Field of the invention

The present subject matter relates to advertisement recommenders for recommending advertisements to a user.

Background of the invention

Patent application US20080140445 discloses systems, methods and computer- executable instructions for customizing health-related advertisements. The system disclosed in US20080140445 may recommend advertisements that may not be personalized to the user.

Summary of the invention

Accordingly it is an object of the present subject matter to recommend personalized advertisements to the user.

The object of the present subject matter is realized by providing an advertisement recommender for recommending advertisements to a user, the advertisement recommender comprising an input module configured to receive information associated with the user; an access module configured to access a health record associated with the user; an analysis module configured to analyze contents of the health record associated with the user and recommend one or more advertisements to a home network based on the analysis of the health record associated with the user; and a learning module configured to receive user feedback on the recommended one or more advertisements and, based on the user feedback, adapt the recommendation of the one or more advertisements to the home network.

The health record of the user contains information about the user's health status and the associated treatment. The health record of the user can be accessed and analyzed. Based on the analysis, advertisements may be recommended to the user.

The learning module obtains the users feedback on the recommended advertisements. The obtained user feedback may be used to suitably adapt the recommendation of advertisements. This allows recommendation of personalized advertisements to the user. Further, the advertisements could reach the intended target audience. The user's feedback could reduce the recommendation of uninteresting advertisements and enhance user satisfaction.

In an embodiment, the analysis module comprises an interpretation and prioritization module configured to interpret the contents of the health record, obtain the health status of the user, and prioritize the obtained health status of the user and associated treatment.

Interpreting the contents of the accessed health record of the user could help in a) obtaining the health status of the user and an indication of the user's lifestyle habits b) obtaining the changes that needs to be made to improve the health status of the user c) recommending advertisements that match user's health requirements

As an illustrative example, based on the interpretation of the contents of the accessed health record of the user, the obtained health status of the user may be obesity. The changes to be made to reduce obesity can be derived from the treatment information available in the accessed health record of the user. This for example could relate to increasing the physical activity by walking or exercising. Hence, the advertisement to be recommended could relate to exercise equipment or benefits of walking and jogging. Recommending such advertisements improves the effectiveness of advertisements and draws the attention of the user. This could also enhance personalization and user satisfaction.

The obtained health status of the user and the associated treatment may help in identifying the various healthcare products and the associated advertisements that may be relevant to the user. Further, the number of healthcare product types, the number of channels for advertising and the number of advertisements related to healthcare products are increasing day by day. Hence, recommending advertisements to the user based on the obtained health status of the user as disclosed in US20080140445 could result in a) recommending more number of advertisements b) recommending advertisements that may not draw the attention of the user c) lower levels of user satisfaction. The obtained health status of the user can be prioritized in order to i) reduce the number of advertisements ii) personalize the advertisements to the user and iii) increase the number of advertisements that can draw the attention of the user. Based on the prioritized health status of the user, the advertisements could be recommended.

As an illustrative example, based on the interpretation of the health record of the user, the obtained health status of the user could be as follows:

The obtained health status of the user could be prioritized. Prioritization could be carried out by comparing the obtained health status of the user with a baseline. The baseline could be persons having similar attributes e.g. gender, age, height, weight and similar health status as the user. As an illustrative example, after comparing the obtained health status of the user with the baseline, the prioritized health status of the user and the associated treatment could be as follows:

Prioritizing the health status of the user and recommending advertisements based on the prioritized health status of the user may a) result in recommending advertisements that can draw the attention of the user b) reduce the number of unwanted advertisements c) enhance the personalization of advertisements d) increase user satisfaction.

As an illustrative example, based on the prioritized health status of the user, advertisements associated with diabetes and obesity could be recommended to the user. The advertisements associated with Asthma, hair loss and sun burn is of less priority and hence need not be recommended. Alternately, the advertisements associated with diabetes and obesity could be recommended to the user more number of times compared to the advertisements associated with asthma, hair loss and sunburn. This could increase the effectiveness of advertising. The system disclosed in US20080140445 recommends advertisements based on the health status of the user. The number of recommended advertisements may be high and uninteresting to the user. The solution disclosed in the present subject matter prioritizes the health status of the user and recommends advertisements based on prioritized health status of the user. Hence, the recommended advertisements could be more interesting and relevant to the user.

