KR101617114B1 - Determining social sentiment using physiological data - Google Patents

Abstract

Methods and systems are disclosed for predicting the social feelings of one or more people using physiological data. In a particular embodiment, the method includes receiving data from one or more people at a computing device, analyzing the data, determining indicator data for one or more people's social emotions so that the indicator data is determined from the data, , And displaying and / or transmitting indicator data. The computing device may include a server or some other remote computing device. Physiological data may be received over the network and / or transmitted over the same or different networks. In certain embodiments, the indicator data includes prewarning information.

Description

[DETERMINING SOCIAL SENTIMENT USING PHYSIOLOGICAL DATA]

The present invention relates to crowd-sourced data collection.

With the spread of Internet technology and new forms of communication such as blogs and social networks, social sentiment has gained considerable attention. Social emotions can entail reviews, ratings and other aspects of other users, corporate referrals, policies, and everyday life.

Typically, the social sensibility is constructed based on various data provided by online users such as text strings and click streams. Even these somewhat limited types of data are valuable tools for companies and other groups (for example, governments) to market products, identify new opportunities and needs, manage reputation, ).

While a number of techniques have been proposed to filter the noise generated by random and non-related information that is widely spread over the Internet, there are a number of limitations on the very nature of the available data. Often, while users are sharing their data, for example, they are pursuing certain goals while driving web traffic to their web pages and accounts, which causes data misunderstandings and confusion. Various other factors such as peer pressure and social stigma may also contribute to the inaccuracy.

Some embodiments are described with reference to the following drawings.
1 is a flow diagram illustrating a method for predicting the social sensibility of one or more persons using physiological data, in accordance with certain embodiments.
Figure 2 illustrates a network used to predict the social emotions of one or more people using physiological data, in accordance with certain embodiments.
3 is a block diagram of an exemplary computing system that may be used to practice aspects of the present invention.
Figure 4 illustrates the social sensibility according to one embodiment.

Physiological data can be used to monitor and predict the social feelings of multiple people. As used herein, the term "physiological data" will be understood to mean physiological data, psychophysiological data, or a combination thereof. In certain embodiments, the method includes receiving physiological data at a computing device, analyzing the physiological data, determining indicator data for the social sensibility such that the indicator data is determined from the physiological data, And transmitting the data. The computing device may include a server or some other remote computing device. Physiological data may be received over the network and transmitted over the same or different networks. The indicator data includes prewarning information and may be sent back to the user in certain embodiments.

In certain embodiments, the physiological data is received from a person using a single sensor. This can be done for a plurality of people whose data is received by a single sensor. In some embodiments, multiple sensors may be used to receive data from a person. For example, the user device may have only a single sensor to obtain physiological data from the user. Even a single sensor implementation may be able to provide various types of physiological data such as body temperature and heart rate. In other words, a single sensor can function as a multifunction sensor. The operation of the sensor and / or the sensor (s) itself may be concealed from the user such that the physiological data is collected from the user without any specific action and possibly in a concealment manner without the knowledge of the user.

When multiple people are involved in providing physiological data, some relationships may exist between users and / or data points provided from different users. The step of analyzing the physiological data may include aggregating data from a plurality of people to provide a more comprehensive analysis. In certain embodiments, the step of analyzing the physiological data may include taking into account current events, personal activities, and / or physical parameters of a person. For example, physical parameters may include health status, age, sex, body weight, percent body fat, genetic traits, biometric and physical location. These parameters may be combined with physiological data or used to interpret physiological data.

Analyzing the physiological data may include predicting one or more correlations between two or more people. The association may be genetic, familial and / or social, and may be based on physiological data of all users or a subset of users. Moreover, analyzing physiological data may include analyzing changes in physiological data due to some external stimuli such as visual stimuli, audio stimuli, and / or other sensory stimuli. In certain embodiments, the method may also include determining a reaction difference between two or more persons based on the presence or absence of the variable.

A system for predicting the social sensibility of one or more people using physiological data includes a memory for storing data, a receiving module, an analysis module, an indicator data determination module, and a transmission module, as well as a receiving module, an analysis module, A determination module, a transmission module, and a processor for executing the display module. The processor receives physiological data from one or more people at a computing device, analyzes the physiological data, determines indicator data for one or more people's social emotions, and the indicator data is determined from physiological data, / RTI > and / or < / RTI > The computing device may be a server, and the physiological data may be received by the receiving module and transmitted by the transmitting module over the network.

