Patent ID: 12231386

DETAILED DESCRIPTION

The following detailed description of example implementations refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements.

Some users keep emails stored on a remote server (e.g., a Gmail® server, a Hotmail® server, and/or another server associated with an email service) while other users keep emails stored locally (e.g., on a desktop computer, a mobile phone, and/or another user device). Emails, however, consume significant amounts of storage space, often beyond five or ten gigabytes (GBs). Additionally, a user may use multiple email addresses, which further multiplies how much storage space is consumed by emails. Accordingly, some implementations described herein provide for a system that intelligently removes emails based on detecting an associated expiry date and/or an associated limited capacity being filled. As a result, the system improves user experience and conserves storage space at either the remote server or at the user device locally.

Similarly, user devices retain caches of website information and other Internet information that a user has accessed in the past. However, searching through caches for relevant information consumes power and processing resources at user devices. Accordingly, some implementations described herein provide for a system that intelligently sorts content in an Internet-based cache based on detecting an associated expiry date and/or an associated limited capacity being filled. For example, an extension associated with a browser application may extract relevant information from the cache in the background while a user accesses websites through the browser application. As a result, the system improves user experience and conserves power and processing resources as compared with searching through the cache.

FIGS.1A-1Dare diagrams of an example100associated with storage space optimization for emails. As shown inFIGS.1A-1D, example100includes an email server, a storage optimization system, a storage, and a user device. These devices are described in more detail in connection withFIGS.3and5.

As shown inFIG.1Aand by reference number105, the storage optimization system may receive emails (e.g., a plurality of emails). For example, the storage optimization system may receive the emails from an email server. In some implementations, the storage optimization system may be at least partially integrated (e.g., physically, virtually, and/or logically) with the email server. Accordingly, the storage optimization system may receive the emails from a storage included in the email server. As an alternative, the storage optimization system may be at least partially integrated (e.g., physically, virtually, and/or logically) with the user device. Accordingly, the storage optimization system may receive the emails from a storage included in the user device. The storage optimization system may receive the emails periodically (e.g., using a push function or a pull function that executes according to a schedule) and/or on-demand (e.g., using a push function each time a new email arrives at the email server and/or the user device or using a pull function based on input from a user).

As shown by reference number110, the storage optimization system may scan the emails. Accordingly, as shown by reference number115, the storage optimization system may identify an email (e.g., at least one email) associated with a limited time window. The storage optimization system may move (or copy) the identified email to a partition (e.g., physical, virtual, and/or logical), of a storage, that is associated with limited time windows. As an alternative, the storage optimization system may store a flag and/or other indicator in the storage having the emails (e.g., included in the email server or included in the user device) such that the identified email is indicated as being associated with a limited time window. The storage optimization system may process the email associated with the limited time window as described in connection withFIG.1B.

Additionally, or alternatively, and as shown by reference number115, the storage optimization system may identify an email (e.g., at least one email) associated with a limited capacity. The storage optimization system may move (or copy) the identified email to a partition (e.g., physical, virtual, and/or logical), of a storage, that is associated with limited capacities. As an alternative, the storage optimization system may store a flag and/or other indicator in the storage having the emails (e.g., included in the email server or included in the user device) such that the identified email is indicated as being associated with a limited capacity. The storage optimization system may process the email associated with the limited capacity as described in connection withFIGS.1C and1D.

In some implementations, the identifications described above may be determined as described in connection withFIGS.1B and1C(e.g., determining an expiry date and determining a website, respectively). As an alternative, the identifications described above may use a sorting function such that the determinations described in connection withFIGS.1B and1Crefine the sorting and reject emails that were incorrectly sorted as associated with a limited time window or a limited capacity. The sorting function may use regular expressions (e.g., one or more regular expressions or regexes) that identify patterns associated with dates (e.g., “##/##/##”; “##/##/####”; “expires on ##/##/##”; “valid until ##/##/####”; and/or other similar patterns) and/or identify patterns associated with capacity (e.g., “while supplies last”; “limited stock available”; “purchase tickets at”; and/or other similar patterns). In some implementations, the regexes may apply to images as well as text. For example, the storage optimization system may convert an image (e.g., at least one image), included in an identified email, to text using an optical character recognition (OCR) algorithm.