In a still further embodiment, the analysis module comprises a logic module configured to compare the prioritized health status of the user and the associated treatment with a pre-determined database having healthcare product information and obtain one or more healthcare products. The pre-determined database can have information on healthcare products and user reference data. The healthcare products could be logically grouped. As an illustrative example, all the healthcare products related to diabetes e.g. glucometer, insulin pen, activity monitor, sugar free tablets can form one logical group; all the healthcare products related to arthritis can form a second logical group. The pre-determined database could act as a baseline reference. As an illustrative example, the pre-determined database could be as follows:

The healthcare products could be associated with the user reference data. The user reference data could have attributes e.g. age, gender, height, weight of a person and associated healthcare products that the user uses. The logic module compares the prioritized health status of the user with the pre-determined database along with the user reference data and obtains healthcare products that match the user's health status.

The pre-determined database could be regularly updated. This allows the logic module to select new healthcare products that are launched in the market. This could further improve personalization and enhance user satisfaction. As an illustrative example, let us assume the user is a male and his age is 45 years. The logic module compares the prioritized health status of the user with the predetermined database and based on the comparison obtains the following healthcare products a) insulin pen b) activity monitors and c) glucometer.

In a still further embodiment, the analysis module comprises a healthcare product rating module configured to gather customer ratings for the obtained one or more healthcare products.

Various customers would have used the healthcare products. Based on their experience of using the healthcare products, they would have shared their experience and given their opinion on the healthcare products indicating the merits/demerits of the healthcare products. This information is generally made available in the public domain e.g. web pages, customer survey results and healthcare product survey results. This information can be used to gather customer ratings associated with the obtained healthcare products. Generally, the healthcare product that is accepted by a wide community of customers may have a higher probability of being interest to the user. Hence, instead of selecting the healthcare product based on prioritized health status of the viewer, it would be advantageous to gather customer rating associated with the healthcare product and recommend the healthcare product based on the gathered customer rating information. This can enhance personalization and user satisfaction.

As an illustrative example, the customer ratings of the obtained healthcare products can be found from web pages. As an example, the gathered customer ratings could be as follows:

In a still further embodiment, the analysis module comprises a retrieval and storage module configured to retrieve an advertisement associated with the obtained one or more healthcare products and store the retrieved advertisement.

Various sources such as television channels, web pages and radio channels generally contain advertisements related to healthcare products. The advertisements associated with the obtained healthcare products can be automatically retrieved from such sources. This embodiment allows off line collection of all advertisements associated with the obtained healthcare products. As an illustrative example, there could be advertisements related to insulin pen, glucometer and activity monitors. These advertisements can be audio advertisements being broadcast on a radio channel or can be video/multimedia advertisements being broadcast on a television channel. These advertisements could be retrieved and stored. Storing has the advantage that the stored advertisements could be used later when required. Further, storing of the retrieved advertisements allow personalization of the recommendation of the advertisements and hence can enhance user satisfaction.

In a still further embodiment, the analysis module comprises a decision module configured to: select the healthcare product and the frequency with which the advertisement associated with the healthcare product is to be played based on the gathered customer ratings; access the retrieval and storage module and retrieve the advertisement associated with the selected healthcare product; and transmit the retrieved advertisement to the home network along with the frequency with which the retrieved advertisement is to be played. The gathered customer ratings could be effectively used to recommend advertisements to the user. The decision module could be a rule base system. As an illustrative example, the rule base system could be as follows:

If the priority of the obtained health status of the user (e.g. diabetes) is very high and the gathered customer rating of the associated healthcare product (e.g. insulin pen) is greater than a pre-determined threshold (e.g. > 6), recommend the advertisement associated with the healthcare product (e.g. insulin pen) to be played 4 times.

Further, the frequency with which the advertisement is to be played could be associated with the gathered customer rating of the healthcare product. The higher the rating, the selected healthcare product can have a higher chance of being accepted by the user. The advertisements associated with healthcare product that have been rated higher could be played more number of times. This can improve the effectiveness of advertising and can improve personalization. This can further enhance user satisfaction.

As an alternative, the user feedback on the recommended advertisements could be associated with the customer rating of the healthcare product. Based on the user's rating of the healthcare product, the recommendation of the advertisements could be suitably adapted.

As an illustrative example, advertisement related to activity monitor that has a customer rating of 7 could be played to the user. The user may give his feedback rating the activity monitor at 5. In such a scenario, the advertisement recommender will not recommend the activity monitor. On the other hand, it recommends insulin pen which has a customer rating of 6.

In a still further embodiment, the advertisement recommender comprises a display mode selection module configured to select the display mode based on user input, the display mode being one of Mode l: displaying the recommended advertisement in a virtual channel

Mode 2: substituting the broadcast advertisement with the recommended advertisement and displaying the recommended advertisement.