In certain embodiments, the physiological data may be received from one or more people using a single sensor per person. Physiological data can be received constantly from a person using single or multiple sensors. When many people are involved, they can have a relationship with each other. Analyzing the physiological data may include aggregating data from a plurality of people. In certain embodiments, the step of analyzing the physiological data may include taking into account current events, personal activities, and / or physical parameters of a person. The physical parameters may include, for example, one or more of health status, age, sex, body weight, percent body fat, genetic trait, biometric and physical location. In certain embodiments, the indicator data includes prewarning information. Some other aspects of analyzing the aforementioned physiological data may include references to various operations that may be implemented within a computer system.

A computer-readable storage medium may have a program embedded thereon executable by a processor to perform a method for monitoring and predicting the social feelings of one or more people using physiological data. The method includes receiving physiological data from one or more people at a computing device, analyzing physiological data, determining indicator data for one or more people's social emotions, the indicator data being determined from physiological data, And displaying and / or transmitting indicator data.

There is a need to objectively monitor and predict social emotions. Monitoring physiological data can provide direct data for monitoring and prediction of social emotions. Social sensibility is an evaluation of feelings or attributes bound to public opinion or public opinion, specific location or locations.

Figure 1 is a flow diagram illustrating a sequence 100 for predicting the social feelings of one or more people (i.e., users) using physiological data, in accordance with certain embodiments. The sequence may be implemented in software, firmware, and / or hardware. In software and firmware embodiments, it may be implemented using computer-executed instructions stored on one or more non-volatile computer-readable media, such as optical, magnetic, or semiconductor storage devices.

The social sensibility typically includes information about these users, their social and / or geographical environment, and various groups to which these people naturally belong or are assigned to the use of this method. As further described herein, this information may be used to generate customized content for the user, such as targeted advertisements and information for a variety of related services, or may be provided to the user again. Social emotions may be shared with other groups, such as corporate and government agencies, for example, to provide services to users. Various security and privacy features may be provided to ensure controlled dissemination of sensitive information to minimize privacy concerns.

Social emotions have gained increased attention due to advances in Internet communication technologies and new forms of communication such as blogs and social networks. Social emotions may include reviews, ratings, and referrals of other users, businesses, policies, and other aspects of everyday life. Typically, the social sensibility is constructed based on various data provided by an online user such as a text string and a click stream. Even these somewhat limited forms of data provide valuable tools for businesses and other groups (eg, governments) to market products, identify new opportunities and needs, manage reputation, and appeal to public opinion . Although a number of techniques have been proposed to filter the noise generated by random non-related information that is prevalent on the Internet, there are a number of limitations on the nature of the available data. Users often pursue certain goals while sharing their data, such as running web traffic on their web pages and accounts, which causes data misunderstandings and confusion. Various other factors such as peer pressure and social stigma can also contribute to the inaccuracy. The methods and systems described herein utilize physiological data, which is inherently more precise and provides a number of new opportunities for service providers.

Physiological data may also be collected via the user device. This data may be sent to the server to aggregate and analyze the data in a more comprehensive manner that was previously available. Some examples of physiological data include heart rate, body impedance, body temperature, and the like. Physiological data may be categorized as objective data because it corresponds to the objective physical characteristics of the user's body. This type of data is easily distinguishable from subjective data types such as verbal and recorded representations in the form of blog posts and news feeds. Subjective data may be easily distorted by various other factors that may not be able to be analytically separated from one another. Thus, physiological data may be much more valuable than other data traditionally used to construct social emotions in general.

During the analysis of the physiological data (and even while obtaining such data), it is possible to determine the geographical location of the user, their demographic information, click-stream data, dictionary entries and post entries, external data feeds, And may be supplemented with other data, such as data of the type. Collectively, all data feeds can be used to organize and refine social sensibilities. In some embodiments, it should be noted that the social sensibility may only be an intermediate product provided by the proposed method and system, and may be used to develop additional services and products to provide to the community of users and users.

