TUESDAY, APRIL 26, 2022
The United States Patent and Trademark Office (USPTO) grants hundreds of new patents every week, showcasing the most exciting developments in technology and innovation.
In this article, we highlight several interesting US patents recently issued by the USPTO.
Looking for more information on patents? Visit our Patents Page here. We also recommend that you check out our Intellectual Property Resources page for guides, videos, and insight on patents and inventorship. We also provide Tips on Choosing a Patent Lawyer.
US PAT. NO. 11,312,415
Volkswagen has been issued a patent for a parking robot for cars. To park the cars, the robot first positions itself under the car using two self-controlled secondary robots. The secondary robots orient themselves under the vehicle’s axel before lifting the vehicle and coordinating with the other secondary robots to position and park the car, not only would this provide a more maneuverable method for parking cars, but the robot would also be compact, allowing it to maneuver through a group of tightly packed cars. With parking always a critical issue, these robots may lead to the more efficient use of parking space, potentially diminishing the need for large lots in the future.
“A parking robot for a transportation vehicle having at least two wheel axles and a method for operating a parking robot. The parking robot includes a main robot part and a secondary robot part which each have a pair of wheel support arms on two opposite sides. The two-part parking robot then moves under the transportation vehicle with respective folded-in wheel support arms and disconnects the secondary robot part from the main robot part to position the main robot part and the secondary robot part each in a region of one of the wheel axles beneath the transportation vehicle and to lift up respective wheels of the respective wheel axle of the transportation vehicle by folding out the respective pairs of wheel support arms.”
PATENT DETAILED DESCRIPTION:
“A parking robot is usually configured to transport a transportation vehicle within a predefined infrastructure environment, for example, a multistory carpark, to a predefined parking positions. To do this, the parking robot moves, for example, at least partially under the transportation vehicle, lifts it up and subsequently moves with the lifted-up motor transportation vehicle to the predefined parking position at which it sets down the transportation vehicle again. By a parking robot, transportation vehicles can therefore be moved fully autonomously, and therefore without the involvement of a driver of the transportation vehicle, within the infrastructure environment, irrespective of whether or not they have, for example, a driver assistance system for at least partially autonomous parking.
IBM has been issued a patent for identifying a brand’s personality based on crowdsourced data. While this may not seem that impactful, this invention relies on natural language processing and the machine learning to help companies determine how their brand may be perceived. As stated in the background section “Brand personality is a key component of brand performance, such as brand identification, brand trust, and brand loyalty”. IBM’s application of natural language processing and machine learning to help companies identify how the public perceives their brand demonstrates new technologies in the marketing field.
“Mechanisms are provided to implement a brand personality inference engine. The mechanisms receive crowdsource information and extract features associated with a brand from the crowdsource information. The crowdsource information comprises natural language content submitted by a plurality of providers to a crowdsource information source. The mechanisms analyze features associated with the brand in accordance with a brand personality model configured to predict a brand personality for the brand based on the features associated with the brand. The mechanisms generate an inferred brand personality data structure, representing a perceived brand personality of providers providing the crowdsource information, and output an output indicating aspects of the perceived brand personality based on the inferred brand personality data structure.”
“The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for performing brand personality inference analysis and generating recommendations as to actions to be performed to achieve a desired brand personality perception based on the brand personality inference analysis.
The term “brand personality,” first introduced by Martineau, “The Personality of the Retail Store,” Harvard Business Review, 36, 1958, pp. 47-55, refers to a set of human characteristics associated with a brand or trademark. A brand has a personality because people tend to associate human attributes with brands. For example, the Apple.TM. brand is considered by many to be a “young” brand while Texas Instruments.TM. is considered by many to be a relatively “old” brand. Within thirty years of Martineau’s introduction to the concept of brand personality, brand personality became widely accepted by both marketing scholars and practitioners as an effective means of business success. Brand personality is a key component of brand performance, such as brand identification, brand trust, and brand loyalty.”
US PAT. NO. 11,315,585
Spotify has been issued a patent for analyzing music, using vector representations. The system extracts characteristics about the music and interprets it into vectors to find other, similar, types of music. For example, this data processing method could be applied to a user’s listening history to analyze and determine what their music preferences are based on the data analysis performed on the extracted vectors from the users listening history.
“A computer receives a first audio content item and applies a process to generate a representation of first audio content item. A portion is extracted from the representation of the first audio content item. A first representative vector that corresponds to the first audio content item is determined by applying a variational autoencoder (VAE) to a first segment of the extracted portion the audio content item. The computer stores the first representative vector that corresponds to the first audio content item.”
“Access to electronic media, such as music and video content, has expanded dramatically over time. As a departure from physical media, media content providers stream media to electronic devices across wireless networks, improving the convenience with which users can digest and experience such content.
Media content streaming platforms provide users with the ability to access content items from large content collections. Navigating through large content collections to determine content of interest can be challenging for users. For example, although a platform may provide information about content items, such as song title, the provided information may be insufficient to help the user decide whether to play back the content. As the amount of media available to users increases, there is a need for systems that reduce the amount of input required from users to obtain content of interest.”