Abstract:
A system provides images or questions to multiple game participants and receives labels or answers in response thereto. The system uses the labels or answers for various data gathering purposes.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     The instant application claims priority from provisional application No. 60/632,706, filed Dec. 3, 2004, the disclosure of which is incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     1. Field of the Invention 
     Implementations consistent with the principles of the invention relate generally to data gathering and, more particularly, to using game responses to gather data. 
     2. Description of Related Art 
     Existing information searching systems use search queries to search data to retrieve specific information that corresponds to the received search queries. Such information searching systems may search information stored locally, or in distributed locations. The World Wide Web (“web”) is one example of information stored in distributed locations. The web contains a vast amount of information, but locating a desired portion of that information can be challenging. This problem is compounded because the amount of information on the web and the number of new users inexperienced at web searching are growing rapidly. 
     Search engines attempt to return hyperlinks to web documents in which a user is interested. Generally, search engines base their determination of the user&#39;s interest on search terms (called a search query) entered by the user. The goal of the search engine is to provide links to high quality, relevant results to the user based on the search query. Typically, the search engine accomplishes this by matching the terms in the search query to a corpus of pre-stored web documents. Web documents that contain the user&#39;s search terms are “hits” and are returned to the user. 
     SUMMARY OF THE INVENTION 
     According to one aspect, a computer-implemented method may include retrieving a first set of question-type queries from a query log having known answers and retrieving a second set of question-type queries from the query log having unknown answers. The method may further include posing the first and second sets of queries to multiple users and receiving the users&#39; answers to the first and second sets of queries. The method may also include aggregating the answers from the users to the second set of queries and determining correct answers for at least some of the queries of the second set of queries using the aggregated answers. 
     According to another aspect, a method may include providing a first digital image to multiple game participants and receiving first words from the multiple participants in response to the first digital image. The method may further include identifying the first words as related terms or synonyms. 
     According to a further aspect, a method may include eliciting user participation in a game hosted by a server and gathering data resulting from the user participation in the game. The method may further include using the gathered data for search related functions performed by a search engine. 
     According to an additional aspect, a method may include providing a digital image to multiple game participants and associating a first word in a first language with the digital image. The method may further include receiving a second word from a first one of the multiple participants in a second language in response to the digital image, where the second language is different than the first language. The method may also include identifying the second word as being a language translation of the first word into the second language. 
     According to a further aspect, a method may include challenging a game participant with multiple tasks, where a first portion of the tasks have known responses and a second portion of the tasks have unknown responses. The method may also include verifying that the game participant is human based on the participant&#39;s responses to the first portion of the tasks and using the participant&#39;s responses to the second portion of the tasks for purposes other than human verification. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, explain the invention. In the drawings, 
         FIG. 1  is a diagram of an exemplary network in which systems and methods consistent with principles of the invention may be implemented; 
         FIG. 2  is an exemplary diagram of a client and/or server of  FIG. 1  in an implementation consistent with the principles of the invention; 
         FIG. 3  is a diagram of an overview of an exemplary quiz game used to gather data to support question-type search queries in a search engine consistent with an aspect of the invention; 
         FIGS. 4A ,  4 B and  4 C are flowcharts of an exemplary process for analyzing participants&#39; answers to quiz questions to determine correct answers for at least some of the quiz questions consistent with principles of the invention; 
         FIG. 5  is a diagram of an exemplary quiz game show document consistent with an aspect of the invention; 
         FIG. 6  is a diagram of an exemplary overview of the use of image labels for identifying the labels associated with an image as related terms; 
         FIGS. 7A and 7B  are flowcharts of an exemplary process for providing labels for images, and for identifying the image labels as related terms; 
         FIG. 8  is a diagram of an exemplary image label guessing document consistent with an aspect of the invention; 
         FIG. 9  is a diagram of an overview of a game challenge, according to an exemplary aspect of the invention, in which a game participant is challenged with multiple tasks, and a portion of the participant&#39;s responses are used to verify that the participant is a human participant, and another portion of the participant&#39;s responses are used for purposes other than human verification; and 
         FIG. 10  is a flowchart of an exemplary process, consistent with principles of the invention, for using participant responses to multiple tasks to verify whether the participant is a human. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description of the invention refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. Also, the following detailed description does not limit the invention. 