The sequence 100 may begin by receiving physiological data from one or more persons at a computing device in operation 102. The received information may be transmitted by one or more user devices. It should be noted that multiple users may share the same user device. These devices can be used to obtain physiological data or to communicate with additional upstream devices used to obtain such data. Some exemplary user devices may include personal computers, laptops, mobile phones, and the like. The user device may be a part of a dedicated data collection operation (e.g., prompting the user to operate or interact with the sensor) or as part of another operation (e.g., while obtaining biometric information for authentication purposes, , Body fat percentage, body temperature, pulse, any other physiological data, etc.), and more than one sensor for collecting such data.

The physiological data obtained at the user device level may then be transmitted to the server. The transmission may be performed over one or more networks as further described with reference to FIG. A server is defined as a computer system separate from the user device used to collect physiological data. In this regard, the server typically does not have the ability to obtain physiological data directly from one or more persons (i. E., It may not have sensors in some embodiments). Instead, the server is communicatively coupled to a plurality of user devices for collecting physiological data from these services. In certain embodiments, one of the user devices used to collect physiological data may perform some or all of the server functions described herein.

Although a single server is referenced, one of ordinary skill in the art will appreciate that various combinations of hardware and software systems may be used for backend reception and processing of physiological data. Specifically, although FIG. 2 illustrates the use of two servers, any number of servers may be used. In a particular embodiment, it is important to note that the server is configured to collect and aggregate such data from multiple users. As will be described below, the data may reflect relationships between some users or may be analyzed in light of some predefined relationships. Sometimes the collection and analysis of physiological data is referred to as "physiological polling ".

At operation 104, the sequence 100 may proceed to analyzing physiological data. In a particular embodiment, this action may be combined with determining indicator data for the social emotions of one or more of the people presented below as action 106. [ Analytical operations may include grouping several physiological data, combining them, and / or more generally analyzing them in the light of other data, and generating an output of some form. Operation 104 may include various alternative embodiments that will now be described in more detail. It should be noted that each of these additional sub-operations may be implemented alone or in combination with various other operations.

In some embodiments, analyzing the physiological data includes aggregating such data from a plurality of users (i. E., A plurality of people (selection action 104a)). Unlike analyzing data from a single user, collecting data from multiple users allows for a more comprehensive analysis of the physiological data of the entire group and even individual users. For example, an indication that a user has a fever has a limited value, while an indication that a group of larger people in the same geographic location has this condition may be, for example, an indication of an epidemic. In addition to being warned about its own fever state, users will also understand information about infectious diseases. For example, this collective information can be used to dispatch medical services and warn all users to evacuate, possibly in hazardous areas.

A particular example may provide a better understanding of some implementations of this optional operation. The user device may capture the temperature of the user during periodic contact of the user with, for example, one or more sensors. The data is transmitted to a server having certain additional information about the user, such as the user's specific geographical location. The server may search for and even request similar data corresponding to other users at this location. A comprehensive analysis of the data may help determine trends (eg, epidemic trends, etc.) and issue warning messages.

As shown in the example above, physiological data may be received from multiple users having various relationships with each other. Some examples of such relationships include blood relatives, social networks, geographical locations and specific demographic groups (e.g., sex, age, etc.). The data may be grouped, sorted and analyzed to reflect these relationships.

In certain embodiments, the physiological data is grouped according to a personal space map and / or a geographic location map. The personal space map is identified based on the user ' s "emotional proximity ", the frequency of their interaction, the required professional / social / family relationship and other relevant factors. The personal space map may help to analyze the physiological data in the particular environment provided by these factors and to derive appropriate conclusions about the social dynamics of a particular group, for example. In certain embodiments, the physiological data itself is used to identify a personal space map. In addition to mappings in terms of interpersonal and geographical / physical relationships, other types of relationships may be mapped and analyzed. These types of relationships may include, but are not limited to, relationships to an organization or hierarchy (e.g., corporations, military, religious organizations, political groups, etc.).

