The programmable communication system supports communication between both user devices message broker server(s) using a processor-based programmable modular block device implementing an execution engine and programmed to communicate with other processors through a message broker server using a predefined communication protocol. The block device includes a device port for coupling to sensor(s) and actuator(s), and a communication port to communicate with other processors using said predefined communication protocol. An editor program discovers and acquires information about the block device and about other devices in communication with the block device directly or via a message broker. The editor generates and downloads to the block device a rules-based program based on the acquired information. The block device uses the execution engine to execute the program and thereby obtain information through the ports and provide information and control signals.

An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include/use in creating/sending messages to/for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages. Personal profile characteristics of senders/recipients can indicate correlations between certain message characteristics and certain outcomes of sending messages.

An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include/use in creating/sending messages to/for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages. Personal profile characteristics of senders/recipients can indicate correlations between certain message characteristics and certain outcomes of sending messages.

A device control method is provided. In the method, a first message is received from a first device of a device messaging group. The first device is associated with a first identifier in the device messaging group. The first message indicates at least one of a state of the first device or detected environment information. A second message is generated based on the first message received from the first device. The second message is sent to a second device of the device messaging group. The second device is associated with a second identifier in the device messaging group.

A system includes a memory and at least one processor to receive text from a client computing device, the text received one character at a time, as each character of the text is received, determine a recommendation in real-time to be added to the text based on at least one of a list of rules, word embedding, an n-gram model, and a co-occurrence model, the recommendation comprising at least one of a word, a list of hashtags, a quotation, and a list of emojis, and send the recommendation to the client computing device.

An information processing apparatus includes a display that displays messages received from other information processing apparatuses, and transmission messages from the information processing apparatus, in a display region in a time sequence, a receiving unit that receives a transmission message responsive to a received message displayed on the display, and a transmission instruction of the transmission message, and a controller that, if a second received message different from a first received message as a message received from a link destination to which the transmission message is directed is displayed in the display region before the receiving unit receives the transmission instruction of the transmission message, controls the display such that the transmission message indicating an association with the first received message is displayed on the display region.

An information processing apparatus includes a display that displays messages received from other information processing apparatuses, and transmission messages from the information processing apparatus, in a display region in a time sequence, a receiving unit that receives a transmission message responsive to a received message displayed on the display, and a transmission instruction of the transmission message, and a controller that, if a second received message different from a first received message as a message received from a link destination to which the transmission message is directed is displayed in the display region before the receiving unit receives the transmission instruction of the transmission message, controls the display such that the transmission message indicating an association with the first received message is displayed on the display region.

An auto-login system and process enable maintaining user accounts on a server without a user having to register or create a user name, password, or other authentication method. An account may be created without user knowledge. The server may transmit a content item to a target user, along with a link. A server identifies the target user from use of the auto-login link and collects interaction or “engagement” data while the user is logged in, to assess user interest in products, for example, a mutual fund investment product, which may be characterized by tags and/or categories. The system may quantify a product salience metric for a given product relative to a target user's interest profile to focus marketing efforts and support engagement with interested target users, especially securities funds and financial advisors.

Systems, methods, devices, instructions, and other examples are described for natural language processing. One example includes accessing natural language processing general encoder data, where the encoder data is generated from a general-domain dataset that is not domain specific. A domain specific dataset is accessed and filtered encoder data using a subset of the encoder data is generated. The filtered encoder data is trained using the domain specific dataset to generate distilled encoder data, and tuning values for the distilled encoder data are generated to configure task outputs associated with the domain specific dataset.

Task assignments are identified. A dataset that includes one or more electronic messages is received. Then, one or more pending tasks in the dataset are identified, and each of a plurality of people who are mentioned in the dataset is also identified. Then, for each of the pending tasks, one or more of the identified people are identified as potentially being people who are assigned to complete the pending task, and the pending task is associated with these identified one or more of the identified people. For each of the pending tasks, one or more of the identified people are also identified as potentially being people for whom the pending task is to be completed, and the pending task is also associated with these identified one or more of the identified people.