Patent Application
20160171110
Users may obtain weather information from various sources, such as a weather app on a mobile device, a weather website, a social network, etc. The weather may affect how users feel and/or what activities users perform. In an example, a user may purchase a winter coat once the weather starts dipping into the 40s. In another example, a user may decide to forego leaving the house on a rainy day, and may instead stay inside and play videogames. Weather may affect different users in different ways. For example, a college student in Colorado may feel comfortable when the temperature is 55°, whereas an elderly woman who grew up in Florida, but resides in the same area, may feel frigid. Thus, different users may have different emotional reactions to weather. Without a better understanding of how a user's mood may be affected by the weather and/or other factors, general assumptions about what content and/or activities may be interesting to the user may be inaccurate (e.g., the college student may be interested in an outdoor activity whereas the elderly woman may be interested in baking, and thus a general recommendation for both the college student and the elderly woman may be inaccurate). Unfortunately, many computing devices and/or content providers may lack technology that can determine a user's interests based upon the weather, and thus a user may expend considerable computing resources, such as network bandwidth, battery life of a mobile device, etc., attempting to locate content that may suit the user's mood.
In accordance with the present disclosure, one or more systems and/or methods for identification of user perception of weather and/or for providing personalized content based upon user perception of weather are provided. In an example of identifying user perception of weather, weather condition information associated with a user may be accessed (e.g., 50° and windy on a Tuesday in December). User contextual information, of the user during a timespan corresponding to the weather condition information, may be accessed (e.g., an email receipt and social network post may indicate that the user bought an ice cream cone on Tuesday). The user contextual information may be evaluated to determine a potential user perception of the weather condition information (e.g., the user may have felt comfortable at 50° with wind). In an example, a confidence metric may be determined for the potential user perception (e.g., a 15% confidence that the user feels comfortable at 50° with wind). In an example, user perceptions of other users that are similar to the user may be used to increase, decrease, or maintain the confidence metric (e.g., the 15% confidence may be increased to a 19% confidence based upon a second user, similar in age and location with the user, buying a slice of ice cream cake when the weather is 50° and windy). A user profile may be generated for the user based upon the potential user perception of the weather condition information.
In an example of providing personalized content based upon user perception of weather, current weather condition information, of a current weather condition associated with a location of a user, may be accessed (e.g., 69° with a high UV index). A user profile of the user may be evaluated utilizing the current weather condition information (e.g., a profile database, comprising the user profile, may be queried using the current weather condition information to identify an entry correlating the weather condition to a user perception) to determine the user perception of the current weather condition (e.g., the user profile may indicate that the user feels uncomfortably hot over 67° and is sensitive to the sun). Content (e.g., a sunscreen lotion advertisement; a recommendation to wear a hat; a homepage where indoor activities, sunscreen lotion, and sun umbrellas are ordered before other sun-based content, etc.), corresponding to the user perception may be identified and accessed. In this way, content, that is relevant and/or interesting to the user, may be provided to the user.
While the techniques presented herein may be embodied in alternative forms, the particular embodiments illustrated in the drawings are only a few examples that are supplemental of the description provided herein. These embodiments are not to be interpreted in a limiting manner, such as limiting the claims appended hereto.
Subject matter will now be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific example embodiments. This description is not intended as an extensive or detailed discussion of known concepts. Details that are known generally to those of ordinary skill in the relevant art may have been omitted, or may be handled in summary fashion.
The following subject matter may be embodied in a variety of different forms, such as methods, devices, components, and/or systems. Accordingly, this subject matter is not intended to be construed as limited to any example embodiments set forth herein. Rather, example embodiments are provided merely to be illustrative. Such embodiments may, for example, take the form of hardware, software, firmware or any combination thereof.
The following provides a discussion of some types of computing scenarios in which the disclosed subject matter may be utilized and/or implemented.
1.1. Networking
The servers 104 of the service 102 may be internally connected via a local area network 106 (LAN), such as a wired network where network adapters on the respective servers 104 are interconnected via cables (e.g., coaxial and/or fiber optic cabling), and may be connected in various topologies (e.g., buses, token rings, meshes, and/or trees). The servers 104 may be interconnected directly, or through one or more other networking devices, such as routers, switches, and/or repeaters. The servers 104 may utilize a variety of physical networking protocols (e.g., Ethernet and/or Fibre Channel) and/or logical networking protocols (e.g., variants of an Internet Protocol (IP), a Transmission Control Protocol (TCP), and/or a User Datagram Protocol (UDP). The local area network 106 may include, e.g., analog telephone lines, such as a twisted wire pair, a coaxial cable, full or fractional digital lines including T1, T2, T3, or T4 type lines, Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links including satellite links, or other communication links or channels, such as may be known to those skilled in the art. The local area network 106 may be organized according to one or more network architectures, such as server/client, peer-to-peer, and/or mesh architectures, and/or a variety of roles, such as administrative servers, authentication servers, security monitor servers, data stores for objects such as files and databases, business logic servers, time synchronization servers, and/or front-end servers providing a user-facing interface for the service 102.