서비스가 이러한 목적으로 사용될 수도 있다. 몇몇 예시적인 실시예에서, 지리학적 위치 맵은 생리학적 데이터의 더 광범위한 분석을 수행하기 위해 개인 공간 맵과 조합된다. 예를 들어, 지리학적 위치 맵은 개인 공간 맵을 수정하는데 사용될 수 있다. 임의의 특정 이론에 제한되지 않고, 가까운 신체적 근접도에 있는 사람들은 서로로부터 멀리 이격되어 위치된 사람들보다 더 많이 서로 영향을 미치는 경향이 있는 것으로 종종 고려된다. 물론, 이 이론은 2개의 유형의 맵의 제안된 종속성에 더 영향을 미칠 사용자에 의해 통신 툴의 이용 가능성 및 사용량을 고려해야 한다. 인구학적 팩터 및 특정 생리학적 데이터 경향 상에 구축된 맵과 같은 다른 유형의 맵이 마찬가지로 사용될 수도 있는 것으로 고려된다. 맵의 그룹화는 비교적 정적이고(예를 들어, 시계, 가족 관계) 또는 동적일 수 있다(예를 들어, 사용자의 신체적 근접도).The geographic location map groups people based on their proximity to each other and / or some geographic boundaries, such as a city limit. Various geographic tracking systems such as Global Positioning System (GPS) and cell phone usage or triangulation may be used to link individuals with specific areas on these maps. For example, GOOGLE LATITUDE available from Google at Menlo Park,

Services may be used for this purpose. In some exemplary embodiments, a geographic location map is combined with a personal space map to perform a more extensive analysis of physiological data. For example, a geographic location map can be used to modify a personal space map. Without being limited to any particular theory, people in close physical proximity are often considered to have a greater tendency to interact with each other than people located far away from each other. Of course, this theory should consider the availability and usage of communication tools by users who will further influence the proposed dependencies of the two types of maps. It is contemplated that other types of maps may be used as well, such as maps built on demographic factors and specific physiological data trends. The grouping of the maps can be relatively static (e. G., A clock, a family relationship) or dynamic (e. G., A user's physical proximity).

Analyzing physiological data may also include aggregating and inspecting / analyzing physiological data over time. This may include determining various time trends since the data varies over time. Some statistical methods may be applied for this purpose. The time dependency of physiological data is an important factor to be considered in constructing an algorithm for analyzing physiological data. For example, after experiencing a specific stimulus (for example, seeing a visual image), the human body may experience various stages. The body may experience an initial response that may be referred to as the initial "shock ". This may include strong or weak reactions and positive or negative reactions such as panic, hate, joy, and the like. During this initial period, the human body can produce a more prominent signal associated with the received stimulus, and the collected physiological data tend to be the most relevant. The body may then recover and receive new non-related stimuli. Physiological data may not only represent a small correlation to the initial stimulant, but may also provide a false signal. Moreover, certain stimuli may produce delayed physical, physical and / or physiological responses.

Continuing with an embodiment in accordance with the teachings of the present invention, analyzing physiological data may include considering current events (optional operation 104b) or, more specifically, some understanding and meaning (which may otherwise be relatively abstract data) Lt; RTI ID = 0.0 > physiological < / RTI > Current events may be defined as a variety of external data (generally presented in the form of some stimuli) that can affect physiological data or at least put physiological data into some circumstances. Examples of these current events include global events such as major political or economic news, disasters, wars and coups, as well as local events such as death or birth of a family and special events (eg birthdays, marriages, promotions, etc.) You may.

One specific example is a trial ballon, which is shared information with a specific purpose to observe the response of the audience (i.e., users of the system). The test apparatus may be used by a company to send out a press release (eg, a predicted product release) to determine a response by a customer, or a politician who deliberately leaks information about policy changes that may be under consideration Or may be used by other entities. In the past, observing aerial reactions was usually difficult to accomplish with expensive, often inaccurate surveys. A new combination of information and sensor technology may be used to capture the relevant data in an efficient and accurate manner. Specifically, once the information is "leaked" to the initial stage of the test apparatus process, the user's physiological data may be collected immediately. This information may be considered by other data points, such as the level of information that reflects how soon the information reaches each user and the level of interest of the information by the user (e.g., clickstream data). As discussed above, physiological data is more generally more rewarding than other types of user responses (e.g., responses to survey queries, etc.), and such data may be collected in a concealment manner, Less intrusive and efficient. In other words, some or all of the users may not even be aware that they are being monitored.

