News Release

  1. Mar 2013
    Acquisition announcement of Media Click, Inc. bussiness
    AIM Corporation(Kanagawa, Japan CEO: Yasumitsu Watanabe) has acquired all business of Media Click, Inc. in February 28, 2013.

    Media Click have obtained many track records and high evaluation from the customer company with music information service.

    Especially Media Click song descriptor database(MCDB) has the track record adopted as much Car Navigation System.

    Media Click have also performed positively service plan and development relevant to them.

    On the other hand, although AIM is developing embedded software for consumer electronics, AIM is also concentrating on the entertainment information service enterprise as one of the marketing strategies.

    AIM will develop new services based on our technologies and Media Click's music information services.
  2. May 2012
    "Book & Writer Feature Amount Generating System" and its Feature Amount began providing data
    Aim Corporation (Kanagawa, Japan CEO: Yasumitsu Watanabe) launched "Book & Writer Feature Amount Generating System" and began providing services and licenses for Feature Amount data generated by the system on May 9, 2012.
    The Book Feature Amount is the numerical value rated as multidimensional information after reviewing and analyzing a book.
    The Writer Feature Amount is the numerical information quantified for features of an author based on the characteristics of his or her works.
    Book Feature Amount and Writer Feature Amount can be used for sales statistics and classification in EC sites, as well as recommendations and similarity searches not contained within the genre.
    Currently Book Feature Amount for approximately 150,000 individual novels and 23,000 individual comic books and Writer Feature Amount for approximately 7,000 authors have been generated.

    ■Plans for future updates
    • Expansion into other genres as well as picture books and poetry
    • Hierarchical classification of Feature Amount
    • Interactive search with other contents
    • Enhancement of external links
    • Generation of a writers correlation map
  3. June 2010
    Kinokuniya adopted "NEXTe" to improve satisfaction of their DVD/CD shopping site "Forest Plus" users
    -"DVD Recommendation For You" function which learns likes and tastes of each user and recommends DVD-
    Kinokuniya Company Ltd (President: Masashi Takai) adopted the Learning Inference Engine "NEXTe" developed and sold by AIM Corporation (President: Yasumitsu Watanabe) as the "DVD Recommendation For You" function which offers products which are most suitable for likes and tastes of members in DVD/CD shopping site "Forest Plus" managed by Kinokuniya, and launched the full-scale operation of the service.

    Kinokuniya and AIM has collaborated to conduct demonstration experiments of recommending DVD suitable for the member's likes and tastes since December 2009 and the high click ratio of recommended DVD and the growth in sales made them decide to adopt the Learning Inference Engine "NEXTe" and launch the full-scale operation of the service. With this time's decision to launch the full-scale operation, Kinokuniya will continue making efforts to improve satisfaction of "Forest Plus" members by offering recommendation of the most suitable DVD.



    *About "Forest Plus" site
    Specialty shopping site of CD and DVD by Kinokuniya Company Ltd
    http://forest.kinokuniya.co.jp/


    *** Comment by Masaya Seo, department manger of store sales, MD development, and DVD/CD online marketing in Kinokuniya Company Ltd ***
    Recommendation function in EC site is now becoming essential, and I was not satisfied with the past methods like collaborative filtering to deal with products whose purchase tendency significantly reflects personal likes and tastes like DVD.
    In contrast, NEXTe shows personalized recommendation results based on customers' purchase and browsing history and the function which can be freely customized for websites and products is unavailable in a package service. Personal likes and tastes vary with time, and the NEXTe feels the changes and automatically recommends the most suitable products.
    These are the reasons why the NEXTe was adopted.

    *About learning inference engine NEXTe
    http://www.aim-inc.co.jp/en/service/reason.html
  4. January 2010
    Full length Ring Song site(Chaku-Uta Full site) “GIGA Music Full” adopted NEXTe
    -Launched a recommendation service using Learning Inference Engine NEXTe for mobile website-
    Faith Wonderworks, Inc. (President: Shin-ichi Yoshida) and Aim Corporation (President: Yasumitsu Watanabe) launched a recommendation service for mobile phones, using Learning Inference Engine NEXTe.