Likewise, the local area network 106 may comprise one or more sub-networks, such as may employ differing architectures, may be compliant or compatible with differing protocols and/or may interoperate within the local area network 106. Additionally, a variety of local area networks 106 may be interconnected; e.g., a router may provide a link between otherwise separate and independent local area networks 106.
In the scenario 100 of
In the scenario 100 of
1.2. Server Configuration
The server 104 may comprise one or more processors 210 that process instructions. The one or more processors 210 may optionally include a plurality of cores; one or more coprocessors, such as a mathematics coprocessor or an integrated graphical processing unit (GPU); and/or one or more layers of local cache memory. The server 104 may comprise memory 202 storing various forms of applications, such as an operating system 204; one or more server applications 206, such as a hypertext transport protocol (HTTP) server, a file transfer protocol (FTP) server, or a simple mail transport protocol (SMTP) server; and/or various forms of data, such as a database 208 or a file system. The server 104 may comprise a variety of peripheral components, such as a wired and/or wireless network adapter 214 connectible to a local area network and/or wide area network; one or more storage components 216, such as a hard disk drive, a solid-state storage device (SSD), a flash memory device, and/or a magnetic and/or optical disk reader.
The server 104 may comprise a mainboard featuring one or more communication buses 212 that interconnect the processor 210, the memory 202, and various peripherals, using a variety of bus technologies, such as a variant of a serial or parallel AT Attachment (ATA) bus protocol; a Uniform Serial Bus (USB) protocol; and/or Small Computer System Interface (SCI) bus protocol. In a multibus scenario, a communication bus 212 may interconnect the server 104 with at least one other server. Other components that may optionally be included with the server 104 (though not shown in the schematic diagram 200 of
The server 104 may operate in various physical enclosures, such as a desktop or tower, and/or may be integrated with a display as an “all-in-one” device. The server 104 may be mounted horizontally and/or in a cabinet or rack, and/or may simply comprise an interconnected set of components. The server 104 may comprise a dedicated and/or shared power supply 218 that supplies and/or regulates power for the other components. The server 104 may provide power to and/or receive power from another server and/or other devices. The server 104 may comprise a shared and/or dedicated climate control unit 220 that regulates climate properties, such as temperature, humidity, and/or airflow. Many such servers 104 may be configured and/or adapted to utilize at least a portion of the techniques presented herein.
1.3. Client Device Configuration
The client device 110 may comprise one or more processors 310 that process instructions. The one or more processors 210 may optionally include a plurality of cores; one or more coprocessors, such as a mathematics coprocessor or an integrated graphical processing unit (GPU); and/or one or more layers of local cache memory. The client device 110 may comprise memory 301 storing various forms of applications, such as an operating system 303; one or more user applications 302, such as document applications, media applications, file and/or data access applications, communication applications such as web browsers and/or email clients, utilities, and/or games; and/or drivers for various peripherals. The client device 110 may comprise a variety of peripheral components, such as a wired and/or wireless network adapter 306 connectible to a local area network and/or wide area network; one or more output components, such as a display 308 coupled with a display adapter (optionally including a graphical processing unit (GPU)), a sound adapter coupled with a speaker, and/or a printer; input devices for receiving input from the user, such as a keyboard 310, a mouse, a microphone, a camera, and/or a touch-sensitive component of the display 308; and/or environmental sensors, such as a global positioning system (GPS) receiver 312 that detects the location, velocity, and/or acceleration of the client device 110, a compass, accelerometer, and/or gyroscope that detects a physical orientation of the client device 110. Other components that may optionally be included with the client device 110 (though not shown in the schematic diagram 300 of
The client device 110 may comprise a mainboard featuring one or more communication buses 312 that interconnect the processor 310, the memory 301, and various peripherals, using a variety of bus technologies, such as a variant of a serial or parallel AT Attachment (ATA) bus protocol; the Uniform Serial Bus (USB) protocol; and/or the Small Computer System Interface (SCI) bus protocol. The client device 110 may comprise a dedicated and/or shared power supply 318 that supplies and/or regulates power for other components, and/or a battery 304 that stores power for use while the client device 110 is not connected to a power source via the power supply 318. The client device 110 may provide power to and/or receive power from other client devices.
