Source: https://patents.google.com/patent/DE212014000220U1/en
Timestamp: 2020-02-16 20:04:34
Document Index: 390804970

Matched Legal Cases: ['Application No. 14', 'Application No. 61', 'art 508', 'arts 510', 'arts 510', 'art 510', 'art 520', 'art 530', 'art.\n6']

DE212014000220U1 - Dynamic creation of lists - Google Patents
Dynamic creation of lists
DE212014000220U1
DE212014000220U1 DE212014000220.6U DE212014000220U DE212014000220U1 DE 212014000220 U1 DE212014000220 U1 DE 212014000220U1 DE 212014000220 U DE212014000220 U DE 212014000220U DE 212014000220 U1 DE212014000220 U1 DE 212014000220U1
DE212014000220.6U
2013-11-22 Priority to US201361908020P priority Critical
2013-11-22 Priority to USUS-61/908,020 priority
2014-10-31 Priority to USUS-14/530,458 priority
2014-10-31 Priority to US14/530,458 priority patent/US20150149298A1/en
2014-11-21 Application filed by PayPal Inc filed Critical PayPal Inc
2014-11-21 Priority to PCT/US2014/066937 priority patent/WO2015077637A1/en
2016-06-23 Publication of DE212014000220U1 publication Critical patent/DE212014000220U1/en
A system for managing an aggregated list, the system comprising at least one processor configured to perform operations, comprising: receiving, from a network, sensor data associated with a first type of data source, the sensor data representing at least one article, which is to be added to the aggregated list from the first data source type, the aggregated list being associated with at least one user, the first data source type representing a connected device; Processing, using at least one processor, the sensor data based on a predictive modeling associated with consumption of the at least one article to be added to the aggregated list from the first data source type to automatically generate learning data, the learning data including a second Data source type and represent at least one article to be added to the aggregated list from the second data source type; Receiving, from the network, non-sensor data associated with a third data source type, the non-sensor data representing at least one article to be added to the aggregated list from the third data source type; Creating the aggregated list of articles representing at least one article added to the aggregated list by each of the first data source type, the second data source type, and the third data source type.
The present international application claims priority from US Patent Application No. 14 / 530,458, filed October 31, 2014, and US Provisional Application No. 61 / 908,020, filed November 22, 2013, the entire contents of which are hereby incorporated by reference Reference is taken in its entirety.
This application relates to systems and methods for creating datasets or lists, and more particularly, but not by way of limitation, to systems and methods for dynamically creating datasets or lists using data from smart devices and other data sources.
Many people rely on lists to help them with their tasks. Manual entry is probably still the most common way people create lists.
Electronic lists have some advantages over manual lists. For example, in an environment, an electronic shopping list may be able to provide an interface for price comparisons or product availability.
Several of the accompanying drawings are merely exemplary embodiments of the present disclosure, and may not be construed as limiting the scope thereof.
1A FIG. 12 shows an exemplary embodiment of a high-level block diagram of a connected system used to create dynamically aggregated shopping lists. FIG.
1B FIG. 11 illustrates an example embodiment of a client-server based network architecture at a high level, according to an example embodiment.
2A FIG. 12 is a block diagram showing components provided in a publishing system, according to some embodiments. FIG.
2 B shows input data and output data for a learning machine and condition system, according to an example embodiment,
2C shows input data and output data for an inventory management system, according to exemplary embodiments;
2D 12 shows a table of metadata fields and values for creating aggregated shopping lists, according to example embodiments;
2E FIG. 10 illustrates an aggregated list creation system that receives multiple data types from multiple data source types, according to an example embodiment.
2F shows examples of data fields that may be included in a user interface (UI) that displays an aggregated shopping list;
2G Figure 12 shows examples of data fields that may be included in a user interface to help a user select recommended products from a number of retailers.
3 FIG. 10 illustrates a system for a client device executing an electronic shopping list application for facilitating purchase transactions with a merchant system, according to an example embodiment.
4 FIG. 10 is a block diagram illustrating components of the inventory management application, according to some example embodiments.
5A FIG. 10 is a flow chart illustrating an example method of automatically recommending a product for ordering based on an analysis of various types of data from a variety of data source types.
5B to 5D show flowcharts for various embodiments for dynamically creating aggregated shopping lists.
6A to 6F show exemplary embodiments of UIs for displaying shopping lists.
7 FIG. 10 illustrates an exemplary mobile device that may execute a mobile operating system according to example embodiments. FIG.
8th is a block diagram 800 depicting a software architecture used in a computing device or machine according to exemplary embodiments.
9 FIG. 10 is a block diagram showing components of a machine according to some example embodiments. FIG.
The following description includes systems, methods, techniques, instruction sequences, and computer machine program products embodying exemplary embodiments of the disclosure. In the following description, numerous specific details are set forth in order to provide an understanding of various embodiments of the subject invention. However, it will be apparent to those skilled in the art that embodiments of the subject invention may be practiced without these specific details. Generally, well-known instruction instances, protocols, and techniques are not necessarily presented in detail.
In various exemplary embodiments, systems and methods for using data from smart devices and other data sources to create an aggregated list are described. In one example, in a publishing system incorporating an online marketplace, an aggregated shopping list may be created and presented to a user on the user's client device via a web browser or application installed on the user's client device. The products on the shopping list can be purchased online for pickup at a store or on delivery, or can be purchased at a store from dealers in a network of connected retailers. The aggregated shopping list includes products that have been added to the aggregated shopping list from multiple data source types. Examples of data source types include connected devices, apps installed on a client device, a learning machine, or a condition system. The data from each of these data source types are intelligently combined to create an aggregated shopping list. The aggregated shopping list is an electronic shopping list that provides product information from one or more merchants in a network of affiliated merchants to help the user select specific products for purchase. The product information may include, for example, product description, brand, availability, price comparisons between matching products, and promotional offers for the matching products. The recommended products may also be based on a user's profile, preferences and historical transaction data for the products on the aggregated shopping lists. The aggregated shopping list may allow a user to place an order online directly from the aggregated shopping list for collection at a store or delivery. The aggregated shopping list may keep the user informed of delivery information, such as the delivery status, and the estimated delivery time. In addition, the aggregated shopping list may be used by a user while shopping in a physical store. While shopping in a physical store, the aggregated shopping list may show or highlight those products on the aggregated shopping list that are available in the physical store as well as any coupons or promotional discounts offered by that physical store. The aggregated shopping list may also check products from the shopping list that have been purchased, and may alert the user when there are products on the shopping list that have not been purchased.
In accordance with one or more embodiments, a method of running an app on a client device to assist the user is disclosed. The application may be a shopping list app, a recipe app, or another type of app that allows lists to be created or provides an existing list that may be modified by one or more users. The app can be used by the user to add items to a shopping list. The same or a different app may be used by the user to view an aggregated shopping list, in the shopping list items that are added by a user being combined with shopping list items added by connected devices that can proactively recognize Articles must be purchased based on recognized situations in an environment such as a home environment or office environment. The aggregated shopping list may also include shopping list items created by a learning machine or condition system based on user-specified data, connected device sensor data, and metadata.
1A FIG. 12 shows an example embodiment of a high level block diagram of a connected system used to create dynamically aggregated shopping lists. FIG. The connected system 108 includes a home environment 129 which connected devices 131 having. The connected devices 131 represent smart devices within the home environment 129 that have a network 104 with a networked system 102 are connected. The connected devices 131 For example, they may be able to connect to the networked system 102 provide sensor data via the Internet. For alternative embodiments, the home environment may be replaced with another type of environment (such as an office environment, club environment, or school environment); where connected devices are present to provide sensor data. 1A shows a number of ways such as shopping list items one of the networked system 102 created aggregated shopping list can be added. The aggregated shopping list may receive sensor data, non-sensor data, learning data, or condition data to dynamically create an aggregated shopping list from multiple data source types.
The networked system 102 can sense sensor data (directly or indirectly) from the connected devices 131 receive. The connected devices 131 include sensors that detect changes in the home environment. In various embodiments, the sensor data refers to a consumption of a product, or information that allows the connected devices to infer consumption. For example, an intelligent refrigerator makes a connected device 131 which has sensors such as cameras and scales. The cameras on the refrigerator can detect that a carton of milk has been removed from the refrigerator and returned to the refrigerator. In addition, the refrigerator scales determine that the milk carton is almost empty, and only one-quarter filled with milk. The smart refrigerator sends information (also referred to as sensor data) to the networked system 102 to put milk on the aggregated shopping list.
In various embodiments, the sensors detect when a food article, a household article, or other article (collectively referred to as "products") in the home environment 129 must be added to a shopping list. The sensors can detect when products have to be ordered because the products have been consumed (or nearly consumed), have to be replaced (or soon need to be replaced), or have expired (or are about to expire). In various embodiments, the connected devices generate 131 Sensor data taken from the connected devices 131 over the network 104 to the networked system 102 be sent. In exemplary embodiments, the sensor data is sent directly to the networked system 102 sent without users 106a or 106b would be involved before they are added to the aggregated shopping list.
The home environment 129 can be any number of connected devices 141 include. Some examples of smart devices that are connected devices 131 may be designated when connected to the network 104 Refrigerators, pantries or pantry shelves, medicine cabinets, wall cabinets, washing machines, coffee makers, diaper trash cans, light bulbs, automobiles or other motor vehicles, smoke detectors, sprinklers and various other kitchen or household appliances. Once the products are added to a shopping list, they may be referred to as a shopping list item.
Embodiments described herein may use data from connected devices to automate, simplify, and facilitate various tasks. In an exemplary embodiment, printers can automatically order ink cartridges when ink runs out, bathrooms can automatically order toiletries (for example, toothpaste and toilet paper), fireplaces can automatically order wood logs, bulbs and lights can automatically order replacement bulbs, battery-powered devices (such as smoke detectors, Toys, flashlights) can automatically order batteries, washing machines can automatically order detergents and softeners when they are running low, refrigerators can use a scale and image recognition to automatically order out of stock items (such as milk and eggs), and cars can automatically schedule appointments to arrange for oil change, battery change and tire change.