In certain embodiments, analyzing physiological data involves analyzing changes in physiological data for external stimuli (optional operation 104c). Some examples of such stimuli include visual images, audio, and various other sensory stimuli (odor, texture, etc.). This type of operation may overlap other similar operations. External stimuli may be provided by the user device using its video and audio output. As described above, the user device may also have a sensor. For example, the user's physiological data may be collected and synchronized with an image on a computer or mobile phone display. In certain embodiments, a photo of a political candidate may be sent to the user's device, and upon viewing of the user's photo, the data is collected and received at the server. This viewing / data collection may be performed over time periods as compared to the more traditional mass media options. This in turn adds to the resilience of this method.

Certain implementations of this optional operation 104c solicit donations for various reasons. Different users may be accommodating for different reasons. These correlations may be set by providing various external stimuli associated with these different causes and selecting those that produce the most significant emotions based on the received physiological data. Solicitations may then be tailored to users based on their different responses to the causes.

In some embodiments, analyzing the physiological data involves considering personal activities (optional operation 104d). Similar to the current events described above with reference to optional action 104b, personal activity may help provide some response to physiological data and may help to better understand this data. Some examples of personal activities include social network usage (e.g., postings, messages, status updates, etc.), click streams, search-streams from user devices, and other types of data (e.g., A weight monitoring application, a physical activity application, a schedule, a to-do list, etc.).

The actions may include designing and providing user specific studies to supplement and interpret the collected physiological data. For example, the user device may register that the user has an exotherm. The user device may then prompt the user to answer certain health related queries to help identify and provide a precise diagnosis and / or prognosis. In some embodiments, the system may analyze other user activities (e.g., intense physical activity registered by another application) to describe the data. Moreover, the system may detect that the user is typing a message (e.g., querying a sick day) on the device that points to some pointers to the health state of the user.

Modern social networks present a vast source of information about users and their environment. People who handle many machines are active members of social networks. The methods and systems disclosed herein may be designed to continue collecting information about the user from such sources. For example, each user account may be associated with a web crawler that is specifically designed to collect information pertaining to that user's personal activity.

In certain embodiments, analyzing the physiological data includes taking into account the physical parameters of the user (optional operation 104e). Some examples of physical parameters include health status, age, sex, weight, genetic traits, biometric and physical location. There may be some overlapping with other analysis operations. For example, a user's physical location may be associated with a personal activity, such as jogging, or with a current event. Moreover, some users may oppose the use and sharing of such personal data. Some proposed methods and systems provide various security measures to limit access to this data, as set by the user or other criteria.

It is noted that various other types of means may be taken and used in accordance with embodiments of the present technique. Some of these types include, but are not limited to, blood flow (for example, to assess sexual arousal), EEG, ECG, eye movement, pupil size (e.g., Skin sweating, and the like.

In one example, the user device has a GPS device or other type of location device and / or system (e.g., a cell phone, a cellular triangulation system, a Wi-Fi hub and an Internet Protocol (IP) address). This location information can be used to form a group for analyzing data based on a particular geographical location. For example, a strong bias in certain physiological parameters of a particular area may indicate an epidemic or an allergy epidemic. People may be warned to avoid these areas.

Some physical parameters may be used to generate and update other parameters. For example, the physical location may be used to determine local weather conditions (e.g., external temperature, rain, etc.) and then correlate this information with physiological data to determine effects on each other. In certain instances, low temperatures may account for the local spur of fever.

In a particular embodiment, one or more heart rate authentication techniques are implemented to provide the user ' s physical parameters. These techniques may be implemented on a user device with local communication (NFC) capabilities. For example, the process may include obtaining heart rate information to be used subsequently to provide mood / emotional feedback. This feedback can be shared with the service provider as an indication of the level of satisfaction (or lack thereof) from the particular transaction completed by the user (e.g., purchase of the product, etc.). In some embodiments, a coupon (or any other) may be sent to one or more individuals based on mood or other data. This may be accomplished via email, SMS, snail mail or any other suitable method.