As shown inFIG.1Band by reference number120, the storage optimization system may determine an expiry date associated with the identified email that is associated with the limited time window. For example, the storage optimization system may scan, from the email associated with the limited time window, text and/or OCR output based on an image (e.g., at least one image) included in the email, to determine an expiry date. In some implementations, the storage optimization system may determine the expiry date using a machine learning model. For example, the storage optimization system may use an artificial neural network (ANN), a linear regression, and/or another model trained on historical emails and associated expiry dates. The machine learning model may use preconfigured features (e.g., words like “expires”; phrases like “valid until”; and/or patterns like “MONTH ##”) to identify an associated expiry date. Additionally, or alternatively, the machine learning model may be a deep learning model such that the model may extract new features to identify an associated expiry date. Accordingly, in some implementations, the storage optimization system may feed back the determined expiry date and the email to update the machine learning model (e.g., to update parameters associated with nodes in a neural network) such that the machine learning model continues to improve in accuracy.

Additionally, or alternatively, to determine the expiry date, the storage optimization system may apply a series of regexes to the text and/or the OCR output. For example, as described above, the regexes may identify patterns associated with dates (e.g., “##/##/##”; “##/##/####”; “expires on ##/##/##”; “valid until ##/##/####”; and/or other similar patterns) to identify the associated expiry date.

As shown by reference number125a, the storage optimization system may transmit a report or an alert to a user (e.g., via the user device), associated with the email, that indicates the expiry date. For example, the report or alert may include a plaintext email or text message, a hypertext markup language (HTML) email, a portable document format (PDF) file, and/or another electronic version of the report. In some implementations, the report or alert may further indicate expiry dates that are associated with additional emails that the storage optimization system identifies as being associated with limited time windows.

In some implementations, the storage optimization system may determine that the expiry date is within a threshold amount of time of a current date such that the storage optimization system transmits the report based thereon. For example, a time difference between the expiry date and the current date satisfying the threshold amount of time may trigger the storage optimization system to generate the report indicating the expiry date. Additionally, or alternatively, the storage optimization system may generate a periodic report (e.g., weekly, daily, and/or according to another interval) such that the expiry date is included in the periodic report when the time difference between the expiry date and the current date satisfies the threshold amount of time. The threshold amount of time may be preconfigured or may be determined according to a setting (e.g., input by the user via the user device).

Additionally, or alternatively, the storage optimization system may determine that an item associated with the expiry date is consistent with a profile associated with the user such that the storage optimization system transmits the report based thereon. For example, the storage optimization system may scan, from the email associated with the limited time window, text and/or OCR output based on an image (e.g., at least one image) included in the email, to identify the item. In some implementations, the storage optimization system may determine the item using a machine learning model. For example, the storage optimization system may use an ANN, a linear regression, and/or another model trained on historical emails and associated items. The machine learning model may use preconfigured features to identify an associated item and/or may be a deep learning model such that the model may extract new features to identify an associated item. Additionally, or alternatively, to identify the item, the storage optimization system may apply a series of regexes to the text and/or the OCR output. For example, as described above, the regexes may identify patterns associated with item titles, descriptions, and/or categories to identify the associated item. The storage optimization system may identify (e.g., using the model and/or the regexes, as described above) an identifier associated with the item (e.g., an alphanumeric sequence associated with the item, such as a model number, a serial number, or an inventor number, and/or another identifier).

In some implementations, the storage optimization system may determine that the item is consistent with the profile using a machine learning model. For example, the storage optimization system may use an ANN, a linear regression, and/or another model trained on historical information regarding item purchases associated with the user. In some implementations, the storage optimization system may receive the historical information from, or be at least partially integrated with, a remote server associated with a financial account of the user and/or a merchant account of the user. The machine learning model may use preconfigured features (e.g., keywords in item titles or descriptions, categories associated with items, and/or other similar features) to identify a similarity score (and/or another measure of similarity between the item and the profile associated with the user). Additionally, or alternatively, the machine learning model may be a deep learning model such that the model may extract new features to identify an associated similarity score. The storage optimization system may determine that the item is consistent with the profile associated with the user when the similarity score satisfies a consistency threshold.

In some implementations, the storage optimization system may receive feedback from the user (e.g., via the user device) to update the machine learning model (e.g., to update parameters associated with nodes in a neural network) such that the machine learning model continues to improve in accuracy. For example, the storage optimization system may generate and transmit (e.g., to the user device) a query regarding whether the user is interested in the item. The storage optimization system may further receive an indication that the user was interested and update the machine learning model based thereon.

Additionally, or alternatively, the storage optimization system may determine that the item is consistent with the profile using a series of regexes. For example, as described above, the regexes may identify patterns associated with item titles, descriptions, and/or categories that match titles, descriptions, and/or categories, respectively, of items associated with the profile.

