JPH07262167A - Forecasting system for response money amount - Google Patents
Forecasting system for response money amountInfo
- Publication number
- JPH07262167A JPH07262167A JP4846594A JP4846594A JPH07262167A JP H07262167 A JPH07262167 A JP H07262167A JP 4846594 A JP4846594 A JP 4846594A JP 4846594 A JP4846594 A JP 4846594A JP H07262167 A JPH07262167 A JP H07262167A
- Authority
- JP
- Japan
- Prior art keywords
- response amount
- catalog
- response
- amount
- predicted
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000010586 diagram Methods 0.000 description 7
- 238000005070 sampling Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
【０００１】[0001]
【産業上の利用分野】本発明は、カタログを顧客に送付
するときのレスポンス金額を予測するレスポンス金額予
測システムに関するものである。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a response amount prediction system for estimating a response amount when a catalog is sent to a customer.
【０００２】[0002]
【従来の技術】通信販売業は、昨年同時期に配布したカ
タログの実績をもとにカタログのレスポンス金額（受注
金額）およびレスポンス率（受注した顧客の割合）を予
測する。昨年と今年とでは景気・気候その他の違いがあ
り、昨年と同様の傾向を示すとは限らない。より正確な
予測をするために、大手通信販売業の多くは、本誌カタ
ログを発行する前に顧客の一部に対してプレカタログを
発刊している。このプレカタログを顧客先に送付してそ
のときの受注実績を参照して管理者が長年の感で今年の
本誌カタログの送付数などを決めていた。2. Description of the Related Art The mail-order business predicts the catalog response amount (order amount) and response rate (ratio of customers who have received an order) based on the results of catalogs distributed at the same time last year. Due to economic, climate, and other differences between last year and this year, the same trends as last year may not always be exhibited. To make more accurate forecasts, many large mail-order businesses publish a pre-catalog to some of their customers before publishing this catalog. This pre-catalog was sent to the customer and the order record at that time was referenced, and the administrator decided the number of times this year's catalog was sent, etc. with a long time sense.
【０００３】[0003]
【発明が解決しようとする課題】このため、正確なレス
ポンス金額を予測することができず、昨年と今年のプレ
カタログを送付したときの受注実績をもとにより正確な
レスポンス金額を予測する手段の提供が望まれていた。Therefore, it is not possible to predict an accurate response amount, and a means of predicting an accurate response amount based on the actual orders received when the pre-catalog of the last year and this year was sent. It was desired to be provided.
【０００４】本発明は、これらの問題を解決するため、
過去と今回のプレカタログによるレスポンス金額（実
績）をもとに調整係数ｋを算出し、過去の顧客情報から
作成した回帰式などに予測対象の顧客情報を当てはめて
予測レスポンス金額を算出して当該調整係数ｋを加味し
た精度の高い予測レスポンス金額を算出し、カタログ送
付数を決定することを目的としている。The present invention solves these problems.
The adjustment coefficient k is calculated based on the response amounts (actual results) from the past and current pre-catalogs, and the estimated response amount is calculated by applying the forecast customer information to the regression formula created from the past customer information. The purpose is to calculate a highly accurate predicted response amount in consideration of the adjustment coefficient k and determine the number of catalogs to be sent.
【０００５】[0005]
【課題を解決するための手段】図１は、本発明の原理ブ
ロック図を示す。図１において、レスポンス金額予測シ
ステム１は、過去の顧客情報６および予測対象の顧客情
報７をもとに予測レスポンス金額を算出し、これに過去
のプレカタログおよび予測対象のプレカタログのレスポ
ンス金額（実績）をもとに予測レスポンス金額を調整す
るものであって、レスポンス金額予測手段２、調整係数
算出手段３、および予測レスポンス金額算出手段４から
構成されるものである。FIG. 1 shows a block diagram of the principle of the present invention. In FIG. 1, the response amount prediction system 1 calculates a predicted response amount based on the customer information 6 in the past and the customer information 7 in the prediction target, and the calculated response amount in the past pre-catalog and the prediction target pre-catalog ( The predicted response amount is adjusted based on the (actual result), and is composed of a response amount prediction unit 2, an adjustment coefficient calculation unit 3, and a predicted response amount calculation unit 4.
【０００６】レスポンス金額予測手段２は、過去の顧客
情報６をもとに作成した式６１に、予測対象の顧客情報
７を当てはめて予測レスポンス金額を算出するものであ
る。調整係数算出手段３は、今回のプレカタログのレス
ポンス金額（実績）９を過去のプレカタログのレスポン
ス金額（実績）８の差に対応する調整係数ｋを算出する
ものである。The response amount predicting means 2 calculates the predicted response amount by applying the customer information 7 to be predicted to the equation 61 created based on the customer information 6 in the past. The adjustment coefficient calculation means 3 calculates an adjustment coefficient k corresponding to the difference between the response amount (actual result) 9 of the current pre-catalog and the response amount (actual result) 8 of the past pre-catalog.
【０００７】予測レスポンス金額算出手段４は、算出し
た予測対象の予測レスポンス金額に対して、調整係数ｋ
を加味した予測レスポンス金額を算出するものである。
過去の顧客情報６は、過去の顧客の受注実績などの顧客
情報である。The predicted response amount calculation means 4 adjusts the calculated predicted response amount of the prediction target by the adjustment coefficient k.
The expected response amount is calculated by adding the above.
The past customer information 6 is customer information such as past customer orders.
【０００８】予測対象の顧客情報７は、予測対象の顧客
情報である。過去のプレカタログのレスポンス金額８
は、過去のプレカタログを送付したときの顧客からのレ
スポンス金額（実績）である。The customer information 7 to be predicted is customer information to be predicted. Past pre-catalog response amount 8
Is the response amount (actual result) from the customer when the past pre-catalog was sent.
