Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

• search engines have been developed to aid users in locating desired content on the Internet.
• a search engine is a computer program that receives a search query from a user (e.g., in the form of a set of keywords) indicative of content desired by the user, and returns information and/or hyperlinks to information that the search engine determines to be relevant to the user's search query.
• Search engines typically work by retrieving a large number of WWW web pages and/or other content using a computer program called a "web crawler" that explores the WWW in an automated fashion (e.g., following every hyperlink that it comes across in each web page that it browses).
• the located web pages and/or content are analyzed and information about the web pages or content is stored in an index.
• the search engine uses the index to identify the web pages and/or content that it determines to best match the user's search query and returns a list of results with the best- matching web pages and/or content.
• this list is in the form of one or more web pages that include a set of hyperlinks to the web pages and/or content determined to best match the user's search query.
• a computer-implemented method for using a knowledge representation to provide information based on an environmental input comprising: receiving at least one environmental input as a user-context information associated with a user; obtaining at least one concept in the knowledge representation, wherein the at least one concept is obtained based on a semantic relevance of the at least one concept to the user- context information; and based on the at least one concept, providing information to the user; wherein a concept is represented by a data structure storing data associated with a knowledge representation.
• the knowledge representation comprises a semantic network and the at least one concept is represented by a data structure storing data associated with a node in the semantic network.
• a system for using a knowledge representation to provide information based on an environmental input adapted to: receive at least one environmental input as a user-context information associated with a user; obtain at least one concept in the knowledge representation, wherein the at least one concept is obtained based on a semantic relevance of the at least one concept to the user-context information; and based on the at least one concept, provide information to the user; wherein a concept is represented by a data structure storing data associated with a knowledge representation.
• a non- transitory computer-readable medium storing computer code that when executed on a computer device adapts the device to provide information based on an environmental input, the computer-readable medium
• code for receiving at least one environmental input as a user-context information associated with a user comprising: code for receiving at least one environmental input as a user-context information associated with a user; code for obtaining at least one concept in the knowledge representation, wherein the at least one concept is obtained based on a semantic relevance of the at least one concept to the user-context information; and code for providing information to the user based on the at least one concept; wherein a concept is represented by a data structure storing data associated with a knowledge representation.
• FIG. 1 is a flowchart of an illustrative process for providing a user with information selected from a large set of digital content, in accordance with some embodiments of the present disclosure.
• FIG. 2 is a block diagram of an illustrative client/server architecture that may be used to implement some embodiments of the present disclosure.
• FIG. 3 is a flowchart of an illustrative process for identifying or generating an active concept representing user context information, in accordance with some embodiments of the present disclosure.
• FIG. 4A-4C is an illustration of generating an active concept representing user context information, in accordance with some embodiments of the present disclosure.
• FIGS. 5A-5H illustrate various approaches for obtaining concepts relevant to an active concept representing user context information, in accordance with some embodiments of the present disclosure.
• FIGS. 6A-6B illustrate techniques for scoring concepts relevant to an active concept, in accordance with some embodiments of the present disclosure.
• FIG. 7 illustrates a system and operating environment in accordance with an embodiment.
• FIG. 8 illustrates a network environment in accordance with an embodiment.
• FIG. 9 illustrates a computing device on which some embodiments of the present disclosure may be implemented.
• the present disclosure relates to a system and method for delivery of promotional content relevant to individiduals in a crowd using synthesis of environmental inputs for context matching.
• the system and method synthesizes
• the relationship represented by an edge in a semantic network may be a "defined-by” relationship or an "is-a” relationship.
• “defined-by” relationships are indicated by edges ending with a filled-in circle and "is-a” relationships are indicated by edges ending with an arrow.
• reference numeral 808 identifies a "defined-by” relationship and reference numeral 806 identifies an "is-a' relationship.
• a concept representing at least a portion of the information associated with a user termed an "active concept,” may be identified or synthesized (i.e., generated) and one or more concepts
• semantic processing for a user can take advantage of previously-performed semantic processing for the user and/or one or more other users. For example, if a user-specific knowledge representation was generated based on information associated with one user in a group of users (e.g., an employee of company X), the same user-specific knowledge representation may be used to identify concepts semantically relevant to information associated with another user in the same group (e.g., another employee of company X).