In embodiments, sensors may be implemented distributed in a home, a building, a car, a lawn, equipment, and so on. These sensors may be communicatively coupled with each other and with an application server or computer.
For example, a lawn sensor or multiple lawn sensors may be embedded over the lawn to capture moisture. The lawn sensor can communicate moisture data to an application server. An application running on the application server can use the lawn sensor data to determine that the lawn needs to be watered. The application can then communicate with a sprinkler system to water the lawn. Many other sensors, conditions and variations can be used.
The networked system 102 can do non-sensor data from a shopping list app 137 received in the cloud computing environment 135 is hosted. The non-sensor data may represent user-specified data. Even if in 1A two users ( 106a and 106b ), any number of users may be associated with creating the aggregated shopping list. For example, a family of four may allow four users (e.g., mother, father and two children) to add items to the family's aggregated shopping list by associating the four family members through their accounts on the shopping list app.
The user 106a or 106b can by means of an app that works on the client device 110a respectively. 110b or over a web browser application running on the client devices 110a and 110b is installed on the shopping list app 137 access. For example, a user 106a or a user 106b Add items to a shopping list by selecting the shopping list app 132 or the shopping list app 133 used. The shopping list app 132 can on the client device 110a reside, and the shopping lists app 133 can on the client device 110b reside and the ability to access the shopping list app 137 in the cloud computing environment 135 Provide where data for the shopping lists (or a copy of the shopping lists) provided by the users 106a and 106b created, can be saved. Data (such as non-sensor data) stored in the cloud computing environment 135 can be saved from the networked system 102 be retrieved to create the aggregated shopping list. It should be noted that the non-sensor data is not limited to a user-specified entry in a shopping list app. Many other types of apps can be used, such as prescription apps or notepads like Evernote, where lists can be created or modified.
A dynamic list system 146 includes a learning machine 141 for creating learning data, a condition system 145 for creating condition data, and an inventory management system 143 to collect, create, track, and store metadata for product inventory within the home environment 129 , The connected devices 131 They can also use their sensors to send information to the inventory management system 143 and the learning machine 141 forward to create the aggregated shopping list. The system 147 to create the aggregated list within the dynamic list system 146 receives learning data from the learning machine 141 , Condition data from the condition system 145 , Metadata from the inventory management system 143 and merchant product data (including product inventory data) and other data from a merchant inventory management system 150 to create the aggregated shopping list. An example of an aggregated shopping list containing data from multiple types of data sources, including the sensor data, non-sensor data, or system-generated data (eg, learning data or condition data) is disclosed in U.S. Patent Nos. 4,378,355 2F , shown. For the in 1A The embodiment shown may include sensor and non-sensor data 127a in a database 126a stored, learning and condition data 127b can in a database 126b be stored, metadata 127c can in the database 126c stored, merchant product data 127d can in a database 126d saved and other data 127e can in a database 126e get saved.
Regarding 1B is an exemplary embodiment of a client-server based network architecture 105 shown at a high level. The networked system 162 provides server-side functionality over a network 104 (For example, the Internet or a wide area network (WAN)) to a client device 110 ready. A user (for example, the user 106 ) can with the networked system 102 by means of the client device 110 to interact. 1B For example, it shows a web client 112 (developed as a browser such as Internet Explorer ® browser ® from Microsoft Corporation of Redmond, Washington State), client application (s) 114 , and a programmatic client 116 the on the client device 110 be executed. The client device 110 can the webclient 112 , the client application (s) 114 , and the programmatic client 116 alone, together, or in any suitable combination. Even if the 1B a client device 110 shows, in the network architecture 100 be included with multiple client devices.
The client device 110 may comprise a computing device having at least one display and communication capabilities over the network 104 access to the networked system 102 allow. The client device 110 may include, but is not limited to, a remote device, a workstation, a computer, a general purpose computer, an internet device, a handheld device, a wireless device, a portable device, a portable computer, a mobile phone, a personal digital assistant (PDA) , a smartphone, a tablet, an ultrabook, a netbook, a laptop, a desktop computer, a multiprocessor system, a microprocessor-based or programmable consumer electronics device, game consoles, set-top boxes, a networked PC, a minicomputer and the like. In further example embodiments, the client device 110 One or more of a touch screen, an acceleration sensor, a gyroscope, a biometric sensor, a camera, a microphone, a GPS device, and the like. In some embodiments, the client device may 110 in one of the connected devices 131 be integrated.
The client device 110 Can connect to the network via a wired or wireless connection 104 communicate. For example, one or more sections of the network may be 104 an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network ( MAN), part of the Internet, part of the public switched telephone network ( "Public Switched Telephone Network", PSTN), a cellular telephone network, a wireless network, a WiFi or wireless fidelity (Wi-Fi ®) network, a Worldwide Interoperability for Microwave Access (WiMax) network, another type of network or a combination of two or more such networks.
The client device 110 may include one or more applications (also referred to as "apps"), such as, but not limited to, web browsers, book reading devices (set up to read electronic books), media apps (adapted to present various media formats , including audio and video), fitness apps, biometric monitoring apps, news apps, email apps, e-commerce apps (also known as "marketplace apps"), and so on. The client application (s) 114 can contain various components designed to present information to the user and to the networked system 102 to communicate. In some embodiments, when the electronic trading page application is in the client device 110 is included, then this application may be configured to provide locally the user interface and at least some of the functionalities, wherein the application is configured with the networked system 102 to communicate, if necessary, data and processing capabilities that are not available locally (for example, accessing a database of articles available for sale to authenticate a user to verify a payment method). Conversely, if the electronic trading page application is not in the client device 110 is included, the client device 110 use their web browser to access the electronic trading page (or a variant of it) running on the networked system 102 is hosted.
In various exemplary embodiments, the users (for example, the user 106 ) a person, machine, or other means of interacting with the client device 110 be. In some example embodiments, users do not like the network architecture 100 but like using the client device 110 or other means with the network architecture 100 to interact. For example, the users may use a client device 110 which may be arranged to receive information input by the users (for example, using a touch-screen input or an alphanumeric input) and to present that information (for example, by means of a graphical representation on a device display). In this case, the users may, for example, be the client device 110 Enter information over the network 104 to the networked system 102 is communicated. The networked system 102 can, in response to the received input information, over the network 102 Information to the client device 110 communicate that is to present to the users. In this way, the user can use the client device 110 with the networked system 102 to interact.
An application program interface (API) server 120 and a web server 122 can be coupled with, and provide programmatic and Web interfaces for, one or more application servers 140 , The application servers 140 can one or more publication system (s) 142 , Payment system (s) 144 and a dynamic list system 146 each of which may include one or more modules or applications, and each of which may be embodied as hardware, software, firmware, or any combination thereof. The application server (s) 140 are in turn as having one or more database servers 124 which provides access to one or more information storage archives or database (s). 126 enable. In an exemplary embodiment, the database (s) is / are 126 Memory devices storing information that is associated with the publication system (s) 142 is to be sent (for example, publications or listings). The database (s) 126 Digital goods may also store information in accordance with some example embodiments. In an exemplary embodiment, the database (s) include 126 databases 126a to 126e ,
Next is a foreign application 132 on a third server 130 which is executed by the API server 120 provided program technical interface programmatic access. on the networked system 102 Has. For example, the foreign application 132 which information benefits from the networked system 102 support one or more features or functions on a web page hosted by the third party. For example, the third party web site may provide one or more promotions, marketplace or payment functions that are relevant to the networked system's applications 102 get supported.
The publication system (s) 142 Can users who work on the networked system 102 provide a number of publication functions and services. The payment system (s) 144 can similarly provide a number of functions to To execute or facilitate payments and transactions. While in the 1B both the publication system (s) 142 as well as the payment system (s) 144 represented part of the networked system 102 It will be understood that in alternative embodiments, each system 142 and 144 may form part of a payment service that is separate and separate from the networked system 102 is. In some example embodiments, the payment system (s) may 144 a part of the publication system (s) 142 form.
The dynamic list system 146 can provide functionality to create an aggregate shopping list based on multiple data types and multiple data source types. For example, non-sensor data from applications that are in a cloud computing environment or within the networked system 102 are stored, provide articles that are to be added to the aggregated shopping list. An example of an application is the Evernote App, which is a multi-functional app that can be used to create notes or lists that will be used as a shopping list. Additionally, sensor data from connected devices 131 Identify changes in an environment within an environment and deploy items to add to the aggregated shopping list. Furthermore, system-generated data (for example from a learning machine 141 or a condition system 145 ) Provide articles to add to the aggregated shopping list. The metadata included in the inventory management system 143 collected, created, tracked and saved, and the system 147 to create an aggregated list are used to create the aggregated shopping list. In one embodiment, the inventory management system 143 with the system 147 be integrated to create an aggregated list. In some example embodiments, the system may 146 for creating an aggregated list with the client device 110 , the third-party server (s) 130 , the publication system (s) 142 (for example, to retrieve inventory and product information) and the payment system (s) 144 (for example, to purchase a shopping list item) communicate. In an alternative embodiment, the system 147 to create an aggregated list, a part of the publishing system (s) 142 be. In some embodiments, the merchant inventory management system may 150 in the publication system (s) 142 or the dynamic list system 146 be included.
Next is while the client-server based network architecture 100 , in the 1B Of course, while using a client-server architecture, the present inventive subject matter is not limited to such an architecture, for example, and may equally be applied to a distributed or peer-to-peer architecture system. The different systems of the application server (s) 140 (For example, the publication system (s) 142 and the payment system (s) 144 ) may also be implemented as stand-alone software programs that do not necessarily have to have network capabilities.
The web client 112 can be over from the webserver 122 supported web interface on the various systems of the networked system 102 (For example, the publication system (s) 142 ) access. Similarly, the programmatic client 116 and the client application (s) 114 over from the API server 120 provided program interface to access the various services and functions provided by the networked system 102 to be provided. The programmatic client 116 For example, it may be a vendor application (such as the Turbo Lister application developed by eBay® Inc., San Jose, California) to give vendors the ability to view listings on the networked system 102 create and manage offline, and batch mode communications between the programmatic client 116 and the networked system 102 perform.