【０００９】予測対象のプレカタログのレスポンス金額
９は、予測対象のプレカタログを送付したときの顧客か
らのレスポンス金額（実績）である。予測レスポンス金
額１０は、予測対象の本誌カタログを送付するときの予
測したレスポンス金額である。The response amount 9 of the prediction target pre-catalog is the response amount (actual result) from the customer when the prediction target pre-catalog is sent. The predicted response amount of money 10 is the predicted amount of response when sending the subject catalog of the prediction target.
【００１０】[0010]
【作用】本発明は、図１に示すように、レスポンス金額
予測手段２が過去の顧客情報６をもとに作成した式６１
（例えば回帰式６１）に、予測対象の顧客情報７を当て
はめて予測レスポンス金額を算出し、調整係数算出手段
３が今回のプレカタログのレスポンス金額（実績）９を
過去のプレカタログのレスポンス金額（実績）８の差に
対応する調整係数ｋを算出し、予測レスポンス金額算出
手段４が算出した予測対象の予測レスポンス金額に対し
て調整係数ｋを加味した予測レスポンス金額を算出する
ようにしている。In the present invention, as shown in FIG. 1, the equation 61 created by the response amount predicting means 2 based on the customer information 6 in the past.
(For example, the regression equation 61) is applied to the customer information 7 to be predicted to calculate the predicted response amount, and the adjustment coefficient calculation means 3 sets the response amount (actual) 9 of the current pre-catalog to the response amount of the past pre-catalog ( The adjustment coefficient k corresponding to the difference of (actual result) 8 is calculated, and the predicted response amount of money in which the adjustment coefficient k is added to the predicted response amount of the prediction target calculated by the predicted response amount calculation means 4 is calculated.
【００１１】また、レスポンス金額予測手段２が過去の
顧客情報６をもとに作成した式６１（例えば回帰式６
１）に、予測対象の顧客情報７を当てはめて予測レスポ
ンス金額を算出し、調整係数算出手段３が今回のプレカ
タログのレスポンス金額（実績）９を過去のプレカタロ
グのレスポンス金額（実績）８で除算した調整係数ｋを
算出し、予測レスポンス金額算出手段４が算出した予測
対象の予測レスポンス金額に対して調整係数ｋを乗算し
た予測レスポンス金額を算出するようにしている。Further, the equation 61 (for example, the regression equation 6) created by the response amount predicting means 2 based on the customer information 6 in the past.
The predicted response amount is calculated by applying the target customer information 7 to 1), and the adjustment coefficient calculating means 3 uses the response amount (actual) 9 of the pre-catalog of this time as the response amount (actual) 8 of the past pre-catalog. The divided adjustment coefficient k is calculated, and the predicted response amount calculated by the predicted response amount calculation means 4 is multiplied by the adjustment coefficient k to calculate the predicted response amount.
【００１２】この際、過去のプレカタログの送付時期
と、今回のプレカタログの送付時期をほぼ同じにして、
変動要因を正確に予測するようにしている。従って、過
去と今回のプレカタログによるレスポンス金額（実績）
をもとに調整係数ｋを算出し、過去の顧客情報から作成
した回帰式６１などに予測対象の顧客情報を当てはめて
予測レスポンス金額を算出して当該調整係数ｋを加味し
た精度の高い予測レスポンス金額を算出することによ
り、より正確な予測レスポンス金額を算出してカタログ
送付数を決定することが可能となる。At this time, the delivery time of the past pre-catalog and the delivery time of this pre-catalog are made almost the same,
We try to accurately predict fluctuation factors. Therefore, the response amount (actual) by the past and this pre-catalog
The adjustment coefficient k is calculated based on the above, and the predicted response amount is calculated by applying the customer information of the prediction target to the regression equation 61 created from the past customer information and the highly accurate predicted response in which the adjustment coefficient k is added. By calculating the amount of money, it becomes possible to calculate a more accurate predicted response amount and determine the number of catalogs to be sent.
【００１３】[0013]
【実施例】次に、図２から図４を用いて本発明の実施例
の構成および動作を順次詳細に説明する。DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Next, the construction and operation of an embodiment of the present invention will be described in detail with reference to FIGS.
【００１４】まず、図２を用いてカタログ送付の全体の
動作を説明する。図２は、カタログ送付の全体説明図を
示す。図２において、Ｓ１は、カタログ企画する。これ
は、通信販売しようとする商品名、価格などを掲載する
カタログの企画をする。First, the entire operation of sending a catalog will be described with reference to FIG. FIG. 2 shows an overall explanatory diagram of sending a catalog. In FIG. 2, S1 plans a catalog. This plans a catalog that lists the product name, price, etc. to be mail-ordered.
【００１５】Ｓ２は、カタログ作成する。Ｓ３は、プレ
カタログを発刊する。これは、今年の本誌カタログを発
刊する前に、前年のプレカタログを発刊したと同一時期
に今年のプレカタログを発刊し、顧客からの受注を受
け、レスポンス金額およびレスポンス率などを算出し、
前年と今年との差（変動要因を数量的に把握する）を計
測するためのものである。At step S2, a catalog is created. S3 publishes a pre-catalog. This is before the publication of this year's magazine catalog, this year's pre-catalog was published at the same time as the previous year's pre-catalog was published, received orders from customers, calculated response amount and response rate, etc.
This is to measure the difference between the previous year and this year (by grasping quantitative factors of fluctuations).
【００１６】Ｓ４は、調整係数を算出する。これは、Ｓ
３で今年のプレカタログを発刊したときの顧客から受注
を受け、集計したレスポンス金額（実績）と、前年の同
一時期のレスポンス金額（実績）との差、ここでは、今
年のレスポンス金額を前年のレスポンス金額で除算した
値（調整係数ｋ）を算出するものである（図３および図
４を用いて後述する）。In step S4, the adjustment coefficient is calculated. This is S
The difference between the response amount (actual) collected from the customer when the pre-catalog of this year was published in 3 and the response amount (actual) at the same period of the previous year, here, the response amount of this year is The value (adjustment coefficient k) divided by the response amount is calculated (described later with reference to FIGS. 3 and 4).
