System And Method For Determining An Authority Rank For Real Time Searching

Abstract

The present invention is directed towards a method and system for processing a real time increase in search requests for a common event. The method and system includes detecting an activity spike in user search request activity based on monitoring of user search requests over a defined period of time and determining source locations associated with the activity spike based on user search result activities. The method and system further includes associating the source locations with the user search request and thereupon applying a machine-learning model to determine a plurality of common features operative to cause the activity spike, including determining associations between the source locations and the activity spike.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation of U.S. patent application Ser. No. 12/706,349 filed Feb. 16, 2010, which is incorporated herein by reference in its entirety.

COPYRIGHT NOTICE

[0002] A portion of the disclosure of this patent document contains material, which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever.

FIELD OF THE INVENTION

[0003] The invention described herein generally relates to search engines and more specifically to systems and methods for processing and improving search results for current real-time trends and/or events.

BACKGROUND OF THE INVENTION

[0004] Traditional search engines deal with multiple sets of information corpora. In response to a search request, the search engine returns result sets in an ordered listing. The reliability of search results often depends on various factors, including the collection of the information, processing of the information, the information source and user feedback on the veracity of this information.

[0005] Problems can arise when there is a spike in activity for a particular search trend because of problems with not only determining the right contemporaneous information, but also the reliability of this information. Standard search terms can be easily and readily handled using existing search technology, for example a user conducting a search to find information on a vacation to Las Vegas.

[0006] But a spike in activity typically represents a corresponding real world occurrence and users seeking information as it becomes available. For example, suppose a natural disaster occurs or a rumor emerges that a company is about to launch a ground-breaking new product, there will be a corresponding in spike in people searching for this information.

[0007] Current web searching technology suffers from an ability to successfully account for contemporaneous information. There is a growing trend for highly contemporaneous information achieving a critical mass of distribution in a very short time frame. This increase in contemporaneous information is predicated on the wide use and quick dissemination of information occurring in the current electronic world.

[0008] The conversion of the Internet from a passive online informational source to a de facto medium for information distribution, combined with the new tools for increases contemporaneous content generation, complicates existing web searching technology. Examples of contemporaneous information may include data feeds, such as social media feeds, really simple syndication (RSS) feeds, web logs, etc. Prior techniques of crawling the Internet, cataloging and then searching these corpora suffer from a lack of proper accounting for these contemporaneous data sources.

[0009] With developments in search engine technology to account for these feeds, problems can arise in the reliability of this information. For example, just because a search engine may describe a social media feed that includes information relating to the event, there is no way to trust the source of this feed. Therefore, there exists a need for improving search results correlating to spikes in real time search activities by accounting for the authority of sources in the search result.

SUMMARY OF THE INVENTION

[0010] The present invention is directed towards a method and system for processing a real time increase in search requests for a common event. The method and system includes detecting an activity spike in user search request activity based on monitoring of user search requests over a defined period of time and determining source locations associated with the activity spike based on user search result activities. The method and system further includes associating the source locations with the user search request and thereupon applying a machine-learning model to determine a plurality of common features operative to cause the activity spike, including determining associations between the source locations and the activity spike.

[0011] The present invention further includes determining a plurality of fresh web content for a search engine and measuring a real-time authority for the fresh content using the machine-learning model. Therein, the method and system includes adjusting a reliability factor for the fresh web content based on the measured real-time authority. In one embodiment, the method and system, the adjustment of the reliability factor based on the measured real-time authority is performed instead of at least one of a link flux calculation and a page rank adjustment.

[0012] The present invention further includes determining the search terms of the user search requests associated with the activity spike and determining a plurality of additional content sources associated with the search terms. The system and method further includes ranking the plurality of additional content sources based on the measured real-time authority. The system and method additionally includes determining an additional processing capacity in the search engine caused by the activity spike and allocating available processing capacity of the search engine proportional to the additional processing capacity.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The invention is illustrated in the figures of the accompanying drawings which are meant to be exemplary and not limiting, in which like references are intended to refer to like or corresponding parts, and in which:

[0014] FIG. 1 illustrates one embodiment of a block diagram of a search result system that includes capabilities of providing search results for contemporaneous information;

[0015] FIG. 2 illustrates one embodiment of a search engine including capabilities for processing contemporaneous information from real time sources and determining an authority rank for the real-time sources;

[0016] FIG. 3 illustrates a flowchart of the steps of one embodiment of a method for determining an authority rank for real time sources; and

[0017] FIG. 4 illustrates a flowchart of the steps of another embodiment of a method for determining an authority rank for real time sources.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In the following description of the embodiments of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration, exemplary embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present invention.