In some embodiments, analyzing the physiological data includes predicting associations or relationships between two or more people (select operation 104f). Some examples of relationship types include genetic linkage, family linkage, and social linkage. The strong correlation of particular physiological data may indicate that two persons have similar genetic makeup, which may also indicate that two persons are relatives. Moreover, this information can be used to identify possible organ donors, although two people may not necessarily be involved. In addition, some physiological data may predict positive or negative relationships of two or more persons with various social and professional settings such as marriage, friendship or other relationships or partnerships. Embodiments of the present disclosure may also be applied to flash mob, demonstration, other conferences, and the like.

In certain embodiments, the method includes determining a difference in response between two or more people based on the presence or absence of one or more variables. In other words, the physiological data corresponding to one or more users is compared to the same type of data of one or more other users. The same type of comparison may be implemented over time for the same group of one or more individuals.

At some point in time, the method 100 may proceed to determine indicator data for the social emotions of one or more people during the action 106. [ It is contemplated by many that it is not limited to any particular theory, but that physiological data may be predictors of more advanced social emotions than other types of user input, such as blog postings and social network postings. This indicator data may include some kind of prewarning information. The indicator data may then be re-transmitted to the user device for display on the user interface at operation 108. For example, the indicator data may include predetermined interpretations of physiological data such as preliminary medical diagnosis, health tips, mental state, mood, and the like. In some embodiments, the system may be configured to compile a health dashboard on the user device based on various data points collected over time. Generally, the indicator data will include some information that will encourage the user to continue to provide physiological data.

 The indicator data may also be valuable to entities other than the user providing the data. In certain embodiments, the data may be transmitted to marketing agencies, government agencies, military organizations, medical entities, and other types of entities interested in data. Various financial and security arrangements may be implemented in this system. For example, government agencies may be interested in various areas of health or other conditions, and collecting physiological data may help such agencies in this regard. In certain embodiments, the indicator data may be used to customize other content that is re-provided to the user device, such as a targeted advertisement.

Various examples of the sequence 100 described above may be implemented for different applications. For example, similar to post and relationship information in social network status, users may be interested in sharing their health and mental state with their network. For example, the physiological data may be interpreted to determine the user's mood, which may be a useful and / or joyful or interesting fact to share with others. There may be clinical uses for broadcasting mood. Those who attempt to quit smoking, overeating or any other addiction or undesirable behavior may be able to implement this aspect so that friends or therapists can monitor their moods and provide ethical support.

Other types of data may also be used for entertainment, social, professional, and medical purposes. For example, an employee's health condition may prompt an employee to provide a monthly difference with a total goal to increase a person's productivity. Indications that many people in a particular area, affiliation, or network are in a bad "mood" or poor health condition may isolate others from this area.

The computer network presented below will help to provide some environment for collecting and analyzing physiological data in a network and the environment of multiple users connected to the service provider through these networks. FIG. 2 illustrates an exemplary network segment 200 in which various embodiments of the above-described methods may be implemented. As shown, a number of user devices or clients 202a-202d may be communicatively coupled to network 204 to provide various types of physiological data to servers 206, 208 hosted by a service provider . User devices 202a through 202d may have one or more sensors for collecting information from their respective users. In a particular embodiment, at least some of the user devices 202a through 202d include only one sensor.

Although the software application associated with the method includes a method that may be implemented on any number of servers, only two servers 206 and 208 are shown. These servers may have access to one or more databases (not shown) in which physiological data and other types of data are held. The servers 206 and 208 may be used to subscribe the user devices 202a through 202d to the services provided. Accordingly, the server 206, 208 may also store various user-related information such as subscription information and demographic information. The servers 206 and 208 may also be used to transmit indicator data to the user devices 202a through 202d.

The network 204 may take any suitable form such as a wide area network (WAN) or the Internet and / or one or more local area networks (LAN). The network 204 may be any number and type of device (e.g., a number of devices) for forwarding requests from each client to a particular server application, re-forwarding application results to the requesting client, , Routers, switches, etc.).

The above-described methods may be implemented in a wide range of network environments (represented by network 204), telecommunication networks, wireless network mobile networks, and the like. In addition, the computer program instructions may be stored on any type of computer readable medium and may be stored on a computer readable medium, in accordance with a variety of computing models including a client / server model, a peer-to-peer model, an autonomous computing device, Various functionalities may be implemented in accordance with distributed computing models that are affected or employed at different locations.