【００１７】Ｓ５は、レスポンス金額を予測する。これ
は、前年の本誌カタログ時の顧客情報をもとに予測した
予測対象の今年の本誌カタログ時の予測レスポンス金額
に対して、Ｓ４で算出した調整係数ｋを乗算したレスポ
ンス金額を予測する（図３および図４を用いて後述す
る）。In step S5, the response amount is predicted. This is to predict the response amount by multiplying the predicted response amount of this year's catalog of this year, which is the forecast target based on the customer information in the previous year's catalog, by the adjustment coefficient k calculated in S4 (Fig. 3 and FIG. 4).
【００１８】Ｓ６は、顧客抽出する。これは、Ｓ５で予
測した今年の本誌カタログ時の予測レスポンス金額をも
とに、例えば所定予測レスポンス金額以上の本誌カタロ
グを送付する顧客を抽出する。At step S6, customers are extracted. For example, based on the predicted response amount for this year's magazine catalog predicted in S5, for example, the customers who send the catalog for the magazine with a predetermined predicted response amount or more are extracted.
【００１９】Ｓ７は、ＤＭラベル発行する。これは、Ｓ
６で抽出した顧客の住所、氏名などをＤＭ（ダイレクト
メール）のラベルに印刷する。Ｓ８は、カタログ送付す
る。これは、Ｓ７でラベルを貼りつけた、あるいは顧客
の住所、氏名を印刷した封筒などを郵送する。In step S7, a DM label is issued. This is S
The customer's address, name, etc. extracted in 6 are printed on the DM (direct mail) label. In S8, the catalog is sent. For this, an envelope or the like with the label attached in S7 or the customer's address and name printed is mailed.
【００２０】Ｓ９は、発注を行う。これは、Ｓ８のカタ
ログを送付したことに対応して、カタログに掲載した商
品が間に合うように発注する。以上のカタログ送付の流
れの中で、Ｓ３からＳ５のレスポンス金額予測を行って
最適な顧客にカタログを送付する場合について以下図３
および図４を用いて詳細に説明する。At step S9, an order is placed. This corresponds to the sending of the catalog in S8, and orders the products listed in the catalog in time. In the flow of the above catalog sending, the case of predicting the response amount from S3 to S5 and sending the catalog to the most suitable customer will be described below with reference to FIG.
And it demonstrates in detail using FIG.
【００２１】図３および図４は、本発明の動作説明図を
示す。図３において、Ｓ１１は、過去の顧客情報６を準
備する。これは、予測しようとする対象と同時期の前年
の顧客情報６として、右側に記載した顧客情報を準備す
る。ここでは、例えば顧客Ａについて、図示のように、 ・顧客Ａ ・９３年春号カタログ送付時 ９３年春号カタログに
対する受注実績 ・最新購入日（ＸA1） ４３００円（ＹA） ・累計購入回数（ＸA2） ・最新購入金額（ＸA3） ・・・ ・顧客Ｂ ・９３年春号カタログ送付時 ９３年春号カタログに
対する受注実績 ・最新購入日（ＸB1） ０円（ＹB） ・累計購入回数（ＸB2） ・最新購入金額（ＸB3） ・・・ Ｓ１２は、サンプリング（ランダム）する。これは、Ｓ
１１の過去の顧客情報６からランダムにサンプリングす
る。FIG. 3 and FIG. 4 are diagrams for explaining the operation of the present invention. In FIG. 3, S11 prepares the past customer information 6. This prepares the customer information described on the right side as the customer information 6 of the previous year at the same time as the target to be predicted. Here, for example, for customer A, as shown in the figure, and customer A · 93 years Spring edition catalog Orders Results and latest purchase date for the 93-year spring issue catalog when sending (X A1) 4300 yen (Y A) · total number of purchases (X A2 ) ・ Latest purchase price (X A3 ) ・ ・ ・ ・ Customer B ・ When sending the Spring 1993 catalog ・ Orders for the Spring 1993 catalog ・ Latest purchase date (X B1 ) 0 yen (Y B ) ・ Cumulative purchase count (X B2 ) ・ Latest purchase amount (X B3 ) ... S12 is sampled (random). This is S
Random sampling is performed from 11 pieces of past customer information 6.
【００２２】Ｓ１３は、ランダムにサンプリングした後
の過去の顧客情報６である。Ｓ１４は、統計解析（数量
化理論１類）を行う。これは、右側に記載したように、
回帰式（全体に対して１つの式）を図示の下記のように
作成する。S13 is past customer information 6 after random sampling. In S14, statistical analysis (quantification theory type 1) is performed. This is as described on the right
A regression equation (one equation for the whole) is created as shown below.
【００２３】 Ｙ＝ａ0 ＋ａ1Ｘ1＋ａ2Ｘ2＋ａ3Ｘ3＋・・・ そして、過去の受注実績などを代入してａ0、ａ1、
ａ2、ａ3・・・を求める。Ｓ１５は、過去のレスポンス
金額を保存する。これは、Ｓ１３の過去の顧客情報６の
うちの過去の受注実績を、過去のレンスポンス金額とし
て保存したものである。Y = a 0 + a 1 X 1 + a 2 X 2 + a 3 X 3 + ... Then, by substituting past order results and the like, a 0 , a 1 ,
seek a 2, a 3 ···. S15 stores the past response amount. This is the past order record of the past customer information 6 in S13 stored as the past response amount.
【００２４】Ｓ１６は、予測対象の顧客情報７を準備す
る。ここでは、９４年春号カタログ送付時点の予測対象
の顧客情報を準備する。Ｓ１７は、レスポンス金額の予
測を行う。これは、右側に記載したように、Ｓ１４で求
めた回帰式６１に、予測対象の顧客情報７を当てはめ、
顧客毎の予測レスポンス金額を求める。例えば右側に図
示のように、顧客ａ、ｂ・・・に対して、回帰式６１に
予測対象の顧客情報７中の当該顧客ａ、ｂ・・・の情報
を当てはめて予測レスポンス金額をそれぞれ求める。In step S16, the customer information 7 to be predicted is prepared. Here, the customer information to be predicted at the time of sending the spring 1994 catalog is prepared. In S17, the response amount is predicted. As described on the right side, this applies the customer information 7 of the prediction target to the regression equation 61 obtained in S14,
Calculate the expected response amount for each customer. For example, as shown on the right side, for the customers a, b, ..., Apply the information of the customers a, b, ... .
