Abstract: According to one embodiment, a method, computer system, and computer program product for personalizing playback of an audio stream is provided. The present invention may include sectioning the audio stream into one or more content blocks and one or more filler blocks; determining one or more topics associated with the one or more filler blocks; determining a level of complexity associated with the one or more filler blocks; determining a listener's level of interest in and level of comprehension of the one or more topics; based on the level of complexity, the level of interest, and level of comprehension corresponding to the one or more topics, assigning a playback speed to the one or more content blocks associated with the one or more topics; and modifying the one or more content blocks to play at the assigned playback speed.

Abstract: According to one embodiment, a method, computer system, and computer program product for personalizing playback of an audio stream is provided. The present invention may include sectioning the audio stream into one or more content blocks and one or more filler blocks; determining one or more topics associated with the one or more filler blocks; determining a level of complexity associated with the one or more filler blocks; determining a listener’s level of interest in and level of comprehension of the one or more topics; based on the level of complexity, the level of interest, and level of comprehension corresponding to the one or more topics, assigning a playback speed to the one or more content blocks associated with the one or more topics; and modifying the one or more content blocks to play at the assigned playback speed.

Abstract: A temperature state of one or more fluids in one or more enclosures is determined. Acoustic data is collected using a microphone. Frequency domain features are determined based on the acoustic data, where the frequency domain features are obtained across a frequency range of the acoustic data. The frequency domain features are correlated to the output of a machine learning acoustic analysis model and the one or more fluids are classified based on temperature based on the correlated one or more frequency domain features. In addition, the machine learning acoustic analysis model is trained by recording acoustic training data using a microphone and collecting temperature data for a fluid across a range of temperatures for the fluid. Frequency domain features are determined across a frequency range of the acoustic training data and the frequency domain features are correlated to the temperature data for a fluid.

Abstract: In an approach to estimating product inventory count, one or more computer processors receive one or more images of one or more products residing on a product storage location from an image capturing device. Based on the received images, one or more computer processors determine a count of the one or more products. One or more computer processors determine a confidence in the count of the one or more products. In response to determining the confidence is below a threshold, one or more computer processors calculate a recommended position of the image capturing device to produce an improved image of the one or more products. One or more computer processors transmit instructions to the image capturing device to move to the recommended position. One or more computer processors determine whether the image capturing device is in the recommended position.

Abstract: In an approach to anonymous inventory tracking, one or more computer processors receive a first image of one or more products from a user. The one or more computer processors remove data associated with an identity of the user from the first image. The one or more computer processors determine that a storage location associated with the one or more products in the first image is detected. The one or more computer processors determine an inventory count of the one or more products in the first image. The one or more computer processors submit the first image and the inventory count of the one or more products in the first image to a blockchain ledger in association with the storage location. The one or more computer processors take an action based on the inventory count.

Abstract: A processor may generate a workstation characteristics model of a workspace. A processor may generate respective user profiles for each of one or more users. Each of the respective user profiles may include user data associated with a respective user. A processor may assign, based on the respective user profiles, a respective risk level to each of the respective user profiles. A processor may compare each of the respective risk levels to a risk threshold level. A processor may apply a user characteristic model. A user characteristic model may be based on each of the respective risk levels and the workstation characteristics model. A processor may assign each of the one or more users to respective workstations based on the user characteristic model.

Abstract: A temperature state of one or more fluids in one or more enclosures is determined. Acoustic data is collected using a microphone. Frequency domain features are determined based on the acoustic data, where the frequency domain features are obtained across a frequency range of the acoustic data. The frequency domain features are correlated to the output of a machine learning acoustic analysis model and the one or more fluids are classified based on temperature based on the correlated one or more frequency domain features. In addition, the machine learning acoustic analysis model is trained by recording acoustic training data using a microphone and collecting temperature data for a fluid across a range of temperatures for the fluid. Frequency domain features are determined across a frequency range of the acoustic training data and the frequency domain features are correlated to the temperature data for a fluid.

Abstract: A first type of data regarding an unidentified product is collected. A first type of analysis on the first type of data is performed. A second type of data regarding an unidentified product is collected. A second type of analysis on the second type of data is performed. Based upon the first type of analysis on the first type of data and the second type of analysis on the second type of data, product identification for the unidentified product is performed. Based on the product identification, an identity of the unidentified product is output. A user is enabled to perform a business interaction with a product matching the identity of the unidentified product.

Abstract: In an approach for determining the optimal space to store goods as well as maximizing the amount of goods in a warehouse by facilitating peer-to-peer subletting of warehouse space, a processor receives a request for storage space from a first user, wherein the request includes a set of requirements for the storage space. A processor determines a suitable storage space for the first user based on the set of requirements. A processor generates a contract between the first user and a second user for the suitable storage space. A processor sends the contract to the first user and the second user while keeping each user anonymous to each other respective user. A processor determines whether the first user and the second user accepted the contract. A processor requests feedback from the first user and the second user about the contract.