    The service launched this time provides users of Full length Ring Song site(Chaku-Uta Full site) "GIGA Music Full", that has various music contents from old hit songs to the latest, with personalized recommend information by analyzing their listening/purchase history and features of music.
    NEXTe can provide recommend information in real-time by learning customer preferences, and contributes to improvement of user experiences.

    In this service, to generate Feature Amounts NEXTe uses code sequence or tempo by applying waveform analysis to music, and repeat learning customer preferences in consideration of their listening and purchase history. This method, first in the industry, NEXTe makes it possible to provide the best recommendation.

    *About "GIGA MUSIC Full" site
    Full length Ring Song site(Chaku-Uta Full site)
    http://www.giga.co.jp/musicfull/(PC)
    http://gmsc.giga.co.jp/jsp/top.jsp(mobile)

    *About learning inference engine NEXTe
    http://www.aim-inc.co.jp/en/service/reason.html
  5. December 2009
    DVD / CD shopping site "Forest Plus" by Kinokuniya adopted NEXTe
    -Launched a recommendation service using Learning Inference Engine "NEXTe"-
    Kinokuniya Company Ltd (President: Masashi Takai) and AIM Corporation (President: Yasumitsu Watanabe) temporarily started a recommendation service using Learning Inference Engine "NEXTe".

    The service offers members of "Forest Plus" suitable recommend information of CD or DVD. The aim of this service is to improve conversion rate of "Forest Plus" site and customer satisfaction. This service is designed to offer each members the personalized recommend information in real-time in consideration of their purchase and browsing history, to accomplish the purpose.

    Learning Inference Engine "NEXTe" utilizes neural network to infer customer preferences.
    A neural network is a technology that imitates operations of a human brain and is therefore able to draw conclusions in the same way that a human brain does. Generally speaking human preferences are changeable. DVDs are good example of commodities that are influenced by its changeable human preferences however, NEXTe can recognize changes of preferences and offers more optimized recommendation, by repeating learning automatically.

    *About "Forest Plus" site
    Specialty shopping site of CD and DVD by Kinokuniya Company Ltd
    http://forest.kinokuniya.co.jp/

    *About learning inference engine NEXTe
    http://www.aim-inc.co.jp/en/service/reason.html
  6. December 2009
    Recommendation portal website "recommen.jp" is renewal
    -Improved usability with new features-
    AIM Corporation (President: Yasumitsu Watanabe) added new features to recommendation portal website "recommen.jp" to improve user convenience.

    [Renewal Point]
    The results of analysis and inference of "NEXTe" is displayed graphically to help users knowing their own preferences.
    Graphically indicating information of contents registered to "Favorite Box", the correlation between contents that users registered as favorite and results of recommendation becomes clearer. And it contributes to improvement of reliability of recommendation result.

    [New Features]
    1. Displays distribution and tendency of recommended contents graphically
      Analysis result of contents registered to "Favorite Box" is displayed in distribution chart and also you can see tendencies of recommendation drawn in circle graph.
    2. Graphs of features and tendencies
      Graphs of contents feature, tendencies of favorite contents, and tendencies of multi-recommendation are displayed in detail information of contents at recommendation ranking page.
    3. Display priority setting
      You can change display priority of contents recommendation in recommendation ranking page.
    4. Search by feelings
      You can use some "Feeling Keywords" like "Happy", "Sad" and so on, in searching contents.
      NEXTe find outs contents that matches keywords you selected.

    [About the future development of NEXTe recommendation service]
    These new features will provide users with more comfortable web shopping experience, and will contribute to improvement of website conversion rate and page view counts.
    We will aim at the development of recommendation solution that pursues the user's convenience improvement in the future.

    *"recommen.jp" -Multi Recommendation portal site-
    http://www.recommen.jp/

    *recommen.jp User's guide
    http://www.recommen.jp/help.ap
  7. August 2009
    New generation portal website that achieves multi-recommendation is opened to public.
    -Introduces contents suitable for your taste-
    On 7th August 2009, AIM Corporation (Kawasaki-shi Kanagawa-ken / President: Yasumitsu Watanabe) opened a new portal website "recommen.jp" (http://www.recommen.jp) that implements Learning inference engine "NEXTe".

    "recommen.jp" is a portal website that recommends suitable items for user's tastes in concept of "New discovery" and "Awareness". By learning and analyzing features of contents registered as favorite by users (feature amount)*, NEXTe can recommend contents in real time.

