# US 2019/0318369 A1 · Method and device for predicting business volume

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- Number: US 2019/0318369 A1
- Document ID: US20190318369A1
- Status: Published application
- Kind: A1
- Application number: 16/469,894
- Filed: 2017-12-13
- Published: 2019-10-17
- Earliest claimed: 2016-12-15
- Assignees: Koubei Holding Limited
- Applicants: Koubei Holding Limited
- Inventors: CHAOFENG MENG; CHI FAN; HAN LUO; YIHUAN SUI; FANGYU DOU
- Web page: https://patentgrep.com/patent/US20190318369A1

## CPC

- G06Q 30/0202: Market predictions or forecasting for commercial activities
- G06Q 30/0205: {based on location or geographical consideration}
- G06Q 50/12: Hotels or restaurants
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Disclosed are a method and a device for predicting business volume. The method includes: determining a prediction time and a historical time corresponding to the prediction time; acquiring historical payment data for a business provider; determining a number of historical payments for the business provider at the historical time on the basis of the historical payment data; and predicting business volume of the business provider at the prediction time on the basis of the number of historical payments.

## Claims

### Claim 1

A method for predicting business volume, comprising:
determining a prediction time and a historical time corresponding to the prediction time;
acquiring historical payment data for a business provider;
determining a number of historical payments for the business provider at the historical time on the basis of the historical payment data; and
predicting business volume of the business provider at the prediction time on the basis of the number of historical payments.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the determining the historical time corresponding to the prediction time comprises:
determining a plurality of historical time in a plurality of historical time periods corresponding to the prediction time, and
the predicting the business volume of the business provider at the prediction time on the basis of the number of historical payments comprises:
calculating the number of historical payments for the business provider at each of the plurality of historical time according to a preset algorithm, and predicting business volume of the business provider at the prediction time on the basis of a calculation result.

### Claim 3 (depends on 1)

The method according to claim 1, wherein the predicting business volume of the business provider at the prediction time on the basis of the number of historical payments comprises:
acquiring payment data for the business provider at a current time;
determining a number of current payments for the business provider at the current time on the basis of the payment data for the business provider at the current time;
determining a reference time in the historical time period corresponding to the current time;
determining a number of reference payments for the business provider at the reference time on the basis of the historical payment data for the business provider; and
predicting business volume of the business provider at the prediction time on the basis of the number of current payments, the number of reference payments and the number of historical payments.

### Claim 4 (depends on 3)

The method according to claim 3, wherein the predicting business volume of the business provider at the prediction time on the basis of the number of current payments, the number of reference payments and the number of historical payments comprises:
determining a difference between the number of current payments and the number of reference payments;
determining a weight corresponding to the difference on the basis of a preset correspondence between weights and differences; and
predicting business volume of the business provider at the prediction time on the basis of the weight and the number of historical payments.

### Claim 5 (depends on 1)

The method according to claim 1, wherein the acquiring historical payment data for the business provider comprises:
determining a geographic location of the user;
determining a business provider within a predetermined range from the geographic location of the user on the basis of the geographic location of the user and a geographical location of each of business providers stored in advance; and
acquiring historical payment data for the determined business provider.

### Claim 6

A method for predicting business volume, comprising:
determining a prediction time and a historical time corresponding to the prediction time;
acquiring historical payment data for a restaurant;
determining a number of historical payments for the restaurant at the historical time on the basis of the historical payment data; and
predicting business volume of the restaurant at the prediction time on the basis of the number of historical payments.

### Claim 7 (depends on 6)

The method according to claim 6, wherein the determining the prediction time and the historical time corresponding to the prediction time comprises:
determining a plurality of historical time in a plurality of historical time periods corresponding to the prediction time,
wherein the predicting business volume of the restaurant at the prediction time on the basis of the number of historical payments comprises:
calculating the number of historical payments for the restaurant at each of a plurality of historical time according to the preset algorithm, and predicting the business volume of the restaurant at the prediction time on the basis of a calculation result.