2A FIG. 12 is a block diagram showing components used in the publication system (s). FIG. 142 is provided according to some embodiments. In various exemplary embodiments, the publication system (s) may include 142 include a marketplace system to provide marketplace functionality (for example, to facilitate the purchase of items associated with item listings on an electronic trading website or aggregated shopping list). The networked system 102 may be hosted on dedicated or shared server machines that are communicatively coupled to facilitate communications between server machines. The components themselves are communicatively coupled to each other and to different data sources (for example via appropriate interfaces) to allow information to be exchanged between the applications or to allow the applications to share and access shared data. These components can also be accessed through the database server (s) 124 to one or more database (s) 126 access.
The networked system 102 can provide a number of mechanisms for publishing, listing, and pricing prices Sellers or Merchants may publish goods or services for sale or barter (or information regarding this), a buyer may express interest in such goods or services, or a desire to buy or trade them, and it may concern the goods or services a transaction (such as a trading transaction) is completed. For this purpose, the networked system 102 a publication 160 and a sales outlet 162 include. The publication work 160 can information on the networked system 102 publish, such as product listings or product description pages. The sales outlet 162 may also have one or more bargains supporting merchant-generated offers for products and services.
A listing work 164 allows a seller to easily create listings of articles or authors to create publications. In one embodiment, the listings refer to goods or services that a merchant over the networked system 102 wishes to act. In some embodiments, the listings may be an offer, a bargain, a coupon, or a discount for the product or service. Each good or service is associated with a particular category. The listing work 164 can receive listing data, such as title, description and name / value pairs. Furthermore, each listing for a good or service may be associated with an article indicator. In other embodiments, a user may create a listing that is an advertisement or other form of informational publication. The listing information can then be stored on one or more storage devices (for example database (s)). 126 ) which are sent to the networked system 102 are coupled. Listings may also include product description pages that represent a product and information associated with the product (for example, product titles, specifications, and reviews). In some embodiments, the product description page may include an aggregation of item listings corresponding to the product described on the product description page. In some embodiments, it allows the listing engine 164 a seller to create offers from the seller's mobile devices. The offers can be made on the networked system 102 be uploaded for storage and tracking.
Browsing the networked system 102 is through a search engine 166 allows. The search engine 166 For example, allows keyword queries from listings made through the networked system 102 were published. In exemplary embodiments, the search engine receives 166 the keyword queries from a user's device and performs a review of the storage device storing the listing information. Review will allow the compilation of a hit set of listings that may be sorted and the client device 110 the user can be returned. The search engine 116 can record the query (for example, keywords) as well as any subsequent user actions and behavior (for example, navigations, selections, or click-throughs).
The search engine 166 can also perform a search based on a location of the user. A user likes on the search engine 166 access via a mobile device and create a search query. Using the search query and the location of the user, the search engine can 166 to provide users with relevant search results for products, services, offers, auctions and so on. The search engine 166 can identify relevant search results both in a list form and graphically on a map. The selection of a graphical indicator on the map may provide additional details regarding the selected search result. In some embodiments, as part of the search query, the user may specify a radius or a distance from the user's current location to restrict search results.
In another example, it allows a navigation system 168 Users, through various categories, catalogs or inventory data structures, according to which listings within the networked system 102 can be classified, to navigate. For example, it allows the navigation system 168 a user to successively navigate down a category tree having a hierarchy of categories (e.g. the category tree structure) until a certain set of listings is reached. Various other navigation applications within the navigation system 168 may be provided to supplement the search and browser applications. The navigation system 168 can record the various user actions (e.g., clicks) performed by the user to navigate down the category tree.
In some example embodiments, it may be a personalization work 170 the users of the networked system 102 allow different aspects of their interactions with the networked system 102 to personalize. For example, users may define, provide, or otherwise communicate personalization settings affecting the personalization work 170 can use to interact with the networked system 102 to determine. In other exemplary embodiments, the personalization work 170 Automatically determine personalization settings and personalize interactions based on automatically determined settings. For example, the personalization work 170 determine a native language of the user and automatically present information in the native language.
2 B FIG. 12 shows input data and output data for a learning machine and a condition system according to an example embodiment. FIG. 2C shows input data and output data for an inventory management system, according to an example embodiment. As in 1A shown are the learning machine 141 , the condition system 145 and the inventory management system 143 Components in the dynamic list system 146 , As in 2 B shown are sensor data 141 , Non-sensor data 141b , and metadata 141c the learning machine 141 provided as inputs. The learning machine 141 generates learning data 141d ,
The learning data 141d represent data that is inferred based on predictive modeling as to when a product needs to be ordered. For example, if milk appears on the aggregated shopping list with a certain frequency, then predictive modeling is used to add milk at the same frequency (for example, every 5 days) to the aggregated shopping list. The frequency with which milk appears on the aggregated shopping list can be determined by the inventory management system 143 collected and as metadata 141c get saved. In addition, refrigerator sensors may also observe the rate at which the user's milk inventory is decreasing to the learning machine 141 provided with sensor data indicating that only a third of the milk in the milk carton is left, so that the learning machine 141 can estimate the remaining time until milk needs to be added to the grocery list and ordered. In addition, the learning machine 141 Receive non-sensor data representing an article designated by the user, that of a shopping list app on the client device 110a add is. In this example, the user has added one gallon or approximately 4 liters of organic whole milk to be added to the list managed by the shopping list app. The learning machine 141 receives this non-sensor data 141b and determines that the sensor data 141 and the non-sensor data 141b Display a duplicate shopping article so that only one gallon of organic whole milk is added to the aggregated shopping list, rather than two gallons of organic whole milk. The sensor data 141 and the non-sensor data 141b typically include sufficient product identification information to identify the product to be ordered. For example, the sensor may be in a connected device 131 include a camera that captures the article number ("stock keeping unit", SKU, number) of the product to be ordered and the product identification information as part of the sensor data 141 provides. The product identification number can be used to match the shopping list item with inventory available from merchants in a network of affiliated merchants. The hits can be exact hits, similar hits or generic hits.
The condition system 145 receives condition criteria 145a , Condition input data 145b and metadata 141c as input. The condition system 145 also receives condition input data from the learning machine 145d were generated. The condition system 145 generates condition data 145c , In some embodiments, the condition system overrides 145 learning data 141d that of the learning machine 141 For example, a conditional criterion is that during the summer, when the temperature is above 90 ° F or above 32 ° C, twice as much water and lemonade are ordered as through the learning data 141d is specified. The condition criteria 145a may in some embodiments relate to the season (for example, calendar seasons, football season, holidays or school year), weather or travel plans (for example, preparation for a camping trip or an absence from the city).
The inventory management system 143 Collects, generates, and tracks metadata for items in a home or other environment to help manage inventory. The inventory management system 143 collects sensor data 141 , Non-sensor data 141b , Condition data 145c , Learning data 141d and other data 143a from different sources. The metadata is stored in one or more tables in at least one database, for example the database 126c , An example of metadata fields used by the inventory management system 143 be tracked is in 2D shown. In the 2D shown table 180 contains a metadata name field 181 and a metadata value field 182 , Various other metadata fields not in 2D can be shown by the inventory management system 143 be collected, tracked and stored.
2E FIG. 10 illustrates an aggregated list creation system that receives multiple data types from multiple data source types, according to an example embodiment. The system 147 To create aggregated lists, combine multiple data types received from multiple data sources into an aggregated shopping list for presentation to the user 106 over a client device 110 , The user 106 The presented aggregated shopping list may represent the data source type and data source to help the user make decisions on whether to order the items, for example, online with or without delivery service, or to procure the items in physical stores that they are in Have consisted.
An example of data fields that may be included in a user interface that displays an aggregate shopping list is in FIG 2F shown. The aggregated shopping list 171 , in the 2F shown contains the following fields: Data Source Type 172 , Data source name 173 , Items 174 , Description or brand 175 and quantity 176 , Other examples of an aggregated shopping list may be compared to the aggregated shopping list 171 , additional fields, modified fields, or deleted fields. The aggregated shopping list contains articles by the users 106a and 106b , the connected devices 131 and by a system such as the dynamic list system 146 or components in the dynamic list system 146 were added. Regarding 2F represents the data source type 172 a field designating the source of the data. The data source types 172 which are specified as the connected device 1, the connected device 2, the connected device 3 and the connected device 4, indicate that the connected devices 131 is the data source type and sensor data is the dynamic list system 146 to be provided. The data source type 172 , which is specified as system, indicates that the data source type is from the learning machine 141 or the condition system 145 is and represents data generated by the system. The data provided by the learning machine 141 are called learning data (for example, learning data 141d ), and the data provided by the condition system 145 are generated are called condition data (for example, condition data 145c ). The data source types 172 , which are specified as the shopping list app and the recipe app, indicate that the data source type is from an app that is owned by the user 106 is used. For example, the user adds items to be ordered or purchased from a shopping list app, or the user selects a recipe and the inventory management system 143 Determines which items must be ordered based on the inventory in the environment (for example, home environment), The system for creating aggregated lists 147 creates the aggregated shopping list based on different data types received from the different data source types. The data provided by the system for creating aggregated lists 147 are received include sensor data 141 , Non-sensor data 141b , Metadata 141c , other data 143a , Learning data 141d and condition data 145c , With reference back to 2E includes the dealer system 150 in an exemplary embodiment, a merchant inventory module 151 , a product recommendation module 152 and an ad generation module 153 , The merchant inventory module 151 puts the system to create aggregated lists 147 Dealer inventory data 155 ready. The product recommendation module 152 Provides the system with recommendation data for creating aggregated lists 156 ready, and the ad generation module 153 puts the system to create aggregated lists 147 Ad data 157 ready. In some embodiments, the merchant system receives 150 a product ID 154 that from the connected devices 131 be provided and in the sensor data 141 may be included. In other embodiments, the product ID 154 from other sources, such as the user. The product ID 154 can represent the article number for the articles that the system has to create aggregated lists 147 add is. The article number may vary for different dealers, and may require some processing to match the received article number with products available from the various dealers. Once the product to be added to the aggregated shopping list is identified, whether by an exact hit, a similar hit, or a generic hit, the merchant system may 150 that of the traders who deal with the merchant system 150 identify available inventory and recommend products and offer advertisements. The dealer system 150 can be associated with a network of affiliates, so the merchant system 150 has access to products offered by the affiliated merchants, has access to the stock of products at affiliated merchants, and access to promotional offers available from affiliated merchants. For example, many companies offering electronic commerce applications, such as eBay, Amazon and Google, each have a network of affiliate merchants who can sell items on their electronic trading websites.
