Amusing Patents – Holiday or Otherwise

In keeping with the American spirit and imagination, here are a few imaginative patents, some holiday and some not

US 1466559A. Head Exerciser. Filed by Charles G. Purdy.

“It is well known that modern methods of preparing food usually result in a cooked food requiring little or no mastication. The use of such foods results in decayed teeth, undeveloped jaws, and various other complications due solely to the lack of exercise attendant on proper mastication.

It is the primary object of the invention to provide a device the use of which will supply the necessary exercise to the muscles of these several organs to keep said organs in a healthy state, and operate as a preventative agent and curative medicine in case of pyorrhea.”

US 0299533A1. Naughty or Nice Meter. Filed by Frank C. Orsini.

“The Naughty or Nice Meter is a visual novelty toy designed to let Children know where they stand on a ‘naughty or nice’ meter.  Originally conceived for Christmas, the Naughty or Nice Meter has widespread applications and can be used for other seasons of the year or events.”

US 8046266B1. Customizing Gift Instrument Experiences for Recipients. Filed by Michal Jonathan Geller, Terrance Douglas Hanold. Current Assignee: Amazon Technologies, Inc.

“Personalizing a gift instrument by customizing the presentation and redemption for a gift instrument recipient. A gift instrument purchaser or other data source associates customization information such as redemption recommendations, restrictions, or other content with the gift instrument based on the gift instrument recipient. The customization information is presented to the gift instrument recipient upon redemption of the gift instrument. In an embodiment, information about the redemption by the gift instrument recipient is provided to the gift instrument purchaser.”

US 8060463B1. Mining of User Data to Identify Users with Common Interests. Filed by Joel R. Spiegel. Current Assignee: Amazon Technologies, Inc.

“A computer-implemented matching service matches users to other users, and/or to user communities, based at least in part on a computer analysis of event data reflective of user behaviors. The event data may, for example, evidence user affinities for particular items represented in an electronic catalog, such as book titles, music titles, movie titles, and/or other types of items that tend to reflect the traits of users. Event data reflective of other types of user actions, such as item-detail-page viewing events, browse node visits, search query submissions, and/or web browsing patterns may additionally or alternatively be considered. By taking such event data into consideration, the matching service reduces the burden on users to explicitly supply personal profile information, and reduces poor results caused by exaggerations and other inaccuracies in such profile information.

To generate people-search results of the type depicted in FIG. 2B, the matching service 62 may take into consideration a wide range of different types of recorded user activity and behaviors. For example, as mentioned above, the matching service may take into consideration each user’s item purchases, item rentals, item viewing activities, item rating activities, search query submissions, and/or web browsing activities. The matching service may additionally or alternatively take into consideration other types of information about each user, including, by way of illustration and not of limitation, any one or more of the following: (1) gift purchases made by other users for this particular user; (2) the gift wrap used by such other users when purchasing gifts for this user, such as when the gift wrap evidences the user’s religion (in the case of Christmas or Hanukkah gift wrap, for example); (3) the time of day at which the user typically engages in online activity; (4) the location (e.g., city) or locations from which the user accesses the matching service 62 or otherwise engages in online activity, as may be determined reasonably accurately based on IP addresses associated with the user computing devices; (5) any blogs, RSS feeds, email newsletters, and/or other content channels to which the user subscribes; (6) the user’s item rating profile, which may be collected, e.g., by systems that provide functionality for users to rate particular items represented in an electronic catalog; (7) the user’s “reputation” for supplying high quality item reviews or other content as determined based on votes cast by other users, as described, for example, in U.S. application Ser. No. 09/715,929, filed Nov. 17, 2000 and Ser. No. 10/640,512, filed Aug. 13, 2003, the disclosures of which are hereby incorporated by reference; (8) the user’s residence location, as determined or inferred based on the shipping or credit card address supplied by the user for purposes of conducting online transactions; (9) the company or type of company the user likely works for, as determined or inferred from the user’s email address (e.g., the email address likely identifies a user who works for Apple Computer); (10) the user’s travel preferences, as determined based on online ticket purchases; (11) the user’s cell phone usage; (12) the user’s music download history; (13) news articles selected by the user for viewing online; (14) the television programs selected by the user via an online television programming guide used to program digital video recorders; (15) the other matching service participants selected by this user to contact or read about; (16) specific communities selected by the user to join or read about; (17) information about how the user has redeemed loyalty points associated with a credit card, frequency flyer program, or other loyalty program; (18) the user’s preferences for particular types or clusters of search results on search results pages, (19) the user’s credit card transactions. Data regarding these and other types of user behaviors may be collected, and incorporated into the matching process, via automated processes such that the users need not affirmatively perform any action to supply the matching service with such information.”


Industries Served

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  • Automotive
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  • Environmental
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  • Government
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  • Life Sciences
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