    It is necessary that NEXTe derives the result as humans do by their brains, to learn, to analyze user's tastes and preferences, and to recommend suitable contents. So NEXTe utilizes neural network to do that.

    A neural network is a technology that imitates operations of a human brain and is therefore able to draw conclusions in the same way that a human brain does. For example, although user's tastes are changeable, NEXTe repeats learning in real time to realize its change, and make tendency of recommendation more suitable.

    NEXTe can also infer relatedness of contents in different category, by analyzing tastes and preferences of contents users selected. Taking an example of multi-recommendation, this "Learning inference engine" technology can recommend "music" and "comics" that will matches user's taste by learning features of their favorite "DVDs", and provides users with opportunity to find "New discovery".

    * "Feature amount" is generated by applying text mining to user reviews or explanations of contents, and applying waveform analysis technology to music contents.


    [ About "recommend.jp" website ]
    On this site, NEXTe recommends four different content genres (DVDs, comics, music and Artists) to provide users an opportunity to discover contents that match their tastes.
    As contents thumbnails are hyperlinked to shopping site, users can enjoy shopping after finding new discovery by multi-recommendation.

    *recommen.jp
    http://www.recommen.jp/

    *About learning inference engine NEXTe
    http://www.aim-inc.co.jp/en/service/reason.html
  8. June 2009
    The "Item Mixer" is opened to the public in Senshukai "Bellemaison lab" web site.
    -This tool can find your favorite items-
    Senshukai Co., Ltd (Kita-ku Osaka-shi Osaka / President: Yasuhiro Yukimachi) and AIM Corporation (Kawasaki-shi Kanagawa / President: Yasumitsu Watanabe) launched proof examination that searches "encounter" with items, applying "amount of feature" generation technology by the text mining.

    The text mining based recommend system by which this time we launched proof examination can search items of "My taste" that we feel vaguely and cannot express in words, by adjusting 6 slider switches of each taste. We developed this system as the next generation system.



    [Features of Recommend]
    The system generates amount of feature by analyzing occurrence rate and correlation of words that is divided from item explanation described in catalog or brochure by the morphological analysis technology.
    This technology, which is of learning inference recommendation service NEXTe, analyses similarity of amount of feature in real-time and enables users to get item images by adjusting slider switches.

    As for generated amount of feature, both evaluation of target item by man and result of this technology generates are compared and adjusted to improve accuracy.

    We improve user convenience and activate EC site by the recommend solution that provides the most appropriate information.

    *About learning inference engine NEXTe
    http://www.aim-inc.co.jp/en/service/reason.html
  9. September 2008
    Exhibited the learning inference Engine, "NEXTe" at "CEATEC JAPAN 2008"
    AIM exhibited the learning inference Engine, "NEXTe" at “CEATEC JAPAN 2008” in Makuhari Messe on 30th September 2008.
    "NEXTe" is a recommendation engine that analyzes the user's likes and tastes from the user's behavior. We can get the personalized recommendation function by "NEXTe".

    -Solution introduced by demonstration-

    The user can experience an appropriate recommendation function by this demonstration based on the history for which the user bought or browsed the commodity (CD and DVD, etc.) on EC site. Also, the demonstration expresses as a chart that visualizes the user's intention.
    And, AIM is introducing the solution that collects characteristics of products(CD and DVD, etc) in the demonstration.

  10. July 2007
    C4 Technology, jointly with AIM, has developed a recommendation system based upon the learning inference engine NEXTe (Neural Extract engine)
    C4 Technology Inc. , from Shinagawa Ward, Tokyo, (President: Mitsuo Misumi), and AIM Corporation from Kawasaki City, Kanagawa Prefecture (President: Kaori Watanabe) have been developing the learning inference engine NEXTe (Neural EXTract Engine). Now, using the engine, they have jointly developed a new recommendation system.

    NEXTe is a learning inference engine that learns and infers in real time with a neural network using C4 Technology's original algorithm. Learning the characteristics of individual likes and tastes makes it possible to provide personalized recommendations.

    AIM is a software company that develops a broad range of products, including mobile phones, car navigation equipment, car stereos and home audio sets, and specializes in the development of embedded software technologies for various devices. In addition to setting up the Japanese CDDB (Compact Disc Data Base) server, AIM is now involved in creating content such as music and pronunciation data for music titles (YOMI). It is now providing mainly home appliance manufactures with comprehensive content, technologies and services.