[0019] As described herein, the search engine technology recognizes a spike or dramatic increase in user search activity for a particular event or theme. From that spike, the search engine is able to process search result options relating to real time sources. From the processing of those sources, the search engine therefore generates an authority rank for the real time sources. Using this authority rank improves the ordering of the search results.

[0020] FIG. 1 illustrates one embodiment of a system 100 that includes a user device 102, a network 104 and a search engine 106. The system 100 further includes a database population module 108 and a content database 110 usable by the search engine 106. In the system 100, additional content sources 120a, 120b and 120c are additionally connected via the network 104.

[0021] The user device 102 may be any suitable type of user processing device as recognized by one skilled in the art. In a typical embodiment, the user device 102 is a personal or mobile computing device that includes local processing capabilities, as well networking capabilities to interact and engage the network 104.

[0022] The network 104 may be any suitable type of network allowing data communication thereacross. In a typical embodiment, the network 104 is the Internet, following known Internet protocols for data communication thereacross.

[0023] The search engine 106 is one or more processing components disposed on one or more processing devices or systems in a networked environment. The search engine 106 may operate similar to known search engine technologies, but with the inclusion of additional processing capabilities describes herein. The search engine is operative to receive search requests and process the requests to generate search results to the user device 102 across the network 104. Whereas, the search engine 106 is additionally capable of recognizing a spike in search activity, recognizing contemporaneous sources of information, processing various details of information and thereby ranking the sources.

[0024] It is recognized that various details of the user device 102, network 104 and search engine 106 have been omitted. Many details, such as techniques for engaging and communication therebetween, not described herein are known within the knowledge of one skilled in the art and are omitted for brevity purposes only.

[0025] The database population module 108 is illustrated as being separate from the search engine 106, but it is recognized that this module may be incorporated therein. The database population module 108 is a processing device or system operative to perform processing operations in response to executable instructions, instructions for extracting search information relating to web-based content and then populating the content database 110. In one embodiment, the module 108 may include technology crawling Internet content to populate the database 110. Additionally, the module 108 includes processing operations for determining contemporaneous sources for real time information.

[0026] The system 100 illustrates three sample contemporaneous sources 120a, 120b and 120c (collectively referred to as 120). The sources 120 can be any type of source that provides real-time information. A typical example may be a social network feed. For example, a Twitter.RTM. feed from various account users can be a real-time source. This real-time data feed provides large amounts of contemporaneous information, with significant uncertainty regarding the veracity of this information. Another source could be a really simple syndication (RSS) feed or other type of news or data feed, e.g. a stock ticker feed.

[0027] It is recognized that there are other types of information sources that provide real-time content and the sources 120 are not limited by the examples listed above. As the speed of information is received, there is the uncertainty of the trustworthiness of this information.

[0028] In the system 100, the user may enter a search request to the search engine 106 via the network 104. The search engine accesses the database 110 to find content results that answer the search inquiry, where based on the population module 108, the database 110 includes real-time information from the contemporaneous sources 120.

[0029] Search results are provided back to the user device 102. The ranking of these results are affected by a recognition in spike in user search activity and generating an authority rank. FIG. 2 describes in further detail one embodiment of processing operations and subsequent methodologies for processing the real time increase in search requests and providing improved search results to the user device 102.

[0030] FIG. 2 illustrates a system 140 including an interface 142, search term monitor 144, search processing engine 146, a source locator, 148, a spike content database 150 and a machine learning processing device 152. The system 140 may be embedded within or part of a larger search engine, such as the search engine 106 of FIG. 1, for example.

[0031] The interface 142 represents the computer executable code that provides the front-end user experience for search operations, such as the user entering search terms and receiving search results in response thereto. The search term monitor 144 may be a processing device or module that monitors search terms over periods of time to determine if there is a particular trend or a spike in activity. For example, a spike in activity may be determined by specific standards, such as if there are X number of search requests to the same common theme within Y seconds. Merely by way of example, a spike may including noting there are in excess of 10,000 searches for the same or common terms within a period of 30 seconds.

