Patent Application
20120041820
The subject matter disclosed herein generally relates to the processing of data. Specifically, the present disclosure addresses systems and methods of structuring data as a composite property.
Nowadays, a machine (e.g., a computing device) may be used to process information pertaining to items and products. As an example, a machine may host a database that tracks an inventory of items, which may be specimens of products. The machine may be all or part of a network-based system for processing such information. For example, a network-based commerce system may include one or more machines that maintain a database, where records in the database contain information pertinent to various items or products. The various items or products may be available for purchase, and accordingly may be merchandised or advertised as being so available.
As used herein, the term “product” may include a tangible product, an intangible product (e.g., downloadable electronic data), an obligation to provide a product, a service, a license to use a service, or any suitable combination thereof. An “item” herein refers to an instance of a product (e.g., a specimen of the product). While a single item may constitute a product (e.g., a unique one-of-a-kind item cataloged as a product), in many cases multiple items constitute multiple instances of a product. For example, a product may be a particular model of digital camera, while a specific digital camera of that model (e.g., having a unique serial number) may be an item constituting an instance of that product.
Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which:
Example methods and systems described herein are directed to structuring data as a composite property. Examples merely typify possible variations. Unless explicitly stated otherwise, components and functions are optional and may be combined or subdivided, and operations may vary in sequence or be combined or subdivided. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of example embodiments. It will be evident to one skilled in the art, however, that the present subject matter may be practiced without these specific details.
Information pertinent to an item or product may be organized (e.g., structured) as a property of the item or product. The property may be represented (e.g., stored) as property data in a data structure of the item or product. For example, the property may be represented in a listing that describes the item or product, or in a database record that stores information regarding the item or product. In many cases, property data takes the form of an attribute-value pair. An attribute-value pair includes an attribute of the item or product and a corresponding value of the attribute. Generally, in a valid attribute-value pair, the value is assignable to the attribute and may be one of multiple potential values that are assignable to the attribute. For example, an attribute-value pair may be expressed as “color=red,” where allowable colors include “red,” “yellow,” and “blue.” The attribute is “color,” and its corresponding value is “red.” The words “red,” “yellow,” and “blue” are values that are assignable to the attribute, and the word “red” is the value actually assigned to the attribute. As another example, an attribute-value pair may be expressed as “color=0x1f,” where “0x1f” is a reference (e.g., an identifier or a pointer) that specifies the color “red.” As a further example, an attribute-value pair may be expressed as “comment=‘I really like this digital camera because it took the best outdoor shots while I was on my vacation to New Zealand,” where the attribute is “comment” and the value is free-form text (e.g., a sentence or a paragraph).
Similarly, information pertinent to a property may also be organized as a “property of a property.” Stated another way, a property may have one or more properties of its own. In particular, the value of an attribute-value pair may correspond to its own property data, which may include a further attribute-value pair. For example, where an attribute-value pair for a digital camera is “color=red,” an attribute-value pair for the color “red” may be “earliest availability=available now.” As further examples, the attribute-value pair for the color “red” may be “earliest availability=available in 90 days” or “earliest availability=Oct. 1, 2010.”
Moreover, information may be organized as a “property of a property of an item” or a “property of a property of a property of a product.” As used herein, the terms “characteristic,” “descriptor,” and “feature” all refer to a property and are used as synonyms of the term “property.” For clarity, a “property of a property of a property of an item” may be described as a “feature of a descriptor of a characteristic of an item.” In this manner, information pertinent to items, products, properties, or any suitable combination thereof may be structured with any level of sophistication or complexity (e.g., beyond three steps removed from an item or product). Accordingly, a data structure may be generated (e.g., by a machine) to contain one or more properties of an item or product, as well as to contain one or more properties of those properties. In the discussion herein, a “composite property” refers to such a data structure. Generally, a composite property may correspond to an item, a product, another composite property, or any suitable combination thereof.
A composite property (e.g., of an item or a product) may be indexed based on any value contained therein, and that value may be used to identify the item or the product (e.g., in response to a search request based on that value). Continuing the above example, suppose the composite property of a digital camera contains the attribute-value pair “color=red.” A query for red digital cameras may result in identification of this digital camera. Furthermore, suppose the composite property for the digital camera includes the attribute-value pair “earliest availability=available now.” A query for presently available digital cameras may result in identification of this digital camera.
