Abstract: A pneumatic tire comprising a vulcanizable rubber composition which comprises: 1) 100 parts by weight of elastomer consisting of 25 to 35 phr of a styrene-butadiene rubber, 45 to 75 phr of a polybutadiene, and 5 to 15 phr of a natural rubber or synthetic polyisoprene, wherein the styrene-butadiene rubber is a functionalized and has a Tg(A) ranging from ?30 to ?10° C.; the natural rubber or synthetic polyisoprene has a Tg(B) ranging from ?60 to ?70° C.; and the polybutadiene is a cis 1,4 polybutadiene having a Tg(C) ranging from ?110 to ?90° C., wherein Tg(A)?Tg(B)?25° C., and Tg(B)?Tg(C)?25° C.; 2) 70 to 100 phr of silica; 3) 1 to 20 phr of carbon black; 4) 1 to 15 phr of rubber processing oil; and 5) 5 to 50 phr of at least one hydrocarbon resin having a Tg?20° C. and a rosin derived resin.

Abstract: This application relates to apparatus and methods for providing recommended items to advertise. In some examples, a computing device determines a first set of items for recommendation based on historical user data associated with a user, and a second set of items for recommendation based on real-time user session data for the user. The computing device may then determine a subset of the first set of items based on associated scores and a predetermined threshold number of first items that can be presented for optimal user interaction. The computing device may generate a set of item recommendations by combining the subset of the first set of items and at least one of the second set of items to present to the user as advertisements.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions including: receiving a respective item description and at least one respective attribute value for each item of a set of items; generating at least one respective text embedding; generating a graph of the set of items based on at least co-view data to create pairs of items that are co-viewed by joining respective pairs of items; training the text embedding model and a machine learning model using a neural loss function based on the graph; and automatically determining, using the machine learning model, as trained, a label for each item of the set of items. Other embodiments are disclosed.

Abstract: This application relates to apparatus and methods for generating preference profiles that may be used to rank search results. In some examples, a computing device obtains browsing session data and determines items that were engaged, such as items that were viewed or clicked. The computing device obtains item property data, such as product descriptions, for the items, and applies a dependency parser to the item property data to identify portions that include certain words, such as nouns or adjectives, which are then identified as attributes. The computing device generates attribute data identifying portions of the item property data as item attributes. In some examples, the computing device applies one or more machine learning algorithms to the session data and/or search query to identify item attributes. The computing device may generate a profile that includes the item attributes, and may rank search results based on the attribute data, among other uses.

Abstract: This application relates to apparatus and methods for automatically determining item relevancy based on textual information. In some examples, a computing device receives a search query, and a plurality of items corresponding to the search query. The computing device may identify one or more features of the search query. The computing device may generate relevancy values for each of the items based on the features of the search query, and features of each of the plurality of items. For example, the computing device may generate, for each of the items, a plurality of relevance values, each relevance value generated based on a feature of the search query and corresponding features of the item. The computing device may transmit the generated relevancy values for the plurality of items. In some examples, the computing device may rank the plurality of items based on the generated relevancy values.

Abstract: A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include training two sets of item embeddings for items in an item catalog and a set of user embeddings for users, using a triple embeddings model, with triplets. The triplets each include a respective first user of the users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket. The method also can include randomly sampling an anchor item from a category of items selected by a user. The method additionally can include generating a list of complementary items using a query vector associated with the user and the anchor item.

Abstract: In some examples, a system may be configured to generate one or more query attributes for a search query received from a computing device of a user. Additionally, the system may be configured to, based at least in part on historical data of the user including data characterizing one or more items associated with the user, generate relevant item data. In various examples, the relevant item data characterizing a set of relevant items. Moreover, the system may be configured to, based on the relevant item data, the historical data of the user and the one or more query attributes, implement a set of operations that generate a set of personalized search results associated with the search query.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital advertisements to targeted users. In some examples, a computing device receives campaign data identifying items to advertise on a website, and generates campaign user data identifying a user that has engaged all of the items on the website. The computing device may then determine a portion of the users based on a relationship between each user and the campaign user data, and may determine user-item values for each of the items for each user of the portion of users, where each user-item value identifies a relational value between the corresponding user and item. The computing device may then identify one or more of the items to advertise to each user of the portion of users based on the user-item values, and may transmit to a web server an indication of the items to advertise for each user.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: inputting one or more pairs of digital images into a neural network; determining, using the neural network, a respective contrastive loss for each respective pair of digital images of the one or more pairs of digital images; receiving one or more new digital images; and determining, using the neural network, the one or more new digital images and the respective contrastive loss, at least one image to which the one or more new digital images is similar. Other embodiments are disclosed herein.

Abstract: A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include monitoring periodically whether a respective recommended bidding price update for a campaign type for a user is required for a respective department of campaign departments based on a respective landscape distribution of respective bidding prices for the campaign type for the respective department.

