Abstract: Disclosed are systems, methods, and devices for managing a personal user inventory. In one embodiment, the method comprises retrieving a message associated with a user identifier, the message including a body content portion; identifying a parser associated with the message; parsing the body content portion using the parser to identify an item of merchandise present within the body content portion, wherein the item of merchandise is associated with an item identifier and a set of item details; associating the item of merchandise with the user identifier; and generating a display listing merchandise associated with the user identifier, wherein the listing of merchandise includes the item of merchandise.

Abstract: Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.

Abstract: An initial list of candidate items automatically evaluated and chosen for an end-user is provided. A selection of one or more items in the initial list of candidate items is received from an expert user different than the end-user to include in an item group set for the end-user. Eligible items are evaluated to identify an additional item to include in the item group set based at least in part on an expert judgement prediction machine learning model trained to predict based at least in part on the one or more items already in the item group set a likelihood of a certain item being evaluated would be selected for inclusion in the item group set. Based on the evaluation, the additional item is included in the item group set. Member items in the item group set are indicated.

Abstract: In an embodiment, a method for optimizing computer machine learning includes receiving an optimization goal. The optimization goal is used to search a database of base option candidates (BOC) to identify matching BOCs that at least in part matches the goal. A selection of a selected base option among the matching BOCs is received. Machine learning prediction model(s) are selected based at least in part on the goal to determine prediction values associated with alternative features for the selected base option, where the model(s) were trained using training data to at least identify weight values associated with the alternative features for models. Based on the prediction values, at least a portion of the alternative features is sorted to generate an ordered list. The ordered list is provided for use in manufacturing an alternative version of the selected base option with the alternative feature(s) in the ordered list.

Abstract: A series of captured images of a user is received. Using a processor, the images are processed to identify a portion of each of the images corresponding to the user. Parameters of a predetermined three-dimensional human model are modified to fit a modified version of the predetermined three-dimensional human model across the identified portions of the images to determine a set of specific parameters representing a body profile of the user.

Abstract: An initial list of candidate items automatically evaluated and chosen for an end-user is provided. A selection of one or more items in the initial list of candidate items is received from an expert user different than the end-user to include in an item group set for the end-user. Eligible items are evaluated to identify an additional item to include in the item group set based at least in part on an expert judgment prediction machine learning model trained to predict based at least in part on the one or more items already in the item group set a likelihood of a certain item being evaluated would be selected for inclusion in the item group set. Based on the evaluation, the additional item is included in the item group set. Member items in the item group set are indicated.

Abstract: In an embodiment, a method for optimizing computer machine learning includes receiving an optimization goal. The optimization goal is used to search a database of base option candidates (BOC) to identify matching BOCs that at least in part matches the goal. A selection of a selected base option among the matching BOCs is received. Machine learning prediction model(s) are selected based at least in part on the goal to determine prediction values associated with alternative features for the selected base option, where the model(s) were trained using training data to at least identify weight values associated with the alternative features for models. Based on the prediction values, at least a portion of the alternative features is sorted to generate an ordered list. The ordered list is provided for use in manufacturing an alternative version of the selected base option with the alternative feature(s) in the ordered list.

Abstract: Disclosed are systems, methods, and devices for managing a personal user inventory. In one embodiment, the method comprises retrieving a message associated with a user identifier, the message including a body content portion; identifying a parser associated with the message; parsing the body content portion using the parser to identify an item of merchandise present within the body content portion, wherein the item of merchandise is associated with an item identifier and a set of item details; associating the item of merchandise with the user identifier; and generating a display listing merchandise associated with the user identifier, wherein the listing of merchandise includes the item of merchandise.

Abstract: Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.

Abstract: A catalog of physical items associated with a target user is accessed. At least a portion of the catalog is at least in part automatically generated based on a retention of one or more of the physical items provided to the target user. A machine learning model trained using outfit combination information gathered from other users is used to automatically determine for the target user, at least a portion of one or more recommended outfit combinations of a plurality of physical items among the physical items within the catalog. An indication of a selected one of the one or more recommended outfit combinations is provided to the target user.