     A “document,” as the term is used herein, is to be broadly interpreted to include any machine-readable and machine-storable work product. A document may include an e-mail, a web site, a file, one or more digital images, a combination of files, one or more files with embedded links to other files, a news group posting, a blog, a web advertisement, etc. In the context of the Internet, a common document is a web page. Web pages often include textual information and may include embedded information (such as meta information, images, hyperlinks, etc.) and/or embedded instructions (such as JavaScript, etc.). A “link” as the term is used here, is to be broadly interpreted to include any reference to or from a document. 
     Exemplary Network Configuration 
       FIG. 1  is an exemplary diagram of a network  100  in which systems and methods consistent with the principles of the invention may be implemented. Network  100  may include multiple clients  110  connected to multiple servers  120  and  115  via a network  130 . Network  130  may include a local area network (LAN), a wide area network (WAN), a telephone network, such as the Public Switched Telephone Network (PSTN), an intranet, the Internet, a memory device, or a combination of networks. Two clients  110  and two servers  120  and  115  have been illustrated as connected to network  130  for simplicity. In practice, there may be more or fewer clients and servers. Also, in some instances, a client may perform the functions of a server and a server may perform the functions of a client. 
     Clients  110  may include client entities. An entity may be defined as a device, such as a wireless telephone, a personal computer, a personal digital assistant (PDA), a laptop, or another type of computation or communication device, a thread or process running on one of these devices, and/or an object executable by one of these devices. Servers  120  and  115  may include server entities that access, fetch, aggregate, process, search, and/or maintain documents in a manner consistent with the principles of the invention. Clients  110  and servers  120  and  115  may connect to network  130  via wired, wireless, and/or optical connections. 
     In an implementation consistent with the principles of the invention, server  120  may include a search engine  125  usable by users at clients  110 . Server  120  may implement a data aggregation service by crawling a corpus of documents (e.g., web pages) hosted on data server(s)  115  and store information associated with these documents in a repository of crawled documents. The data aggregation service may be implemented in other ways, such as by agreement with the operator(s) of data server(s)  115  to distribute their hosted documents via the data aggregation service. Search engine  125  may execute a query, received from a user, on the corpus of documents hosted on data server(s)  115 . Server  120  may also host various games, as described below with respect to  FIGS. 3-10 , to gather data for various purposes, such as, for example, search-related functions. 
     Server(s)  115  may store or maintain documents that may be crawled by server  120 . Such documents may include data related to published news stories, products, images, user groups, geographic areas, or any other type of data. For example, server(s)  115  may store or maintain news stories from any type of news source, such as, for example, the Washington Post, the New York Times, Time magazine, or Newsweek. As another example, server(s)  115  may store or maintain data related to specific product data, such as product data provided by one or more product manufacturers. As yet another example, server(s)  115  may store or maintain data related to other types of web documents, such as pages of web sites. 
     While servers  120  and  115  are shown as separate entities, it may be possible for one or more of servers  120  and  115  to perform one or more of the functions of another one or more of servers  120  and  115 . For example, it may be possible that two or more of servers  120  and  115  are implemented as a single server. It may also be possible for a single one of servers  120  or  115  to be implemented as two or more separate (and possibly distributed) devices. 
     Exemplary Client/Server Architecture 
       FIG. 2  is an exemplary diagram of a client or server entity (hereinafter called “client/server entity”), which may correspond to one or more of clients  110  and servers  120  and  115 , according to an implementation consistent with the principles of the invention. The client/server entity may include a bus  210 , a processing unit  220 , an optional main memory  230 , a read only memory (ROM)  240 , a storage device  250 , an input device  260 , an output device  270 , and a communication interface  280 . Bus  210  may include a path that permits communication among the components of the client/server entity. 
     Processing unit  220  may include any type of software, firmware or hardware implemented processing device, such as a microprocessor, a field programmable gate array (FPGA), combinational logic, etc. Main memory  230  may include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by processing unit  220 , if processing unit  220  includes a microprocessor. ROM  240  may include a conventional ROM device or another type of static storage device that stores static information and/or instructions for use by processing unit  220 . Storage device  250  may include a magnetic and/or optical recording medium and its corresponding drive. 
     Input device  260  may include a conventional mechanism that permits an operator to input information to the client/server entity, such as a keyboard, a mouse, a pen, voice recognition and/or other biometric mechanisms, etc. Output device  270  may include a conventional mechanism that outputs information to the operator, including a display, a printer, a speaker, etc. Communication interface  280  may include any transceiver-like mechanism that enables the client/server entity to communicate with other devices and/or systems. For example, communication interface  280  may include mechanisms for communicating with another device or system via a network, such as network  130 . 