Abstract: A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include receiving, via a computer network, an ad request. The method also can include retrieving ad candidates from an ad database. The method further can include determining a respective ad ranking score for each of the ad candidates, based at least in part on the ad request and respective historical retrieval scores for each of the ad candidates.

Abstract: A system including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, perform: receiving user session activity information and campaign impression information; determining a sample of the user session activity information and the campaign impression information based on a sampling criterion; analyzing the sample using (i) a first logistic regression model and (ii) a second linear regression model; determining a weighting value for the campaign impression information based on a first output of the first logistic regression model and a second output of the second linear regression model; and determining a sub cut lift measurement for the campaign impression information based on a first lift measurement for the campaign impression information and the weighting value for the campaign impression information. Other embodiments are described.

Abstract: This application relates to apparatus and methods for automatically generating item information, such as item descriptions, and providing the item information to customers. For example, the embodiments may generate and provide personalized item descriptions to customers during conversational interactions in speech-based systems. In some examples, the embodiments determine entities (e.g., attributes) from item information, and apply trained machine learning processes to the extracted entities to generate textual data, such as item descriptions. For example, a computing device may apply a trained natural language processing, such as a trained transformer-based machine learning technique, to the extracted entities to generate the item descriptions. In some examples, the computing device applies post processing techniques to the generated textual data. The generated textual data may include descriptive phrases that are user friendly to customers in an e-commerce system.

Abstract: A system and method are described for collecting and providing health-related information of a patient’s medical record or information from a plurality of health-service providers using a clinical data connector and clinical document collector. In some instances, all or a part of the collected information are received through a web application or web service (e.g., of a third-party requestor).

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions including: receiving a respective item description and at least one respective attribute value for each item of a set of items; generating at least one respective text embedding; generating a graph of the set of items based on at least co-view data to create pairs of items that are co-viewed by joining respective pairs of items; training the text embedding model and a machine learning model using a neural loss function based on the graph; and automatically determining, using the machine learning model, as trained, a label for each item of the set of items. Other embodiments are disclosed.

Abstract: A category recommender system includes a computing device configured to obtain customer information characterizing a customer's interactions on an ecommerce marketplace. The computing device is further configured to determine discovery category rankings for each category of items available on the ecommerce marketplace for the customer based on the customer information, determine repeat category rankings for each category of items available on the ecommerce marketplace for the customer based on the customer information, and determine item rankings for each item in the ecommerce marketplace based on the customer information. The computing device is further configured to merge the discovery category rankings, the repeat category rankings and the item recommendation rankings into final recommendations based on one or more predetermined merging criteria and provide the final recommendations to the customer.

Abstract: A seasonal recommender system includes a computing device configured to obtain periodic sales data characterizing a number of purchases made of each item of a plurality of items in a specified period and to obtain periodic buyers data characterizing a number of unique customers of each item in the plurality of items in the specified period. The computing device is further configured to determine a final item seasonality embedding for each item based on the periodic sales data and the periodic buyers data and to determine a final user seasonality embedding for each user based on the periodic purchase data. The computing device is further configured to determine a final user-item score for each item based on the final item seasonality embedding and the final user seasonality embedding and to send a recommendation to a user based on the final user-item score.

Abstract: This application relates to apparatus and methods for automatically generating item information, such as item descriptions, and providing the item information to customers. For example, the embodiments may generate and provide personalized item descriptions to customers during conversational interactions in speech-based systems. In some examples, the embodiments determine entities (e.g., attributes) from item information, and apply trained machine learning processes to the extracted entities to generate textual data, such as item descriptions. For example, a computing device may apply a trained natural language processing, such as a trained transformer-based machine learning technique, to the extracted entities to generate the item descriptions. In some examples, the computing device applies post processing techniques to the generated textual data. The generated textual data may include descriptive phrases that are user friendly to customers in an e-commerce system.

Abstract: A method includes receiving a request for clinical information associated with one or more patients from a requestor, the request being associated with requested values for one or more parameters associated with a plurality of network sources for retrieving the clinical information; comparing the requested values with characteristic values for the one or more parameters of each of the plurality of network sources to generate a comparison result; selecting one of the plurality of network sources based on the comparison result; and communicating the request for clinical information to the one of the plurality of network sources that was selected.

Abstract: This application relates to apparatus and methods for automatically determining item relevancy based on textual information. In some examples, a computing device receives a search query, and a plurality of items corresponding to the search query. The computing device may identify one or more features of the search query. The computing device may generate relevancy values for each of the items based on the features of the search query, and features of each of the plurality of items. For example, the computing device may generate, for each of the items, a plurality of relevance values, each relevance value generated based on a feature of the search query and corresponding features of the item. The computing device may transmit the generated relevancy values for the plurality of items. In some examples, the computing device may rank the plurality of items based on the generated relevancy values.