• a user-specific knowledge representation was generated based on information associated with one user in a group of users (e.g., an employee of company X)
• the same user-specific knowledge representation may be used to identify concepts semantically relevant to information associated with another user in the same group (e.g., another employee of company X).
• the primary set of content may be any suitable set of content and, for example, may be content accessible via a particular website (e.g., an e-commerce website, a website of a business, a website providing access to one or more databases, etc.).
• a secondary or reference set of content may be any suitable set of content and, for example, may be content in any information repository (e.g., Wikipedia, WordNet, etc.), database, or content-provisioning source. Though it should be recognized that these are only examples and that the target set of content and the reference set of content may be any suitable sets of content, as aspects of the present invention are not limited in this respect.
• a reference domain comprising information about diseases commonly known in the public sphere may help to relate terms in users' search queries in a medical website to terms in content accessible through that website. Indeed, it may be easier to relate a user's search query, such as "Flu Virus,” to content accessible through the medical website, which may refer to viral diseases only by using official
• Process 100 then continues to act 104, where an active concept representing at least a portion of the user-context information may be identified in a knowledge representation.
• the knowledge representation may be any suitable knowledge representation and, in some embodiments, may be a user-specific knowledge representation associated with the user(s).
• Process 100 then continues to act 108, where one or more of the obtained concept(s) may be selected.
• the concept(s) may be selected based at least in part on a score that one or more of the concept(s) may be assigned by using a relevance measure.
• Process 100 then proceeds to act 1 10, where content may be provided to the one or more users based at least in part on the active concept, identified or generated in act 104, and the concept(s) selected in act 108.
• Such content may be selected from a large set of content by using the active concept and the concept(s) selected in act 108.
• Each of the acts of the process of FIG. 1 may be performed in any of a variety of ways, and some examples of the ways in which these acts may be performed in various embodiments are described in greater detail below.
• receiving user context information from a user's client computer is not a limiting aspect of the present invention as user context information may be obtained in any other suitable way as part of act 102 of process 100.
• user context information may be obtained, actively by requesting and/or passively by receiving, from any source with, or with access to, user context information associated with one or more users.
• an active concept representing at least a portion of the user-context information obtained during act 102, may be either identified in a knowledge representation, which may be a user-specific knowledge representation or any other suitable knowledge representation, or generated and used to construct a user-specific knowledge representation comprising the active concept. Any of a variety of possible techniques may be used to identify or generate an active concept representing user context information. An example of one such technique that may be used in some embodiments is illustrated in process 300 of FIG. 3.
• the order of words in the strings may be ignored. For instance, it may be determined that the string "The Board of Directors,” matches the concept label “Board Directors” as well as the concept label “Directors Board.” [0069] If it is determined, in decision block 302, that the relevant portion of the user context information matches a concept in the knowledge representation, process 300 proceeds to decision block 304, where it is determined whether there are multiple concepts in the knowledge representation matching the relevant portion.
• user input comprising a selection of one or more disambiguation terms may be received as part of act 308 of process 300.
• application program 204 that received the set of candidate disambiguation terms generated in act 306 may accept input from the user selecting one or more of the terms, and may send an indication of the user-selected term(s) to the server executing process 100.
• process 300 proceeds to act 310, where the relevant portion of the user context information may be decomposed into one or more knowledge representation entities.
• the relevant portion of the user context information may be decomposed into individual concepts, keywords, and/or morphemes. This may be done in any suitable way. For example, when the portion of the user context information is represented by an alphanumeric string, the string may be tokenized or separated into more elemental knowledge representation entities. Stop words such as "the” and "and” may be filtered out or ignored.
• Process 300 continues to act 312, where concepts in the knowledge representation that cover the KR entities, which were derived in act 310, are identified. This may be done in any suitable way and, for example, may be done by comparing each of the KR entities with concepts in the KR to see if there is a match. In some embodiments, a string associated with a KR entity may be compared with labels of concepts in the KR. For example, consider semantic network 401 shown in FIG. 4A comprising concepts 402 and 406 labelled "Board of Directors" and “Board,” respectively. Concepts 402 and 406 are connected by a "defined-by" edge 404.