As a result, a composite property may be used to identify an item as an instance of a virtual product. A “virtual product,” as used herein, is a set of items that share at least one attribute-value pair within their respective composite properties. Thus, following the previous example, although an item may be an instance of a particular product (e.g., serial number 00010 of a Model ABC digital camera), the same item may also be instances of multiple virtual products (e.g., all digital cameras that are red, all digital cameras that are available now, and all digital cameras that are both red and available now). Moreover, a composite property may be used to identify a virtual product as being related to another product, virtual or otherwise. For example, where one product has a composite property with the attribute-value pair “manufacturer=Sony,” a related virtual product may have a composite property with the attribute-value pair “nationality of manufacturer=Japan.”
As shown in
The characteristic 120 is a basis of a virtual product and corresponds to property data 122. The property data 122 specifies an attribute 124 of the characteristic 120. The property data 122 also specifies a value 126 of the attribute 124, thereby specifying an attribute-value pair pertinent to the characteristic 120. The value 126 specifies the descriptor 130 of the characteristic 120.
The descriptor 130 is also a basis of a virtual product and corresponds to property data 132. The property data 132 specifies an attribute 134 of the descriptor 130. The property data 132 also specifies a value 136 of the attribute 134, thereby specifying an attribute-value pair pertinent to the descriptor 130. The value 136 specifies the feature 140 of the descriptor 130.
The feature 140 is another basis of a virtual product and corresponds to property data 142. The property data 142 specifies an attribute 144 of the feature 140. The property data 142 also specifies a value 146 of the attribute 144, thereby specifying an attribute-value pair pertinent to the feature 140. The value 146 may specify a downstream property (not shown) of the feature 140. This chain of relationships may extend to any length and, in some example embodiments, may include one or more circular relationships (e.g., direct or indirect loopback relationships). As noted above, any level of sophistication or complexity may be supported by a composite property.
In an example embodiment shown in
The characteristic 120 of the item 110, as specified by the value 116, is “Michael Crichton.” The attribute 124 of the characteristic 120 is “producer of,” and the corresponding value 126 is “ER” (the title of a television show). Accordingly, a query for “producer of: ER” may result in identification (e.g., as instances of a virtual product) of items for which Michael Crichton is a producer (e.g., the show “ER” and the movie “Twister”). Hence, a virtual product based on “ER” may be a product that is related to “Michael Crichton,” “Jurassic Park,” or both.
The descriptor 130 of the characteristic 120, as specified by the value 126, is “ER.” The attribute 134 of the descriptor 130 is “co-producer,” and the corresponding value 136 is “Steven Spielberg.” Accordingly, a query for “co-producer: Steven Spielberg” may result in identification (e.g., as instances of a virtual product) of items for which Steven Spielberg is a co-producer (e.g., the show “ER” and the movie “The Goonies”). Hence, a virtual product based on “Steven Spielberg” may be a product that is related to “ER,” “Michael Crichton,” “Jurassic Park,” or any suitable combination thereof.
The feature 140 of the descriptor 130, as specified by the value 136, is “Steven Spielberg.” The attribute 144 of the feature 140 is “actor in,” and the corresponding value 146 is “The Blues Brothers.” Accordingly, a query for “actor in: The Blues Brothers” may result in identification (e.g., as instances of a virtual product) of items in which Steven Spielberg is an actor (e.g., the movie “The Blues Brothers” and the movie “Vanilla Sky”). Hence, a virtual product based on “The Blues Brothers” may be a product that is related to “Steven Spielberg,” “ER,” “Michael Crichton,” “Jurassic Park,” or any suitable combination thereof.
In an example embodiment shown in
The characteristic 120 of the item 110, as specified by the value 116, is “Stephen King.” The attribute 124 of the characteristic 120 is “birth year,” and the corresponding value 126 is “1947.” Accordingly, a query for “birth year: 1947” may result in identification of items that are related to a person born in 1947 (e.g., a book by Stephen King and a song by David Bowie). Hence, a virtual product based on “1947” may be a product that is related to “Stephen King,” “The Stand,” or both.