Once the aggregated shopping list the user 106 presented, the user can 106 Provide additional input to select products that have been recommended to the user. The user 106 can also receive discounts on the recommended items. The user 106 can from the client device 110 Access the aggregated shopping list using an app or web browser, which can be the same app as the shopping list app 132 and 133 , or another app, such as an aggregated shopping list app. The aggregated shopping list app may be from one of the connected devices 131 , such as the fridge, can be accessed. 2G shows some additional fields to the user 106 can be displayed to the user 106 helping to select recommended products from a number of dealers. The affiliated dealers 1 to 4 are as 191a to 191d shown. The fields 192 to 198 represent information to the user 106 can be given together with the aggregated shopping list. fields 192 and 193 refer to inventory and price for exact matches. The fields 194 and 195 represent inventory and price for similar items. The field 196 Represents advertisements made by affiliated merchants 191a to 191d Tobe offered. fields 197 and 198 refer to shopping in stores and provide location-based information related to advertisements and inventory while the user 106 at one of the affiliated dealers 191a to 191d buys.
3 FIG. 10 illustrates a system for a client device executing a shopping list application to facilitate purchase transactions with a merchant system, according to an example embodiment. In some embodiments, the merchant system may 302 in the publication system (s) 142 be included. The seller 330 can represent a network of affiliated merchants. A dealer system 302 can be a delivery service module 336 , a dealer inventory system 150 , a check-out application 334 and an inventory management application 362 contain. The user 106a uses a client device 110a to a shopping list application 132 to execute transactions with one or more sellers 330 using a dealer system 302 to do. The shopping list application 132 allows the user 106a , a shopping list with information updates from the sellers 330 create, view, organize and manage. For example, the user 106a the shopping list application 132 use to create a shopping list of items to buy and to get detailed information from items on the shopping list from the sellers 330 to obtain. The detailed information allows the user 106a , Products from the sellers 330 compare to the user 106a helping to select specific products to buy and the particular seller from whom he wants to buy them. The user 106a can the shopping list application 132 Also use to automatically check off items that have been purchased and to be reminded during check-out of items that still need to be purchased.
The client device 110a that the shopping list application 132 may be a smartphone (for example, iPhone, Google Phone, or other phones that run Android, Windows Mobile or other operating systems), a tablet computer (for example, iPad, Galaxy), a PDA, a notebook computer, or various other types of wireless or wired computer devices. In some embodiments, the client device may 110a partially or completely in a connected device 131 be integrated. For example, a refrigerator may include a display with a user interface, as in 6A shown. The client device 110a can be from a network 104 with the dealer system 302 communicate.
The shopping list application 132 contains a user interface 306 , a product query interface 308 , a check-out interface 310 and a smart device interface 312 , The shopping list application 132 can be inputs from one or more connected devices 131 received in the inventory management system for connected devices 340 are located.
In an exemplary embodiment, the inventory management system manages connected devices 340 the stock of food and household goods in a home environment (or other environment) so that products and articles can be purchased when inventory is low. The connected devices 131 can dynamically identify products that a user needs to buy based on detecting or detecting changes in the environment. Examples of connected devices include a refrigerator that uses a scale and image recognition to automatically detect out-of-stock items, such as milk and eggs, a printer that can automatically detect when it runs out of ink cartridges, and a pantry that uses image recognition. Ordered for non-stock items. In alternative embodiments, food and other household items may be automatically ordered based on historical consumption patterns. The inventory management system for connected devices 340 Represents the shopping list application 132 Updates ready. The updates may incorporate sensor data from various sensors. Based on updates made by the connected device inventory management system 340 can be provided, and additional input from the user, the shopping list application 132 present various product recommendations to the user for purchase. Sensor data from the smart devices can be accessed via the path 342 to the network and then via the data path 346 to the dealer system 302 be transmitted. Alternatively, the sensor data can be transmitted via the data path 344 to the network 104 and then over the data path 346 to the dealer system 302 be sent. Further, non-sensor data may be from the shopping list application 132 via the data path 344 the network 104 and then over the data path 346 to the dealer system 302 to be provided. In some embodiments, the non-sensor data from the shopping list application may be stored in a cloud computing environment (not shown) connected to the network 104 connected, and on the the dealer system 302 can access.
The user interface 306 allows the user 106a , with the shopping list application 132 to interact and transactions with the sellers 330 using the dealer system 302 over the network 104 perform. For example, it allows the user interface 306 the user 106a to enter the shopping list of items to be purchased, and to see and manage the items and detailed information of items on the shopping list. Entering items to be purchased can be done in several ways. In one example, the user manually types in individual items or product types with a keypad or keyboard, in another example, the user may select items or product types from a list, such as from a drop-down menu of items / types available for purchase Sellers, or items / types previously purchased by the user. If the user 106a plans to go to a particular store, the shopping list may contain only those items or types available in that store. Creating an aggregated shopping list may include a combination of manual input with user-specified input data (also referred to as non-sensor data), sensor data, and system-generated data, along with a product / type selection. In one embodiment, the user interface includes 306 a software program, such as a user interface, that can be executed by a processor and that is configured to interface with the user 106a to build. The user 106a can also use the graphical user interface to access and browse product information of products that match one of the items on the shopping list where the products are for sale by the sellers 330 Are available.
The product query interface 308 allows the shopping list application 132 , Product information for items on the shopping list of sellers 330 over the network 104 to obtain. The product information of products in the stocks of the seller 330 is in a merchant inventory system 150 also known as a vendor inventory database. The dealer inventory system 150 can compare the queried items with products in its data back, check the availability of the products and product information of the product query interface 308 provide. The user 106a can through the product query interface 308 also received product recommendations. Alternatively, in other embodiments, the user may 106a wish, product information from the sellers 330 over the network 104 download. For example, the product query interface 358 make a query for product information of products that match items on the shopping list, over the Internet from one or more sellers 330 that the user 106a has designated. In addition, the product query interface 308 the dealer inventory system 150 query to offer preferential rates when the user 106a a loyalty program from one of the sellers 330 belongs.
The product information from the merchant inventory system 150 Products that match items on the shopping list may include trademarks, descriptions, pricing information and so on of the product. If the item on the shopping list is a generic product category, the product information may include information about a selection of products belonging to the general product category. The dealer inventory system 150 may also include information on discounts, sales promotions, special offers and the like for products that match the items on the shopping list, or may provide product information of products related to items on the shopping list. The user 106a can through the user interface 306 the product information from one or more sellers 330 consider. Based on product information and recommendations presented to the user, including special offers, pricing and delivery options, the user may 106a select the specific products to be purchased by a seller and / or buy items from multiple sellers' shopping lists to get the best price, the best selection and the best delivery options.
Once the user 106a has selected the products to be purchased and is ready to check out, the check-out interface 310 the shopping list application 132 the shopping list application 132 help track which items have been purchased from the shopping list and can inform the user 106a draw attention to all items that remain on the shopping list. For example, the check-out interface 310 with a check-out application 334 of the seller 330 over the network 104 communicate to receive information about purchased products. The check-out interface 310 can check out the application 334 of the seller 330 ask for information about the proof of purchase to receive information about the purchased products. The check-out interface 310 Can the items on the shopping list with the Compare purchased products on the receipt to identify the items that have been purchased and the remaining items that need to be purchased. Before the checkout is completed, if the shopping list contains items that have not yet been purchased, with one or more matching products available from the seller, the user interface may 306 the user 106a to draw attention to the not purchased item.
The delivery service module 336 is responsible to the user 106a To provide updates to the delivery. For example, the user becomes 106a Notify which courier has been assigned to the order, if the courier is at the store, to pick up the ordered items, if the courier is about to deliver the items, if the courier arrived at the buyer's place, and if the order is shipped has been.
The inventory management application 362 can provide data to other systems or applications (for example, the publishing system 140 or the payment systems 144 ). In some embodiments, the inventory management application 362 in the publication system 142 or the dynamic list system 146 be integrated.
The inventory management application 362 can store data about items (for example, food, household products, books, cars, guitars, and other tangible or intangible goods). For example, the database may include tables storing information about wood, paper, food, and electronic subscriptions. These tables may not only display static information about the articles, such as a name and a picture, but also dynamic information, such as a current inventory and a consumption rate. The inventory management application 362 can also save data about users. The inventory management application 362 can also have tables that indicate which of these articles belongs to a particular user. For example, in a home, multiple users of the inventory management application may 362 each owner of different items. By way of illustration, a roommate may consume one brand of soda (for example, brand X) while another roommate consumes another brand of soda (for example, brand Y). An image sensor (for example, a camera) in the refrigerator coupled to a processor configured to analyze images and identify the number of doses of each type of lemonade may determine when the amount of brand X lemonade or the mark Y falls below a predetermined threshold. Based on an assignment of the lemonade to the corresponding roommate can make an order for the lemonade and this will be charged to the right roommate.
4 FIG. 10 is a block diagram showing components of the inventory management application according to some example embodiments. FIG. The inventory management application shown 362 has a sensor module 410 , a learning module 428 , a condition module 430 and an order module 440 all of which are configured to communicate with each other (for example, via a bus, shared memory, or a switch). Any one or more of the modules described herein may be implemented using hardware (eg, a processor of a machine), or a combination of hardware and software. For example, each module described herein may configure a processor to perform the operations described herein for that module. Further, any two or more of these modules may be combined into a single module, and the functions described herein for a single module may be shared across multiple modules. Further, according to various exemplary embodiments, modules described herein as implemented in a single machine, database, or device may be distributed across multiple machine databases or devices. In some embodiments, one or more components of the inventory management application 362 into the inventory management system for connected devices 340 or the dealer system 302 be installed (as in 3 shown).