FIG. 3 is a graphical representation of an example of a method for receiving physiological data from one or more people, analyzing physiological data, determining indicator data for one or more people's social feelings from physiological data, And / or < / RTI > The computer system 300 is coupled to a storage device that includes a primary storage 306 (typically a random access memory (RAM)) and a primary storage 304 (typically a read only memory (Also referred to as a central processing unit (CPU)). The processor 302 may be of various types, including microcontrollers and microprocessors, e.g., programmable devices (e.g., CPLDs and FPGAs) and non-programmable devices such as gate array ASICs or general purpose microprocessors. The primary storage 304 serves to transfer data and instructions in a unidirectional manner to the processor 302 and the primary storage 306 is typically used to transfer data and instructions in a bidirectional manner. All of these primary storage devices may include any suitable computer-readable media such as those described herein. The mass storage device 308 may also be bidirectionally coupled to the processor 302 and provide additional data storage capacity and may include any of the computer-readable media described herein. The mass storage device 308 can be used to store programs, data, and the like, and is typically a secondary storage medium (such as a hard disk). It will be appreciated that the information held in the mass storage device 308 may be incorporated in a standard manner as part of the primary storage 306 as a virtual memory, if appropriate. Certain high capacity devices, such as CD-ROM 313, may also pass data unidirectionally to processor 302.

The processor 302 may also be coupled to a processor 302 such as a video monitor, trackball, mouse, keyboard, microphone, touch-sensitive display, transducer card reader, magnetic or paper tape reader, tablet, stylus, voice or hand- And an interface 310 communicatively coupled to one or more input / output devices, Finally, the processor 302 may optionally be coupled to an external device, such as a database, computer, or telecommunications network, using external connections, generally as shown at 312. With such a connection, it is contemplated that the processor 302 may receive information from the network, or may output information to the network while performing the method steps described herein.

In some embodiments, the physiological data may include, for example, a configuration that is integral with a processor-based system such as a computer, a tablet, a cell phone, a medical test rig and a television remote control, a mouse and a touch pad, It can also be collected by elements. 3, a pair of contacts 324 may be provided on the button 322 of the mouse 320, in one embodiment. Next, physiological data may be taken when the user has left the user's finger on the mouse button 322 only. This physiological data may be used to continue collecting information about the physiological state of a user of a plurality of computing devices. If enough data can be collected, more meaningful trends can be developed.

According to some embodiments, the physiological data may be associated with location information. Specifically, a location with a relatively fine granularity of the user, whose physiological data may be taken, may be determined by the mapping software in such a way that the mapping software may be used to direct physiological data changes and thereby changes in the social sensibility on a geographic basis . ≪ / RTI >

For example, as shown in FIG. 4, areas of the map indicating the counties indicated as boxes 402, 404, 406 and 408 are indicated. Area 402 may have a different social sensibility than regions 404, 406, 408, as shown compiled on the map. In this way, geographic differences and geographical based trends may be better understood.

In some embodiments, the sensor may be connected to an interface, such as the interface shown in Fig. 3, which performs filtering and signal processing using conventional techniques. This data, once analyzed, may then be transmitted over a computer network for aggregation with information from a wide variety of other users. In embodiments where the data is combined in a particular geographical location, the social sensibility changes of the geographic basis may be identified and visualized in some embodiments.

Regardless of the configuration of the system, one or more memory or memory modules configured to store general purpose processing operations and / or data, program instructions for the inventive techniques described herein may be employed. The program instructions may, for example, control the operation of the operating system and / or one or more applications. The memories or memories may also be configured to store expressive information about one or more of account or subscription information, messages, message semantic features, classification information, feature vectors, class lexicons, topic models, .

It is worthwhile that the various modules and engines can be located in different locations in various embodiments. Modules and engines referred to herein may be stored as software, firmware, hardware, combinations, or in various other ways. It is contemplated that the various modules and engines may be eliminated and included in other suitable locations other than those specifically disclosed herein. In various embodiments, additional modules and engines may be included in the exemplary embodiments described herein.

While the above concepts have been described in some detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. It should be noted that there are a number of alternative ways of implementing processes, systems, and devices. Accordingly, the embodiments of the present invention should be considered as illustrative rather than limiting.