[0032] The search processing engine 146 is a processing device operative to process various aspects of the search engine operations. The engine 146 may include receipt of the search term, accessing a database of search results and then generating the search results page in response thereto. The engine 146 includes additional processing capabilities for real-time rank authority as described in further detail below.

[0033] The source locator 148 may be a processing device or a module of executable instructions, operative to perform operations relating to determining a content source and allowing for various processing operations relating to that source. As described in further detail below, it is important to find various information sources, typically contemporaneous sources, in real-time event search response scenarios. And when those sources are discovered, the system 140 is operative to thereby rank the sources to quantify the reliability of these sources, typically illustrated via the search result rankings.

[0034] The spike content database 150 is any suitable type of data storage device that stores spike information. This database 150 may include the storage of search query information, e.g. search query terms, search query rewrites, search result actions, etc. This information is then usable for tracking search query information over a period of time, as described in further detail below.

[0035] In this system 140, the machine learning processing device 152 is one or more processing devices operative to generate authority ranks The device 152 uses machine learning operations to evaluate the authority of the determined sources, where in one embodiment the device 152 may use known machine learning techniques for ranking source authority, techniques used in existing search engines for evaluating sources with existing crawling techniques to crawl web content. Whereas, in the present system 140, the timeliness of the real time authority rank complicates the machine learning process, such that the machine learning is modified to be performed in a more expedited manner.

[0036] For further illustration of the systems of FIGS. 1 and 2, the operations of these systems are described in further detail regarding the methodologies of FIGS. 3 and 4.

[0037] FIG. 3 illustrates a flowchart of one embodiment of a method processing a real time increase in search requests for a common event. The method begins, step 160, by detecting an activity spike in user search request activity based on monitoring of user search requests over a defined period of time.

[0038] As described above, in a typical search engine, various amounts of searches are conducted on a regular basis. There are larger trends relating to common events, such as for example there may be an increase in searches for an actor or actress around the time a movie premieres, or general searches to a sporting event around the time the sporting event occurs.

[0039] By contrast, a spike in activity relates to an immediate jump in searching for information as may be caused by an immediate, typically unplanned event. Simply by way of example, an unplanned event may be a natural disaster, e.g. an earthquake in Haiti. The search engine 106, using the search term monitoring processing device 144, is therefore able to determine an activity spike by detecting that over a very short period of time there is an increase in the number of common searches. Using the example of a Haitian earthquake, the activity spike may be recognized as tens of thousands of searches for same or common terms, such as "Earthquake" and "Haiti."

[0040] The period of time can readily be adjusted to determine differences between a trend and an activity spike. A trend is more likely over an extended period of time, whereas an activity spike occurs in a truncated time period, whether it be seconds, minutes, hours, etc.

[0041] In the method of FIG. 3, a next step, step 162, is determining source locations associated with the activity spike based on user search result activities. The source locations for real-time activities include contemporaneous sources. Examples of contemporaneous sources may be any source that includes information in real time or in a timely fashion likely to be missed by web crawling techniques, for example a social network feed.

[0042] In this step, one example may be an event of a rumor of a high-tech product launch. A source location may be a technology blog dedicated to tracking and reporting on high tech rumors and news releases. Another source location may be a technical journal reporting on the blog article. This step may include determining that this web log and the journal articles are the sources of the activity spike. In the example of a natural disaster, the source of the activity spike may be a news web location reporting on the event in a breaking news fashion. Other sources could be social network feeds, for example, from individuals at the specification location.

[0043] A next step, step 164, is associating the source locations with the user search requests. Reference back to FIG. 2, the source locations 148 may determine these sources and the locations are stored in the spike content database 150. The locations can be stored and referenced by the associated search request and/or search terms. For example, in the example of a product launch, the web log and journal article may be cataloged in the spike content database 150 referenced by the search terms used in the spike of activity.

[0044] With a database cataloging the information, the machine learning processing device 152 is operative to perform the next step, step 166, of FIG. 3. The step includes applying a machine-learning model to determine a plurality of common features operative to cause the activity spike including determining associations between the source locations and the activity spike. It is recognized that there are any number of suitable machine-learning techniques operative to process the associations described herein, including in one embodiment machine-learning techniques applicable to web-crawling techniques in non-real time data processing and cataloging operations.