The descriptor 130 of the characteristic 120, as specified by the value 126, is “1947.” The attribute 134 of the descriptor 130 is “era,” and the corresponding value 136 is “post-WWII.” Accordingly, a query for “era: post-WWII” may result in identification of items that are related to a period of time between 1946 and 1960 (e.g., a book by Richard Matheson and a film by Alfred Hitchcock). Hence, a virtual product based on “post-WWII” may be a product that is related to “1947,” “Stephen King,” “The Stand,” or any suitable combination thereof.
The feature 140 of the descriptor 130, as specified by the value 136, is “post-WWII.” The attribute 144 of the feature 140 is “literature style(s),” and the corresponding value 146 is “pulp fiction.” Accordingly, a query for “literature style(s): pulp fiction” may result in identification of items for which the literature style is pulp fiction (e.g., books by Frank Herbert and books by H. P. Lovecraft). Hence, a virtual product based on “pulp fiction” may be a product that is related to “post-WWII,” “1947,” “Stephen King,” “The Stand,” or any suitable combination thereof.
In an example embodiment shown in
The characteristic 120 of the item 110, as specified by the value 116, is “X1 Universal Muffler.” The attribute 124 of the characteristic 120 is “in stock at,” and the corresponding value 126 is “Bob's Car Parts.” Accordingly, a query for “in stock at: Bob's Car Parts” may result in identification of items that are in stock at Bob's Car Parts (e.g., an X1 Universal Muffler and a dashboard cover).
The descriptor 130 of the characteristic 120, as specified by the value 126, is “Bob's Car Parts.” The attribute 134 of the descriptor 130 is “shipping policy,” and the corresponding value 136 is “free shipping.” Accordingly, a query for “shipping policy: free shipping” may result in identification of items for which shipping is free (e.g., a X1 Universal Muffler and a set of snow tires). Hence, a virtual product based on “free shipping” may be a product that is related to “Bob's Car Parts,” “X1 Universal Muffler,” “2006 Honda Civic 2 Door Coupe LX,” or any suitable combination thereof.
The feature 140 of the descriptor 130, as specified by the value 136, is “free shipping.” The attribute 144 of the feature 140 is “offered by,” and the corresponding value 146 is “Tires By Mail.” Accordingly, a query for “offered by: Tires By Mail” may result in identification of items that are offered by Tires By Mail (e.g., a set of snow tires and a set of racing tires). Hence, a virtual product based on “Tires By Mail” may be a product that is related to “free shipping,” “Bob's Car Parts,” “X1 Universal Muffler,” “2006 Honda Civic 2 Door Coupe LX,” or any suitable combination thereof.
The property 530 (“Author”) has its own properties 531 (“Birth Name”), 533 (“Birthdate”), 535 (“Biography”), 537 (“Books”), and 539 (“Films”). Likewise, the property 550 (“Reviews”) has its own properties 552 (“Title”), 554 (“Date”), 556 (“Author”), 558 (“Text”), 562 (“Title”), 564 (“Date”), 566 (“Publication”), and 568 (“Author”). The properties 531, 533, 535, 537, 539, 552, 554, 556, 558, 562, 564, 566, and 568 constitute “properties of properties” and may be designated as “descriptors” of “characteristics” of the product 500, using the nomenclature of
The property 531 (“Name”) has its own property 532 (“Aliases”). Similarly, the property 539 (“Films”) has its own properties 541 (“Title”), 542 (“Release Date”), and 543 (“Cast”). The property 558 (“Text”) has its own property 559 (“Keywords”), and the property 568 (“Author”) has its own properties 571 (“Other Works”), 572 (“Style”), and 573 (“Biography”). The properties 532, 541, 542, 543, 559, 571, 572, and 573 constitute “properties of properties of properties” and may be designated as “features” of “descriptors” of “characteristics” of the product 500, using the nomenclature of
The relationships among these properties 510-573 may be represented in a data structure as a composite property, which may be stored as a composite property of the product 500. This may have the effect of organizing (e.g., structuring) data that otherwise would be unstructured with respect to the product. For example, suppose that the author of the product 500 used a fictitious name (e.g., a nom de plume) for the product 500, but has a legal birth name specified in the property 531 and a list of known aliases (e.g., nicknames) specified in the property 532. While a vendor of the product 500 may neglect to provide the legal birth name or the aliases (e.g., due to limited space on packaging for the product 500), the legal birth name and the aliases may be available from an alternative source of information (e.g., another vendor of the product, the manufacturer of the product, or an information service).
