By way of example, where the user brings the communication device into a location which has loud conversations, the communication device could determine the name of the establishment by correlating the location with map data. The loud conversations in correlation with a specific time might indicate different purchasing preferences, and thus result in different purchasing scenarios. For instance, the specific time might be in a range more typical of lunch versus dinner, in a range more typical of happy hour with friends versus dinner, or in a range more indicative of desert versus lunch or dinner. The specific time would be usable in the social signature to select different social templates even in the same establishment. Thus, the social signature could be used to identify the type of shopping, dining, or purchasing experience of the user by applying a social template reflecting this type of experience. 

 In one embodiment, when a purchasing experience is detected from the social signature, the social template could be used to prevent interruptions or to inform others of the user's activities according to a set social hierarchy. In addition, different social templates could be set up to provide recommendations on possible purchases to the user and/or others in the social hierarchy. Returning to the restaurant example, the social template could be programmed so that it is used by the system to provide to the user a suggestion based upon prior experiences in the same establishment or a common category of establishments. In this manner, the social template, and optionally data on past purchases, could be used to suggest future purchases, and can use this data across categories of establishments. Thus, if the social signature indicates a purchasing experience of purchasing coffee at a coffee shop, and the past purchase data indicates that the user typically wants a cappuccino based upon the location, time, and acoustic data, the social template is used by the system and might suggest to the user to purchase the cappuccino in any establishment when they are in a category of "coffee shop". 

 Additionally, motion, acceleration, light, temperature, can yield an activity (running, biking, driving, etc.) followed by a food purchase. Then, the next time the activity is pursued, suggestions or coupons related to the food purchase could be offered for a similar purchase. 

The purchase data can be captured and stored in the system in many ways. Illustrative examples include, but are not limited to, the purchase data being captured from direct keying of the purchase into the system or from optical character recognition (OCR) of images of prior receipts, or from a history of online purchases, or from transactions made with a mobile device through an electronic wallet application, or from lists indicating what was purchased. The past purchase data could also come through an interface with purchasing recommendation websites, such as where the user has an affinity or loyalty card with a particular establishment which records past purchases, or has signed onto a particular service which records prior purchase history (such as is available on AMAZON or through ITUNES). Such past purchase data could be segregated according to particular establishments or stores, or could be used for establishments or stores having the same basic categories. In this manner, where a user goes to a new restaurant, the user's past purchases in the same basic restaurant category (i.e., Italian food, fast food, etc.) can be used to predict possible purchases at the new restaurant. 
 In addition to or instead of past purchase data, purchase data could be available based upon third party input, such as is available through social networking sites and/or recommendation websites such as YELP or OPENTABLE. Additionally, purchase data can be made available based upon third party input, such as one or more of a user's contacts, general community, and/or general population having made certain purchases or identification of relative purchase trends/fads, which may influence a user's purchase decision. Conversely, purchase data indicating that no one has made a certain purchase may influence a user's purchase decision. Thus, even when a user has not purchased anything at a particular location, purchase data can be used as part of the social signature which then allows the system to provide suggestions according to a particular social template.