective action taken by the second vehicle.

Abstract: A method for enhancing speech performance includes communicating, via an input/output (I/O) device, speech data of a patient with speech problems, segmenting the speech data, generating one or more feature vectors based on at least the segmented speech data, determining whether the one or more feature vectors match with one or more recognition objects pre-trained using clinical data of one or more other patients, determining a speech disorder based on a matched result between the one or more feature vectors and the one or more recognition objects, and communicating, via the I/O device, one or more ameliorative actions for mitigating the determined speech disorder.

Abstract: A method, system, and computer product for enhancing speech performance include includes communicating, via an input/output (I/O) device, speech data of a patient with speech problems, segmenting the speech data, generating one or more feature vectors based on at least the segmented speech data, determining whether the one or more feature vectors match with one or more recognition objects pre-trained using clinical data of one or more other patients, determining a speech disorder based on a matched result between the one or more feature vectors and the one or more recognition objects, and communicating, via the I/O device, one or more ameliorative actions for mitigating the determined speech disorder.

Abstract: Embodiments of the present invention provide a method, system and computer program product for smart elevator car destination management according to probabilistic destination determination. In an embodiment of the invention, a method for smart elevator car destination management according to probabilistic destination determination includes predicting a set of passengers requesting use of an elevator car in a bank of elevator cars in a building and determining a probability for each of the passengers that each passenger will select as a destination a particular floor in the building. The method also includes grouping ones of the passengers in the set according to a common floor determined to be probable for the grouped ones of the passengers. Finally, the method includes displaying in connection with the bank of elevator cars an assignment of the grouped ones of the passengers to one of the elevator cars in the bank.

Abstract: Embodiments of the present invention provide a method, system and computer program product for smart elevator car destination management according to probabilistic destination determination. In an embodiment of the invention, a method for smart elevator car destination management according to probabilistic destination determination includes predicting a set of passengers requesting use of an elevator car in a bank of elevator cars in a building and determining a probability for each of the passengers that each passenger will select as a destination a particular floor in the building. The method also includes grouping ones of the passengers in the set according to a common floor determined to be probable for the grouped ones of the passengers. Finally, the method includes displaying in connection with the bank of elevator cars an assignment of the grouped ones of the passengers to one of the elevator cars in the bank.

Abstract: A computer generates a navigation profile corresponding to a user by identifying one or more user preferences within an associated social media network. The computer receives a user input identifying a starting location and a destination, from which the computer identifies one or more potential routes between the starting location and destination. The computer generates one or more route profiles corresponding to the one or more potential routes detailing one or more characteristics associated with each of the potential routes. The computer then compares the navigation profile associated with the user to the route profiles associated with the one or more potential routes and, based on the comparison, determines an optimal route of the one or more potential routes.

Abstract: A method and system for automatically assessing the mental state of a user from a drawing made by the user. The mental state of the user is automatically assessed by digitizing a drawing and determining and analyzing drawing characteristics, including color, objects, and location. Video analytics are used to determine and analyze user time characteristics and mood characteristics. The mental state of the user is automatically determined by interpreting the color, object, location, time and mood characteristics and to automatically select a user action. A machine learning algorithm can learn developmental patterns of the user from historical data about a plurality of the user's drawings and the characteristics of the drawings to determine the mental state of the user. The machine learning algorithm can be used in selecting the user action.