[0045] In step 168, the method further includes measuring a real-time authority rank for search result items based on the machine learning models. Based on this real time machine learning, the real-time authority indicates an authority ranking determinative of the veracity of the source. Using the above example of a rumored product launch, the web log may be given a high authority ranking based on the machine-learning factors indicating it is a highly trustworthy source.

[0046] By contrast, it is also possible that another source could be a secondary, less reliable web log indicating the product rumor. This less reliable web log may be less reliable for any number of reasons, such as it regularly broadcasts various rumors, is associated with a competing business, is associated with an illegal stock manipulation scheme, just by way of example. Using the machine learning operations of the machine learning processing device 152, this particular web location is then given a low authority for search results.

[0047] Based on determination of various sources, machine-learning processing and generating authority ranks, the search processing engine 146 is operative to generate search results for users performing search requests.

[0048] Step 170 of the method of FIG. 3 includes generating or updating search results including updating the ranking of search result items based on the authority rank. Thereby, in the system of FIG. 1, the client 102 submits the search request to the search engine 106, the search request being directed to a real time event causing a spike in user search activity. The search engine 106 is thereby operative to provide updated search results included results adjusted based on the authority rank generated via the methodology of the steps of FIG. 3.

[0049] FIG. 4 illustrates another embodiment, whereby processing operations provides for improving search results relating to real time event and activity spikes in searching, including processing information from contemporaneous sources. The method of FIG. 4 may be processed in the systems of FIGS. 1 and 2 or any other suitable processing environment.

[0050] In this embodiment, a first step, step 180, is determining a plurality of fresh web content for a search engine based on the active spike. The method of FIG. 4 may be predicated on the events of the method of FIG. 3, including the determination of an activity spike. The method of FIG. 4 provides additional processing operations to augment and/or supplement the search results with new and/or additional search results, such as may be found from contemporaneous sources, or at least via sources not yet captured using the web crawling techniques.

[0051] The determination of fresh web content may include direct web crawling techniques or searching contemporaneous feeds. For example, one technique may include searching a social network data feed of user submissions. The user submissions may be short messages, such as status updates or real-time messages, also colloquially known as a "tweet." A next step, step 182, is measuring real-time authority for the fresh content using the machine-learning model. The measuring of the real-time authority for the fresh content may be performed using the machine learning processing device 152 of FIG. 2 as described in further detail above.

[0052] A next step, step 184, is adjusting a reliability factor for the fresh web content based on the measured real-time authority. The adjustment of the reliability factor includes utilizing the authority information from step 182. If the authority information indicates a high degree of trustworthiness, the reliability factor can be improved and if the authority information indicates a low degree of trustworthiness, the reliability factor can be lowered.

[0053] In this embodiment, a next step, step 186, is adjusting the reliability factor based on the real-time authority instead of either a link flux calculation or a page rank adjustment. Similar to step 184, the reliability factor is adjusted, but in step 186, there is a reduction in factors used for this calculation. The methodology of FIG. 4 does not require the steps to be in sequential order and various embodiments can include step 186 instead of step 184 or vice versa. Step 186 further modifies step 184 by eliminating the adjustment factors of the link flux calculation and the page rank adjustment.

[0054] A next step, step 188, is determining an additional processing capacity in the search engine caused by the activity spike. In this embodiment, a processing operation examines the processing load search engine 106 of FIG. 1 to account for this activity spike and the processing of real-time authority ranks as described above. This determination may be performed using any suitable processing technique, such as monitoring processing load allocations to search operations on a search processing environment and measuring the delta between before the activity spike and during the activity spike.

[0055] In the embodiment including step 188, an additional step, step 190, is allocating available processing capacity of the search engine proportional to the additional processing capacity. Therefore, in this embodiment, the search engine provides for processing capacity as needed, without seeking to lose or otherwise compromise processing operations relating to the non-activity spike information. It is recognized that just because there is a spike in user activity, there is still a need to maintain standard search engine operations, therefore by the allocation of step 190, attempts are made to maintain the search engine but also efficiently and effectively provide real-time source information whereby there is a real-time authority ranking for this information.