• Process 300 next continues to acts 314-318, where an active concept
• an active concept associated with the relevant portion of the user context information may be generated. This may be done in any suitable way. For example, a new node may be added to the knowledge representation and may be associated with the relevant portion of the user context information. As such, the node may be assigned an identifier (i.e., a concept label) comprising the relevant portion of the user context information. For example, as shown in FIG. 4B, node 408 associated with the generated active concept and labelled "BP Board of Directors" was added to semantic network 401 to form semantic network 403. In this and other diagrams of semantic networks, the node corresponding to an active concept may be indicated by a rectangle.
• the new node may be connected by one or more new edges to one or more concepts already present in the knowledge representation.
• the new node, representing the generated active concept may be connected to any suitable concepts in the knowledge representation and, for example, may be connected to one or more concepts in the knowledge representation that cover the knowledge representation entities derived from the relevant portion of the user context information.
• node 408 may be connected to the node associated with the concept "Board of Directors,” to the node associated with the concept "Board,” and/or to the node associated with the concept "Directors.”
• the new node may be connected to nodes associated with the most complex concepts that cover the KR entities derived in act 310.
• Complexity of a concept may be defined in any of numerous ways. For example, complexity of a concept may be indicative of the number of other concepts that are "defined-by” the concept; the greater the number of concepts "defined-by” the concept, the greater its complexity. Thus, complexity of a concept may be proportional to the number of outgoing "defined-by" edges from the node corresponding to that concept.
• semantic network 401 for example, the concept "Board of Directors" has a greater complexity than the concept "Board.”
• complexity of a concept may be indicative of the number of words in the label of the concept; the greater the number of words, the greater its complexity.
• complexity of a concept may be proportional to the number of words in the concept label.
• the concept "Board of Directors” also has a greater complexity than the concept "Board.” Accordingly, node 408, associated with the concept "BP Board of Directors,” is connected, in semantic network 405, by a new "defined-by" edge 410 to node 402, corresponding to the "Board of Directors" concept.
• the knowledge representation may be further augmented such that the knowledge representation includes concepts that may cover all of the KR entities derived from the relevant portion of the user context information in act 310.
• a new node may be added to the knowledge representation for each KR entity derived in act 310 but not covered by a concept already in the knowledge representation.
• Each such new node may be connected to one or more concepts existing in the knowledge representation and, for example, may be connected via a "defined-by" edge to the concept associated with the generated active concept.
• the keyword "BP" was derived from the new "BP Board of Directors” concept, but is not covered by any of the concepts in semantic networks 401 or 403.
• node 416 associated with the concept "BP” may be added to the knowledge representation in act 318 and may be connected to node 408, associated with the active concept, via a "defined- by" edge 414.
• a new user-specific knowledge representation is created after acts 314-318 have been executed.
• the created knowledge representation is user-specific because it comprises one or more concepts derived from user context information associated with one or more users and the knowledge representation used in act 302.
• semantic network 405 was created by incorporating two concepts (i.e., "BP" and "BP Board of Directors" obtained from user context information) into semantic network 401.
• FIGS. 4A-4C are merely illustrative and are not limiting on aspects of the present invention.
• process 300 continues to act 320, where the active concept generated in acts 314-318 may be provided for subsequent processing, and after act 320, process 300 completes.
• one or more concepts relevant to the active concept may be obtained by using a knowledge representation comprising the active concept.
• the knowledge representation may be any suitable knowledge representation and, in some instances, may be a user-specific knowledge representation associated with the user(s) whose context information was obtained in act 102 of process 100.
• the active concept may be any suitable concept in the knowledge representation and may be identified based at least in part on the user context information, for instance, by using process 300, or in any other suitable way.
• any of numerous techniques for obtaining concepts relevant to the active concept based on the graph structure of the semantic network comprising the active concept may be used in act 106 of process 100.
• concepts relevant to the active concept may be obtained by performing one or more types of operations with respect to the graph structure of the semantic network. Three such operations, namely: (1) retrieval, (2) addition, and (3) substitution, are described in greater detail below. These three operations are merely illustrative, however, and any other suitable operations for identifying concepts relevant to the active concept, based at least in part on the graph structure of the semantic network, may be used.
• no explicit distinction is made between a node in a graph used to represent a concept and the concept itself.
• an edge between two concepts corresponds to an edge between the nodes in the semantic graph used to represent those two concepts.
• a retrieval operation may be used to identify concepts in the semantic network that are relevant to the active concept.
