Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise one or more physical conditions of the user; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a circumstance when the user is exhibiting a particular physical condition to output subsequent questions.

Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.

Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise one or more physical conditions of the user; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a circumstance when the user is exhibiting a particular physical condition to output subsequent questions.

Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.

Abstract: A method of selecting data, the method comprising, receiving data indicating a first requirement and identifying first records stored in a first database. The first records relate to first data subjects and the identification is based upon the first requirement. The first data subjects cannot be identified from the records of the first database. Second records are identified, the second records being stored in a second database and relating to the first data subjects. The first data subjects can be identified from the identified records of the second database. Each of the second records corresponds to a respective one of the first records, and the identification of the second records is based upon the first requirement.

Abstract: A method of selecting data, the method comprising, receiving data indicating a first requirement and identifying first records stored in a first database. The first records relate to first data subjects and the identification is based upon the first requirement. The first data subjects cannot be identified from the records of the first database. Second records are identified, the second records being stored in a second database and relating to the first data subjects. The first data subjects can be identified from the identified records of the second database. Each of the second records corresponds to a respective one of the first records, and the identification of the second records is based upon the first requirement.

Abstract: A method of selecting data, the method comprising, receiving data indicating a first requirement and identifying first records stored in a first database. The first records relate to first data subjects and the identification is based upon the first requirement. The first data subjects cannot be identified from the records of the first database. Second records are identified, the second records being stored in a second database and relating to the first data subjects. The first data subjects can be identified from the identified records of the second database. Each of the second records corresponds to a respective one of the first records, and the identification of the second records is based upon the first requirement.

Abstract: A method of selecting data, the method comprising, receiving data indicating a first requirement and identifying first records stored in a first database. The first records relate to first data subjects and the identification is based upon the first requirement. The first data subjects cannot be identified from the records of the first database. Second records are identified, the second records being stored in a second database and relating to the first data subjects. The first data subjects can be identified from the identified records of the second database. Each of the second records corresponds to a respective one of the first records, and the identification of the second records is based upon the first requirement.