A machine (e.g., a data structure machine) may generate the composite property to include all this information. The machine may then index the composite property based on any one or more of the properties 510-573, thereby enabling identification of the product 500 using any one or more of the properties 510-573 (e.g., in response to a query submitted by a user). Furthermore, the machine may identify multiple products (e.g., multiple items from multiple products) as a single virtual product, based on any one or more of the properties 510-573 being respectively contained in composite properties of the multiple products. In other words, meaningful relationships (e.g., commonalities) among products may be identified through “properties of properties” of those products.
The data structure machine 610 and the database 670 may be associated with a network-based commerce system and accordingly may form all or part of such a network-based commerce system. The data structure machine 610 is configured to generate a data structure as a composite property for the item 110, as discussed in greater detail below with respect to
Any of the machines shown in
The database 670 may be any kind of database that stores information (e.g., a data structure stored as one or more data records). For example, the database 670 may be a single file (e.g., a tab-delimited text file), a spreadsheet, a relational database, a triple-store, or any suitable combination thereof. Moreover, the database 670 may be implemented by one or more machines, which may be co-located together (e.g., a database server “farm”) or separated in location (e.g., a cloud computing environment).
The network 690 may be any network that enables communication between machines (e.g., data structure machine 610 and client machine 660). For example, the network 690 may be a wired network, a wireless network, or any suitable combination thereof. The network 690 may include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof.
The property data 112 (of the item 110) includes an attribute-value pair that specifies the attribute 114 and its corresponding value 116. The data structure 700 includes information correlating the value 116 with the property data 122 (of the characteristic 120). For example, the information may be a reference (e.g., a pointer) to the property data 122. Additional attribute-value pairs are also shown.
The property data 122 (of the characteristic 120) includes an attribute-value pair that specifies the attribute 124 and its corresponding value 126. The data structure 700 includes information correlating the value 126 with the property data 132 (of the descriptor 130). For example, the information may be a reference (e.g., a pointer) to the property data 132. Additional attribute-value pairs are also shown.
The property data 132 (of the descriptor 130) includes an attribute-value pair that specifies the attribute 134 and its corresponding value 136. The data structure 700 includes information correlating the value 136 with the property data 142 (of the feature 140). For example, the information may be a reference (e.g., a pointer) to the property data 142. Additional attribute-value pairs are also shown.
The property data 142 (of the feature 140) includes an attribute-value pair that specifies the attribute 144 and its corresponding value 146. The data structure 700 may include information correlating the value 146 with other property data contained in the data structure 700, in another data structure elsewhere, or both. Additional attribute-value pairs are also shown.
The access module 810 is configured to access various property data (e.g., property data 112, 122, 132, or 142). In various example embodiments, the access module 810 is further configured to receive information usable to generate (e.g., create or modify) one or more properties (e.g., property data 112, 122, 132, or 142). The information may be received, in whole or in part, from different machines (e.g., partially from the vendor machine 620, partially from the vendor machine 630, partially from the manufacturer machine 640, partially from the information service machine 650, and partially from the client machine 660). Accordingly, the access module 810 may receive full or partial updates to the data structure 700, and these updates may be received from one or more sources (e.g., the different machines shown in
The generator module 820 is configured to generate the data structure 700 based on property data (e.g., property data 112, 122, 132, or 142) accessed by the access module 810. Specifically, the generator module 820 may generate the data structure 700 based on one or more attributes (e.g., attributes 114, 124, 134, or 144) specified by the property data, one or more values (e.g., values 116, 126, 136, or 146) specified by the property data, or any suitable combination thereof. The generator module 820 is further configured to store the data structure 700 in the database 670 as a composite property of the item 110.
