Content selection for mobile device

• US 10,565,650 B2
• Filed: 03/04/2015
• Issued: 02/18/2020
• Est. Priority Date: 03/13/2014
• Status: Active Grant

• Current Assignee: Op-Palvelut Oy (OP Financial Group)
• Original Assignee: Op-Palvelut Oy (OP Financial Group)
• Inventors: Jomppanen, Tuomas, Juntunen, Jussi
• Primary Examiner(s): Apple, Kirsten S
• Application Number:
  US15/125,795
• Publication Number:
  US 20170024815A1
• Time in Patent Office: 1,812 Days
• Field of Search: 705 35
• CPC Class Codes: G06Q 10/109 Time management, e.g. calendars, reminders, meetings or time accounting
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

Content selection for a mobile device. An apparatus obtains classified weekly consumption data of a consumer, calculates for each consumption class a relative daily value for each day of the week, calculates for each consumption class a content relevance probability in a selected day of the week on the basis of a relative daily value of the selected day of the week affected by a relative daily value of at least one day following the selected day of the week, controls a content selection for the consumer on the selected day of the week on the basis of the calculated content relevance probabilities for the selected day, and transmits the selected content to a mobile device of the consumer on the selected day.

Claims

1. A server computer apparatus configured to provide content selection for a mobile device, the server computer apparatus comprising:

one or more processors; and

one or more memories including computer program code,

the one or more memories and the computer program code configured to, with the one or more processors, cause the apparatus at least to;

obtain classified weekly spending data of a consumer, the classified weekly spending data comprising daily spending classes for each day of the week,

calculate for each spending class a relative daily value for each day of the week such that the relative daily value is the sum of the payments in the spending class during the day in proportion to the sum of the payments in all spending classes during said day,

calculate for each spending class a content relevance probability in a selected day of the week based on a sum of a relative daily value of the selected day of the week and a relative daily value of at least one day following the selected day of the week,

control a content selection for the consumer on the selected day of the week based on the calculated content relevance probabilities for the selected day, and

transmit wirelessly the selected content to the mobile device of the consumer on the selected day in a subsequent week after the classified weekly spending data is obtained,

wherein the selected content comprises information on at least one of the following;

services, brands, offers, advertisements, vouchers, coupons, and social media updates.

2. The apparatus of claim 1, wherein the classified weekly spending data is based on payments of trades of various commodities during at least one week.

3. The apparatus of claim 1, wherein the classified weekly spending data is based on payments of trades of various commodities during a fortnight, four weeks, eight weeks, twelve weeks, one month, two months, three months, six months, or one year.

4. The apparatus of claim 1, wherein each daily spending class includes payments of trades of commodities belonging to the daily consumption class.

5. The apparatus of claim 1, wherein the at least one day following the selected day of the week comprises at least one of the following:

two days, three days, four days, five days, and six days.

6. The apparatus of claim 1, wherein the selected day and the at least one day following the selected day of the week comprises at least one of the following combinations:

Monday as the selected day, and Tuesday as the at least one day following the selected day of the week,

Monday as the selected day, and Tuesday and Wednesday as the at least one day following the selected day of the week,

Monday as the selected day, and Tuesday, Wednesday and Thursday as the at least one day following the selected day of the week,

Tuesday as the selected day, and Wednesday as the at least one day following the selected day of the week,

Tuesday as the selected day, and Wednesday and Thursday as the at least one day following the selected day of the week,

Wednesday as the selected day, and Thursday as the at least one day following the selected day of the week,

Friday as the selected day, and Saturday as the at least one day following the selected day of the week,

Friday as the selected day, and Saturday and Sunday as the at least one day following the selected day of the week,

Saturday as the selected day, and Sunday as the at least one day following the selected day of the week,

Sunday as the selected day, and Monday as the at least one day following the selected day of the week,

Sunday as the selected day, and Monday and Tuesday as the at least one day following the selected day of the week,

Sunday as the selected day, and Monday, Tuesday and Wednesday as the at least one day following the selected day of the week, and

Sunday as the selected day, and Monday, Tuesday, Wednesday and Thursday as the at least one day following the selected day of the week.

7. The apparatus of claim 1, wherein the one or more memories and the computer program code are further configured to, with the one or more processors, cause the apparatus further to:

calculate for each spending class the content relevance probability in the selected day of the week based on the sum of the relative daily value of the selected day of the week and the relative daily value of the at least one day following the selected day of the week such that the relative daily value of the selected day of the week is multiplied by a first weighting coefficient and summed up with the relative daily value of the at least one following day of the week multiplied by at least one further weighting coefficient.

8. The apparatus of claim 1, wherein the one or more memories and the computer program code are further configured to, with the one or more processors, cause the apparatus further to:

control the content selection for the consumer on the selected day of the week based on the calculated content relevance probabilities for the selected day such that each content item of the content is classified to at least one spending class, and a content item classified to a spending class with a higher content relevance probability is favored over a content item classified to a spending class with a lower content relevance probability than the higher content relevance probability.

9. The apparatus of claim 8, wherein the one or more memories and the computer program code are further configured to, with the one or more processors, cause the apparatus further to:

control the content selection for the consumer on the selected day of the week based on the calculated content relevance probabilities for the selected day such that demographic information of the consumer further influences the content selection such that the content item matching the demographic information of the consumer is favored in the content selection.

10. The apparatus of claim 8, wherein the one or more memories and the computer program code are further configured to, with the one or more processors, cause the apparatus further to:

obtain brand loyalty ranking information of the consumer; and

control the content selection for the consumer on the selected day of the week based on the calculated content relevance probabilities for the selected day such that each content item of the content is classified to at least one brand, and the brand loyalty ranking information of the consumer further influences the content selection such that the content item with a higher brand loyalty rank is favored over a content item with a lower brand loyalty rank than the higher brand loyalty rank.

11. The apparatus of claim 8, wherein the one or more memories and the computer program code are further configured to, with the one or more processors, cause the apparatus further to:

divide the selected day into parts;

control the content selection for the consumer on the selected day of the week based on the calculated content relevance probabilities for the selected day such that each content item of the content is classified to at least one part of the day, and each selected content item is assigned for at least one part of the selected day; and

transmit the selected content to the mobile device of the consumer on the selected day such that each selected content item is transmitted during the at least one assigned part of the selected day.

12. A non-transitory computer readable storage medium comprising computer program code configured to provide content selection for a mobile device, the computer program code, when loaded into a server computer apparatus, causes the apparatus at least to:

obtain classified weekly spending data of a consumer, the classified weekly spending data comprising daily spending classes for each day of the week;

calculate for each spending class a relative daily value for each day of the week such that the relative daily value is the sum of the payments in the spending class during the day in proportion to the sum of the payments in all spending classes during said day;

calculate for each spending class a content relevance probability in a selected day of the week based on a sum of a relative daily value of the selected day of the week and a relative daily value of at least one day following the selected day of the week;

control a content selection for the consumer on the selected day of the week based on the calculated content relevance probabilities for the selected day; and

transmit wirelessly the selected content to the mobile device of the consumer on the selected day in a subsequent week after the classified weekly spending data is obtained,

wherein the selected content comprises information on at least one of the following;

services, brands, offers, advertisements, vouchers, coupons, and social media updates.