【０００１】[0001]
【産業上の利用分野】本発明は使用者の個性を学習させ
ることによって使用者の必要性に合致する情報源を選び
出す機能を持つ自動情報源選択装置に関するものであ
る。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an automatic information source selecting device having a function of selecting an information source that meets the needs of the user by learning the individuality of the user.
【０００２】[0002]
【従来の技術】情報端末機器では、使用者によりアクセ
ス可能な情報源が一義的に構造化されている。従って、
このような情報源をアクセスする使用者は、例えば階層
構造を持つメニュー方式などによって、所望の情報源を
選択していた。特に、これまでの放送受信装置において
は、新聞や雑誌などの情報からその情報源である番組内
容を判断するか、実際の番組を見ながらチャンネルを変
えることにより、受信者自身が所望の番組（情報源）を
選択していた。2. Description of the Related Art In an information terminal device, an information source accessible by a user is uniquely structured. Therefore,
A user who accesses such an information source selects a desired information source by, for example, a menu system having a hierarchical structure. In particular, in the conventional broadcast receiving apparatus, the receiver himself / herself desires the desired program (by judging the content of the program as the information source from information such as newspapers and magazines or changing the channel while watching the actual program). Source) was selected.
【０００３】[0003]
【発明が解決しようとする課題】しかしながら、従来の
方法による番組選択を行なった場合、番組情報を常に把
握・参照したり、他の番組を常に見る必要があり、時間
的な制約があったり、煩雑である。また、新聞・雑誌等
の情報媒体からの番組情報からだけではその番組の内容
を的確に判断できない場合もある。つまり、自分にとっ
て重要な情報を漏らさずに選び出すことは非常に困難で
ある。また、番組情報の持つ意味は同一の言葉でも個人
によってその解釈が異なり、利用者個人の関心に合致し
た番組情報を選択する方法が求められていたが、多様な
番組情報の意味を統合して記述する方法がなかったた
め、実用化されていなかった。However, when the program is selected by the conventional method, it is necessary to always grasp and refer to the program information and to watch other programs at all times. It is complicated. In addition, there are cases where the content of the program cannot be accurately determined only from the program information from information media such as newspapers and magazines. In other words, it is very difficult to select information that is important to you without leaking it. Also, even if the meaning of program information is the same, the interpretation differs depending on the individual, and there has been a demand for a method of selecting program information that matches the individual interests of the user. It was not put to practical use because there was no way to describe it.
【０００４】本発明は上記の問題点に鑑みてなされたも
のであり、多様な情報源の中から使用者が関心のある情
報源を自動的に抽出することができるようにして、使用
者を情報源選択のための情報検索作業から解放し、使用
者が必要としている情報源の見落しの可能性を小さくす
る自動情報源選択装置を提供することを目的とする。The present invention has been made in view of the above problems, and enables a user to automatically extract an information source of interest from a variety of information sources, and An object of the present invention is to provide an automatic information source selecting device which is free from the information retrieval work for selecting the information source and reduces the possibility of overlooking the information source required by the user.
【０００５】[0005]
【課題を解決するための手段】上記の目的を達成するた
めに、本発明による自動情報源選択装置は、複数の情報
源データを記憶してあるデータベースと、前記データベ
ースに記憶された情報源データの内、使用者が選択した
情報源データの履歴から使用者の関心内容を学習する学
習手段と、前記学習手段による学習内容に基づいて使用
者の関心に合致する情報源データに対応する情報源を自
動的に選択する選択手段とを備える。In order to achieve the above object, an automatic information source selecting device according to the present invention includes a database storing a plurality of information source data, and information source data stored in the database. Among these, learning means for learning the user's interest content from the history of the information source data selected by the user, and the information source corresponding to the information source data matching the user's interest based on the learning content by the learning means. And selecting means for automatically selecting.
【０００６】[0006]
【作用】上記の構成において、過去に使用者が選択した
情報源の内容を記憶し、使用者の個性との間に経験的な
関係付けを行い、使用者の関心に合致した情報源を自動
的に選択する。In the above structure, the contents of the information source selected by the user in the past are memorized, the empirical relation is established with the individuality of the user, and the information source which matches the interest of the user is automatically generated. To choose.