The sensor module 410 can be configured to receive sensor data. For example, a temperature may be received by a thermometer, a weight received by a scale, or an image received by a camera. The sensor module 410 can process the sensor data to determine a quantity of an item in the user's inventory. For example, an image may be processed to count individual imaged articles or to estimate a volume occupied by the article. As an example, a number of soda cans may be counted from the image, or the size of a paper stack may be estimated and used to estimate a number of sheets of stock in the stock.
The learning module 420 may be configured to suit the usage patterns of the users 106a and 106b to learn. For example, data from the sensor module 410 periodically the learning module 420 be supplied. By observing the rate at which the inventory of an article of the user is decreasing, one may observe estimated remaining time to be exhausted. Even more complex usage patterns can be learned. For example, the rate of decline of a user's inventory may vary depending on the temperature or the season, and this variation may be taken into account when estimating the time remaining until exhaustion.
The condition module 430 may be configured to access and store conditions for triggering the order of an article. Conditions (also referred to as conditional criteria) that are determined by the condition module 430 can be stored by a graphical user interface (for example, from the client devices 110a or 110b ) or from the learning module 420 be received. In an exemplary embodiment, the user enters the precise conditions that must be met in order for items to be added to a shopping list or to trigger an order for those items. This can be done using graphical user interface components such as text boxes, drop-down menus, data selectors, and the like. In another exemplary embodiment, no conditions are entered by the user. Instead, sensors monitor the amount of different items and the inventory management application 362 monitors orders made by the user for the various items. The learning module 420 correlates the orders with the sensor data and automatically generates conditions for the condition module 430 , In another exemplary embodiment, a combination of these two approaches is used.
The order module 440 may be configured to determine if that of the condition module 430 stored conditions and to add an item to a shopping list or place an order for the corresponding items. For example, the condition module 430 access a condition indicating that when the number of eggs in the refrigerator falls below 3, a dozen eggs should be added to the shopping list and ordered. The order module 440 can read data from the sensor module 410 Receive the ads that exist in the user's inventory of 2 eggs, and conclude that the condition to which the condition module 430 has been accessed. In response, the order module 440 with the electronic commerce application (for example, from the publication system 142 ) to place an order. For example, the order module may be 440 send the user's address and credit card information along with the quantity of the item to be ordered. The publication system (s) 142 may cause the user's account to be charged for the ordered items and the order to be sent to the appropriate parties (for example, the warehouse storing the ordered physical items).
5A FIG. 10 is a flow chart showing an exemplary method for automatically recommending a product to order based on an analysis of sensor data, non-sensor data, and inventory data. As in 5A shown contains the flowchart 508 operations 501 to 505 , In one embodiment, the sensor data and the non-sensor data may be used in the operation 501 be received. The sensor data may be received via a number of different means. In an exemplary embodiment, the sensor data is from a network 104 transfer. The network communication may take place via near field communication (NFC), WLAN, Bluetooth or other means of wired or wireless data transmission. In some embodiments, the network communication is over the network 104 (for example the internet). In other embodiments, the network communication may be peer-to-peer. In yet further embodiments, the communication may take place via NFC, Bluetooth low energy (BLE), RFID tag, audio, infrared, or other physical means of data transmission. The sensor data can be retrieved from numerous types of sensors. For example, light sensors, image sensors, tactile sensors, temperature sensors, humidity sensors, motion sensors and so on.
For example, a refrigerator may have an image sensor that is implemented inside and that can capture images of the contents of the refrigerator. Image recognition software or hardware may then be used to identify products in the refrigerator, along with other information such as quantity, brand and so on.
The operation 502 can retrieve stock data. The stock data can be stored in databases, for example 126 be saved. The inventory data may include a wide range of information. For example, the inventory data may include a product and a quantity. In further embodiments, the inventory data may include information about the products, such as shelf life data, product brand names, previous product brand name purchases, price, weight, dimensions, seasonal sales of the product, color, and so forth.
The operation 503 can determine products to order based on an analysis of sensor data, non-sensor data, and inventory data. For example, the surgery 501 Retrieve sensor data from a refrigerator indicating a low number of eggs. Then the operation can 502 Retrieve stock data related to eggs and determine that there are no more eggs in other refrigerators in a home, that the current eggs have exceeded their expiration date and are likely to be bad, that the eggs are grade A, that in the past only eggs purchased in grade A, that on average a dozen eggs were purchased per week, and so on. For example, an analysis of inventory data and sensor data may be used to determine that a dozen Grade A eggs should be ordered this week. In other embodiments, other information may be included in the analysis, such as the user's historical purchases, current trends in different products, information retrieved from social networks, and other information.
The operation 504 can recommend products to be ordered to a user and enable the delivery of products. The user can buy products, for example, using the check-out application 334 , The products can be recommended using a variety of user interfaces. In an exemplary embodiment, the product may be added to a shopping cart, and the contents of the shopping cart may be presented to the user. In other embodiments, a variety of comparable products may be recommended to the user, and the user may make a selection by comparing the products. Product information can be obtained from the application servers. The product information may include the product price, product images, brand name, current discounts and so on.
In some embodiments, the product recommendations may be based on other information, such as ingredients for a prescription, products that are consistent with a particular meal plan, products that are consistent with certain dietary restrictions, products that are consistent with certain medical conditions. for example, diabetes or allergies) and so on.
After the products have been recommended to the user, the surgery can 504 enable the delivery of the products, for example using the delivery service module 336 , For example, if a user places an order for a product, the operation may 504 Provide status updates and product delivery status notifications. The product status, in an exemplary embodiment, may be from the third-party application servers 130 be retrieved. For example, the delivery status may be represented as a position of the product on a map for the user.
Notifications can be sent using a variety of means. For example, notifications using e-mail, instant messages, SMS, text, fax or voice messages (for example, Voice over IP (VoIP)) over wired networks (for example, the Internet), the plain telephone service ("Plain Old Telephone Service"). , POTS), or wireless networks (for example, cellular, WLan, WiMAX).
The operation 505 can update inventory data based on ordered products. For example, if a user made a purchase, the inventory may be updated to reflect the purchase. Other data associated with the purchase may also be stored (for example, the time of purchase, the brand name, the price, whether the product was reduced in price, whether a coupon was used, and so on). The data can be stored in databases, for example 126 get saved.
5B to 5D show flowcharts for various embodiments for dynamically creating aggregated shopping lists. In various embodiments, additional operations may be to each of the flowcharts 510 to 530 can be added, or one or more operations can be from each of the flowcharts 510 to 530 be removed. In further embodiments, the operations of the flowcharts may be 510 to 530 , or variants of these flowcharts, can be combined.
5B FIG. 10 is a flowchart illustrating a method for dynamically creating aggregated shopping lists according to an example embodiment. The flowchart 510 includes operations 511 to 514 , For one embodiment, the operations may be 511 to 514 by one or more systems, modules, or components of the networked system 102 be implemented. For example, the operations can 511 to 514 through the dynamic list system 146 in combination with the dealer inventory system 150 be implemented.
At the operation 511 Sensor data associated with a first data source type is from a network 104 receive. The sensor data represents at least one article to be added to an aggregated shopping list from the first data source type, wherein the aggregated shopping list is associated with at least one user, and the first data source type represents a connected device.
At the operation 512 For example, the sensor data is processed based on predictive modeling associated with the consumption of the at least one article to be added to an aggregated shopping list from the first data source type to automatically generate learning data. The learning data is associated with a second type of data source.
The learning data represents at least one article to be added to the aggregated shopping list from the second data source type.
At the operation 513 Non-sensor data associated with a third type of data source is taken from the network 104 receive. The non-sensor data represents at least one article to be added to the aggregated shopping list from the third data source type.
At the operation 514 the aggregated shopping list is created of articles representing at least one article added to the aggregated shopping list by each of the first data source type, the second data source type, and the third data source type.
5C FIG. 10 is a flowchart showing a method of dynamically creating aggregated shopping lists according to another example embodiment. The flowchart 520 includes operations 521 to 527 , For one embodiment, the operations may be 521 to 527 by one or more systems, modules or components of the networked system 102 be implemented. For example, the operations can 521 to 527 through the dynamic list system 146 in combination with the dealer inventory system 150 to be implemented
At the operation 521 The method includes receiving, from a network, sensor data associated with a first type of data source. The sensor data represents at least one article to be added to an aggregated shopping list from the first data source type, wherein the aggregated shopping list is associated with at least one user. The first data source type represents a connected device.
At the operation 522 The method includes processing the sensor data based on predictive modeling associated with consumption of the at least one article to be added to an aggregated shopping list from the first data source type to automatically generate learning data. The learning data is associated with a second type of data source. The learning data represents at least one article to be added to the aggregated shopping list from the second data source type.
At the operation 523 The method includes receiving, over the network, non-sensor data associated with a third type of data source. The non-sensor data represents at least one article to be added to the aggregated shopping list from the third data source type.
At the operation 524 the method includes receiving, over the network, condition input data and condition criteria. The condition input data is associated with a fourth data source type. At the operation 525 The method includes processing the condition input data to determine whether the condition input data meets the condition criteria. At the operation 526 The method includes automatically generating condition data representing at least one article to be added to the aggregated shopping list from the fourth data source type.
At the operation 527 The method includes creating the aggregated shopping list of items that represent at least one item added to the aggregated shopping list by each of the first data source type, the second data source type, the third data source type, and the fourth data source type.
5D FIG. 10 is a flowchart showing a method of matching articles added to a shopping list with a merchant inventory according to an example embodiment. The flowchart 530 includes operations 531 to 533 , For one embodiment, the operations 531 to 533 by one or more systems, modules or components of the networked system 102 be implemented. For example, the operations can 531 to 533 through the dynamic list system 146 in combination with the dealer inventory system 150 be implemented. In other examples, the operation may be the operations 531 to 533 through the merchant inventory system 150 or the dealer system 302 be implemented.