[0056] It is understood that search engines provides effective solutions to standard searching operations, but based on the crawling data cataloging nature of these systems, problems can arise in real time activities. Based on the detection of the activity spike and machine-learning processing, the present method and system provides not only time sensitive search results, but also performs machine-learning authority rank to improve the accuracy and benefit of the search results. The authority ranking allows for presentation of users with highest quality results in primary result positions, including account for contemporaneous sources as described above.

[0057] FIGS. 1 through 4 are conceptual illustrations allowing for an explanation of the present invention. It should be understood that various aspects of the embodiments of the present invention could be implemented in hardware, firmware, software, or combinations thereof. In such embodiments, the various components and/or steps would be implemented in hardware, firmware, and/or software to perform the functions of the present invention. That is, the same piece of hardware, firmware, or module of software could perform one or more of the illustrated blocks (e.g., components or steps).

[0058] In software implementations, computer software (e.g., programs or other instructions) and/or data is stored on a machine readable medium as part of a computer program product, and is loaded into a computer system or other device or machine via a removable storage drive, hard drive, or communications interface. Computer programs (also called computer control logic or computer readable program code) are stored in a main and/or secondary memory, and executed by one or more processors (controllers, or the like) to cause the one or more processors to perform the functions of the invention as described herein. In this document, the terms "machine readable medium," "computer program medium" and "computer usable medium" are used to generally refer to media such as a random access memory (RAM); a read only memory (ROM); a removable storage unit (e.g., a magnetic or optical disc, flash memory device, or the like); a hard disk; or the like.

[0059] Notably, the figures and examples above are not meant to limit the scope of the present invention to a single embodiment, as other embodiments are possible by way of interchange of some or all of the described or illustrated elements. Moreover, where certain elements of the present invention can be partially or fully implemented using known components, only those portions of such known components that are necessary for an understanding of the present invention are described, and detailed descriptions of other portions of such known components are omitted so as not to obscure the invention. In the present specification, an embodiment showing a singular component should not necessarily be limited to other embodiments including a plurality of the same component, and vice-versa, unless explicitly stated otherwise herein. Moreover, applicants do not intend for any term in the specification or claims to be ascribed an uncommon or special meaning unless explicitly set forth as such. Further, the present invention encompasses present and future known equivalents to the known components referred to herein by way of illustration.

[0060] The foregoing description of the specific embodiments will so fully reveal the general nature of the invention that others can, by applying knowledge within the skill of the relevant art(s) (including the contents of the documents cited and incorporated by reference herein), readily modify and/or adapt for various applications such specific embodiments, without undue experimentation, without departing from the general concept of the present invention. Such adaptations and modifications are therefore intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance presented herein, in combination with the knowledge of one skilled in the relevant art(s).

[0061] While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example, and not limitation. It would be apparent to one skilled in the relevant art(s) that various changes in form and detail could be made therein without departing from the spirit and scope of the invention. Thus, the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. A method, implemented one or more machines having at least one processor, memory, and a communication platform connected to a network for allocating processing capacity of a search engine, the method comprising: determining a plurality of content for a search engine based on user search activity satisfying a condition; measuring a real-time authority for the plurality of content using a machine-learning model; adjusting a reliability factor for the plurality of content; determining additional processing capacity of the search engine as a result of the user search activity satisfying the condition; and allocating available processing capacity to the search engine based on the additional processing capacity.

2. The method of claim 1, wherein the user search activity satisfying the condition comprises: determining that, during a period of time, user search request activity included an increased amount of that one or more search terms within one or more search requests, the one or more search terms being associated with an event.

3. The method of claim 1, wherein the plurality of content comprises fresh web content, the method further comprises: searching one or more contemporaneous data feeds; and identifying the plurality of content within the one or more contemporaneous data feeds.

4. The method of claim 1, wherein measuring the real-time authority comprises: generating a rank for a source authority by evaluating the source authority of a source associated with each of the plurality of content.

5. The method of claim 1, wherein adjusting the reliability factor comprises: utilizing the real-time authority to determine whether each of the plurality of content indicates a high degree of trustworthiness or a low degree of trustworthiness, wherein: the reliability factor is increased for content that indicates the high degree of trustworthiness, and the reliability factor is decreased for content that indicating the low degree of trustworthiness.

6. The method of claim 1, wherein adjusting the reliability factor comprises at least one of: adjusting the reliability factor based on the real-time authority; and adjusting the reliability factor by eliminating adjustment factors of link flux calculation and page rank adjustment.