Additionally, or alternatively, the storage optimization system may delete the email associated with the limited time window based on determining that the expiry date is passed. As a result, the storage optimization system automatically conserves storage space at the email server or the user device. Additionally, because the storage optimization system may perform operations described above in the background, the user experience is improved as compared with using retention rules programmed by the user.

In some implementations, the storage optimization system may further determine, using a database, a traversal path and an application programming interface (API) call (e.g., at least one API call) associated with a website indicated in the email. For example, the storage optimization system may identify the website indicated in the email as described in connection withFIG.1C. Further, the storage optimization system may query the database (e.g., a tabular database, a graphical database, and/or another type of data structure that stores first information in association with at least second information), using a string or other data structure indicating the website, to receive an indication of a traversal path (e.g., a series of uniform resource locators (URLs) and/or other identifiers) and an indication of an API call (e.g., an identification of the API function and one or more parameters to include with the API call) that are associated with the website in the database.

Accordingly, the storage optimization system may traverse the website using the traversal path and the API. For example, the storage optimization system may execute one or more get functions or otherwise move through the series of URLs indicated by the traversal path. Additionally, the storage optimization system may perform the API call to determine whether the item associated with the email is unavailable. For example, the storage optimization system may include an identifier associated with the item (e.g., identified as described above) as an argument to the API call to receive output from the API indicating whether the item is unavailable (e.g., a Boolean indicating whether the item is in stock, an integer indicating a quantity of the item that remains in inventory, and/or similar output). Accordingly, the storage optimization system may delete the email further based on the item being unavailable. As a result, the storage optimization system automatically conserves storage space at the email server or the user device. Additionally, because the storage optimization system may perform operations described above in the background, the user experience is improved as compared with using retention rules programmed by the user.

In some implementations, as further shown by reference number125b, the storage optimization system may delete the email associated with the limited time window by moving the email associated with the limited time window to a new folder (shown as a trash folder in example100). The storage optimization system may move the email by physically or logically moving an electronic copy (e.g., a file) of the email across storages (or partitions of a storage) or by flagging the email for deletion. Accordingly, the storage optimization system may delete the email associated with the limited time window from the new folder after a threshold amount of time has passed. Accordingly, in some implementations, the user may recover the email from the new folder before the threshold amount of time has passed.

Although described using a single expiry date and/or single item associated with the email, the description similarly applies when the email is associated with a plurality of expiry dates and/or a plurality of items. For example, the storage optimization system may delete the email when the plurality of expiry dates have passed, when the plurality of items are not available, or a combination thereof.

Additionally with, or alternatively to, processing the email associated with the limited time window as described in connection withFIG.1B, the storage optimization system may process the email associated with the limited capacity. For example, as shown inFIG.1Cand by reference number130, the storage optimization system may determine a website associated with the identified email that is associated with the limited capacity. For example, the storage optimization system may scan, from the email associated with the limited capacity, hyperlinks (e.g., one or more hyperlinks) to determine a website associated with the email. In some implementations, the storage optimization system may additionally or alternatively scan OCR output based on an image (e.g., at least one image) included in the email, to determine the website. The storage optimization system may determine the website using a machine learning model. For example, the storage optimization system may use an ANN, a linear regression, and/or another model trained on historical emails and associated websites. The machine learning model may use preconfigured features (e.g., words like “link”; phrases like “click here”; and/or patterns like “https://WEBSITE”) to identify an associated website. Additionally, or alternatively, the machine learning model may be a deep learning model such that the model may extract new features to identify an associated website. Accordingly, in some implementations, the storage optimization system may feed back the determined website and the email to update the machine learning model (e.g., to update parameters associated with nodes in a neural network) such that the machine learning model continues to improve in accuracy.

Additionally, or alternatively, to determine the website, the storage optimization system may apply a series of regexes to the text and/or the OCR output. For example, as described above, the regexes may identify patterns associated with website names (e.g., “https://WEBSITE”; “www.WEBSITE”; and/or other similar patterns) to identify the associated website. Accordingly, the storage optimization system may apply pattern recognition to the hyperlinks to determine the website. The storage optimization system may further identify (e.g., using the model and/or the regexes, as described above) an identifier associated with the event (e.g., an alphanumeric sequence associated with the event). For example, the storage optimization system may apply pattern recognition (e.g., extracting a portion between “?event_id=” and a terminus like “/”) to the hyperlinks to determine the identifier.