At the operation 531 the method includes determining, based on the product identification information, whether at least one dealer of the network of affiliated merchants has an exact match in inventory for an item on the aggregated shopping list. At the operation 532 if the exact match is not successfully determined, the method includes determining which of the at least one dealer of the network of affiliates has the closest match in the match Inventory has for the one article on the aggregated shopping list. At the operation 533 if the closest hit is not successfully determined, the method includes determining whether at least one dealer of the network of affiliated merchants has a generic product having the same product category as the one article on the aggregated shopping list.
6A shows an exemplary embodiment of a user interface. The user interface 601 shows a list of products that need to be ordered. An ad 602 shows an exemplary location where the user interface 601 may be. In this example, the user interface 601 be on the front of a refrigerator. The user interface element 603 may be an electronic shopping cart that displays the number of articles in the cart and the total order value. The user interface element 604 may be a key that, when activated, responds to voice commands. For example, the voice commands may place additional items on a shopping list or place an order.
6B shows an exemplary embodiment of a user interface. The user interface element 605 FIG. 12 shows an exemplary user interface that may be used to add items to an electronic shopping cart and to place an order from an online marketplace (eg, eBay). The user interface element 606 may be a button that, when activated, adds an article to a shopping cart. A variety of metadata related to the products may be included in the user interface element 605 are displayed. Many brands can be compared on this user interface. In addition, a price comparison and a feature comparison of the products can also be displayed. It may be in the user interface element 605 Also, any current promotional activity or estate will be displayed. This user interface can be used to recommend products for sale as described herein.
6C shows an exemplary embodiment of a user interface. The user interface element 607 shows an example user interface that can display items that have been added to an electronic shopping cart. In this example, eggs were added to the cart. The user interface element 608 For example, in one exemplary embodiment, a key may be that, when activated, causes the purchase and delivery of the items in the electronic shopping cart.
6D shows an exemplary embodiment of a user interface. The user interface 609 can display the current delivery status of an order. The user interface element 610 can see the current location of the products on a map relative to the destination 611 Show. The status area 612 can be a graphical display of the current order status.
6E shows an exemplary embodiment of a user interface. The user interface 613 can indicate that the delivery has been made or that the delivery is imminent. The user interface element 614 can graphically show on a map that the delivery has been made or that delivery is imminent. Notifications about the upcoming delivery can be given.
6F shows an exemplary embodiment of a user interface. The user interface 615 can display a shopping list of items to be purchased. After a previous order has been completed, the shopping list can be dynamically updated to indicate that a product has been ordered on the list and no longer needs to remain on the shopping list.
Herein, certain embodiments are described as including logic or a number of components, modules, or mechanisms. Modules may be either software modules (eg, code embodied on a machine-readable medium or in a broadcast signal) or hardware modules. A "hardware module" is a tangible unit for performing certain operations and may be configured or arranged in a particular physical manner. In various exemplary embodiments, one or more computer systems (eg, a stand-alone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (eg, a processor or group of processors) may be software-implemented (eg, an application or a part an application) may be configured as a hardware module that operates to perform certain operations as described herein.
In some embodiments, a hardware module may be implemented mechanically, electronically, or in any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module may be a special purpose processor, such as a field programmable gate logic (FPGA) or an application specific integrated circuit (ASIC). A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module may include software contained within a general purpose processor or other programmable processor. It should be understood that the decision to implement a hardware module mechanically, in a dedicated and permanently configured circuit, or in a temporarily configured circuit (configured, for example, by software) may be influenced by cost and time considerations.
Accordingly, the term "hardware module" should be understood to include a tangible entity, whether an entity that is physically constructed, permanently configured (eg, hardwired), or temporarily configured (eg, programmed) to a certain extent or to perform certain operations described herein. As used herein, the term "hardware-implemented module" refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at all times. For example, where a hardware module includes a general purpose processor that is software configured to become a special purpose processor, the general purpose processor may be configured as different special purpose processors at different times (e.g., including another hardware module). Accordingly, software may configure a particular processor or processors to form, for example, one particular hardware module at a time and to form another hardware module at a different time.
A hardware module can provide information and receive information from other hardware modules. Accordingly, the described hardware module may be considered communicatively coupled. Where multiple hardware modules exist simultaneously, communications may be accomplished by signal transmission (eg, via appropriate circuitry and buses) between or under two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be accomplished, for example, by the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, a hardware module may perform an operation and store the output of that operation in a memory device with which it is communicatively coupled. Another hardware module may then access the storage device at a later time to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and may operate on a resource (eg, a collection of information).
The various operations of example methods described herein may be performed, at least in part, by one or more processors that are temporarily configured (for example, by software), or that are permanently configured to perform the relevant operations. Regardless of whether they are temporarily or permanently configured, such processors may form processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, a "processor-implemented module" refers to a hardware module that is implemented using one or more processors.
Similarly, the methods described herein may be at least partially processor implemented, with a particular processor or processors being examples of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. Further, the one or more processors may also operate to assist in performing the relevant operations in a "cloud computing" environment or as a "software as a service" (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines that include processors), these operations being performed over a network (e.g., the Internet) and through one or more suitable interfaces (e.g., an API ) can be accessed.
The execution of certain of the operations may be distributed across the processors, not just residing on a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented modules may reside in a single geographic location (eg, in a home environment, office environment, or server farm). In other example embodiments, the processors or processor implemented modules may be distributed over a number of geographic locations.
7 FIG. 12 shows an example mobile device that may be executing a mobile operating system (eg, iOS ™ , Android ™ , Windows® Phone, or other mobile operating systems) in accordance with exemplary embodiments. In one embodiment, the mobile device may 700 a touch screen, the tactile information from a user 702 can receive. For example, the user 702 physically the mobile device 700 touch 704 , and in response to the touch 704 can the mobile device 700 determine tactile information such as the location of touch, touch, gesture, and so on. In various exemplary embodiments, the mobile device may 700 a home screen 706 Show (for example, Springboard on iOS TM ) the user 702 the mobile device 700 can use to launch applications and the mobile device 700 to manage otherwise. In various exemplary embodiments, the home screen may 706 Provide status information such as battery life, connection status, or other hardware status. The home screen 706 may also include a plurality of icons that may be activated to launch applications, for example, by touching the area occupied by the icon. Similarly, other user interface elements may be activated by touching an area occupied by a particular user interface element. This way the user can 702 interact with the applications.
There may be a wide variety of applications (also referred to as "apps") on the mobile device 700 be executed. The applications can be native applications (for example, applications programmed in Objective-C running on iOS TM , or applications programmed in Java running on Android TM ), mobile web applications (such as HTML5), or hybrid Applications (for example, a native shell application that starts an HTML5 session). In a specific example, the mobile device may 700 a news app 720 , an audio recording app 722 , a camera app 724 , a book reading app 726 , a media app 728 , a fitness app 730 , a file management app 732 , a positioning app 734 , a browser app 736 , a settings app 738 , a contact app 740 , a phone call app 742 , other apps (for example, gaming apps, social networking apps, biometric monitoring apps), an app 744 a third party and so on. Examples of other apps may include a shopping list app, a recipe app, a notepad such as Evernote, a productivity app that allows to track tasks and lists, a shopping app that includes shopping list functionality, or other apps that include shopping list functionalities.
8th is a block diagram 800 , which is a software architecture 802 which can be installed on any one or more of the above devices. 8th is merely a non-limiting example of a software architecture, and it will be understood that many other architectures may be implemented to enable the functionality described herein. The software 802 can on a hardware like the machine 900 of the 9 which processors are running 910 , Storage 930 and input / output (I / O) components 950 having. In the exemplary architecture of 8th can the software 802 be conceptualized as a stack of layers wherein each layer can provide a particular functionality. For example, the software may 802 Layers like an operating system 804 , Libraries 806 , Frameworks 808 and applications 810 include. Operationally, the applications can 810 API calls 812 through the software stack and news 814 in response to the API calls 812 receive.
The operating system 804 can manage hardware resources and provide shared services. The operating system 804 can, for example, a kernel 820 , Services 822 and drivers 824 include. The kernel 820 can serve as an abstraction layer between the hardware and the other software layers. For example, the kernel 820 be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so forth. The services 822 Others can provide shared services for the other software layers. The drivers 824 may be responsible for controlling or interfacing with the underlying hardware. For example, the drivers 824 Display driver, camera driver, Bluetooth ® driver, flash memory driver, serial communication driver (for example, Universal Serial Bus (USB) driver), Wi-Fi ® or WLan driver, audio driver, performance management driver and so on.
The libraries 806 can provide a common infrastructure at a lower level by the applications 810 can be used. The libraries 806 can system libraries 830 (for example, the C standard library) which include functions such as memory allocation functions, string manipulation functions, can provide mathematical functions and the like. In addition, the libraries can 806 API libraries 832 such as media libraries (for example, libraries to support the presentation and handling of various media formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (for example, an OpenGL framework that can be used to support 2D and 3D render in graphical content on a display), database libraries (eg, SQLite that can be used to provide various relational database functions), web libraries (eg, WebKit that can provide web browsing functionality), and the like. The libraries 806 You can also use a large variety of other libraries 834 involve to the applications 810 to provide many other APIs.
The frameworks 808 can provide a common infrastructure at a high level by the applications 810 can be used. For example, the frameworks 808 provide various user interface functions, high-level resource management, high-level positioning services and so on. The frameworks 808 can provide a wide range of other APIs used by the applications 810 may be used, some of which may be specific to a particular operating system or platform.
The applications 810 include a home application 850 , a contact application 852 , a browser application 854 , a book reading application 856 , a positioning application 858 , a media application 860 , a news application 862 , a game application 864 and a wide collection of other applications like an application 866 a third party. In a specific example, the application 866 a third party (for example, an application that has been developed by an entity which is not the vendor of the particular platform. using the Android TM or IOS TM software development kits (SDK) may be a mobile software running on a mobile operating system such as iOS TM , Android TM , Windows® Phone, or other mobile operating systems. In this example, the application may 866 a third party the API calls 812 call from the mobile operating system 804 provided to enable the function described herein.