• the retrieval operation may be used to identify concepts that were represented in the semantic network before the active concept was identified and/or generated.
• the retrieval operation may be used to identify concepts that were added to the semantic network when the active concept was generated (e.g., in act 318 of process 300).
• the retrieval operation may identify a concept that is connected by one or more edges, of any suitable type and/or direction, to the active concept as a concept relevant to the active concept.
• the retrieval operation may identify a concept that is connected by one or more "is-a" edges to the active concept as a concept relevant to the active concept.
• the retrieval operation may identify a concept that has outgoing "is-a" edge towards the active concept or a concept that has an incoming "is-a” edge from the active concept as a concept relevant to the active concept.
• the addition and substitution operations described below may be used to obtain concepts relevant to the active concept by synthesizing new concepts based at least in part on the active concept and on the concepts in the semantic network. Note that in FIGS. 5B-5H, concepts added to the active concept to synthesize a new concept are indicated by a hexagon.
• An addition operation may synthesize a new concept by using the active concept and at least one other concept in the semantic network, and return the new concept as a concept relevant to the active concept.
• the new concept may be synthesized using any of numerous techniques including at least: (1) attribute co-definition, (2) analogy-by-parent, (3) analogy-by- sibling, (4) attribute commonality or any suitable combination thereof.
• a new concept may be synthesized by combining the active concept with any of the other concepts co-defining a concept with the active concept.
• the concept “press sets” may be synthesized by combining "press” and “sets.”
• the concept “bench press” may be synthesized by combining "press” and "bench.”
• one or more attributes of the active concept may be pruned in order to produce a candidate that is more general than the active concept. This may be done in any suitable way. For instance, attributes may be pruned by performing an "inverse" addition operation, wherein an attribute of the active concept may be removed if, according to any of the above-described techniques, that attribute may be combined with the "broader" concept that results from the pruning. For example, if in the semantic network shown in FIG.
• substitution operation Another operation that may be used to obtain one or more concepts relevant to the active concept is the substitution operation.
• the substitution operation may be used to synthesize a new concept by replacing one or more attributes of the active concept with another concept, which may be a broader or a narrower concept than the attribute that it replaces.
• a retrieval or an addition operation may be performed on one or more attributes of the active concept.
• the concept identified or generated by the retrieval or addition operations, respectively, when performed on a specific attribute of the active concept may be used to replace the specific attribute to synthesize a new concept.
• substitution operations using any other type of addition operation e.g., analogy-by-parent, analogy-by-sibling, and attribute commonality
• addition operations using any other type of addition operation e.g., analogy-by-parent, analogy-by-sibling, and attribute commonality
• a weight may be assigned to an edge in a semantic network in any of numerous ways.
• the weight assigned to an edge may be computed based on a measure of certainty associated with traversing that edge.
• the amount of certainty associated with traversing an edge may depend on the type of the edge (i.e., is the edge a "defined-by" edge or an "is-a" edge) and/or on the direction of traversal.
• the weight assigned to an edge may be a number between 0 and 1, but in other embodiments the weight may be a number in any other suitable range.
• traversal of a "defined-by" edge may reduce the certainty associated with traversing the edge by a factor of x, where x may be any suitable number between 0 and 1 and, for example, may be any factor greater than or equal to 0.25, 0.5, 0.75, 0.9, etc.
• traversal of an "is-a" edge may reduce the certainty of traversing the edge by a factor of y, where y may be any suitable number between 0 and 1 and, for example may be any factor greater than or equal to 0.25, 0.5, 0.75, 0.9, etc.
• the factor x may be equal to the factor y, but, in some instances, these factors may be different such that the amount of certainty associated with traversing an edge may depend on the type of edge being traversed.
• the Jaccard index may be applied in our case as follows. Let the set A represent the set of concepts that may be neighbors, or may be within a predetermined number of degrees of separation in the semantic network, from the active concept. Let the set B represent the set of concepts that may be neighbors, or may be within a predetermined number of degrees of separation in the semantic network, from the concept to be scored. Thus, the denominator in the above equation represents the total number of concepts that may be neighbors (or may be within a predetermined number of degrees of separation) of the active concept and/or the concept to be scored while the numerator represents the total number of concepts that are both a neighbor (or may be within a predetermined number of degrees of separation) of the active concept and the concept under evaluation. Accordingly, the Jaccard score of a concept may be computed as the Jaccard index.