【０００７】[0007]
【実施例】以下に添付の図面を参照して本発明の好適な
実施例を説明する。DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT A preferred embodiment of the present invention will be described below with reference to the accompanying drawings.
【０００８】＜実施例１＞本実施例では、情報源として
テレビ、ラジオ等の公共放送による番組を例にとって説
明する。<Embodiment 1> In the present embodiment, an information source will be described by taking a program broadcast by public broadcasting such as television and radio as an example.
【０００９】図１は実施例の番組セレクタを示す。オー
トモード選択スイッチ１がオフの場合、記憶・自動選択
動作は行われず、チャンネルを指示する入力部２の信号
に基づいて、操作部３を経由してチャンネル選択動作部
４にチャンネル選択信号が送られ、番組の選択が実施さ
れる。FIG. 1 shows a program selector of the embodiment. When the auto mode selection switch 1 is off, the memory / automatic selection operation is not performed, and the channel selection signal is sent to the channel selection operation unit 4 via the operation unit 3 based on the signal of the input unit 2 indicating the channel. Then, the program is selected.
【００１０】オートモード選択スイッチ１がオンの場
合、学習モードと自動選択モードが一定の時間間隔で交
互に行われる。学習モードの場合、次のように記憶動作
が行われる。まず、学習データ入力経路８を経由する操
作部３からのチャンネルデータが記憶部７へ送られる。
記憶部７は図２に示すようなニューラルネット回路を有
している。番組データベース５には既に見た番組情報が
放送時間帯とともに記憶されている。そして、この番組
データベース５からは番組情報が、時計６からは視聴時
間データが記憶部７へ送られる。これらのデータを基
に、これまで見た番組情報（番組名、番組ジャンル、テ
ーマ、出演者、イベントｅｔｃ．）を入力可能な形に因
子化し、記憶部７の対応するニューロンを発火させる。
また、その番組を実際に視聴した時間がサンプリング回
数頻度の形で入力される。この入力データの発火が図２
における出力層１３の使用者の関心度ニューロンを発火
させるよう記憶部７のニューロン結合の重み付けを変化
させることで記憶を形成させる。When the automatic mode selection switch 1 is turned on, the learning mode and the automatic selection mode are alternately performed at regular time intervals. In the learning mode, the storage operation is performed as follows. First, the channel data from the operation unit 3 via the learning data input path 8 is sent to the storage unit 7.
The storage unit 7 has a neural network circuit as shown in FIG. In the program database 5, the program information that has already been seen is stored together with the broadcasting hours. Then, program information is sent from the program database 5 and viewing time data is sent from the clock 6 to the storage unit 7. Based on these data, the program information (program name, program genre, theme, performer, event etc.) seen so far is factored into an inputtable form, and the corresponding neuron in the storage unit 7 is fired.
In addition, the time when the program is actually viewed is input in the form of sampling frequency. The firing of this input data is shown in Figure 2.
The memory is formed by changing the weighting of the neuron connection of the storage unit 7 so as to fire the degree-of-interest neuron of the user of the output layer 13 in FIG.
【００１１】自動選択モードの場合、この学習が終わっ
たニューラルネット回路を持つ記憶部７に、自動選択モ
ードを作動させる時間帯における全番組の情報を、順次
番組データベース５から入力する。これら入力された全
番組データの中から、記憶部７にある使用者の関心度ニ
ューロンの発火が強いものを選ぶことで、使用者の関心
が強い番組を選択することが可能である。そして、選択
した結果をチャンネル選択動作部４に判断データ出力経
路９を経由して伝達し、放送中あるいは開始直前の番組
の選択の場合はチャンネル変更の指示をしたり、選択し
た番組が放送時間帯に映るように予約設定したり、選択
された番組の情報を表示部１０に表示したりする。In the automatic selection mode, information about all programs in the time zone in which the automatic selection mode is activated is sequentially input from the program database 5 to the storage unit 7 having the neural network circuit for which learning has been completed. It is possible to select a program with a strong interest from the user by selecting, from all the inputted program data, one in which the degree of interest neurons of the user in the storage unit 7 are strong. Then, the selected result is transmitted to the channel selection operation unit 4 via the judgment data output path 9, and when the program being broadcast or just before the start is selected, a channel change instruction is given, or the selected program is broadcast time. Reservation is set so that it appears in the band, and information on the selected program is displayed on the display unit 10.