9 is a block diagram, the components of a machine 900 in accordance with some example embodiments configured to execute instructions from a machine-readable medium (eg, a machine-readable storage medium) and one or more of the methodologies described herein. Exactly shows the 9 a diagrammatic representation of the machine 900 in the exemplary form of a computer system within which instructions 916 (for example, software, a program, an application, an applet, an app, or other executable code) to the machine 908 to cause any one or more of the methodologies described herein to be practiced. In alternative embodiments, the machine operates 980 as a stand-alone device or may be coupled to other machines (eg, networked). In a networked use, the machine can 900 work as a server machine or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 900 may include, but is not limited to, a server computer, a client computer, a tablet computer, a laptop computer, a netbook, a set-top box, a PDA, a media entertainment system, a mobile phone, a smartphone, a mobile device, a portable device ( a smartwatch, for example), a smart home device (eg, a smart device), other smart devices, a weaving device, a network router, a network switch, a network bridge, or any other machine that is set up the instructions 916 execute, sequentially or otherwise, which actions specify by the machine 900 are to be executed. Continue, while only a single machine 900 is shown, the term "machine" should also be understood to include a collection of machines 900 includes, individually or collectively, the instructions 916 to perform one or more of the methodologies discussed herein.
The machine 900 can processors 910 , Storage 930 , and input / output components 950 include, which may be configured via a bus 902 communicate with each other. In an exemplary embodiment, the processors may 910 (for example, a central processing unit (CPU), a reduced instruction set processor (RISC), a complex instruction set processor (CISC), a graphics processing unit (GPU), a digital signal processor (DSP), an ASIC, an integrated radio circuit ("Radio Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof), for example the processor 912 and the processor 914 include what instructions 916 can execute. The term "processor" is intended to encompass multi-core processors that include two or more independent (also referred to as "cores") executing instructions simultaneously. Although the 9 can show multiple processors machine 900 a single single-core processor, a single multi-core processor (e.g., a multi-core processor), multiple single core processors, multiple multi-core processors, or any combination thereof.
The memory 930 can be a main memory 932 , a static memory 934 and a storage unit 936 include on which the processors 910 over the bus 902 can access. The storage unit 936 can be a machine readable medium 938 include, on the instructions 916 which embody one or more of the methodologies or functions described herein. The instructions 916 may also, in whole or in part, within the main memory 932 , inside the static memory 934 , within at least one of the processors 910 (for example, within the cache memory of the processor) or any suitable combination thereof, during their execution by the machine 900 , Accordingly, the main memory 932 , the static memory 934 and the processors 910 as machine-readable media 938 to be viewed as.
As used herein, the term "memory" refers to a machine-readable medium 938 , which is arranged to store data temporarily or permanently, and may be understood to include, but is not limited to, random access memory (RAM), read only memory (ROM), buffer memory, flash memory, and cache memory , While the machine-readable medium 938 In an exemplary embodiment, as a single medium is shown, the term "machine-readable medium" should be understood to include a single medium or multiple media (eg, a centralized or distributed database, or associated caches and servers) established are, instructions 916 save. The term "machine-readable medium" should also be understood to encompass any medium, or any combination of multiple media that is capable of providing instructions (e.g., instructions 916 ) for execution by a machine (for example the machine 900 ), so the instructions if they are from the one or more processors of the machine 900 (for example, the processors 910 ) the machine 900 cause one or more of the methodologies described herein to be performed. Accordingly, a "machine-readable medium" refers to a single storage device or storage device as well as "cloud-based" storage systems or storage networks that include multiple storage devices or devices. Accordingly, the term "machine-readable medium" should be understood to include, but not limited to, one or more data archives in the form of a semiconductor memory (eg, flash memory), an optical medium, a magnetic medium, other nonvolatile memory (e.g. erasable read-only memory (EPROM)), or any suitable combination thereof. The term "machine-readable medium" specifically excludes non-legal signals as such.
The input / output components 950 may include a wide variety of components to receive inputs, generate outputs, transmit information, exchange information, collect measurements, and so forth. It will be understood that the input / output components 950 Other components included in the 9 are not shown. The input / output components 950 are grouped by functionality only for ease of discussion, and grouping is in no way limiting. In various exemplary embodiments, the input / output components 950 output components 952 and input components 954 include. The output components 952 For example, visual components (for example, a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)) may be used. , acoustic components (for example speakers), haptic components (for example a vibration motor), other signal generators and so on. The input components 954 For example, alphanumeric input components (eg, a keyboard, a touch screen configured to receive alphanumeric inputs, a photooptical keyboard, or other input alphanumeric components), point-based tagging components (eg, a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing device), tactile input components (eg, a physical button, a touch screen that provides the location and force of touch or touch gestures, or other tactile input components), audio input components (eg, a microphone), and the like.
In other exemplary embodiments, the input / output components 958 , under a wide range of other components, biometric components 956 , Movement components 958 , Environmental components 960 or positional components 962 contain. For example, the biometric components 956 Components for recognizing expressions (for example, hand, facial expression, vocal Expressions, body gestures, or eye tracking) for measuring biosignals (eg, blood pressure, heart rate, body temperature, sweating, or brain waves) to identify a person (eg, voice recognition, retina recognition, face recognition, fingerprint recognition, or encephalogram-based recognition), and the like. The movement components 958 may include acceleration sensor components (eg, an acceleration sensor), gravity sensor components, rotation sensor components (eg, a gyroscope), and so on. The environment components 960 For example, illumination sensor components (eg, a photometer), temperature sensor components (eg, one or more ambient temperature detectors), humidity sensor components, pressure sensor components (eg, a barometer), acoustic sensor components (eg, one or more microphones that detect background noise), proximity sensor components (For example, infrared sensors that detect nearby objects), gas sensors (for example, gas detection sensors to detect concentrations of safety-threatening gases or to measure contaminants in the atmosphere), or other components that provide indications, measurements, or signals that are environmental physical environment. The position components 962 For example, position sensor components (for example, a GPS receiver component), height sensor components (eg, altimeters or barometers that detect the air pressure from which the altitude can be derived), direction sensor components (eg, magnetometers), and the like.
Communication can be implemented using a wide variety of technologies. The input / output components 958 can communication components 964 which are set up, the machine 900 to a network 980 or devices 970 to couple over the coupling 982 or the coupling 972 , For example, the communication components 964 a network interface component or other suitable device configured to interface with the network 980 to build. In other examples, the communication components 964 wired communication components, wireless communication components, cellular communication components, components NFC, Bluetooth ® components (for example, Bluetooth ® Low Energy), Wi-Fi ® or WLAN components, and other communication components to provide communication over other modalities include. The devices 970 may be another machine or one of a wide variety of peripherals (for example, a peripheral device connected via USB).
Next, the communication components 964 Identify identifiers or include components that are set up to recognize identifiers. For example, the communication components 964 RFID tag reading components, NFC smart tag recognition components, optical reading components (for example, an optical sensor to one-dimensional bar codes such as the universal product code (UPC) bar code, multi-dimensional bar codes such as the Quick Response (QR) code, Aztec code , Data Matrix, Dataglyph, MaxiCode, PDF417, UltraCode, UCC RSS-2D barcode and other optical codes), or audible recognition components (such as microphones to identify tagged audio signals). In addition, a variety of information about the communication components 964 are derived, such as the place of Internet Protocol (IP) location determination, position determination by means of Wi-Fi ® or WLAN Signaltriangulation, position determination by means of detecting an NFC beacon signal, which may indicate a specific position, and so on.
In various exemplary embodiments, a portion or may include multiple portions of the network 980 an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a part of the Internet, a part of the PSTN, a POTS network, a mobile network his wireless network, a Wi-Fi ® or wiFi network, a different type of network, or a combination of two or more such networks. For example, the network 980 or part of the network 980 a wireless or cellular network, and the coupling 982 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling can 982 implement any of a variety of types of data transmission technologies, such as Single Carrier Radio Transmission Technology (IxRTT), Evolution Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, 3rd Generation Partnership Project (3 GPP) including 3G, 4th Generation Wireless Networks (4G), Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) Standard, others defined by different standardization bodies, others Wide area protocols, or other data transmission technology include.
The instructions 916 can over the network 980 be transmitted or received using a transmission medium via a network interface device (eg, a network interface component included in the communication components 964 and using any of a number of well-known transmission protocols (e.g., HTTP). Similarly, the instructions can 916 be transmitted or received using a transmission medium via the coupling 972 (for example, peer-to-peer coupling) on devices 970 , The term "transmission medium" should be understood to encompass any intangible medium capable of instructions 91 for execution by the machine 900 store, encode or carry, and includes digital or analog communication signals or other medium to facilitate the communication of such software.
Next is the machine-readable medium 938 non-transitory (in other words, has no transitory signals), in the sense that it does not embody a propagation signal. The name of the machine-readable medium 938 however, as "non-transitory" should not be construed as meaning that the medium is unable to move; the medium should be considered as being transportable from one physical place to another. In addition, since the machine-readable medium 938 tangible, the medium may be considered as a machine-readable device.
Throughout the description, multiple instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are described as separate operations, one or more of the individual operations may be performed concurrently and there is no requirement that the operations be performed in the illustrated order. Structures and functionalities illustrated as separate components in exemplary embodiments may be implemented as a combined structure or component. Similarly, structures and functionalities presented as a single component may be implemented as separate components. These and other modifications, modifications, additions and improvements are within the scope of the subject matter.
Although an overview of the inventive subject matter has been described with respect to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term "invention" for convenience only and without intending to voluntarily limit the scope of its application to any single disclosure or inventive concept, if indeed more than one is disclosed.
The embodiments shown herein are described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and may deviate from such that structural and logical substitutions may be made without departing from the scope of this disclosure. The detailed description is therefore not to be considered in a limiting sense, and the scope of the various embodiments is defined solely by the appended claims, along with the full scope of equivalents to which such claims are entitled.
As used herein, the term "or" may be understood in both an inclusive and an exclusionary sense. Further, multiple instances may be provided for resources, operations, or structures, described herein as individual instances.