• the Jaccard score may be obtained by applying the above-described techniques not to the synthesized concept, but to the concept that was used to substitute an attribute of the active concept as part of the substitution (e.g., the concept "press sets" shown in FIG. 5H).
• a TF score for a concept to be scored may be calculated for each document in the subset and the statistical coherence score may be calculated as the average or median of the computed TF scores.
• the statistical coherence score may be calculated as the largest calculated TF score. This may be advantageous in situations when a concept to be scored appears infrequently in a large number of documents within the subset of documents used for calculating the statistical coherence score.
• the way in which the statistical coherence score is calculated may depend on whether the concept to be scored was retrieved from the semantic network or, instead, was synthesized during act 106 of process 100.
• the statistical coherence score may be obtained by applying the above- described techniques not to the synthesized concept, but rather to the concept that was combined with the active concept to produce the synthesized concept (e.g., the concept "Shiatsu” shown in FIG. 5E).
• the cosine similarity score of a particular concept may be calculated by using the cosine similarity metric for evaluating semantic proximity between pairs of concepts.
• each of the concepts is mapped to two vectors in Euclidean space of any suitable dimension.
• the cosine similarity between the two concepts may then be computed as the ratio between the inner product between the two vectors and the product of the magnitudes of the two vectors. This ratio represents the cosine of the angle between the two vectors, giving rise to the name "cosine similarity.”
• one or more of the scored concepts may be selected for subsequent use based on the calculated scores.
• the score-based selection may be done in any suitable way. In some embodiments, for example, concepts associated with a score above a predetermined threshold may be selected for subsequent use. Alternatively, a predetermined number or percentage of the top-scoring concepts may be selected. Though, it should be recognized that many other ways of utilizing scores to select one or more concepts will be apparent to one skilled in the art. V. Provide Content to User(s) Based on Active Concept and Selected Concept(s)
• process 100 proceeds to act 1 10, where information may be provided to the user(s) associated with the user context information obtained in act 102, based at least in part on the active concept identified or generated in act 104 and the relevant concept(s) selected in act 108.
• information to present to the user(s) may be selected from among a larger set of information by using the active concept and relevant concept(s) selected in act 108.
• the type of information provided to the user(s) and the manner in which the information may be provided to the user(s) may vary depending on the specific scenario in which the techniques described herein may be applied.
• the search results returned from the search service may not include the identified pieces of content themselves, but rather may include a list of hyperlinks to these pieces of content along with an excerpt of each piece of content.
• the list of hyperlinks may be filtered and ranked using the associated excerpt, and the excerpt may be semantically annotated.
• a user may be presented with personalized product and service recommendations based on the active concept and the selected concepts. Consequently, the personalized recommendations may reflect one or more of the user's interests.
• the personalized recommendations may include promotional content including, but not limited to, advertisements for products and/or services.
• an active concept derived from user context information, may indicate that the user is interested in "recliners.”
• the user may be presented with advertisements related to "recliners” and to "massage recliners,” which is a concept relevant to the active concept "recliners” as described with reference to FIG. 5D. As such, the user may not be presented with irrelevant promotional content.
• the present system and method may use one or more methods to, using a knowledge representation, provide information based on environmental and surrounding input by collecting such inputs volunteered by or authorized by individuals or members of a crowd.
• the knowledge representation may be used, according to some aspects, to perform a semantic operation (as described in more detail above) in order to provide information to the user.
• Such inputs may be leveraged as a user-context that provides the basis for the semantic operation that may be performed on a computing device operable to perform one or more methods as previously described with respect to FIG. 1 and the description associated with FIG. 1.
• the semantic operations may be integrated with local electronic media such as digital displays, and at least one wireless service within a localized area, such as a Wi-Fi connection made available to people within a certain operating range.
• local electronic media such as digital displays
• at least one wireless service within a localized area such as a Wi-Fi connection made available to people within a certain operating range.
• a Wi-Fi connection made available to people within a certain operating range.
• members of a crowd sitting in front of one or more electronic displays may participate as members of one or more target groups in the crowd for crowd based advertising.
• the members may be offered incentives, such as a free Wi-Fi connection or reduced data transmission rates by their carrier, in exchange for non- identifiable collection of information.
• the collected information may be such that it cannot identify any personal information of any individual member of the crowd, such that privacy is maintained.