【００２５】 Ｙa＝ａ0 ＋ａ1Ｘa1＋ａ2Ｘa2＋ａ3Ｘ3＋・・・ Ｙb＝ａ0 ＋ａ1Ｘb1＋ａ2Ｘb2＋ａbＸ3＋・・・ ・・・ Ｓ１８は、Ｓ１７で求めた、予測対象の顧客の予測レス
ポンス金額を保存したものである。Y a = a 0 + a 1 X a1 + a 2 X a2 + a 3 X 3 + ... Y b = a 0 + a 1 X b1 + a 2 X b2 + a b X 3 + ... , S17, the predicted response amount of the prediction target customer is stored.
【００２６】図４において、Ｓ１９は、過去のプレカタ
ログのレスポンス金額（実績）である。これは、前年の
本誌カタログ発刊前のプレカタログを発刊してそのとき
の顧客からのレスポンス金額（実績）を保存したもので
ある。In FIG. 4, S19 is the response amount (actual result) of the past pre-catalog. This is a publication of a pre-catalog before the publication of the main catalog of the previous year, and the response amount (actual result) from the customer at that time is stored.
【００２７】Ｓ２０は、今年の予測対象のプレカタログ
のレスポンス金額（実績）である。これは、今年の予測
対象の本誌カタログ発刊前のプレカタログを発刊してそ
のときの顧客からのレスポンス金額（実績）を保存した
ものである。S20 is the response amount (actual result) of the pre-catalog of this year's forecast target. This is a pre-catalog published before the publication of this year's catalog, which is the forecast target of this year, and the response amount (actual result) from the customer at that time is saved.
【００２８】Ｓ２１は、調整係数を算出する。これは、
右側に記載したように、 を算出する。これは、Ｓ２０で保存した予測対象のプレ
カタログのレスポンス金額（実績）をＳ１９で保存した
過去のプレカタログのレスポンス金額（実績）で除算し
て調整係数ｋを算出したものである。In step S21, the adjustment coefficient is calculated. this is,
As mentioned on the right, To calculate. This is an adjustment coefficient k calculated by dividing the response amount (actual) of the pre-catalog of the prediction target saved in S20 by the response amount (actual) of the past pre-catalog saved in S19.
【００２９】Ｓ２２は、調整係数ｋを加味した予測レス
ポンス金額を算出する。これは、Ｓ１８で保存した、予
測対象の今年の本誌カタログの予測対象の予測レスポン
ス金額に、Ｓ２１で算出した調整係数ｋを加味、例えば
乗算して変動要因を加味した予測レスポンス金額を算出
する。In S22, the predicted response amount including the adjustment coefficient k is calculated. This is performed by adding the adjustment coefficient k calculated in S21 to the predicted response amount of the prediction target of this year's magazine catalog stored in S18, for example, multiplying the predicted response amount to calculate the predicted response amount.
【００３０】Ｓ２３は、Ｓ２２で算出した最終版の予測
レスポンス金額を保存したものである。Ｓ２４は、Ｓ２
３で保存した本誌カタログの調整係数ｋを加味した予測
レスポンス金額をもとにカタログ送付数の決定を行う。
例えば予測レスポンス金額が１０，０００円以上の顧客
（顧客数）に本誌カタログを送付すると決定する。In step S23, the final version predicted response amount calculated in step S22 is stored. S24 is S2
The number of catalogs to be sent is determined based on the predicted response amount in consideration of the adjustment coefficient k of the magazine catalog stored in 3.
For example, it is determined that the catalog of the magazine is to be sent to customers (the number of customers) whose estimated response amount is 10,000 yen or more.
【００３１】以上によって、今年の同時期のプレカタロ
グのレスポンス金額（実績）を過去の同時期のプレカタ
ログのレスポンス金額（実績）で除算した調整係数ｋを
求め、前年の本誌カタログの顧客情報（実績）をもとに
作成した回帰式６１に今年の予測対象の顧客情報を当て
はめて予測レスポンス金額を算出し、これに調整係数ｋ
を乗算して今年の変動要因を加味した予測レスポンス金
額（最終版）を算出し、この予測レスポンス金額（最終
版）をもとに本誌カタログの送付数を決定する。これら
により、前年と今年の変動要因を加味したより正確な予
測レスポンス金額を算出し、的確なカタログ送付数を決
定することが可能となる。From the above, the adjustment coefficient k obtained by dividing the response amount (actual) of the pre-catalog for the same period of this year by the response amount (actual) of the pre-catalog for the same period in the past is obtained, and the customer information of the catalog of the previous year ( The estimated response amount is calculated by applying the customer information for this year's forecast to the regression equation 61 created based on the
Multiply by to calculate the forecast response amount (final version) that takes into account the factors of change this year, and determine the number of catalogs to be sent based on this forecast response amount (final version). From these, it becomes possible to calculate a more accurate predicted response amount in consideration of the fluctuation factors of the previous year and this year, and to determine the correct number of catalogs to be sent.
【００３２】[0032]
【発明の効果】以上説明したように、本発明によれば、
過去の顧客情報６をもとに作成した回帰式６１に予測対
象の顧客情報７を当てはめて予測レスポンス金額を算出
し、この予測レスポンス金額に、前年と今年の同時期の
プレカタログのレスポンス金額（実績）から算出した調
整係数ｋを乗算して変動要因を加味した予測レスポンス
金額を求める構成を採用しているため、変動要因を加味
したより正確なレスポンス金額を予測ることができる。
そして、この算出した予測レスポンス金額を参照して、
予測レスポンス金額が例えば上位から８０％までの人数
の顧客にカタログを送付すると容易に決定できる。As described above, according to the present invention,
The forecast response amount is calculated by applying the forecast target customer information 7 to the regression equation 61 created based on the past customer information 6, and the response amount of the pre-catalog of the same period of the previous year and this year ( Since the configuration is adopted in which the predicted response amount including the variation factor is obtained by multiplying the adjustment coefficient k calculated from the (actual result), a more accurate response amount including the variation factor can be predicted.