     As will be described in detail below, the client/server entity, consistent with the principles of the invention, may perform certain data processing operations. The client/server entity may, in some implementations, perform these operations in response to processing unit  220  executing software instructions contained in a computer-readable medium, such as memory  230 . A computer-readable medium may be defined as one or more physical or logical memory devices and/or carrier waves. 
     The software instructions may be read into memory  230  from another computer-readable medium, such as data storage device  250 , or from another device via communication interface  280 . The software instructions contained in memory  230  may cause processing unit  220  to perform processes that will be described later. Alternatively, hardwired circuitry may be used in place of, or in combination with, software instructions to implement processes consistent with the principles of the invention. Thus, implementations consistent with principles of the invention are not limited to any specific combination of hardware circuitry and software. 
     Exemplary Quiz Game Overview 
       FIG. 3  illustrates an overview of a game that may be used to gather data to support question-type search queries. A log  305  of question-type search queries may be accumulated from multiple search queries of the form “what is xxx?,” “who is xxx?,” “where is xxx?,” or other types of question formats, received at search engine  125 . The question-type queries may be accumulated from one or more users over a period of time. From the question-type query log  305 , queries having known answers  310  may be identified, and queries having unknown answers  315  may be identified. For example, the question-type query “where is Akron?” may have the known answer “Ohio.” However, the question-type query “who is Joe Smith?” may have an unknown answer. In some implementations, queries having known answers  310  may be retrieved from any query source  370 , and need not be retrieved only from query log  305 . The queries having known answers  310  and queries with unknown answers  315  may be issued to a participant  320  and a participant  325  from server  120 . Participant  320  may provide, to server  120 , his answers  330  for the queries with known answers  310 , and his answers  335  for the queries with unknown answers  315 . A score may be assigned  350 , by server  120 , to participant  320  based on the participant&#39;s own answers  330  to the queries having known answers. For example, a score may be assigned based on a number of the queries that participant  320  answered correctly. 
     Participant  325  may also provide, to server  120 , his answers  340  for the queries with known answers  310 , and his answers  345  for the queries with unknown answers  315 . A score may also be assigned  355 , by server  120 , to participant  325  based on the participant&#39;s answers  340  to the queries having known answers. A score, for example, may be assigned based on a number of the queries that participant  325  answered correctly. 
     Participant  1   320 &#39;s answers  335  to the queries with unknown answers  315 , and participant  2   325 &#39;s answers  345  to the queries with unknown answers  315 , may then be aggregated  360 . The answers  335  and  345 , for example, may be aggregated in a database associated with server  120 . A correct answer for each question-type search query may then be determined from the participants&#39; aggregated answers  360 . For example, an analysis of the answers  335  and  345  may determine that both participants (or a majority of participants, if more than two participants are involved) chose the same answer for a given question-type query and that, therefore, this answer is the correct answer for the query. 
     Exemplary Quiz Questioning Process 
       FIGS. 4A ,  4 B and  4 C are flowcharts of an exemplary process, consistent with principles of the invention, for analyzing participants&#39; answers to quiz questions to determine correct answers for at least some of the quiz questions. As one skilled in the art will appreciate, the process exemplified by  FIGS. 4A ,  4 B and  4 C can be implemented in software and stored on a computer-readable memory, such as main memory  230 , ROM  240  or storage device  250  of server  120 . In other implementations, the processing exemplified by  FIGS. 4A ,  4 B and  4 C can be implemented in hardwired circuitry, such as combinational logic, within processing unit  220  of server  120 . 
     The exemplary process may begin with the retrieval of question-type search queries from a query log (block  405 ) ( FIG. 4A ). The query log may be accumulated from multiple search queries of the form “what is xxx?,” “who is xxx?,” “where is xxx?,” or other types of question formats, received at search engine  125  and stored in a database associated with server  120 . Queries from the retrieved question-type search queries that have known answers may be identified (block  410 ). For example, a portion of the logged question-type search queries may have previously known, correct answers. Queries from the retrieved question-type search queries that do not have known answers may be identified (block  415 ). For example, a portion of the logged question-type search queries may not have any known, correct answers associated with them. The determination of questions with known or unknown answers may be made based on an analysis of query logs and users&#39; selections of search results. In some implementations, queries having known answers may be retrieved from any source, and need not be retrieved only from the query log. 