In some implementations, the storage optimization system may determine the website using a data structure that associates at least a portion of the hyperlinks with an identifier of the website. For example, a database (e.g., a tabular database, a graphical database, and/or another type of data structure that stores first information in association with at least second information) may associate portions of hyperlinks (e.g., a portion between “http://” or “https://” and a terminus like “.com” or “.org”) with identifiers of website names such that the storage optimization system queries the database to determine the website.

Accordingly, as shown by reference number135, the storage optimization system may traverse the website using the traversal path and the API. For example, the storage optimization system may execute one or more get functions or otherwise move through the series of URLs indicated by the traversal path. In some implementations, the storage optimization system may use the identifier associated with the event in the URLs (e.g., after a “/event/” portion of a URL and/or in another location indicated by the traversal path). Additionally, the storage optimization system may perform the API call to determine whether the limited capacity is filled. For example, the storage optimization system may include an identifier associated with the event (e.g., identified as described above) as an argument to the API call to receive output from the API indicating whether the limited capacity is filled (e.g., a Boolean indicating whether the event is sold out, an integer indicating a quantity of tickets available, and/or similar output). Accordingly, as shown by reference number140, the storage optimization system may receive capacity information associated with the event based on using the traversal path and the API. Additionally, as shown inFIG.1Dand by reference number145, the storage optimization system may determine the limited capacity (e.g., a Boolean indicating whether the limited capacity is low, an integer indicating a quantity of tickets available, and/or similar output).

As shown by reference number150a, the storage optimization system may transmit a report or alert to a user (e.g., via the user device), associated with the email, that indicates a low capacity associated with the event. For example, the report or alert may include a plaintext email or text message, an HTML email, a PDF file, and/or another electronic version of the report. In some implementations, the report or alert may further indicate capacities that are associated with additional emails that the storage optimization system identifies as being associated with limited capacities.

In some implementations, the storage optimization system may determine that the limited capacity associated with the event satisfies a capacity threshold such that the storage optimization system transmits the report based thereon. For example, the limited capacity associated with the event satisfying the capacity threshold may trigger the storage optimization system to generate the report indicating the limited capacity. Additionally, or alternatively, the storage optimization system may generate a periodic report (e.g., weekly, daily, and/or according to another interval) such that the limited capacity is indicated in the periodic report when the limited capacity associated with the event satisfies the capacity threshold. The capacity threshold may be preconfigured or may be determined according to a setting (e.g., input by the user via the user device).

Additionally, or alternatively, the storage optimization system may determine that the event associated with the limited capacity is consistent with a profile associated with the user such that the storage optimization system transmits the report based thereon. For example, the storage optimization system may scan, from the email associated with the limited time window, text and/or OCR output based on an image (e.g., at least one image) included in the email, to identify the event. In some implementations, the storage optimization system may determine the event using a machine learning model. For example, the storage optimization system may use an ANN, a linear regression, and/or another model trained on historical emails and associated events. The machine learning model may use preconfigured features to identify an associated event and/or may be a deep learning model such that the model may extract new features to identify an associated event. Additionally, or alternatively, to identify the item, the storage optimization system may apply a series of regexes to the text and/or the OCR output. For example, as described above, the regexes may identify patterns associated with event titles, descriptions, and/or categories to identify the associated event.

In some implementations, the storage optimization system may determine that the event is consistent with the profile using a machine learning model. For example, the storage optimization system may use an ANN, a linear regression, and/or another model trained on historical information regarding event purchases associated with the user. In some implementations, the storage optimization system may receive the historical information from, or be at least partially integrated with, a remote server associated with a financial account of the user and/or a merchant account of the user. The machine learning model may use preconfigured features (e.g., keywords in event titles or descriptions, categories associated with events, and/or other similar features) to identify a similarity score (and/or another measure of similarity between the event and the profile associated with the user). Additionally, or alternatively, the machine learning model may be a deep learning model such that the model may extract new features to identify an associated similarity score. The storage optimization system may determine that the event is consistent with the profile associated with the user when the similarity score satisfies a consistency threshold.

In some implementations, the storage optimization system may receive feedback from the user (e.g., via the user device) to update the machine learning model (e.g., to update parameters associated with nodes in a neural network) such that the machine learning model continues to improve in accuracy. For example, the storage optimization system may generate and transmit (e.g., to the user device) a query regarding whether the user is interested in the event. The storage optimization system may further receive an indication that the user was interested and update the machine learning model based thereon.