• the present system and method may then collect, in real-time, information about the various websites that individual members of the target crowd are visiting, searches that members of the crowd are performing, or status updates posted by members of the crowd.
• This information may be considered as part of the context information associated with a user, as described above with respect to FIG. 1.
• Synthesis or retrieval operation as described in more detail above, may then be performed on an active concept derived from these user contexts to semantically expand the range of concepts known to be relevant to the crowd.
• the intersection or most prevalent concepts in the group may be deemed the areas that are likely to appeal to the largest constituency of the crowd.
• Content may then be delivered to the crowd based on concepts representing interests with global appeal to the group of people.
• the interests may be the concepts obtained by semantic operations, in other instances the concepts may provide the starting point for further operations that are used to derive such interests, which in turn may be used to identify relevant content.
• Such content may serve a wide range of purposes as described above, whether merely for entertaining the crowd or for a specific commercial purpose such as generating messaging such as advertising, or influencing the creation of new content by presenting ideas from a domain that embody "looser" associations to encourage lateral thinking by the consumers.
• the present system and method receives environmental and surrounding inputs from one or more individuals in a crowd and generates one or more contexts relevant to one or more target groups or individuals in the crowd. These one or more contexts may form the basis for one or more active concepts representing user context information, each of which may then be used to generate one more sets of relevant concepts utilizing a knowledge representation as described in more detail above. Generating a number of semantically relevant concepts to a number of individuals in the crowd may present a rich landscape of information for ascertaining interests relevant to the crowd at large. In some embodiments, these concepts may be used to select one or more promotional messages, or other germane content, that may be more relevant to one or more groups of individuals within the crowd.
• the techniques and methodologies may be employed to identify an interest applicable to crowds of mass scale, such as those that may be found at a sports stadium. For example, a crowd in a hockey game may be exposed to various digital advertisement banners over the course of an evening attending a hockey game.
• information from individuals within large audience may be semantically expanded to produce a large pool of relevant concepts, the intersection or most prevalent of which may be identified.
• Prevalence, as applied to the crowd at large may be determined using any of the scoring techniques described above. This information may then be used to dynamically customize the advertisements being presented to the crowd in real time. Thus, any shift in the trending interests may be immediately addressed by corresponding changes to the advertisements.
• the present disclosure also relates to a system and method for identifying interest based on concepts deemed relevant to an individual form a semantic operation performed on the user's environmental context.
• the present system and method may be practiced in various embodiments.
• a suitably configured computer device, and associated communications networks, devices, software and firmware may provide a platform for enabling one or more embodiments as described above.
• An illustrative computer device and an embodiment of the system is shown in FIGS. 7-9 as described below.
• the present system and method may also be implemented as a computer- readable/useable medium that includes computer program code to enable one or more computer devices to implement each of the various process steps in a method in accordance with the present invention.
• computer-readable medium or computer useable medium comprises one or more of any type of physical embodiment of the program code.
• the computer-readable/useable medium can comprise program code embodied on one or more portable storage articles of manufacture (e.g. an optical disc, a magnetic disk, a tape, etc.), on one or more data storage portioned of a computing device, such as memory associated with a computer and/or a storage system.
• sensing devices may be used to detect and analyze environmental inputs including, and not by way of limitation, image and video input sensors, acoustic input sensors, touch and pressure sensors, motion and orientation sensors, global positioning and speed sensors, temperature and humidity sensors, electric/magnetic field sensors, vapour and chemical sensors, and the like.
• image and video input sensors acoustic input sensors
• touch and pressure sensors motion and orientation sensors
• global positioning and speed sensors global positioning and speed sensors
• temperature and humidity sensors temperature and humidity sensors
• electric/magnetic field sensors vapour and chemical sensors, and the like.
• an acoustic input sensor may be applied to detect ambient music. Any of a number of available commercial applications, especially popular on smart phones and tablet devices, may then be used to identify the song, artist genre of music. The genre of music may for example then be used as the context for generating the active concept. As another non- limiting example, categorizations may be assigned to weather conditions, so that when humidity and temperature detected by sensors exceed certain levels, a context of "hot and humid" may be provided as the user-context. It may be envisioned that certain products (e.g. fans) or literature on certain topics (e.g. body hydration) may be of interest to individuals in this setting. Such product advertisements and article suggestions may be ascertained from relevant interests and concepts (e.g. "body hydration") derived from semantic operations performed on an active concept identified from the user context (e.g. "hot and humid").