Then, referring to the calculated predicted response amount,
The expected response amount can be easily determined by sending the catalog to customers having the highest 80% of response amounts.
【図１】本発明の原理ブロック図である。FIG. 1 is a principle block diagram of the present invention.
【図２】カタログ送付の全体説明図である。FIG. 2 is an overall explanatory diagram of sending a catalog.
【図３】本発明の動作説明図（続く）である。FIG. 3 is an operation explanatory diagram (continued) of the present invention.
【図４】本発明の動作説明図（続き）である。FIG. 4 is an operation explanatory diagram (continuation) of the present invention.
１：レスポンス金額予測システム ２：レスポンス金額予測手段 ３：調整係数算出手段 ４：予測レスポンス金額算出手段 ６：過去の顧客情報 ６１：回帰式 ７：予測対象の顧客情報 ８：過去のプレカタログのレスポンス金額（実績） ９：予測対象のプレカタログのレスポンス金額（実績） １０：予測レスポンス金額 1: Response amount prediction system 2: Response amount prediction means 3: Adjustment coefficient calculation means 4: Predicted response amount calculation means 6: Past customer information 61: Regression formula 7: Prediction target customer information 8: Past pre-catalog response Amount (actual) 9: Response amount of the pre-catalog of the prediction target (actual) 10: Predicted response amount
Claims (3)
ス金額を予測するレスポンス金額予測システムにおい
て、 過去の顧客情報（６）をもとに作成した式（６１）に、
予測対象の顧客情報（７）を当てはめて予測レスポンス
金額を算出するレスポンス金額予測手段（２）と、 今回のプレカタログのレスポンス金額（実績）（９）と
過去のプレカタログのレスポンス金額（実績）（８）と
の差に対応する調整係数ｋを算出する調整係数算出手段
（３）と、 上記算出した予測レスポンス金額に対して上記調整係数
ｋを加味した予測レスポンス金額を算出する予測レスポ
ンス金額算出手段（４）とを備えたことを特徴とするレ
ンスポンス金額予測システム。1. In a response amount prediction system for predicting a response amount when a catalog is sent to a customer, an equation (61) created based on past customer information (6) is added to
Response amount predicting means (2) for calculating the predicted response amount by applying the customer information (7) to be predicted, the response amount (actual) of this pre-catalog (9) and the response amount (actual) of the past pre-catalog An adjustment coefficient calculation means (3) for calculating an adjustment coefficient k corresponding to the difference from (8), and a predicted response amount calculation for calculating the predicted response amount in which the adjustment factor k is added to the calculated predicted response amount. A response amount forecasting system comprising means (4).
ス金額を予測するレスポンス金額予測システムにおい
て、 過去の顧客情報（６）をもとに作成した式（６１）に、
予測対象の顧客情報（７）を当てはめて予測レスポンス
金額を算出するレスポンス金額予測手段（２）と、 今回のプレカタログのレスポンス金額（実績）（９）を
過去のプレカタログのレスポンス金額（実績）（８）で
除算した調整係数ｋを算出する調整係数算出手段（３）
と、 上記算出した予測レスポンス金額に上記調整係数ｋを乗
算した予測レスポンス金額を算出する予測レスポンス金
額算出手段（４）とを備えたことを特徴とするレンスポ
ンス金額予測システム。2. A response amount prediction system for predicting a response amount when a catalog is sent to a customer, wherein a formula (61) created based on past customer information (6)
Response amount predicting means (2) for applying the customer information (7) of the prediction target to calculate the predicted response amount, and the response amount (actual) of this pre-catalog (9) to the response amount (actual) of the past pre-catalog Adjustment coefficient calculation means (3) for calculating the adjustment coefficient k divided by (8)
And a predicted response amount calculation means (4) for calculating a predicted response amount calculated by multiplying the calculated predicted response amount by the adjustment coefficient k.