In some scenarios, as a user 112 interacts with a software application on a client device 110 (e.g., an instant messenger and/or electronic mail application), descriptive content in the form of signals or stored physical states within memory (e.g., an email address, instant messenger identifier, phone number, postal address, message content, date, and/or time) may be identified. Descriptive content may be stored, typically along with contextual content. For example, the source of a phone number (e.g., a communication received from another user via an instant messenger application) may be stored as contextual content associated with the phone number. Contextual content, therefore, may identify circumstances surrounding receipt of a phone number (e.g., the date or time that the phone number was received), and may be associated with descriptive content. Contextual content, may, for example, be used to subsequently search for associated descriptive content. For example, a search for phone numbers received from specific individuals, received via an instant messenger application or at a given date or time, may be initiated. The client device 110 may include one or more servers that may locally serve the client device 110 and/or other client devices of the user 112 and/or other individuals. For example, a locally installed webserver may provide web content in response to locally submitted web requests. Many such client devices 110 may be configured and/or adapted to utilize at least a portion of the techniques presented herein.
One or more systems and/or techniques for identification of user perception of weather and/or for providing personalized content based upon user perception of weather are provided. Many computing devices and/or environments may lack computing resources, detection techniques, and/or functionality to determine what content, such as advertisements, recommendations, and/or other information (e.g., a video, text, an activity suggestion, an app to download, etc.) may be interesting to a user (e.g., the user be in the mood for enjoying a bike ride). As provided herein, weather condition information of a current weather condition (e.g., precipitation, temperature, humidity, wind, UV index, pollution, etc.) may be leveraged to determine a user perception, such as a current mood and/or interest in doing an activity, of the user. The user perception may be tailored to the user based upon age, location, gender, culture, user specified information (e.g., the user may create a social network post “this rain just bums me out”), user activities (e.g., the user may purchase a scarf when the weather dips below 45°), and/or other information specified through a user profile of the user. Content, associated with the user perception (e.g., the user may be in an energetic mood based upon a current weather condition of 85° and sunny), may be identified, accessed, and provided to the user (e.g., a bike rental recommendation for a bike rental reservation website may be provided to the user).
The ability to provide users with relevant content may reduce network bandwidth, time, and/or computing resources otherwise utilized by users in an attempt to locate such content on their own (e.g., manually searching websites for activities to do or losing interest in a weather app because wind, humidity, and a raw temperature value may not provide an accurate indicator as to how the user may feel based upon the weather or what the user may want to do). Many content providers may not have information, processing resources, and/or network bandwidth to leverage weather information and user contextual information to determine a user perception of a current weather condition that may be indicative of a mood of the user to engage in a particular activity.
An embodiment of identification of user perception of weather is illustrated by an example method 400 of
At 408, the user contextual information may be evaluated to determine a user perception of the weather condition information. For example, this particular user may be in an indoor activity mood (e.g., a gloomy mood, a gaming mood, a low key mood, a bored mood, an uncomfortable mood, etc.) based upon the weather being humid and 76° or above. In an example, a confidence metric may be determined for the user perception (e.g., a 21% confidence metric based upon the social network post and the 6 hours of videogame playtime). In an example, other users, such as a second user, that are similar to the user above a user similarity threshold (e.g., similar in age, gender, location, career, culture, hobbies, etc.), may be identified. Responsive to the user perception of the weather condition information (e.g., the indoor activity mood when the weather is humid and 76° or above) corresponding to the second user, the confidence metric may be increased (e.g., increased to 25%). At 410, a user profile may be generated based upon the user perception of the weather condition information (e.g., the user profile may indicate that there is a 25% confidence that the user may be in an indoor activity mood, such as a mood to play videogames, when the weather is humid and 76° or above). It may be appreciated that different user profiles may be created and/or updated for different users because users may have different perceptions for the same weather conditions due to personal preferences of such users.
In an example, machine learning may be utilized to determine user perceptions of users regarding various weather condition information. For example, a plurality of users may be clustered based upon user identifying information of the plurality of user. Users may be clustered based upon age, such as clustering grade-schoolers into a first cluster and elderly people into a second cluster because the grade-schoolers may be more resilient to cold than the elderly. Users may be clustered based upon gender and occupation, such as clustering business women in their 40s into a third cluster and 20 year old college students into a fourth cluster because a 40 year old business woman may prefer different clothing recommendations when feeling cold than a 20 year old college student. Location, culture, and/or a variety of user traits may be used to cluster similar users that may share similar user perceptions of weather conditions. For example, a first cluster may comprise a first set of users, such as the user, that are similar above a similarity threshold. Responsive to determining that the user has the user perception of the weather condition (e.g., the indoor activity mood when the weather is humid and 76° or above), the user perception may be assigned to users within the first set of users to create propagated user perceptions. Confidence metrics may be assigned to the propagated user perceptions. For example, a confidence metric for a second user may correspond to a similarity between the user and the second user (e.g., the more similar the users the higher the confidence that both users will have the indoor activity mood when the weather is humid and 76° or above). In this way, machine learning functionality may identify user perceptions, of users, for generating user profiles that may be used to identify content that may be relevant and/or interesting to a particular mood, which may be inferred from the weather, of a user. At 412, the method ends.