• relevant interests and concepts e.g. "body hydration”
• semantic operations performed on an active concept identified from the user context e.g. "hot and humid
• the various types of sensors may be connected to one or more analyzers for analyzing the collected inputs. For example, if the input is from an image sensor, analysis may include determination of light intensity and color to determine ambient mood, or the use of facial recognition technology to determine if a subject is smiling, and even infer whether a subject is male or female.
• Images can be processed by utilizing computer animation technologies, or by comparing to a repository of images and identifying a whole or a part of an image from one or more images stored in a database. Image can be captured from high resolution networked cameras or cameras based in the frame of the display.
• Video sensors may be used to detect motion and simple gestures, and compare them to a motion or gesture repository in order to find a match.
• Video input may also be a video that is being displayed or broadcast within the local environment on a LAN or internet. Any or all of these video inputs may then be processed by associated content/meta tags and/or speech, images or text within the video.
• Environmental input may include other types of audio input, such as speech or other acoustic inputs.
• the audio inputs may be generally classified as object generated or human generated.
• Object generated inputs may provide a way to capture multimedia inputs without interfering with an individual user's privacy. Some examples of object generated inputs include: laughter, music, public announcements or other publicly available inputs that may not affect the privacy of the user.
• the audio input may distinguish between dialects when the user's voice is considered as an environmental input, and this may be used to build a profile of the user based on their dialect. For example, a dialect identified as being of Cajun origins may be used to identify or generate an active concept "Cajun.” A semantically relevant concept, using the techniques as described above with respect to Fig. 1, may then obtain "French Cuisine" as a relevant concept to the user, from which advertisements for local dining establishments to that effect may be suggested. Speech may also be converted into text and the text may be analyzed for semantic meaning or translated into different languages as may be appropriate.
• audio input indicative of emotion or mood may also be captured and processed for further synthesis.
• the analysis may include determination of volume and intensity of a user's voice to determine the mood of the user, or the level of ambient noise to determine if the user is in the middle of a crowd, or sitting in a quiet room.
• Another type of sensor may receive motion input to determine how active the user currently is - whether moving around rapidly during the middle of a busy weekday, or relaxing comfortably at home on a weekend (this context may be considered along with temporal inputs such as the actual time of day and day of the week, which may further suggest in which type of activity the user may be engaged).
• constant fidgeting may suggest a context of uneasiness that may in turn be used to identify or generate "anxiousness” or “restlessness” as an active concept.
• the approaches, outlined above for example in Fig. 1 may be used to generate semantically relevant concept that may be of interest to the user, such as "relaxation techniques.”
• Such concepts of interest may be employed to retrieve and present content, such as articles on relaxation techniques, to the user.
• the general computing device may be a mobile device, such as a smart phone or touch pad, for example.
• the mobile device may include a gestural input, accelerometers, a GPS for location data, picture and sound (or music) sensors, a built-in camera, and other types of sensors for receiving input from the surrounding environment. These different types of inputs may be collected in different ways, and may require participation from the user to collect the input.
• multi-media and motion sensor inputs may be received and analyzed in real time, or near real time, and the analyzed data may be aggregated in order to better assess a user's surrounding environment at any particular moment.
• machine vision sensors and motion sensors may be used to determine a motion or orientation of a user to determine a user's activity or mood.
• the sensors may determine what type of motion or gesture is occurring by comparing the motion to a databank of pre-recorded motions.
• the machine vision or motion sensor analyzers can translate the motion into meaningful data. For example, detection of a feature on a user's face may be translated into a text or graphic equivalent (i.e.
• a linguistic recitation of that state may provide a context, that in turn may be used to generate semantically relevant concepts that are of particular interest to the user given his or her current emotional state).
• the input data may be analyzed to determine a predominant type of motion, as long as it can be identified.
• the different types of gestures or motions may be considered in sequence, and the sequence may be used to consider what types of changes may be occurring based on signatures or patterns within the data.
• the present system and method may also utilize sensor-based capabilities embedded or affixed to various articles that may be worn by a user.
• sensors may be built into a shoe, or worn in some article of clothing.