     A number of question-type search queries, from the retrieved queries with known answers, may be issued to one or more participants (block  420 ). For example, a set of search queries of the form “who is X i ,” “what is X 2 ,” and “where is X 3 ” may be issued to the one or more participants, where the correct answers to these questions are all known. A number of question-type search queries, from the retrieved queries having unknown answers, may be issued to the one or more participants (block  425 ) ( FIG. 4B ). For example, a set of search queries of the form “where is X 4 ,” “who is X 5 ,” and “what is X 6 ” may be issued to the one or more participants, where the correct answers to these questions are not known.  FIG. 5  illustrates the issuance of question-type queries  505  to a participant via a game show document  510 . 
     Answers, for each issued question-type search query, may be received from the participant(s) (block  430 ). As shown in  FIG. 5 , a participant may enter his answers in answer fields  515  in game show document  510  and submit them via, for example, an answer submission  520  “button.” In another implementation, a document may present several candidate answers in a multiple choice format to a participant, and the participant may choose his answer from the several candidate answers. A determination may then be made, for each participant, how many of the question-type search queries having known answers that the participant answered correctly (block  435 ). A score may be assigned to each participant based on the number of the question-type search queries, having known answers, that the participant answered correctly (block  440 ). For example, if a given participant answered 90 of 100 question-type search queries having known answers correctly, then the participant may be assigned a score of 90%. As shown in  FIG. 5 , a participant score document  525  may be presented to the participant indicating the participant&#39;s assigned score. 
     The answers received from the participant(s) may be aggregated (block  445 ) ( FIG. 4C ) and stored in a memory, such as memory  230  of server  120 , or in a database associated with server  120 . A correct answer for each of the question-type search queries having unknown answers may be determined using the aggregated answers (block  450 ). The answers from the participants, for the question-type search queries having unknown answers, may be analyzed to ascertain which answer has been given the most for a specific question-type query. The answer that has been given the most by participants for a specific query may be determined to be the correct answer. For example, if there have been  100  participants, and 10 of the participants selected the same answer, but everyone else selected different answers, then the answer picked by the 10 participants may not actually be the “correct” answer. A given answer may have to be selected a minimum number of times to be considered the “correct” answer. If each participant selected an answer from a multiple choice list of candidate answers, then the answer that was chosen by a majority of participants may be determined to be the correct answer for the question-type query. In one implementation, the answers given by participants that have been assigned a high score (see block  440  above) may be weighted more heavily when determining a correct answer for each of the question-type search queries. Weighting high scoring participants more heavily when determining a correct answer for a search query may remove the activity of “bots” (i.e., computer programs that run automatically), since “bots” would likely not answer the question-type queries correctly that have previously known answers. The determined “correct” answers to queries with previously unknown answers may be used by a search engine for answering subsequently received question-type search queries. 
     Exemplary Image Labeling Game Overview 
       FIG. 6  illustrates an exemplary overview of the use of image labels for identifying the labels associated with an image as related terms to one another. Multiple images (e.g., images  615 - 1  through  615 - 4 ) may be presented to multiple participants (e.g., participant  605  and participant  610 ). The images may include, for example, digital images in any digital image format (e.g., MPEG, JPEG, etc.). For each image  615 , each participant (e.g., participants  605  and  610 ) may supply a guess word (e.g., guess words  620 - 1  through  620 - 4  for participant  605  and guess words  625 - 1  through  625 - 4  for participant  610 ) that the participant believes describes the presented image. Each guess word supplied by a participant may then be identified as an image label (i.e., guess word  1 _ 1   620 - 1  identified as label  1 _ 1   630 - 1  for image  1   615 - 1 ; guess word  2 _ 2   625 - 2  identified as label  2 _ 2   635 - 2 , etc.). The identified image labels may then also be identified as related terms. For example, as shown in  FIG. 6 , label  1 _ 1  and label  2 _ 1 , previously supplied as guess words from participant  605  and participant  610  for image  1   615 - 1 , may be identified as “related terms.” The labels identified as “related terms” may, for example, represent synonyms of one another, or be otherwise related. 
     If the participants to the image labeling game are playing the game in different countries, the identification of image labels as “related terms” may be used for language translation also. For example, if participants playing from France label the image of a car “voiture,” it can be inferred that “voiture” is French for “car.” This technique may be particularly useful in translating languages that are more obscure than French. 