【００１２】図２は学習を行うニューラルネットの結合
の様子を示すモデルの１例を示す図である。ニューラル
ネットのモデルとして、ここでは多層型のモデルを使用
する。これは番組情報を記述するｎ個の入力層１１、ｍ
個の中間層１２、１個の出力層１３から成る。記憶の学
習は入力データに対して出力層１３にある使用者の関心
度ニューロンが強く発火するように入力層１１と中間層
１２の間及び中間層１２と出力層１３の間のニューロン
結合の重み付けを変更することで実行される。このよう
にして使用者の番組選択に関する個性が記憶部７に記憶
される。FIG. 2 is a diagram showing an example of a model showing the state of connection of neural networks for learning. A multi-layer model is used here as a model of the neural network. This is the n input layers 11, m that describe the program information.
It comprises one intermediate layer 12 and one output layer 13. The learning of the memory is performed by weighting the neuron connections between the input layer 11 and the intermediate layer 12 and between the intermediate layer 12 and the output layer 13 so that the user's degree of interest neurons in the output layer 13 fire strongly for the input data. It is executed by changing the. In this way, the personality regarding the user's program selection is stored in the storage unit 7.
【００１３】以上のようにして、本実施例は使用者が選
んだ番組のうち、意味のレベルが異なる内容を学習する
手段としてニューラルネットを設けることにより、番組
を選択するうえで多様な番組情報から形成される使用者
の個性を装置に記憶させたものである。ニューラルネッ
トは数値化して相関をとることが困難なデータ群の関係
をブラックボックス化して学習を行い関係付けをするも
ので、論理的な構造化や一般的な関係付けが困難な人間
の個性にかかわるようなデータを取り扱う時に有効な手
段である。このニューラルネットにより、これまで困難
であった互いに関連を持ちながらその関連が論理的に記
述できない多様な番組情報の意味を学習できるようにな
り、使用者の個性の記述が可能となるのである。As described above, according to the present embodiment, the neural network is provided as a means for learning the contents of different meaning levels among the programs selected by the user, so that various program information can be selected for selecting the programs. The personality of the user formed from is stored in the device. A neural network is a method of black-boxing the relationships of a group of data that is difficult to quantify and correlate, and performs the relationships to establish relationships. It is an effective means when dealing with related data. With this neural network, it becomes possible to learn the meanings of various program information, which have been difficult until now, but the relationships cannot be logically described, and it becomes possible to describe the user's individuality.
【００１４】以上説明してきたように本実施例１によれ
ば、ヒューマンテクノロジーの観点から、番組情報の意
味を利用者個人の関心に合致した形で、経験的な関係付
けを行うニューラルネットを用いて、これまでに使用者
が選んだ番組の情報を記憶させることによって、誰もが
簡単に自分が興味を持つ番組に自動的にアクセスできる
装置が提供される。As described above, according to the first embodiment, from the viewpoint of human technology, a neural network for empirically relating the meaning of program information to a user's individual interest is used. Thus, by storing the information of the program selected by the user so far, an apparatus is provided in which anyone can easily and automatically access the program of his / her interest.
【００１５】＜実施例２＞実施例２では、学習手段とし
てエキスパートシステムを用いる。この場合学習後の記
憶に該当するのは知識ベースであり、番組選択履歴デー
タから知識ベースを構築するソフトウェア・ツールを用
意する。記憶の用途と作用については実施例１と同様で
ある。ただしこの場合、履歴データの項目を絞り、その
頻度によって他の項目とは無関係に記憶内容を構成する
ため、実施例１の簡易版となる。<Second Embodiment> In the second embodiment, an expert system is used as a learning means. In this case, the knowledge base corresponds to the memory after learning, and a software tool for constructing the knowledge base from the program selection history data is prepared. The use and operation of the memory are the same as in the first embodiment. However, in this case, since the items of the history data are narrowed down and the stored contents are configured irrespective of other items depending on the frequency, the simplified version of the first embodiment is obtained.