Additionally, or alternatively, the storage optimization system may determine that the event is consistent with the profile using a series of regexes. For example, as described above, the regexes may identify patterns associated with event titles, descriptions, and/or categories that match titles, descriptions, and/or categories, respectively, of events associated with the profile.

Additionally, or alternatively, and as shown by reference number150b, the storage optimization system may delete the email associated with the limited capacity based on determining that the limited capacity is filled. As a result, the storage optimization system automatically conserves storage space at the email server or the user device. Additionally, because the storage optimization system may perform operations described above in the background, the user experience is improved as compared with using retention rules programmed by the user.

In some implementations, the storage optimization system may further determine a date associated with the event. For example, the storage optimization system may identify the date associated with the event using a machine learning model and/or regexes similarly as described for expiry dates in connection withFIG.1B. Further, the storage optimization system may determine whether the date associated with the event has passed. Accordingly, the storage optimization system may delete the email further based on the event having already occurred. As a result, the storage optimization system automatically conserves storage space at the email server or the user device. Additionally, because the storage optimization system may perform operations described above in the background, the user experience is improved as compared with using retention rules programmed by the user.

In some implementations, the storage optimization system may delete the email associated with the limited capacity by moving the email associated with the limited time window to a new folder (shown as a trash folder in example100). Accordingly, the storage optimization system may delete the email associated with the limited capacity from the new folder after a threshold amount of time has passed. Accordingly, in some implementations, the user may recover the email from the new folder before the threshold amount of time has passed.

Although described using a single limited capacity and/or single event associated with the email, the description similarly applies when the email is associated with a plurality of limited capacities and/or a plurality of events. For example, the storage optimization system may delete the email when the plurality of limited capacities are filled, when the plurality of events are associated with dates that have passed, or a combination thereof.

As indicated above,FIGS.1A-1Dare provided as an example. Other examples may differ from what is described with regard toFIGS.1A-1D.

FIGS.2A-2Care diagrams of an example200associated with content prioritization for websites. As shown inFIGS.2A-2C, example200includes a remote server, a storage optimization system, a storage, and a user device. These devices are described in more detail in connection withFIGS.4and5.

As shown inFIG.2Aand by reference number205, the user device may receive information associated with a website. For example, the user device may request the information from a remote server, and the remote server may provide the information to the user device (e.g., for display to the user via a browser application and/or another piece of software executing on the user device).

As shown by reference number210, the user device may scan the information received from the remote server. In some implementations, the user device may scan the information during reception. As an alternative, the user device may scan the information after the information is stored in a cache (e.g., a cache associated with the browser application).

Accordingly, as shown by reference number215, the user device may identify content (e.g., at least a portion of the website information) associated with a limited time window. The user device may move (or copy) the identified content to a partition (e.g., physical, virtual, and/or logical), of a storage, that is associated with limited time windows. As an alternative, the user device may store a flag and/or other indicator in a storage having the content (e.g., included in a storage optimization system or included in the user device) such that the identified content is indicated as being associated with a limited time window. The storage optimization system may process the content associated with the limited time window as described in connection withFIG.2B.

Additionally, or alternatively, and as shown by reference number215, the user device may identify content (e.g., at least a portion of the website information) associated with a limited capacity. The user device may move (or copy) the content to a partition (e.g., physical, virtual, and/or logical), of a storage, that is associated with limited capacities. As an alternative, the user device may store a flag and/or other indicator in the storage having the content (e.g., included in a storage optimization system or included in the user device) such that the identified content is indicated as being associated with a limited capacity. The storage optimization system may process the content associated with the limited capacity as described in connection withFIG.2C.

In some implementations, the storage optimization system may be at least partially integrated (e.g., physically, virtually, and/or logically) with the user device. Accordingly, the storage optimization system may receive the content (as shown inFIG.2BandFIG.2C) from a storage included in the user device. As an alternative, the storage optimization system may be a remote server to which the user device transmits the identified content.

As shown inFIG.2Band by reference number220, the storage optimization system may determine an expiry date associated with the identified content that is associated with the limited time window. For example, the storage optimization system may determine the expiry date similarly as described above for emails in connection withFIG.1B.

Accordingly, as shown by reference number225, the storage optimization system may transmit a report or alert to a user (e.g., via the user device), associated with the content, that indicates the expiry date. For example, the report or alert may include a plaintext email or text message, an HTML email, a PDF file, and/or another electronic version of the report. In some implementations, the report or alert may further indicate expiry dates that are associated with additional content that the storage optimization system identifies as being associated with limited time windows. The storage optimization system may transmit the report or alert according to the triggers described above in connection withFIG.1B.