     Exemplary Image Labeling Process 
       FIGS. 7A and 7B  are flowcharts of an exemplary process, according to an implementation consistent with the principles of the invention, for providing labels for images, and for identifying the image labels as related terms. As one skilled in the art will appreciate, the process exemplified by  FIGS. 7A and 7B  can be implemented in software and stored on a computer-readable memory, such as main memory  230 , ROM  240  or storage device  250  of server  120 . In other implementations, the processing exemplified by  FIGS. 7A and 7B  can be implemented in hardwired circuitry, such as combinational logic, within processing unit  220  of server  120 . 
     The exemplary process may begin with sending an image (image_i) to game participants (block  705 ) ( FIG. 7A ). Multiple participants may participate in each image labeling game. The image may be retrieved from memory and sent from server  120  to each participant at a client  110 .  FIG. 8  illustrates an exemplary image label guessing document  805 , that is sent to each game participant, and that includes an image  810 . A determination may be made whether a “pass” has been received from the participants (block  710 ), meaning that the participant does not have a label for image  810 . A participant may indicate a “pass” via, for example, a “pass” button  815  provided in document  805  provided to the participant, as shown in  FIG. 8 . If a “pass” has been received from the participants, a determination may be made whether the image (image_i) is the last image (block  715 ). Each game may only include a given sequence of images, spanning a first image to a last image. If the image is not the last image, then a counter i, used to designate a particular image in an image sequence, may be incremented (i=i+1) (block  720 ). If the image is the last image, then the exemplary process may continue at block  765  below. 
     Returning to block  710 , if a “pass” has not been received from the participants, then a guess may be received from each participant (block  725 ). For example, each participant may type their next guess word in field  820  of document  805 , as shown in  FIG. 8 . A determination may then be made whether any of the guesses is a “taboo” word (block  730 ). A “taboo” word may include a word that the game has excluded as an acceptable label for a given image. As shown in  FIG. 8 , document  805  may, for each image  810 , present a list  825  of “taboo” words to each participant. If any of the guesses from the participants of the game is a “taboo” word, then an error message may be sent to the participant who submitted the “taboo” word (block  735 ), and the exemplary process may return to block  725  above. If none of the guesses are “taboo” words, then a determination may be made whether the guesses of the participants match (block  740 ) ( FIG. 7B ). For example, if there are two participants to the game, then both participants may submit guess words that match. If the participants have not submitted matching guess words, then a determination may be made whether a time period has expired (block  745 ). The time period may be configurable, and may represent an allotted amount of time that the participants to the game have to provide matching labels for a given image. If the time period has not expired, then the exemplary process may return to block  725  above. If the time period has expired, then the exemplary process may return to block  720  above with the sending of a new image (image_i+1) to the game participants. 
     Returning to block  740 , if the guesses of the participants match, then the matching guesses may be stored as a label for the image (image_i) sent to the participants (block  750 ). A determination may be made whether the image is the last image (block  755 ). If not, then the exemplary process may return to block  720  above. If the image is the last image, then, in one implementation, labels associated with each image, from multiple games, or possibly multiple, different participants, may be identified as related terms (block  760 ). Labels, associated with each image, may be aggregated from multiple different games and the labels may be identified as “related.” The image labels may be identified, for example, as synonyms. The labels (and their synonyms) may be used by a search engine for, for example, supplying related search terms for a given input search term. 
     In another implementation, if the image is the last image, then a given label from a participant in a first country in a first language may be identified as a language translation of a corresponding label from a participant in a second country in a second language (block  765 ). For example, if one of the participants to the image labeling is playing the game in the United States in English, and labels an image of an automobile a “car,” and another participant is playing the game in France in French, and labels the image of the automobile “voiture,” it can be inferred that “voiture” is French for “car.” “Voiture,” thus, may be identified as the French translation of the English label “car.” 
     Exemplary Game Challenge Overview 
       FIG. 9  illustrates an exemplary overview of a game challenge, according to an exemplary aspect of the invention, in which a game participant is challenged with multiple tasks, and a portion of the participant&#39;s responses are used to verify that the participant is a human participant, and another portion of the participant&#39;s responses are used for purposes other than human verification. As shown in  FIG. 9 , a participant may be challenged with multiple tasks, with a portion  905  of the tasks being tasks that have known responses, and another portion  910  of the tasks being tasks that have unknown responses  910 . The tasks may include any type of task, such as labeling an image, as described above with respect to  FIGS. 6-8 , or answering questions, as described above with respect to  FIGS. 3-5 . 