    Now, C4 Technology and AIM have developed M-NEXTe, a music recommendation system, their first joint-development project.
    M-NEXTe, embedded in music players and music distribution sites, is a recommendation system that shows you music suitable to your tastes. It learns from your listening history (how many times a track is played, which category the music belongs to, and what time you listen) and based on characteristics of the music it infers and recommends music you might like. The system accepts any music information, even extracted features, to learn and infer from.

    Now, C4 Technology and AIM are promoting embedding services, based upon the M-NEXTe system, for media player software, car navigation systems, information appliances and music distribution sites.
  11. March 2005
    Media Click Inc. Ltd., and AIM have started a business collaboration to develop entertainment services.
    Media Click Inc. Ltd., from Chiyoda Ward, Tokyo, (President: Ikuo Oota), a total solutions company providing entertainment services for automobiles, and AIM Corporation from Kawasaki City, Kanagawa Prefecture, (President: Kaori Watanabe), with excellent technologies and achievements as an embedded software developer, have reached an agreement for business partnership to develop embedded technologies for entertainment services. They have also established a system for speedy business operations.

    The purpose of this business partnership is to provide manufacturers with Media Click's services more quickly at lower prices. To accomplish this purpose, AIM comprehensively covers both development and embedding operations for standard applications, which makes it much easier to embed entertainment services, deployed by Media Click, into in-vehicle devices such as car navigation systems and car stereos, as well as portable and home audio products in the near future.

    The partnership businesses are:
    1. To standardize the application for embedding Media Click's core product, MCDB (Media Click Database), into hardware devices
    2. To standardize the application for embedding FMdeTITLE, an innovative service that integrates broadcasting systems with information services, into hardware devices
    3. To give support for developing services, and to develop standard applications for embedding the products into devices

    Media Click and AIM are promoting thier businesses together in order to accomplish these purposes, bringing out the best in both companies.
  12. April 13, 2001
    A Business Partnership with Gracenote in the U.S. Is Announced:
    KCDB (Japanese CDDB) and U.S. CDDB integrated
    A Business Partnership with Gracenote in the U.S. Is Announced: KCDB (Japanese CDDB) and U.S. CDDB integrated Gracenote (CDDB LLC) in Berkeley, California, U.S., announced that it has started a business partnership with AIM Corporation in Kawasaki City, Kanagawa Prefecture, (President: Yasumitsu Watanabe). In addition, AIM announced that its music players, "CD Partners" and "What? Audio Player", will contain a new Japanese music database which utilizes the technology of the Gracenote music database, the CCDB (Compact Disc Data Base) and Music Recognition Service. According to the AIM announcement, it will also adopt the Gracenote Japanese music distribution system with Unicode 2-byte character encoding for the three Japanese writing systems: Kanji, Hiragana, and Katakana. The CCDB database has been the industry standard in the music CD recognition market. The announcement said that the KCDB (CDDB server in Japanese) run by AIM would be integrated into the Gracenote CDDB server, along with all Japanese data belonging to KCDB.

    Gracenote (http://www.gracenote.com) develops, manages and provides the CDDB database, the biggest music database in the world. Thanks to the high volume of data and high-level technology, Gracenote has been gaining global attention from media player manufacturers, encoder makers, home appliance manufacturers and recording labels. On top of that, its music recognition technology is evaluated so highly that more than 1000 partner companies currently utilize Gracenote's database and technologies.

    AIM is a software company that has been dealing with the development of systems and programs in various fields, including embedded firmware, dam control systems and building management systems. Furthermore, it has been developing and managing the Japanese KCDB music database, as well as developing media players for KCDB. It also has proprietary rights to the YOMI pronunciation database, which allows users to add audio recognition technology to media players.

    The idea of the business partnership between Gracenote and AIM started with Gracenote's entering into the Japanese market. The partnership, along with the integration of CDDB/KCDB, allows users in Japan of both Japanese and English to make use of the biggest music database and best music recognition technology in the world. With AIM's YOMI Pronunciation Database, the users can fully enjoy the merits of audio recognition technology for music replay as well as sound recognition technology in PC-based products: media players, car navigation systems, and home information appliances.
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