回のプレカタログの送付時期をほぼ同じにして、変動要
因を正確に予測することを特徴とする請求項１および請
求項２に記載のレスポンス金額予測システム。3. The variable factor is accurately predicted by making the delivery time of the past pre-catalog and the delivery time of the current pre-catalog almost the same. Response amount prediction system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4846594A JP3400068B2 (en) | 1994-03-18 | 1994-03-18 | Response amount prediction system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4846594A JP3400068B2 (en) | 1994-03-18 | 1994-03-18 | Response amount prediction system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH07262167A true JPH07262167A (en) | 1995-10-13 |
| JP3400068B2 JP3400068B2 (en) | 2003-04-28 |
Family
ID=12804129
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP4846594A Expired - Fee Related JP3400068B2 (en) | 1994-03-18 | 1994-03-18 | Response amount prediction system |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JP3400068B2 (en) |
Cited By (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014508336A (en) * | 2011-03-18 | 2014-04-03 | ザ ニールセン カンパニー （ユー エス） エルエルシー | Method and apparatus for identifying media impressions |
| US9215288B2 (en) | 2012-06-11 | 2015-12-15 | The Nielsen Company (Us), Llc | Methods and apparatus to share online media impressions data |
| US9237138B2 (en) | 2013-12-31 | 2016-01-12 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US9313294B2 (en) | 2013-08-12 | 2016-04-12 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
| US9519914B2 (en) | 2013-04-30 | 2016-12-13 | The Nielsen Company (Us), Llc | Methods and apparatus to determine ratings information for online media presentations |
| US9596151B2 (en) | 2010-09-22 | 2017-03-14 | The Nielsen Company (Us), Llc. | Methods and apparatus to determine impressions using distributed demographic information |
| US9838754B2 (en) | 2015-09-01 | 2017-12-05 | The Nielsen Company (Us), Llc | On-site measurement of over the top media |
| US9852163B2 (en) | 2013-12-30 | 2017-12-26 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
| US9912482B2 (en) | 2012-08-30 | 2018-03-06 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US9953330B2 (en) | 2014-03-13 | 2018-04-24 | The Nielsen Company (Us), Llc | Methods, apparatus and computer readable media to generate electronic mobile measurement census data |
| US10045082B2 (en) | 2015-07-02 | 2018-08-07 | The Nielsen Company (Us), Llc | Methods and apparatus to correct errors in audience measurements for media accessed using over-the-top devices |
| US10068246B2 (en) | 2013-07-12 | 2018-09-04 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
| US10147114B2 (en) | 2014-01-06 | 2018-12-04 | The Nielsen Company (Us), Llc | Methods and apparatus to correct audience measurement data |
| US10205994B2 (en) | 2015-12-17 | 2019-02-12 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
| US10270673B1 (en) | 2016-01-27 | 2019-04-23 | The Nielsen Company (Us), Llc | Methods and apparatus for estimating total unique audiences |
| US10311464B2 (en) | 2014-07-17 | 2019-06-04 | The Nielsen Company (Us), Llc | Methods and apparatus to determine impressions corresponding to market segments |
| US10333882B2 (en) | 2013-08-28 | 2019-06-25 | The Nielsen Company (Us), Llc | Methods and apparatus to estimate demographics of users employing social media |
| US10380633B2 (en) | 2015-07-02 | 2019-08-13 | The Nielsen Company (Us), Llc | Methods and apparatus to generate corrected online audience measurement data |
| US10803475B2 (en) | 2014-03-13 | 2020-10-13 | The Nielsen Company (Us), Llc | Methods and apparatus to compensate for server-generated errors in database proprietor impression data due to misattribution and/or non-coverage |
| US10956947B2 (en) | 2013-12-23 | 2021-03-23 | The Nielsen Company (Us), Llc | Methods and apparatus to measure media using media object characteristics |
| US10963907B2 (en) | 2014-01-06 | 2021-03-30 | The Nielsen Company (Us), Llc | Methods and apparatus to correct misattributions of media impressions |
| US11381860B2 (en) | 2014-12-31 | 2022-07-05 | The Nielsen Company (Us), Llc | Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information |
| US11562394B2 (en) | 2014-08-29 | 2023-01-24 | The Nielsen Company (Us), Llc | Methods and apparatus to associate transactions with media impressions |
| US12015681B2 (en) | 2010-12-20 | 2024-06-18 | The Nielsen Company (Us), Llc | Methods and apparatus to determine media impressions using distributed demographic information |
-
1994
- 1994-03-18 JP JP4846594A patent/JP3400068B2/en not_active Expired - Fee Related
Cited By (78)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9596151B2 (en) | 2010-09-22 | 2017-03-14 | The Nielsen Company (Us), Llc. | Methods and apparatus to determine impressions using distributed demographic information |
| US11144967B2 (en) | 2010-09-22 | 2021-10-12 | The Nielsen Company (Us), Llc | Methods and apparatus to determine impressions using distributed demographic information |
| US10504157B2 (en) | 2010-09-22 | 2019-12-10 | The Nielsen Company (Us), Llc | Methods and apparatus to determine impressions using distributed demographic information |
| US11682048B2 (en) | 2010-09-22 | 2023-06-20 | The Nielsen Company (Us), Llc | Methods and apparatus to determine impressions using distributed demographic information |
| US12015681B2 (en) | 2010-12-20 | 2024-06-18 | The Nielsen Company (Us), Llc | Methods and apparatus to determine media impressions using distributed demographic information |
| JP2014508336A (en) * | 2011-03-18 | 2014-04-03 | ザ ニールセン カンパニー （ユー エス） エルエルシー | Method and apparatus for identifying media impressions |
| JP2015212959A (en) * | 2011-03-18 | 2015-11-26 | ザ ニールセン カンパニー （ユーエス） エルエルシー | Method and apparatus for identifying media impression |
| JP2017152006A (en) * | 2011-03-18 | 2017-08-31 | ザ ニールセン カンパニー （ユー エス） エルエルシー | Method and apparatus for identifying media impression |
| US9497090B2 (en) | 2011-03-18 | 2016-11-15 | The Nielsen Company (Us), Llc | Methods and apparatus to determine an adjustment factor for media impressions |
| US12010191B2 (en) | 2012-06-11 | 2024-06-11 | The Nielsen Company (Us), Llc | Methods and apparatus to share online media impressions data |
| US9215288B2 (en) | 2012-06-11 | 2015-12-15 | The Nielsen Company (Us), Llc | Methods and apparatus to share online media impressions data |