Additionally with, or alternatively to, processing the content associated with the limited time window as described in connection withFIG.2B, the storage optimization system may process the content associated with the limited capacity. For example, as shown inFIG.2Cand by reference number230, the storage optimization system may determine a website associated with the identified content that is associated with the limited capacity. For example, the storage optimization system may determine the website similarly as described above for emails in connection withFIG.1C.

Accordingly, as shown by reference number235, the storage optimization system may traverse the website using the traversal path and the API. For example, the storage optimization system may traverse the website as described above in connection withFIG.1C. Accordingly, as shown by reference number240, the storage optimization system may receive capacity information associated with the event based on using the traversal path and the API. Additionally, the storage optimization system may determine the limited capacity (e.g., a Boolean indicating whether the limited capacity is low, an integer indicating a quantity of tickets available, and/or similar output).

As shown by reference number245, the storage optimization system may transmit a report or alert to a user (e.g., via the user device), associated with the content, that indicates a low capacity associated with the event. For example, the report or alert may include a plaintext email or text message, an HTML email, a PDF file, and/or another electronic version of the report. In some implementations, the report or alert may further indicate capacities that are associated with additional content that the storage optimization system identifies as being associated with limited capacities. The storage optimization system may transmit the report or alert according to the triggers described above in connection withFIG.1D.

As indicated above,FIGS.2A-2Care provided as an example. Other examples may differ from what is described with regard toFIGS.2A-2C.

FIG.3is a diagram of an example environment300in which systems and/or methods described herein may be implemented. As shown inFIG.3, environment300may include a storage optimization system301, which may include one or more elements of and/or may execute within a cloud computing system302. The cloud computing system302may include one or more elements303-306, as described in more detail below. As further shown inFIG.3, environment300may include a network320and a user device330. Devices and/or elements of environment300may interconnect via wired connections and/or wireless connections.

The cloud computing system302includes computing hardware303, a resource management component304, a host operating system (OS)305, and/or one or more virtual computing systems306. The cloud computing system302may execute on, for example, an Amazon Web Services platform, a Microsoft Azure platform, or a Snowflake platform. The resource management component304may perform virtualization (e.g., abstraction) of computing hardware303to create the one or more virtual computing systems306. Using virtualization, the resource management component304enables a single computing device (e.g., a computer or a server) to operate like multiple computing devices, such as by creating multiple isolated virtual computing systems306from computing hardware303of the single computing device. In this way, computing hardware303can operate more efficiently, with lower power consumption, higher reliability, higher availability, higher utilization, greater flexibility, and lower cost than using separate computing devices.

Computing hardware303includes hardware and corresponding resources from one or more computing devices. For example, computing hardware303may include hardware from a single computing device (e.g., a single server) or from multiple computing devices (e.g., multiple servers), such as multiple computing devices in one or more data centers. As shown, computing hardware303may include one or more processors307, one or more memories308, and/or one or more networking components309. Examples of a processor, a memory, and a networking component (e.g., a communication component) are described elsewhere herein.

The resource management component304includes a virtualization application (e.g., executing on hardware, such as computing hardware303) capable of virtualizing computing hardware303to start, stop, and/or manage one or more virtual computing systems306. For example, the resource management component304may include a hypervisor (e.g., a bare-metal or Type 1 hypervisor, a hosted or Type 2 hypervisor, or another type of hypervisor) or a virtual machine monitor, such as when the virtual computing systems306are virtual machines. Additionally, or alternatively, the resource management component304may include a container manager, such as when the virtual computing systems306are containers. In some implementations, the resource management component304executes within and/or in coordination with a host operating system305.

A virtual computing system306includes a virtual environment that enables cloud-based execution of operations and/or processes described herein using computing hardware303. A virtual computing system306may execute one or more applications using a file system that includes binary files, software libraries, and/or other resources required to execute applications on a guest operating system (e.g., within the virtual computing system306) or the host operating system305.

Although the storage optimization system301may include one or more elements303-306of the cloud computing system302, may execute within the cloud computing system302, and/or may be hosted within the cloud computing system302, in some implementations, the storage optimization system301may not be cloud-based (e.g., may be implemented outside of a cloud computing system) or may be partially cloud-based. For example, the storage optimization system301may include one or more devices that are not part of the cloud computing system302, such as device500ofFIG.5, which may include a standalone server or another type of computing device. The storage optimization system301may perform one or more operations and/or processes described in more detail elsewhere herein.