An embodiment of providing personalized content based upon user perception of weather is illustrated by an example method 700 of
At 706, the user profile may be evaluated utilizing the current weather condition information (e.g., a profile database, comprising the user profile, may be queried using the current weather condition information to identify an entry correlating the weather condition to a user perception) to determine the user perception of the current weather condition. For example, the user profile may indicate that there is a 25% chance that the user may be in a skiing mood (e.g., the user may have previously engaged in winter sports when the weather dipped below 60° during February). At 708, content, corresponding to the user perception, may be accessed. The content may comprise a recommendation (e.g., “Try the new Coolest winter sport—Snow Soccer . . . ”), a media clip (e.g., a skiing resort promotional video), a website (e.g., a vacation website), an advertisement (e.g., a snowboard sale), an app suggestion (e.g., a sports app), and/or any other content that may be consumed by a user. Because multiple content from various content sources may correspond to the user perception, content candidates may be identified and prioritized. For example, a first content candidate (e.g., the skiing resort promotional video) may be prioritized over a second content candidate (e.g., a skiing movie suggestion) as the content based upon the first content candidate having a stronger correlation to the user perception than the second content candidate (e.g., the mood for participating in winter sports may correlate more to visiting a skiing resort than merely passively watching a skiing movie).
At 710, the content may be provided to the user. In an example, a recommendation of the content may be generated, and the recommendation may be sent to the user (e.g., a mobile alert comprising the text “Try the new Coolest winter sport—Snow Soccer . . . ”). In an example, a demand side platform may be invoked to identify an advertisement as the content based upon the advertisement corresponding to the user perception, and the advertisement may be provided to the user (e.g., displayed through an application interface, sent as an email, displayed through an advertisement interface on a webpage, etc.). In an example, the user perception may be provided to an advertising entity, and an advertisement may be received as the content from the advertising entity for display to the user. In an example, content may be arranged based upon the user perception, where content candidates with stronger correlations to the user perception may be displayed more prominently within a user interface than content candidates with weaker correlations to the user perception (e.g., a homepage may display winter sports activities in user interface elements having higher display prominence than summer sports activities).
In an example, user feedback may be received from the user. The user feedback may specify whether the user associated the user perception with the current weather condition information. In an example, the user may explicitly provide feedback that the user is not interested in winter sports activities when the weather dips below 60° and is windy. In an example, the user may implicitly provide feedback by ignoring the winter sports content and instead stays inside to read a surfing book.
Various users may perceive the current weather condition differently, and thus different content may be provided to different users for the same weather condition. For example, a determination may be made that the current weather condition information (e.g., 60° and windy) corresponds to a second location of a second user (e.g., a 70 year old man that lives in Florida and recently visited the doctor with a cold). A second user profile of the second user may be evaluated utilizing the current weather condition information to determine a second user perception of the current weather condition (e.g., the user may be in a gloomy mood, and thus may be interested in renting a movie and staying inside). Second content (e.g., a recommendation to download a movie rental app), but not the content (e.g., the skiing resort promotional video), corresponding to the second user perception may be accessed. The second content may be provided to the second user. At 712, the method ends.
As used in this application, “component,” “module,” “system”, “interface”, and/or the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers.
Unless specified otherwise, “first,” “second,” and/or the like are not intended to imply a temporal aspect, a spatial aspect, an ordering, etc. Rather, such terms are merely used as identifiers, names, etc. for features, elements, items, etc. For example, a first object and a second object generally correspond to object A and object B or two different or two identical objects or the same object.
Moreover, “example” is used herein to mean serving as an example, instance, illustration, etc., and not necessarily as advantageous. As used herein, “or” is intended to mean an inclusive “or” rather than an exclusive “or”. In addition, “a” and “an” as used in this application are generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Also, at least one of A and B and/or the like generally means A or B or both A and B. Furthermore, to the extent that “includes”, “having”, “has”, “with”, and/or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising”.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing at least some of the claims.
Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. Of course, many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter.
Various operations of embodiments are provided herein. In an embodiment, one or more of the operations described may constitute computer readable instructions stored on one or more computer readable media, which if executed by a computing device, will cause the computing device to perform the operations described. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated by one skilled in the art having the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment provided herein. Also, it will be understood that not all operations are necessary in some embodiments.
Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application.