Where the access module 810 receives information usable to generate one or more properties (e.g., property data 112, 122, 132, or 142), the generator module 820 is configured to generate one or more properties (e.g., property data 112, 122, 132, or 142) based on the received information. Specifically, the generator module 820 may generate one or more attributes, one or more values, or any suitable combination thereof, based on the received information. Generation of an attribute or a value, as discussed herein, includes modification (e.g., updating) of an existing attribute or value (e.g., already stored in the database 670), as well as creation of a new attribute or value. Accordingly, the generator module 820 may update the data structure 700, in whole or in part, in response to the access module 810 receiving information from one or more sources (e.g., the different machines shown in
The search module 830 is configured to index the data structure 700 generated by the generator module 820. The search module 830 may index the data structure 700 based on one or more values (e.g., values 116, 126, 136, or 146) specified (e.g., contained) therein. The search module 830 may receive a search request (e.g., in the form of one or more search terms submitted by a user of the client machine 660) and, in response to the search request, perform a query of the database 670 to identify one or more items. The search module 830 may determine that one or more of the values matches (e.g., identically or non-identically) the search request (e.g., matches one or more search terms of the search request) and accordingly identify the item 110 based on the matching value (e.g., value 126, value 136, or value 146).
The recommendation module 840 is configured to identify one or more further items based on the matching value determined by the search module 830. The one or more further items identified by the recommendation module 840 may constitute a virtual product, and the one or more further items may be presented as one or more instances of the virtual product. For example, the recommendation module 840 may transmit a description (e.g., in a listing or in an advertisement) of one of the further items to the client machine 660 for presentation to a user of the client machine 660.
As shown in
In operation 930, the generator module 820 generates the data structure 700 based on the property data 122 accessed by the access module 810 in operation 920. For example, the data structure 700 may be generated based on the value 126 specified by the property data 122. The generator module 820 may reference the item 110 in the data structure 700, such that the data structure 700 is a data structure of the item 110. In operation 940, the generator module 820 stores the data structure 700 in the database 670 as a composite property of the item 110.
In an example of method 900 shown in
Operation 902 involves receiving an update of the value 116 and an update of the value 126, and the operation 902 may be performed by the access module 810. As an example, the access module 810 may receive the update of the value 116 from the vendor machine 620, and may receive the update of the value 126 from the vendor machine 630. In operation 904, the generator module 820 modifies the value 116 and modifies the value 126, based on the updates received in operation 902 by the access module 810.
As shown in
In operation 932, the generator module 820 generates further property data of the item 110. The further property data may specify an identifier of the data structure 700 (e.g., an item number or a product number), and this identifier may be communicated (e.g., by the generator module 820) to any one or more of the machines shown in
In operation 950, the search module 830 indexes the data structure 700 based on the value 126. The search module 830, in operation 960, performs a query of the database 670 based on the value 126 (e.g., in response to a search request), and in operation 970 identifies the item 110 based on some or all of the property data 122 (e.g., based on the value 126).
In operation 980, the recommendation module 840 identifies a further item based on some or all of the property data 122 (e.g., based on the value 126). The recommendation module 840, in operation 990, presents the further item as an instance of a virtual product. In some example embodiments, the recommendation module 840 also presents the item 110 as an instance of the same virtual product. In certain example embodiments, the recommendation module 840 presents the item 110 as an instance of a product that is related to the virtual product. Alternatively, the recommendation module 840 may present the item 110 as an instance of a project that is unrelated to the virtual product (e.g., as a serendipitous recommendation).
In an example of method 900 shown in
Operation 901 may be performed by the access module 810 and involves receiving information pertinent to the item 110 from a vendor of the item 110 (e.g., from the vendor machine 620). In operation 903, the generator module 820 generates the value 126 based on the information received in operation 901. Similarly, in operation 905, the generator module 820 generates the value 136 based on the received information.
In operation 909, the generator module 820 accesses the property data 132 of the descriptor 130. The property data 132 specifies the value 136 generated in operation 905.
As shown in
In operation 952, the search module 830 indexes the data structure 700 based on the value 136. The search module 830, in operation 962, performs a query of the database 670 based on the value 136 (e.g., in response to a search request), and in operation 972 identifies the item 110 based on some or all of the property data 132 (e.g., based on the value 136).