| US10063378B2 (en) | 2012-08-30 | 2018-08-28 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US10778440B2 (en) | 2012-08-30 | 2020-09-15 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US11870912B2 (en) | 2012-08-30 | 2024-01-09 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US9912482B2 (en) | 2012-08-30 | 2018-03-06 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US11483160B2 (en) | 2012-08-30 | 2022-10-25 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US11792016B2 (en) | 2012-08-30 | 2023-10-17 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US11669849B2 (en) | 2013-04-30 | 2023-06-06 | The Nielsen Company (Us), Llc | Methods and apparatus to determine ratings information for online media presentations |
| US12093973B2 (en) | 2013-04-30 | 2024-09-17 | The Nielsen Company (Us), Llc | Methods and apparatus to determine ratings information for online media presentations |
| US10192228B2 (en) | 2013-04-30 | 2019-01-29 | The Nielsen Company (Us), Llc | Methods and apparatus to determine ratings information for online media presentations |
| US9519914B2 (en) | 2013-04-30 | 2016-12-13 | The Nielsen Company (Us), Llc | Methods and apparatus to determine ratings information for online media presentations |
| US10643229B2 (en) | 2013-04-30 | 2020-05-05 | The Nielsen Company (Us), Llc | Methods and apparatus to determine ratings information for online media presentations |
| US11410189B2 (en) | 2013-04-30 | 2022-08-09 | The Nielsen Company (Us), Llc | Methods and apparatus to determine ratings information for online media presentations |
| US10937044B2 (en) | 2013-04-30 | 2021-03-02 | The Nielsen Company (Us), Llc | Methods and apparatus to determine ratings information for online media presentations |
| US11205191B2 (en) | 2013-07-12 | 2021-12-21 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
| US11830028B2 (en) | 2013-07-12 | 2023-11-28 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
| US10068246B2 (en) | 2013-07-12 | 2018-09-04 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
| US10552864B2 (en) | 2013-08-12 | 2020-02-04 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
| US9928521B2 (en) | 2013-08-12 | 2018-03-27 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
| US11222356B2 (en) | 2013-08-12 | 2022-01-11 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
| US9313294B2 (en) | 2013-08-12 | 2016-04-12 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
| US11651391B2 (en) | 2013-08-12 | 2023-05-16 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
| US10333882B2 (en) | 2013-08-28 | 2019-06-25 | The Nielsen Company (Us), Llc | Methods and apparatus to estimate demographics of users employing social media |
| US11496433B2 (en) | 2013-08-28 | 2022-11-08 | The Nielsen Company (Us), Llc | Methods and apparatus to estimate demographics of users employing social media |
| US10956947B2 (en) | 2013-12-23 | 2021-03-23 | The Nielsen Company (Us), Llc | Methods and apparatus to measure media using media object characteristics |
| US9852163B2 (en) | 2013-12-30 | 2017-12-26 | The Nielsen Company (Us), Llc | Methods and apparatus to de-duplicate impression information |
| US10846430B2 (en) | 2013-12-31 | 2020-11-24 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US9237138B2 (en) | 2013-12-31 | 2016-01-12 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US9979544B2 (en) | 2013-12-31 | 2018-05-22 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US11562098B2 (en) | 2013-12-31 | 2023-01-24 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US9641336B2 (en) | 2013-12-31 | 2017-05-02 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions and search terms |
| US10147114B2 (en) | 2014-01-06 | 2018-12-04 | The Nielsen Company (Us), Llc | Methods and apparatus to correct audience measurement data |
| US11727432B2 (en) | 2014-01-06 | 2023-08-15 | The Nielsen Company (Us), Llc | Methods and apparatus to correct audience measurement data |
| US10963907B2 (en) | 2014-01-06 | 2021-03-30 | The Nielsen Company (Us), Llc | Methods and apparatus to correct misattributions of media impressions |
| US12073427B2 (en) | 2014-01-06 | 2024-08-27 | The Nielsen Company (Us), Llc | Methods and apparatus to correct misattributions of media impressions |
| US11068927B2 (en) | 2014-01-06 | 2021-07-20 | The Nielsen Company (Us), Llc | Methods and apparatus to correct audience measurement data |
| US11887133B2 (en) | 2014-03-13 | 2024-01-30 | The Nielsen Company (Us), Llc | Methods and apparatus to generate electronic mobile measurement census data |
| US12045845B2 (en) | 2014-03-13 | 2024-07-23 | The Nielsen Company (Us), Llc | Methods and apparatus to compensate for server-generated errors in database proprietor impression data due to misattribution and/or non-coverage |
| US9953330B2 (en) | 2014-03-13 | 2018-04-24 | The Nielsen Company (Us), Llc | Methods, apparatus and computer readable media to generate electronic mobile measurement census data |
| US10217122B2 (en) | 2014-03-13 | 2019-02-26 | The Nielsen Company (Us), Llc | Method, medium, and apparatus to generate electronic mobile measurement census data |
| US11037178B2 (en) | 2014-03-13 | 2021-06-15 | The Nielsen Company (Us), Llc | Methods and apparatus to generate electronic mobile measurement census data |
| US11568431B2 (en) | 2014-03-13 | 2023-01-31 | The Nielsen Company (Us), Llc | Methods and apparatus to compensate for server-generated errors in database proprietor impression data due to misattribution and/or non-coverage |
| US10803475B2 (en) | 2014-03-13 | 2020-10-13 | The Nielsen Company (Us), Llc | Methods and apparatus to compensate for server-generated errors in database proprietor impression data due to misattribution and/or non-coverage |
| US11854041B2 (en) | 2014-07-17 | 2023-12-26 | The Nielsen Company (Us), Llc | Methods and apparatus to determine impressions corresponding to market segments |
| US10311464B2 (en) | 2014-07-17 | 2019-06-04 | The Nielsen Company (Us), Llc | Methods and apparatus to determine impressions corresponding to market segments |
| US11068928B2 (en) | 2014-07-17 | 2021-07-20 | The Nielsen Company (Us), Llc | Methods and apparatus to determine impressions corresponding to market segments |
| US11562394B2 (en) | 2014-08-29 | 2023-01-24 | The Nielsen Company (Us), Llc | Methods and apparatus to associate transactions with media impressions |
| US11983730B2 (en) | 2014-12-31 | 2024-05-14 | The Nielsen Company (Us), Llc | Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information |
| US11381860B2 (en) | 2014-12-31 | 2022-07-05 | The Nielsen Company (Us), Llc | Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information |
| US10380633B2 (en) | 2015-07-02 | 2019-08-13 | The Nielsen Company (Us), Llc | Methods and apparatus to generate corrected online audience measurement data |