【００１６】以上説明してきたように本実施例によれ
ば、多様な番組の中から自分が関心ある番組を自動的に
抽出することができ、番組選択の情報検索から解放さ
れ、かつ、特別に番組検索に労力をかけることなく見落
としの可能性を小さくする番組選択装置が提供される。As described above, according to this embodiment, a program of interest to oneself can be automatically extracted from various programs, which is freed from the information search for program selection and specially performed. Provided is a program selection device that reduces the possibility of overlooking without making efforts to search programs.
【００１７】尚、上記の実施例においては、情報源とし
てテレビ、ラジオ等の公共放送を例にとって説明してあ
るがこれに限られるものではない。In the above embodiment, public broadcasting such as television and radio has been described as an information source, but the information source is not limited to this.
【００１８】また、本発明は、複数の機器から構成され
るシステムに適用しても１つの機器から成る装置に適用
しても良い。また、本発明は、システム或は装置にプロ
グラムを供給することによって達成される場合にも適用
できることはいうまでもない。Further, the present invention may be applied to a system composed of a plurality of devices or an apparatus composed of a single device. Further, it goes without saying that the present invention can be applied to the case where it is achieved by supplying a program to a system or an apparatus.
【００１９】[0019]
【発明の効果】以上説明してきたように本発明によれ
ば、多様な情報源の中から使用者が関心のある情報源を
自動的に抽出することができ、情報源選択のための情報
検索作業から解放され、見落としの可能性を小さくする
効果がある。As described above, according to the present invention, the information source of interest to the user can be automatically extracted from various information sources, and the information search for selecting the information source can be performed. It is free from work and has the effect of reducing the possibility of oversight.
【図１】番組セレクタの構成を示すブロック図である。FIG. 1 is a block diagram showing a configuration of a program selector.
【図２】多層型ニューラルネットのモデル例を表す図で
ある。FIG. 2 is a diagram illustrating a model example of a multilayer neural network.
１ オートモード選択スイッチ ２ 入力部 ３ 操作部 ４ チャンネル選択動作部 ５ 番組データベース ６ 時計 ７ 記憶部 ８ 学習データ入力経路 ９ 判断データ出力経路 １０ 使用者の個性に合う番組を選択した結果を示す表
示部 １１ ｎ個の入力ニューロンからなる入力層 １２ ｍ個のニューロンからなる中間層 １３ １個の出力ニューロンからなる出力層1 Auto mode selection switch 2 Input section 3 Operation section 4 Channel selection operation section 5 Program database 6 Clock 7 Storage section 8 Learning data input path 9 Judgment data output path 10 Display section showing the result of selecting a program that matches the individuality of the user 11 Input layer consisting of n input neurons 12 Intermediate layer consisting of m neurons 13 Output layer consisting of 1 output neuron
───────────────────────────────────────────────────── フロントページの続き (72)発明者 松岡 宏 東京都大田区下丸子３丁目30番２号 キヤ ノン株式会社内 (72)発明者 小俣 智司 東京都大田区下丸子３丁目30番２号 キヤ ノン株式会社内 ─────────────────────────────────────────────────── ─── Continuation of front page (72) Inventor Hiroshi Matsuoka 3-30-2 Shimomaruko, Ota-ku, Tokyo Canon Inc. (72) Inventor Satoshi Omata 3-30-2 Shimomaruko, Ota-ku, Tokyo Canon Within the corporation
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4113483AJPH05314186A (en) | 1992-05-06 | 1992-05-06 | Automatic information source selector |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4113483AJPH05314186A (en) | 1992-05-06 | 1992-05-06 | Automatic information source selector |
| Publication Number | Publication Date |
|---|---|
| JPH05314186Atrue JPH05314186A (en) | 1993-11-26 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP4113483AWithdrawnJPH05314186A (en) | 1992-05-06 | 1992-05-06 | Automatic information source selector |
| Country | Link |
|---|---|
| JP (1) | JPH05314186A (en) |
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