In operation 982, the recommendation module 840 identifies a further item based on some or all of the property data 132 (e.g., based on the value 136). The recommendation module 840, in operation 992, presents the further item as an instance of a virtual product. In some example embodiments, the recommendation module 840 also presents the item 110 as an instance of the same virtual product. In certain example embodiments, the recommendation module 840 presents the item 110 as an instance of a product that is related to the virtual product. Alternatively, the recommendation module 840 may present the item 110 as an instance of a product that is unrelated to the virtual product (e.g., as a serendipitous recommendation).
In an example of method 900 shown in
Operation 907 involves receiving an update of the value 126 and an update of the value 136, and the operation 907 may be performed by the access module 810. As an example, the access module 810 may receive the update of the value 126 from the vendor machine 630, and may receive the update of the value 136 from the manufacturer machine 640. In operation 908, the generator module 820 modifies the value 126 and modifies the value 136, based on the updates received in operation 907 by the access module 810.
As shown in
Moreover, as shown in
According to various example embodiments, one or more of the methodologies described herein may facilitate the provision of recommendations for products, items, or both, to a user (e.g., of the client machine 660). This may have the effect of providing recommendations that are perceived by the user as being enhanced (e.g., more interesting, more unexpected, or more instructive) compared to existing recommendation technology. Provision of such enhanced recommendations may therefore result in a reduction in search time spent by the user in identifying a desirable item or product. Accordingly, one or more of the methodologies discussed herein may have the technical effect of reducing demand for one or more computing resources used by one or more devices within the system 100 (e.g., the client machine 660). Examples of such computing resources include processor cycles, network traffic, memory usage, storage space, power consumption, and cooling capacity.
The machine 1300 includes a processor 1302 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), or any suitable combination thereof), a main memory 1304, and a static memory 1306, which are configured to communicate with each other via a bus 1308. The machine 1300 may further include a graphics display 1310 (e.g., a plasma display panel (PDP), a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)). The machine 1300 may also include an alphanumeric input device 1312 (e.g., a keyboard), a cursor control device 1314 (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument), a storage unit 1316, a signal generation device 1318 (e.g., a speaker), and a network interface device 1320.
The storage unit 1316 includes a machine-readable medium 1322 on which is stored the instructions 1324 (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions 1324 may also reside, completely or at least partially, within the main memory 1304, within the processor 1302 (e.g., within the processor's cache memory), or both, during execution thereof by the machine 1300. Accordingly, the main memory 1304 and the processor 1302 may be considered as machine-readable media. The instructions 1324 may be transmitted or received over a network 1326 (e.g., network 190) via the network interface device 1320.
As used herein, the term “memory” refers to a machine-readable medium able to store data temporarily or permanently and may be taken to include, but not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, and cache memory. While the machine-readable medium 1322 is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions (e.g., instructions 1324). The term “machine-readable medium” shall also be taken to include any medium that is capable of storing instructions (e.g., software) for execution by the machine, such that the instructions, when executed by one or more processors of the machine (e.g., processor 1302), cause the machine to perform any one or more of the methodologies described herein. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, a data repository in the form of a solid-state memory, an optical medium, a magnetic medium, or any suitable combination thereof.
Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.
Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware modules. A “hardware module” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) 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 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 array (FPGA) or an 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 encompassed within a general-purpose processor or other programmable processor. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “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 any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.
Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information).
The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors.
Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations.
The one or more processors may also operate to support performance of 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 including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an application program interface (API)).
The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations.
Some portions of this specification are presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals within a machine memory (e.g., a computer memory). These algorithms or symbolic representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. As used herein, an “algorithm” is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, algorithms and operations involve physical manipulation of physical quantities. Typically, but not necessarily, such quantities may take the form of electrical, magnetic, or optical signals capable of being stored, accessed, transferred, combined, compared, or otherwise manipulated by a machine. It is convenient at times, principally for reasons of common usage, to refer to such signals using words such as “data,” “content,” “bits,” “values,” “elements,” “symbols,” “characters,” “terms,” “numbers,” “numerals,” or the like. These words, however, are merely convenient labels and are to be associated with appropriate physical quantities.
Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or any suitable combination thereof), registers, or other machine components that receive, store, transmit, or display information. Furthermore, unless specifically stated otherwise, the terms “a” or “an” are herein used, as is common in patent documents, to include one or more than one instance. Finally, as used herein, the conjunction “or” refers to a non-exclusive “or,” unless specifically stated otherwise.