### Claim 8 (depends on 6)

The method according to claim 6, wherein the predicting business volume of the restaurant at the prediction time on the basis of the number of historical payments comprises:
acquiring payment data for the restaurant at a current time;
determining a number of current payment for the restaurant at the current time on the basis of the payment data for the restaurant at the current time;
determining a reference time in the historical time period corresponding to the current time;
determining a number of reference payments for the restaurant at the reference time on the basis of the historical payment data for the restaurant; and
predicting the business volume of the restaurant at the prediction time on the basis of the number of current payments, the number of reference payments and the number of historical payments.

### Claim 9 (depends on 8)

The method according to claim 8, wherein the predicting the business volume of the restaurant at the prediction time on the basis of the number of current payments, the number of reference payments and the number of historical payments comprises:
determining a difference between the number of current payments and the number of reference payments;
determining a weight corresponding to the difference on the basis of a preset correspondence between weights and differences; and
predicting business volume of the restaurant at the prediction time on the basis of the weight and the number of historical payments.

### Claim 10 (depends on 6)

The method according to claim 6, wherein the acquiring historical payment data for the restaurant comprises:
determining a geographic location of the user;
determining a restaurant within a predetermined range from the geographic location of the user on the basis of the geographic location of the user and a geographical location of each of restaurants stored in advance; and
acquiring historical payment data for the determined restaurant.

### Claim 11 (depends on 6)

The method according to claim 6, further comprising:
determining a popularity value of the restaurant at the prediction time on the basis of the predicted business volume of the restaurant at the prediction time and a preset rule; and
displaying the popularity value.

### Claim 12

A device for predicting business volume, comprising:
one or more processors; and
a memory;
wherein one or more programs are stored in the memory, and when executed by the one or more processors, the one or more programs cause the one or more processors to:
determine a prediction time and a historical time corresponding to the prediction time;
acquire historical payment data for a business provider;
determine a number of historical payments for the business provider at the historical time on the basis of the historical payment data; and
predict business volume of the business provider at the prediction time on the basis of the number of historical payments.

### Claim 13 (depends on 12)

The device according to claim 12, wherein the one or more processors are further caused to:
determine a plurality of historical time in a plurality of historical time periods corresponding to the prediction time, and
calculate the number of historical payments for the business provider at each of the plurality of historical time according to a preset algorithm, and predicting business volume of the business provider at the prediction time on the basis of a calculation result.

### Claim 14 (depends on 12)

The device according to claim 12, wherein the one or more processors are further caused to:
acquire payment data for the business provider at a current time;
determine a number of current payments for the business provider at the current time on the basis of the payment data for the business provider at the current time;
determine a reference time in the historical time period corresponding to the current time;
determine a number of reference payments for the business provider at the reference time on the basis of the historical payment data for the business provider; and
predict business volume of the business provider at the prediction time on the basis of the number of current payments, the number of reference payments and the number of historical payments.

### Claim 15 (depends on 14)

The device according to claim 14, wherein the one or more processors are further caused to:
determine a difference between the number of current payments and the number of reference payments;
determine a weight corresponding to the difference on the basis of a preset correspondence between weights and differences; and
predict business volume of the business provider at the prediction time on the basis of the weight and the number of historical payments.

### Claim 16 (depends on 12)

The device according to claim 12, wherein the one or more processors are further caused to:
determine a geographic location of the user;
determine a business provider within a predetermined range from the geographic location of the user on the basis of the geographic location of the user and a geographical location of each of business providers stored in advance; and
acquiring historical payment data for the determined business provider.

### Claim 17

(canceled)

### Claim 18

(canceled)

### Claim 19

(canceled)

### Claim 20

(canceled)

### Claim 21

(canceled)

### Claim 22

(canceled)

### Claim 23

(canceled)

### Claim 24

(canceled)

### Claim 25

(canceled)

### Claim 26

(canceled)