7. The method of claim 1, wherein determining the additional processing capacity of the search engine comprises: monitoring processing load allocations by measuring a difference between search operations of the search engine prior to the user search activity satisfying the condition being detected and during the user search activity satisfying the condition.

8. The method of claim 1, wherein allocating the available processing capacity to the search engine comprises: allocating the available processing capacity to the search engine such the available processing capacity of the search engine is proportional to the additional processing capacity of the search engine.

9. A system for allocating processing capacity of a search engine, comprising: a computer readable medium comprising instructions stored thereon; and a processing device that, in response to executing the instructions, is operative to: determine a plurality of content for a search engine based on user search activity satisfying the condition; measure a real-time authority for the plurality of content using a machine-learning model; adjust a reliability factor for the plurality of content; determine additional processing capacity of the search engine as a result of the user search activity satisfying the condition; and allocate available processing capacity to the search engine based on the additional processing capacity.

10. The system of claim 9, wherein the processing device, in response to executing the instructions corresponding to the user search activity satisfying the condition, is further operative to: determine that, during a period of time, user search request activity included an increased amount of that one or more search terms within one or more search requests, the one or more search terms being associated with an event.

11. The system of claim 9, wherein the plurality of content comprises fresh web content, the processing device, in response to executing the instructions, is further operative to: search one or more contemporaneous data feeds; and identify the plurality of content within the one or more contemporaneous data feeds.

12. The system of claim 9, wherein the processing device, in response to executing the instructions corresponding to the real-time authority being measured, is operative to: generate a rank for a source authority by evaluating the source authority of a source associated with each of the plurality of content.

13. The system of claim 9, wherein the processing device, in response to executing the instructions corresponding to the reliability factor being adjusted, is operative to: utilize the real-time authority to determine whether each of the plurality of content indicates a high degree of trustworthiness or a low degree of trustworthiness, wherein: the reliability factor is increased for content that indicates the high degree of trustworthiness, and the reliability factor is decreased for content that indicating the low degree of trustworthiness.

14. The system of claim 9, wherein the processing device, in response to executing the instructions corresponding to the reliability factor being adjusted, is operative to at least one of: adjust the reliability factor based on the real-time authority; and adjust the reliability factor by eliminating adjustment factors of link flux calculation and page rank adjustment.

15. The system of claim 9, wherein the processing device, in response to executing the instructions corresponding to the additional processing capacity of the search engine being determined, is operative to: monitor processing load allocations by measuring a difference between search operations of the search engine prior to the user search activity satisfying the condition being detected and during the user search activity satisfying the condition.

16. The system of claim 9, wherein the processing device, in response to executing the instructions corresponding to the available processing capacity to the search engine being allocated, is operative to: allocate the available processing capacity to the search engine such the available processing capacity of the search engine is proportional to the additional processing capacity of the search engine.

17. A non-transitory computer readable medium comprising instructions that, when executing by at least one processor, cause a device to: determine a plurality of content for a search engine based on user search activity satisfying a condition; measure a real-time authority for the plurality of content using a machine-learning model; adjust a reliability factor for the plurality of content; determine additional processing capacity of the search engine as a result of the user search activity satisfying the condition; and allocate available processing capacity to the search engine based on the additional processing capacity.

18. The non-transitory computer readable medium of claim 17, wherein the instructions corresponding to adjusting the reliability factor, when executed by the at least one processor, cause the device to: utilize the real-time authority to determine whether each of the plurality of content indicates a high degree of trustworthiness or a low degree of trustworthiness, wherein: the reliability factor is increased for content that indicates the high degree of trustworthiness, and the reliability factor is decreased for content that indicating the low degree of trustworthiness.

19. The non-transitory computer readable medium of claim 17, wherein the instructions comprising the additional processing capacity of the search engine being determined, when executed by the at least one processor, cause the device to: monitor processing load allocations by measuring a difference between search operations of the search engine prior to the user search activity satisfying the condition being detected and during the user search activity satisfying the condition.

20. The non-transitory computer readable medium of claim 17, wherein the instructions comprising the available processing capacity to the search engine being allocated, when executed by the at least one processor, cause the device to: allocate the available processing capacity to the search engine such the available processing capacity of the search engine is proportional to the additional processing capacity of the search engine.