In addition, boundaries between different resources, operations, modules, works, and datastores are somewhat arbitrary, and certain operations are presented in a context of specific example configurations. Other allocations of functionality are contemplated and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality may be implemented as separate resources in the example configurations as a combined structure or resource. Similarly, structures and functionality represented as a single resource may be implemented as separate resources. These and other modifications, modifications, additions and improvements are within the scope of embodiments of the present disclosure as represented by the appended claims. Accordingly, the description and drawings are to be considered as illustrative and not in a limiting sense.
A system for managing an aggregated list, the system comprising at least one processor configured to perform operations comprising: Receiving, from a network, sensor data associated with a first data source type, the sensor data representing at least one article to be added to the aggregated list from the first data source type, the aggregated list being associated with at least one user, the first Data source type represents a connected device; Processing, using at least one processor, the sensor data based on a predictive modeling associated with consumption of the at least one article to be added to the aggregated list from the first data source type to automatically generate learning data, the learning data including a second Data source type and represent at least one article to be added to the aggregated list from the second data source type; Receiving, from the network, non-sensor data associated with a third data source type, the non-sensor data representing at least one article to be added to the aggregated list from the third data source type; Creating the aggregated list of articles representing at least one article added to the aggregated list by each of the first data source type, the second data source type, and the third data source type.
The system of claim 1, wherein the operations further comprise: Receiving, from the network, condition input data and condition criteria, wherein the condition input data is associated with a fourth data source type; Processing, using the at least one processor, the condition input data to determine whether the condition input data meets the condition criteria; automatically generating, using at least one processor, condition data representing at least one article to be added to the aggregated list from the fourth data source type.
The system of claim 2, wherein the learning data for the at least one article to be added to the aggregated list may be overruled by the condition data.
The system of claim 2, wherein the operations for creating the aggregated list further include: Creating the aggregated list of articles representing at least one article added to the aggregated list of each of the first data source type, the second data source type, the third data source type, and the fourth data source type.
The system of claim 1, wherein the third type of data source includes one or more persons associated with the at least one user; and wherein the non-sensor data includes user-specified data representing the at least one article to be added to the aggregated list of the one or more persons.
The system of claim 1, wherein the sensor data includes the at least one article to be added to the aggregated list and associated product identification information.
The system of claim 6, wherein the product identification information includes a part number of the at least one article on the aggregated list.
The system of claim 7, wherein the article number is usable by a merchant inventory system associated with a network of affiliate merchants to determine if one or more affiliated merchants have an available inventory of the at least one article on the aggregated list.
The system of claim 6, wherein the operations further include: Determining, based on the product identification information, whether at least one dealer of the network of affiliated merchants has an exact inventory hit for an article on the aggregated list; if the exact hit is not successfully determined, determining which of the at least one dealer of the network of affiliated merchants has a closest hit with inventory for the one article on the aggregated list; and if the closest hit is unsuccessful, determining if at least one dealer of the network of affiliated merchants has a generic product that has the same product category as the one article on the aggregated list.
The system of claim 1, wherein the operations for receiving, from the network, the non-sensor data further comprise: Receiving the non-sensor data from a cloud computing environment, the cloud computing environment hosting a list application accessible to a client device, the list application receiving the non-sensor data via the client device.
The system of claim 1, wherein the operations further comprise: Identifying available inventory for the at least one item on the aggregated list from one or more merchants in a network of affiliated merchants.
The system of claim 11, wherein the operations further comprise: Identifying available promotional items associated with the at least one item on the aggregated list offered by one or more merchants within the network of affiliated merchants.
The system of claim 1, wherein the second data source type represents a learning engine.
The system of claim 1, wherein the third data source type represents a list application.
The system of claim 1, wherein the operations further comprise: Receiving, from the network, non-sensor data associated with a fifth data source type, the non-sensor data representing at least one article to be added to the aggregated list from the fifth data source type, the fifth data source type representing a recipe application; and wherein creating the aggregated list further comprises: Creating an aggregated list of articles representing at least one article added by each of the first data source type, the second data source type, the third data source type, and the fifth data source type.
System for managing system resources, comprising: at least one processor configured to execute operations for processor-implemented modules, which include: an inventory management system configured to: Receiving sensor data associated with a first data source type, the sensor data representing at least one article to be added to an aggregated list from the first data source type, the aggregated list being associated with at least one user, the first data source type representing a connected device ; and non-sensor data associated with a third data source type, the non-sensor data representing at least one article to be added to the aggregated list from the third data source type; a learning engine configured to process the sensor data based on predictive modeling associated with consumption of the at least one article to be added to the aggregated list from the first data source type to automatically generate learning data, wherein the learning data includes are associated with a second data source type and represent at least one article to be added to the aggregated list from the second data source type; and an aggregated list creation system configured to create the aggregated list of articles representing at least one article added to the aggregated list of each of the first data source type, the second data source type, and the third data source type.
The system of claim 16, further comprising: a condition system configured to: Receiving condition input data and condition criteria, wherein the condition input data is associated with a fourth data source type; Processing the condition input data to determine whether the condition input data satisfies the condition criteria; and automatically creating condition data representing at least one article to be added to the aggregated list from the fourth data source type.
The system of claim 16, further comprising: a merchant inventory system configured to identify an available inventory for the at least one item on the aggregated list from one or more merchants within the network of affiliated merchants.
The system of claim 18, further comprising: an ad generation module configured to identify available adware discounts associated with the at least one item on the aggregated list offered by one or more merchants within the network of affiliate merchants.
A machine readable medium storing instructions which, when executed by at least one processor of a machine, cause the machine to perform operations comprising: receiving sensor data associated with a first type of data source, the sensor data representing at least one article , which is an aggregated list of the first data source type wherein the aggregated list is associated with at least one user, the first data source type representing a connected device; Processing the sensor data based on predictive modeling associated with consumption of the at least one article to be added to the aggregated list from the first data source type to automatically generate learning data, wherein the learning data is associated with a second data source type and at least one article representing to be added to the aggregated list from the second data source type; Receiving non-sensor data associated with a third data source type, the non-sensor data representing at least one article to be added to the aggregated list from the third data source type; and creating the aggregated list of articles representing at least one article added to the aggregated list of each of the first data source type, the second data source type, and the third data source type.
A machine readable medium storing instructions which, when executed by at least one processor of a machine, cause the machine to: Receiving, from a network, sensor data associated with a first data source type, the sensor data representing at least one article to be added to an aggregated list from the first data source type, the aggregated list being associated with at least one user, the first one Data source type represents a connected device; Processing, using at least one processor, the sensor data based on a predictive modeling associated with consumption of the at least one article to be added to the aggregated list from the first data source type to automatically generate learning data, the learning data including a second Data source type and represent at least one article to be added to the aggregated list from the second data source type; Receiving, from the network, non-sensor data associated with a third data source type, the non-sensor data representing at least one article to be added to the aggregated list from the third data source type; Creating the aggregated list of articles representing at least one article added to the aggregated list by each of the first data source type, the second data source type, and the third data source type.
The machine-readable medium of claim 21, wherein the instructions further cause the machine to: Receiving, from the network, condition input data and condition criteria, wherein the condition input data is associated with a fourth data source type; Processing, using the at least one processor, the condition input data to determine whether the condition input data meets the condition criteria; automatically generating, using at least one processor, condition data representing at least one article to be added to the aggregated list from the fourth data source type.
The machine readable medium of claim 22, wherein the learning data for the at least one article to be added to the aggregated list may be overruled by the condition data.
The machine-readable medium of claim 22, wherein the instructions for creating the aggregated list further include instructions for: Creating the aggregated list of articles representing at least one article added to the aggregated list of each of the first data source type, the second data source type, the third data source type, and the fourth data source type.
The machine readable medium of claim 21, wherein the third type of data source includes one or more persons associated with the at least one user; and wherein the non-sensor data includes user-specified data representing the at least one article to be added to the aggregated list of the one or more persons.
The machine-readable medium of claim 21, wherein the sensor data includes the at least one article to be added to the aggregated list and associated product identification information.
The machine readable medium of claim 26, wherein the product identification information includes a article number of the at least one article on the aggregated list.
The machine readable medium of claim 27, wherein the article number is usable by a merchant inventory system associated with a network of affiliate merchants to determine if one or more affiliated merchants have an available inventory of the at least one article on the aggregated list.
The machine-readable medium of claim 26, further comprising instructions for: Determining, based on the product identification information, whether at least one dealer of the network of affiliated merchants has an exact inventory hit for an article on the aggregated list; if the exact hit is not successfully determined, determining which of the at least one dealer of the network of affiliated merchants has a closest hit with inventory for the one article on the aggregated list; and if the closest hit is unsuccessful, determining whether at least one merchant of the network of affiliated merchants has a generic product having the same product category as the one item on the aggregated list.
The machine readable medium of claim 21, wherein the instructions for receiving, from the non-sensor data network, further instructions include: Receiving the non-sensor data from a cloud computing environment, the cloud computing environment hosting a list application accessible to a client device, the list application receiving the non-sensor data via the client device.
The machine-readable medium of claim 21, further comprising instructions for: Identifying available inventory for the at least one item on the aggregated list from one or more merchants in a network of affiliated merchants.
The machine-readable medium of claim 31, further comprising instructions for: Identifying available promotional items associated with the at least one item on the aggregated list offered by one or more merchants within the network of affiliated merchants.
The machine readable medium of claim 21, wherein the second data source type represents a learning engine.
The machine readable medium of claim 21, wherein the third data source type represents a list application.
The machine-readable medium of claim 21, further comprising instructions for: Receiving, from the network, non-sensor data associated with a fifth data source type, the non-sensor data representing at least one article to be added to the aggregated list from the fifth data source type, the fifth data source type representing a recipe application; and wherein creating the aggregated list further comprises: Creating an aggregated list of articles representing at least one article added by each of the first data source type, the second data source type, the third data source type, and the fifth data source type.
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2014-11-21 DE DE212014000220.6U patent/DE212014000220U1/en active Active
2014-11-21 CA CA2929957A patent/CA2929957A1/en not_active Abandoned
2014-11-21 AU AU2014352762A patent/AU2014352762A1/en not_active Abandoned
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2016-08-04 R207 Utility model specification
2017-11-22 R150 Term of protection extended to 6 years