Network320includes one or more wired and/or wireless networks. For example, network320may include a cellular network, a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a private network, the Internet, and/or a combination of these or other types of networks. The network320enables communication among the devices of environment300.

The user device330may include one or more devices capable of using the storage optimization system301to optimize email storage space. The user device330may include a communication device. For example, the user device330may include a wireless communication device, a user equipment (UE), a mobile phone (e.g., a smart phone or a cell phone, among other examples), a laptop computer, a tablet computer, a handheld computer, a desktop computer, a gaming device, a wearable communication device (e.g., a smart wristwatch or a pair of smart eyeglasses, among other examples), an Internet of Things (IoT) device, or a similar type of device. The user device330may communicate with the storage optimization system301to determine which emails to delete, as described elsewhere herein.

The number and arrangement of devices and networks shown inFIG.3are provided as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown inFIG.3. Furthermore, two or more devices shown inFIG.3may be implemented within a single device, or a single device shown inFIG.3may be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of environment300may perform one or more functions described as being performed by another set of devices of environment300.

FIG.4is a diagram of an example environment400in which systems and/or methods described herein may be implemented. As shown inFIG.4, environment400may include an operating system410, a browser application420(e.g., supported by the operating system410), and a browser extension430(e.g., supported by the browser application420), as described in more detail below. Additionally, as further shown inFIG.4, environment400may include a remote server440. Devices and/or elements of environment400may interconnect via wired connections and/or wireless connections.

The operating system410may include system software capable of managing hardware of a user device (which may include, for example, one or more components of device500ofFIG.5) and providing an environment for execution of higher-level software, such as the browser application420. For example, the operating system410may include a kernel (e.g., a Windows-based kernel, a Linux kernel, a Unix-based kernel, such as an Android kernel, an iOS kernel, and/or another type of kernel) managing the hardware and library functions that may be used by the higher-level software. The operating system410may additionally provide a graphical user interface (GUI) and process input from a user.

The browser application420may include an executable capable of running on a user device using the operating system410. The browser application420may communicate with the remote server440. For example, the browser application420may user hypertext transfer protocol (HTTP), file transfer protocol (FTP), and/or other Internet- or network-based protocols to request information from, transmit information to, and receive information from the remote server440. Additionally, the browser application420may communicate with the browser extension430to determine portions of a cache, associated with the remote server440, to prioritize content for the user of the user device, as described elsewhere herein.

The browser extension430may include a plug-in or another type of software that executes on top of the browser application420. In some implementations, the browser extension430may be at least partially integrated into the browser application420. Additionally, or alternatively, the browser extension430may at least partially operate independently of the browser application420. The browser extension430may determine portions of a cache, associated with the remote server440, to prioritize for the user of the user device, as described elsewhere herein.

The remote server440may include remote computing devices that provide information to requesting devices over the Internet and/or another network (e.g., similar to network320ofFIG.3). The remote server440may include a standalone server, one or more servers included on a server farm, or one or more servers spread across a plurality of server farms. In some implementations, the remote server440may include a cloud computing system (e.g., similar to cloud computing system302ofFIG.3). As an alternative, the remote server440may include one or more devices, such as device500ofFIG.5, that may include a standalone server or another type of computing device.

The number and arrangement of devices and networks shown inFIG.4are provided as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown inFIG.4. Furthermore, two or more devices shown inFIG.4may be implemented within a single device, or a single device shown inFIG.4may be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of environment400may perform one or more functions described as being performed by another set of devices of environment400.

FIG.5is a diagram of example components of a device500, which may correspond to an authentication system and/or a mobile device. In some implementations, an authentication system and/or a mobile device include one or more devices500and/or one or more components of device500. As shown inFIG.5, device500may include a bus510, a processor520, a memory530, an input component540, an output component550, and a communication component560.

Bus510includes one or more components that enable wired and/or wireless communication among the components of device500. Bus510may couple together two or more components ofFIG.5, such as via operative coupling, communicative coupling, electronic coupling, and/or electric coupling. Processor520includes a central processing unit, a graphics processing unit, a microprocessor, a controller, a microcontroller, a digital signal processor, a field-programmable gate array, an application-specific integrated circuit, and/or another type of processing component. Processor520is implemented in hardware, firmware, or a combination of hardware and software. In some implementations, processor520includes one or more processors capable of being programmed to perform one or more operations or processes described elsewhere herein.