• sensors built into clothing or shoes may transmit a signal that may be detected by wireless detection means.
• Such sensors may be connected to user's mobile devices via short-range communication protocols such as Bluetooth or IrDA.
• the present system and method may receive inputs from multiple different types of sensors, such that a more comprehensive profile of a user and his/her environment may be created.
• Any suitable computing device may be used which allows collection of inputs to be processed locally on the device that is directly coupled to the sensors picking up
• any suitable environmental input may be considered to capture a user-context, based on linguistic expressions associated with any of the multi-modal data derived from the environmental input.
• Such an environmental input may be the basis of user-context consistent the approaches described above with respect to acts 102 of FIG. 1 and other teachings for identifying a user-context as detailed in this disclosure.
• obtaining semantically relevant concepts to each user may be accomplished by any number of suitable approaches, including but not limited to those outlined above in acts 104-108 and other methodologies as explained herein. Accordingly, the present system and method provide a means for considering the user's environment in order to enrich the type of information and/or content that user experiences.
• FIG. 9 is a block diagram an illustrative computing device 1000 that may be used to implement any of the above-discussed computing devices.
• the computing device 1000 may include one or more processors (e.g., microprocessors) 1001 and one or more tangible, non-transitory computer-readable storage media (e.g., memory 1003).
• Memory 1003 may store, in tangible non-transitory computer- readable storage media computer instructions that implement any of the above-described functionality.
• Processor(s) 1001 may be coupled to memory 1003 and may execute such computer instructions to cause the functionality to be realized and performed.
• Computing device 1000 may also include a network input/output (I/O) interface 1005 via which the computing device may communicate with other computers (e.g., over a network).
• I/O network input/output
• the computing device may also include one or more user I/O interfaces, via which the computer may provide output to and receive input from a user.
• the user I/O interfaces may include devices such as a keyboard, a mouse, a microphone, a display device (e.g., a monitor or touch screen), speakers, a camera, and/or various other types of I/O devices.
• the above-described embodiments of the present invention can be implemented in any of numerous ways.
• the embodiments may be implemented using hardware, software or a combination thereof.
• the software code may be embodied as stored program instructions that may be executed on any suitable processor or collection of processors (e.g., a microprocessor or microprocessors), whether provided in a single computer or distributed among multiple computers.
• a computer may be embodied in any of numerous forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer.
• a computer may be embodied in a device not generally regarded as a computer, but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone, a tablet, a reader, or any other suitable portable or fixed electronic device.
• PDA Personal Digital Assistant
• a computer may have one or more input and output devices. These devices may be used, among other things, to present a user interface. Examples of output devices that may be used to provide a user interface include printers or display screens for visual presentation of output, and speakers or other sound generating devices for audible presentation of output. Examples of input devices that may be used for a user interface include keyboards, microphones, and pointing devices, such as mice, touch pads, and digitizing tablets.
• Such computers may be interconnected by one or more networks in any suitable form, including networks such as a local area network (LAN) or a wide area network (WAN), such as an enterprise network, an intelligent network (IN) or the Internet.
• networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks, and/or fiber optic networks.
• the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of numerous suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a virtual machine or a suitable framework.
• inventive concepts may be embodied as at least one non- transitory tangible computer-readable storage medium (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, etc.) article(s) encoded with one or more programs that, when executed on one or more computers or other processors, implement the various process embodiments of the present invention.
• the non-transitory computer-readable medium or media may be transportable, such that the program or programs stored thereon may be loaded onto any suitable computer resource to implement various aspects of the present invention as discussed above.
• program or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion among different computers or processors to implement various aspects of the present invention.
• Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices.
• program modules include routines, programs, items, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
• functionality of the program modules may be combined or distributed as desired in various embodiments.
• data structures may be stored in non-transitory tangible computer-readable storage media articles in any suitable form.
• data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a non-transitory tangible computer-readable medium that convey relationship between the fields.
• any suitable mechanism may be used to establish relationships among information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationships among data elements.
• inventive concepts may be embodied as one or more methods, of which multiple examples have been provided (e.g., processes 100, 300).
• the acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments, or vice versa.
• All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.
• the phrase "at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements.
• This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase "at least one" refers, whether related or unrelated to those elements specifically identified.
• At least one of A and B can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.
• a reference to "A and/or B", when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
• “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items.

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