     The participant may provide a response  925  to each task  915 - 1  through  915 - x  having known responses, and to each task  920 - 1  through  920 - z  having unknown responses. The participant may be verified  930  as being human based on the responses  925  to each task having known responses. For example, if the task involves the participant providing answers to question-type queries, then the participant&#39;s responses  925  to tasks  915 - 1  through  915 - x  can be compared to the known response to those questions to verify whether the participant is human, or whether the participant may possibly be a “bot”. A “bot” may not respond correctly to the tasks to which the correct responses are known. The participant responses  925  to the tasks  920 - 1  through  920 - z  having unknown responses may be used  935  for purposes other than human verification. For example, as described above with respect to  FIGS. 3-5 , participant answers to question-type queries that have unknown answers may be used to determine a correct answer to the question-type queries. As another example, as described above with respect to  FIGS. 6-8 , image guess words provided by game participants may used as related terms. 
     Exemplary Process 
       FIG. 10  is a flowchart of an exemplary process, according to an implementation consistent with the principles of the invention, for using participant responses to multiple tasks to verify whether the participant is a human. As one skilled in the art will appreciate, the process exemplified by  FIG. 10  can be implemented in software and stored on a computer-readable memory, such as main memory  230 , ROM  240  or storage device  250  of server  120 . In other implementations, the processing exemplified by  FIG. 10  can be implemented in hardwired circuitry, such as combinational logic, within processing unit  220  of server  120 . 
     The exemplary process may begin with challenging a game participant with multiple tasks (block  1005 ). Server  120  may send the multiple tasks to the game participant at a client  110  via network  130 . The multiple tasks may include any type of task, such as issuing question-type queries to a participant, or presenting an image for the participant to label. Prior to the start of the game, correct responses to a first portion of the multiple tasks are identified, and correct responses to a second portion of the tasks are determined to be unknown. The game participant may then be verified as being human based on the participant&#39;s responses to the first portion of tasks (block  1010 ). The participant&#39;s responses to the tasks having known responses may be compared with the correct responses to determine how many of the participant&#39;s responses are incorrect. A high error rate may indicate that the participant is not human, and may, for example, be a “bot.” Responses identified as being potentially from a non-human source, such as a “bot” may be discarded. Responses from the game participant to the second portion of tasks may then be used for other than human verification (block  1015 ). For example, as described above with respect to  FIGS. 3-5 , participant answers to question-type queries that have unknown answers may be used to determine a correct answer to the question-type queries. As another example, as described above with respect to  FIGS. 6-8 , image guess words provided by game participants may used as related terms. 
     CONCLUSION 
     The foregoing description of preferred embodiments of the present invention provides illustration and description, but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. For example, while series of acts have been described with regard to  FIGS. 4A ,  4 B,  4 C,  7 A,  7 B and  10 , the order of the acts may be modified in other implementations consistent with the principles of the invention. For the processes described with respect to  FIGS. 4A ,  4 B,  4 C,  7 A,  7 B and  10 , answers or responses from game participants may be solicited from participants in many different ways than those described. In some implementations, answers or responses may be solicited using freeform input (e.g., if the correct answer/response is not known), or they may be solicited using a multiple choice format (i.e., if a set of candidate answers/responses is known that is believed to include a correct answer/response). Additionally, participants may be incentivized to play the games described above with respect to  FIGS. 4A ,  4 B,  4 C,  7 A,  7 B and  10 . Game participants may be rewarded (i.e., monetarily or otherwise) for participating in the games, and rewards may be used to leverage a significant number of participants to participate in the games. In some implementations, only a small subset of participants may be rewarded (e.g., a reward lottery), but the participants may not now who will receive the reward until some time after participating in a given game. 
     It will also be apparent to one of ordinary skill in the art that aspects of the invention, as described above, may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. The actual software code or specialized control hardware used to implement aspects consistent with the principles of the invention is not limiting of the present invention. Thus, the operation and behavior of the aspects of the invention were described without reference to the specific software code—it being understood that one of ordinary skill in the art would be able to design software and control hardware to implement the aspects based on the description herein. 
     No element, act, or instruction used in the present application should be construed as critical or essential to the invention unless explicitly described as such. Also, as used herein, the article “a” is intended to include one or more items. Where only one item is intended, the term “one” or similar language is used. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.