200: Network segment
202a-202d: a user device or client
204: Network 206, 208: Server
302: processor (s) 304: primary storage device
306: primary storage device 308: mass storage device
310: Interface 312: Network connection

Claims (25)

A method for predicting social emotions of a plurality of people using physiological data,
Receiving physiological data from a plurality of people on a plurality of computing devices;
Analyzing the physiological data;
And using the physiological data to determine the social sensibility of the plurality of people,
Wherein analyzing the physiological data comprises predicting the association between two or more people
Social Sensitivity Prediction Method.
The method according to claim 1,
Wherein the computing device comprises a server, the physiological data being received over a network
Social Sensitivity Prediction Method.
The method according to claim 1,
Comprising receiving said physiological data from a person using a single sensor
Social Sensitivity Prediction Method.
The method according to claim 1,
And receiving the physiological data from the plurality of persons having a relationship of each other
Social Sensitivity Prediction Method.
The method according to claim 1,
Wherein analyzing the physiological data comprises aggregating data from the plurality of persons
Social Sensitivity Prediction Method.
The method according to claim 1,
Wherein analyzing the physiological data comprises considering current events
Social Sensitivity Prediction Method. The method according to claim 1,
Wherein analyzing the physiological data comprises taking into account individual activities
Social Sensitivity Prediction Method.
The method according to claim 1,
Wherein analyzing the physiological data comprises taking into account the physical parameters of a person
Social Sensitivity Prediction Method.
9. The method of claim 8,
The physical parameters may include one or more of health status, age, sex, body weight, percent body fat, genetic traits, biometric and physical location
Social Sensitivity Prediction Method.
delete The method according to claim 1,
Such an ointment may include one or more of genetic linkage, family linkage, and social linkage
Social Sensitivity Prediction Method.
The method according to claim 1,
Wherein analyzing the physiological data comprises analyzing a change in the physiological data due to an external stimulus
Social Sensitivity Prediction Method.
A system for predicting a social sensibility of one or more people using physiological data,
A memory for storing the reception module, the analysis module, the indicator data determination module, and the transmission module;
And a processor for executing the receiving module, the analyzing module, the indicator data determining module, and the transmitting module,
The processor
Receiving physiological data from one or more people at a computing device,
Analyzing the physiological data by predicting the association between two or more people,
And to determine the social feelings of the one or more people using the physiological data
Social Sensitivity Prediction System.
14. The method of claim 13,
Wherein the computing device comprises a server, the physiological data being received via the network by the receiving module and transmitted over the network by the transmitting module
Social Sensitivity Prediction System.
14. The method of claim 13,
The physiological data may be received from a person using a single sensor
Social Sensitivity Prediction System.
14. The method of claim 13,
The physiological data may be received seamlessly from a person using a single sensor or multiple sensors
Social Sensitivity Prediction System.
14. The method of claim 13,
The physiological data is received from a plurality of persons having a relationship with each other
Social Sensitivity Prediction System.
14. The method of claim 13,
Wherein analyzing the physiological data comprises aggregating data from a plurality of people
Social Sensitivity Prediction System.
14. The method of claim 13,
Analyzing the physiological data may include considering current events
Social Sensitivity Prediction System.
14. The method of claim 13,
Analyzing the physiological data may include considering personal activities
Social Sensitivity Prediction System.
14. The method of claim 13,
Analyzing the physiological data may include considering a person's physical parameters
Social Sensitivity Prediction System.
22. The method of claim 21,
The physical parameters may include one or more of health status, age, sex, body weight, percent body fat, genetic traits, biometric and physical location
Social Sensitivity Prediction System.
14. The method of claim 13,
Wherein the indicator data includes prewarning information
Social Sensitivity Prediction System.
At least one non-volatile computer-readable storage medium having an implemented program,
The program being executable by a processor to perform a method for predicting a social sensibility of one or more people using physiological data,
Receiving physiological data from one or more people at a computing device;
Analyzing the physiological data;
Determining the social sensibility of one or more people based on the physiological data,
Wherein analyzing the physiological data comprises predicting the association between two or more people
Computer-readable storage medium.
25. The method of claim 24,
Further storing instructions for receiving global positioning system coordinates with said data to identify a geographical location of a person
Computer-readable storage medium.

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