Abstract: An article type adjustment approximation value for each physical article type of a plurality of physical article types of limited quantities is predetermined. A determination is made to assign to a selected client among a set of clients, a set of physical article types among the plurality of physical article types. A group of eligible physical article types for the selected client among the plurality of physical article types is identified. Using one or more machine learning prediction models, a desirability prediction value for each physical article type in the group of eligible physical article types is determined. The desirability prediction values are adjusted using the corresponding predetermined article type adjustment approximation value and a corresponding predetermined client adjustment approximation value to determine corresponding adjusted prediction values. The corresponding adjusted prediction values are used to determine the set of physical article types to be assigned to the selected client.

Abstract: A series of captured images of a user is received. Using a processor, the images are processed to identify a portion of each of the images corresponding to the user. Parameters of a predetermined three-dimensional human model are modified to fit a modified version of the predetermined three-dimensional human model across the identified portions of the images to determine a set of specific parameters representing a body profile of the user.

Abstract: A machine learning model for predicting a size fit satisfaction for a variable size component is trained using at least sizing profiles of a plurality of items and feedbacks of subjects regarding sizing of the plurality of items. The machine learning model is used to determine a value for the variable size component that corresponds to an optimal predicted size fit satisfaction. The determined value of the variable size component is provided for use in creating a new item with a sizing variation based on the determined value.

Abstract: Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and/or the item data to provide recommendations. The system also includes a batch-process results data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing.

Abstract: Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and/or the item data to provide recommendations. The system also includes a batch-process resultsP data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing.

Abstract: Data about clients, items included in an item inventory, executable code for recommendation processes, and executable code for feature selection process(es), are stored. The recommendation processes are used to identify items in the item inventory that are recommended for specific clients, and the feature selection process(es) is/are used to select features that are displayed to entities tasked with selecting items from the item inventory for clients. Selection feedback and client feedback are received. One or more feature selection process(es) is/are modified based on the selection feedback and/or the client feedback, to thereby change which, where or/or how certain portion of the data about clients is displayed to the entities tasked with selecting items from the item inventory for the clients, and/or change which, where and/or how certain portion of the data about items is displayed to the entities tasked with selecting items from the item inventory for the clients.

Abstract: Systems and methods described herein, which utilize a combination of batch-processing and on-demand processing to provide recommendations, can include database(s) that store client data, item data, and executable code for composable algorithms that utilize the client data and/or the item data to provide recommendations. The system also includes a batch-process resultsP data store that stores results of composable algorithms executed using batch-processing. Additionally, the system includes an algorithm engine that accepts calls to composable algorithms and output results thereof. The algorithm engine determines which called composable algorithms are designated as being executable using batch-processing, and which are designated as being executable using on-demand processing.

Abstract: Computer implemented systems and methods intelligently distribute tasks received from clients among worker resources. One or more databases store information about worker resources and information about clients. A task assignment server, communicatively coupled to the database(s), receives a plurality of tasks that are to be performed for the clients, accesses the stored information about the worker resources, accesses the stored information about the clients, and assigns each of a majority of the tasks to one of the plurality of worker resources, in dependence on the information about the plurality of worker resources and in dependence on the information about the plurality of clients, so that the plurality of tasks are distributed among two or more of the plurality of worker resources. The system can also include a plurality of queues adapted to store information about tasks assigned to the worker resources associated with the queues.

Abstract: Systems and methods described herein employ adaptive machine learning to provide recommendations to an entity that selects one or more items for a client from an item inventory. Client information, item information, and recommendation algorithms are stored and are accessible by a recommendation engine. The recommendation algorithms utilize the client information and the item information in different manners to identify different subsets of items recommended for a client. Information about two or more of the subsets of the items in the item inventory that are identified are selected for display to the entity tasked with selecting items for the client.

Abstract: Computer implemented systems and methods intelligently distribute tasks received from clients among worker resources. One or more databases store information about worker resources and information about clients. A task assignment server, communicatively coupled to the database(s), receives a plurality of tasks that are to be performed for the clients, accesses the stored information about the worker resources, accesses the stored information about the clients, and assigns each of a majority of the tasks to one of the plurality of worker resources, in dependence on the information about the plurality of worker resources and in dependence on the information about the plurality of clients, so that the plurality of tasks are distributed among two or more of the plurality of worker resources. The system can also include a plurality of queues adapted to store information about tasks assigned to the worker resources associated with the queues.