Memory530includes volatile and/or nonvolatile memory. For example, memory530may include random access memory (RAM), read only memory (ROM), a hard disk drive, and/or another type of memory (e.g., a flash memory, a magnetic memory, and/or an optical memory). Memory530may include internal memory (e.g., RAM, ROM, or a hard disk drive) and/or removable memory (e.g., removable via a universal serial bus connection). Memory530may be a non-transitory computer-readable medium. Memory530stores information, instructions, and/or software (e.g., one or more software applications) related to the operation of device500. In some implementations, memory530includes one or more memories that are coupled to one or more processors (e.g., processor520), such as via bus510.

Input component540enables device500to receive input, such as user input and/or sensed input. For example, input component540may include a touch screen, a keyboard, a keypad, a mouse, a button, a microphone, a switch, a sensor, a global positioning system sensor, an accelerometer, a gyroscope, and/or an actuator. Output component550enables device500to provide output, such as via a display, a speaker, and/or a light-emitting diode. Communication component560enables device500to communicate with other devices via a wired connection and/or a wireless connection. For example, communication component560may include a receiver, a transmitter, a transceiver, a modem, a network interface card, and/or an antenna.

Device500may perform one or more operations or processes described herein. For example, a non-transitory computer-readable medium (e.g., memory530) may store a set of instructions (e.g., one or more instructions or code) for execution by processor520. Processor520may execute the set of instructions to perform one or more operations or processes described herein. In some implementations, execution of the set of instructions, by one or more processors520, causes the one or more processors520and/or the device500to perform one or more operations or processes described herein. In some implementations, hardwired circuitry is used instead of or in combination with the instructions to perform one or more operations or processes described herein. Additionally, or alternatively, processor520may be configured to perform one or more operations or processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

The number and arrangement of components shown inFIG.5are provided as an example. Device500may include additional components, fewer components, different components, or differently arranged components than those shown inFIG.5. Additionally, or alternatively, a set of components (e.g., one or more components) of device500may perform one or more functions described as being performed by another set of components of device500.

FIG.6is a flowchart of an example process600associated with storage space optimization for emails. In some implementations, one or more process blocks ofFIG.6may be performed by a system (e.g., storage optimization system301). In some implementations, one or more process blocks ofFIG.6may be performed by another device or a group of devices separate from or including the system, such as user device330. Additionally, or alternatively, one or more process blocks ofFIG.6may be performed by one or more components of device500, such as processor520, memory530, input component540, output component550, and/or communication component560.

As shown inFIG.6, process600may include receiving a plurality of emails (block610). As further shown inFIG.6, process600may include identifying at least one email, associated with a limited capacity, in the plurality of emails (block620). Additionally, or alternatively, and as shown inFIG.6, process600may include identifying at least one email, associated with a limited time window, in the plurality of emails (block620). As further shown inFIG.6, process600may include scanning, from the at least one email associated with the limited capacity, one or more hyperlinks to determine a website (block630). Additionally, as further shown inFIG.6, process600may include traversing the website to perform at least one API call to determine that the limited capacity is filled (block640). Accordingly, as shown inFIG.6, process600may include deleting the at least one email associated with the limited capacity based on determining that the limited capacity is filled (block650). Additionally, or alternatively, and as further shown inFIG.6, process600may include scanning, from the at least one email associated with the limited time window, text and OCR output based on at least one image to determine an expiry date (block660). Accordingly, as shown inFIG.6, process600may include deleting the at least one email associated with the limited time window based on determining that the expiry date is passed (block670).

AlthoughFIG.6shows example blocks of process600, in some implementations, process600may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted inFIG.6. Additionally, or alternatively, two or more of the blocks of process600may be performed in parallel.

The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise forms disclosed. Modifications may be made in light of the above disclosure or may be acquired from practice of the implementations.

As used herein, the term “component” is intended to be broadly construed as hardware, firmware, or a combination of hardware and software. It will be apparent that systems and/or methods described herein may be implemented in different forms of hardware, firmware, and/or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and/or methods are described herein without reference to specific software code—it being understood that software and hardware can be used to implement the systems and/or methods based on the description herein.

As used herein, satisfying a threshold may, depending on the context, refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, not equal to the threshold, or the like.

Although particular combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of various implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of various implementations includes each dependent claim in combination with every other claim in the claim set. As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiple of the same item.

No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more.” Further, as used herein, the article “the” is intended to include one or more items referenced in connection with the article “the” and may be used interchangeably with “the one or more.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, or a combination of related and unrelated items), and may be used interchangeably with “one or more.” Where only one item is intended, the phrase “only one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and/or,” unless explicitly stated otherwise (e.g., if used in combination with “either” or “only one of”).