| US11259086B2 (en) | 2015-07-02 | 2022-02-22 | The Nielsen Company (Us), Llc | Methods and apparatus to correct errors in audience measurements for media accessed using over the top devices |
| US10045082B2 (en) | 2015-07-02 | 2018-08-07 | The Nielsen Company (Us), Llc | Methods and apparatus to correct errors in audience measurements for media accessed using over-the-top devices |
| US11706490B2 (en) | 2015-07-02 | 2023-07-18 | The Nielsen Company (Us), Llc | Methods and apparatus to correct errors in audience measurements for media accessed using over-the-top devices |
| US11645673B2 (en) | 2015-07-02 | 2023-05-09 | The Nielsen Company (Us), Llc | Methods and apparatus to generate corrected online audience measurement data |
| US10785537B2 (en) | 2015-07-02 | 2020-09-22 | The Nielsen Company (Us), Llc | Methods and apparatus to correct errors in audience measurements for media accessed using over the top devices |
| US12015826B2 (en) | 2015-07-02 | 2024-06-18 | The Nielsen Company (Us), Llc | Methods and apparatus to correct errors in audience measurements for media accessed using over-the-top devices |
| US10368130B2 (en) | 2015-07-02 | 2019-07-30 | The Nielsen Company (Us), Llc | Methods and apparatus to correct errors in audience measurements for media accessed using over the top devices |
| US9838754B2 (en) | 2015-09-01 | 2017-12-05 | The Nielsen Company (Us), Llc | On-site measurement of over the top media |
| US11272249B2 (en) | 2015-12-17 | 2022-03-08 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
| US10205994B2 (en) | 2015-12-17 | 2019-02-12 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
| US10827217B2 (en) | 2015-12-17 | 2020-11-03 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
| US11785293B2 (en) | 2015-12-17 | 2023-10-10 | The Nielsen Company (Us), Llc | Methods and apparatus to collect distributed user information for media impressions |
| US11232148B2 (en) | 2016-01-27 | 2022-01-25 | The Nielsen Company (Us), Llc | Methods and apparatus for estimating total unique audiences |
| US11971922B2 (en) | 2016-01-27 | 2024-04-30 | The Nielsen Company (Us), Llc | Methods and apparatus for estimating total unique audiences |
| US10270673B1 (en) | 2016-01-27 | 2019-04-23 | The Nielsen Company (Us), Llc | Methods and apparatus for estimating total unique audiences |
| US11562015B2 (en) | 2016-01-27 | 2023-01-24 | The Nielsen Company (Us), Llc | Methods and apparatus for estimating total unique audiences |
| US10536358B2 (en) | 2016-01-27 | 2020-01-14 | The Nielsen Company (Us), Llc | Methods and apparatus for estimating total unique audiences |
| US10979324B2 (en) | 2016-01-27 | 2021-04-13 | The Nielsen Company (Us), Llc | Methods and apparatus for estimating total unique audiences |
Also Published As
| Publication number | Publication date |
|---|---|
| JP3400068B2 (en) | 2003-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP3400068B2 (en) | Response amount prediction system | |
| Alfares | Inventory model with stock-level dependent demand rate and variable holding cost | |
| JP3698433B2 (en) | Safety stock quantity calculation device, safety stock quantity calculation program and order point calculation device | |
| Tan et al. | Modelling imperfect advance demand information and analysis of optimal inventory policies | |
| JP4154373B2 (en) | Power supply business risk management support system | |
| US20060265310A1 (en) | Realtime transparent commodity index and trading database | |
| Nguyen et al. | Capacity and lead-time management when demand for service is seasonal and lead-time sensitive | |
| Reiner et al. | Demand forecasting for supply processes in consideration of pricing and market information | |
| US20020194071A1 (en) | Reward system and apparatus used therefor | |
| JP2006221315A (en) | Management method of purchase cost difference and sales margin profit amount interlocking with exchange fluctuation | |
| JP4921572B2 (en) | Bond characteristic calculation system and spread change rate calculation system | |
| JPH04135271A (en) | Merchandise ordering determination device | |
| Behfard et al. | Last Time Buy and repair decisions for fast moving parts | |
| US8122448B2 (en) | Estimation method and system | |
| JP2023133469A (en) | Information visualization processing apparatus, information visualization processing system, information visualization processing method, and information visualization processing computer program | |
| JPH09198448A (en) | System and device for automatically ordering commodity | |
| JP7144945B2 (en) | Sales forecast simulation device, sales forecast simulation method and sales forecast simulation program | |
| JP6622678B2 (en) | Information processing apparatus, information processing method, and program | |
| JPH07239884A (en) | Purchasing and ordering system | |
| Chen et al. | Lease expiration management for a single lease term in the apartment industry | |
| CN114626660A (en) | Method and apparatus for surge regulation forecasting | |
| JP2023071296A (en) | Coupon value quantifying program, recording medium, coupon value quantifying method and coupon value quantifying device | |
| JP7033325B2 (en) | Information visualization processing device, information visualization processing system, information visualization processing method, and information visualization processing computer program | |
| JP3400069B2 (en) | Response number prediction system | |
| JP2022133829A (en) | Coupon value quantification program, coupon value quantification method, and coupon value quantification device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20030128 |
|
| FPAY | Renewal fee payment (event date is renewal date of database) |
Free format text: PAYMENT UNTIL: 20080221 Year of fee payment: 5 |
|
| FPAY | Renewal fee payment (event date is renewal date of database) |
Free format text: PAYMENT UNTIL: 20090221 Year of fee payment: 6 |
|
| FPAY | Renewal fee payment (event date is renewal date of database) |
Free format text: PAYMENT UNTIL: 20090221 Year of fee payment: 6 |
|
| FPAY | Renewal fee payment (event date is renewal date of database) |
Free format text: PAYMENT UNTIL: 20100221 Year of fee payment: 7 |
|
| FPAY | Renewal fee payment (event date is renewal date of database) |
Free format text: PAYMENT UNTIL: 20110221 Year of fee payment: 8 |
|
| FPAY | Renewal fee payment (event date is renewal date of database) |
Free format text: PAYMENT UNTIL: 20110221 Year of fee payment: 8 |
|
| FPAY | Renewal fee payment (event date is renewal date of database) |
Free format text: PAYMENT UNTIL: 20120221 Year of fee payment: 9 |
|
| LAPS | Cancellation because of no payment of annual fees |