Abstract: An application on a computing device of a user is initiated. The application allows the user to complete a purchase via the computing device instead of a point-of-sale (POS) terminal. A set of preferences of the user is retrieved. A new interface of the POS terminal is generated on the computing device. The new interface is based on the retrieved set of preferences. The new interface includes one or more screens and icons that perform one or more POS terminal capabilities when selected by the user. The generated new interface is rendered on the computing device.

Abstract: A method utilizes Artificial Intelligence (AI) system to broadcast a real-time event to a particular broadcast audience. The AI system receives a profile of a particular broadcast audience for a real-time event, and concurrent real-time Audio/Video (/V) streams for a particular portion of the real-time event. The AI system ranks the concurrent real-time A/V streams for the particular portion of the real-time event based on the types of event activities depicted in each of the concurrent real-time A/V streams and the profile of the particular broadcast audience for the real-time event; selects a particular real-time A/V stream, from the concurrent real-time A/V streams, for the particular broadcast audience based on the ranking; and directs a performance of a broadcast action related to the selected particular real-time A/V stream.

Abstract: A method utilizes Artificial Intelligence (AI) system to broadcast a real-time event to a particular broadcast audience. The AI system receives a profile of a particular broadcast audience for a real-time event, and concurrent real-time Audio/Video (/V) streams for a particular portion of the real-time event. The AI system ranks the concurrent real-time A/V streams for the particular portion of the real-time event based on the types of event activities depicted in each of the concurrent real-time A/V streams and the profile of the particular broadcast audience for the real-time event; selects a particular real-time A/V stream, from the concurrent real-time A/V streams, for the particular broadcast audience based on the ranking; and directs a performance of a broadcast action related to the selected particular real-time A/V stream.

Abstract: In an approach to anonymous inventory tracking, one or more computer processors receive a first image of one or more products from a user. The one or more computer processors remove data associated with an identity of the user from the first image. The one or more computer processors determine that a storage location associated with the one or more products in the first image is detected. The one or more computer processors determine an inventory count of the one or more products in the first image. The one or more computer processors submit the first image and the inventory count of the one or more products in the first image to a blockchain ledger in association with the storage location. The one or more computer processors take an action based on the inventory count.

Abstract: In an approach to estimating product inventory count, one or more computer processors receive one or more images of one or more products residing on a product storage location from an image capturing device. Based on the received images, one or more computer processors determine a count of the one or more products. One or more computer processors determine a confidence in the count of the one or more products. In response to determining the confidence is below a threshold, one or more computer processors calculate a recommended position of the image capturing device to produce an improved image of the one or more products. One or more computer processors transmit instructions to the image capturing device to move to the recommended position. One or more computer processors determine whether the image capturing device is in the recommended position.

Abstract: A first type of data regarding an unidentified product is collected. A first type of analysis on the first type of data is performed. A second type of data regarding an unidentified product is collected. A second type of analysis on the second type of data is performed. Based upon the first type of analysis on the first type of data and the second type of analysis on the second type of data, product identification for the unidentified product is performed. Based on the product identification, an identity of the unidentified product is output.

Abstract: A first type of data regarding an unidentified product is collected. A first type of analysis on the first type of data is performed. A second type of data regarding an unidentified product is collected. A second type of analysis on the second type of data is performed. Based upon the first type of analysis on the first type of data and the second type of analysis on the second type of data, product identification for the unidentified product is performed. Based on the product identification, an identity of the unidentified product is output. A user is enabled to perform a business interaction with a product matching the identity of the unidentified product.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: responsive to a determination that a mobile client computer device is at a location of an item venue, sending to the client computer device prompting data, wherein the client computer device is associated to a user; examining feedback data defined by the user, wherein the feedback data is defined by the user in response to the prompting data; and transmitting to the mobile client computer device second prompting data in dependence on the examining feedback data defined by the user.

Abstract: Providing prior-purchase (prior-acquisition) notification determining available storage capacity based on dimensions of total space for storage, dimensions of occupied space for storage, and attributes of available space for storage. Available storage capacity is based on dimensions of total storage capacity and occupied storage space. Identification of a product (item) selected by the user for purchase or acquisition is made. A profile of the product that includes storage requirements is retrieved, and storage requirements include required storage space and storage space attributes. A storage option is determined based on the product selected, the storage requirements of the product selected, and the available storage capacity. A notification recommending a storage option is generated, based on the storage requirements of the product, available storage space, and the attributes of the available storage space, and the notification is sent to a computing device of the user prior to acquiring the product.

Abstract: A computer receives one or more user preferences regarding a product characteristic. The computer performs, using one or more sensors, product testing of a product to gather characteristic data of the product. The computer determines, using the characteristic data of the product, a measured characteristic value of the product. The computer compares the measured characteristic value with an expected characteristic value associated with the one or more user preferences. The computer determines, based on the comparison, a product suitability value for use by a user. The computer outputs, using the product suitability value, an indication whether the product is suitable for use by the user in accordance with the one or more user preferences.