Display mode selection has several advantages. While the user is watching a program, during the commercial breaks the recommended advertisements could substitute the commercial break advertisements. Alternately, the user can opt to view all the recommended advertisements by selecting a virtual channel. By providing the display mode selection, the recommended advertisements can be displayed in a personalized manner. This can improve personalization and enhance user satisfaction. The object of the present subject matter is further realized by providing a method for recommending advertisements to a user, the method comprising receiving information associated with the user; accessing a health record associated with the user; analyzing contents of the health record associated with the user and recommending one or more advertisements to a home network based on the analysis of the health record associated with the user; and receiving user feedback on the recommended one or more advertisements and, based on the user feedback, adapting the recommendation of the one or more advertisements to the home network.

Brief description of the drawings The above-mentioned aspects, features and advantages will be further described, by way of example only, with reference to the accompanying drawings, in which the same reference numerals indicate identical or similar parts, and in which:

Fig. 1 shows an exemplary schematic block diagram of an advertisement recommender according to an embodiment of the present subject matter; Fig. 2 shows an exemplary block diagram of the analysis module according to an embodiment of the present subject matter;

Fig. 3 shows an exemplary block diagram of the analysis module according to a still further embodiment of the present subject matter;

Fig. 4 shows an exemplary block diagram of the analysis module according to a still further embodiment of the present subject matter;

Fig. 5 shows an exemplary block diagram of the analysis module according to a still further embodiment of the present subject matter;

Fig. 6 shows an exemplary block diagram of the analysis module according to a still further embodiment of the present subject matter; Fig. 7 shows an exemplary block diagram of the advertisement recommender according to a further embodiment of the present subject matter; and Fig. 8 schematically shows an exemplary flowchart illustrating the method of recommending advertisements according to an embodiment of the present subject matter.

Detailed description of the embodiments

Referring now to Fig. 1, the advertisement recommender 100 includes a) an input module 102 b) an access module 104 c) an analysis module 106 d) a learning module 108

The input module 102 is configured to receive information associated with the user 130. This information is useful for identifying the user 130. The user information for example can be name, gender, age, height and weight of the user.

The health record of the user 110 is generally available over a network. The health record of the user contains medical history, laboratory test results, radiology images, and treatment data. Once the user 130 is identified, the access module 104 accesses the health record 110 associated with the user 130.

The analysis module 106 analyzes the contents of the accessed health record 110 associated with the user 110. The analysis of the accessed health record 110 of the user 130 results in finding the various healthcare products that are relevant to the user's 130 health status. One or more advertisements related to the healthcare products that are relevant to the user's 130 health status are recommended to the home network 140. The recommended one or more advertisements could be audio advertisements, video advertisements/multimedia advertisements. The recommended one or more advertisements can be played to the user 130.

The learning module 108 receives the users 130 feedback on the recommended advertisements. The user's feedback can be useful in recommending advertisements that match the user's 130 health status. Based on the user's feedback, the recommendation of one or more advertisements is adapted to match the user's 130 health requirements.

The disclosed advertisement recommender a) can recommend personalized advertisements to the user b) can reduce the recommendation of uninteresting advertisements c) can enhance user satisfaction. Referring now to Fig. 2, in an embodiment the analysis module 106 includes an interpretation and prioritization module 200. The interpretation and prioritization module 200 is configured to a) interpret the contents of the accessed health record 110 of the user 130 b) obtain the health status of the user 130 based on the interpretation c) prioritize the obtained health status of the user 130 and the associated treatment.

Interpreting the contents of the accessed health record 110 of the user 130 could help in a) obtaining the health status of the user 130 and an indication of the user's 130 habits b) obtaining the changes that needs to be made to improve the health status of the user 130 c) recommending advertisements that match user's 130 health requirements

As an illustrative example, based on the interpretation of the contents of the accessed health record 110 of the user 130, the obtained health status of the user 130 may be obesity. The changes to be made to reduce obesity can be derived from the treatment information available in the accessed health record of the user 130. This can for example relate to increasing the physical activity by walking or exercising. Hence, the advertisement to be recommended could relate to exercise equipment or benefits of walking and jogging. Recommending such advertisements improves the effectiveness of advertisements and draws the attention of the user 130. This could also enhance personalization and user satisfaction. The obtained health status of the user 130 and the associated treatment may help in identifying the various healthcare products and the associated advertisements that may be relevant to the user.

Further, the number of healthcare products, the number of channels for advertising and the number of advertisements related to healthcare products are increasing day by day. Hence, recommending advertisements to the user 130 based on the obtained health status of the user 130 as discussed in US20080140445 may result in a) recommending more number of advertisements b) recommending advertisements that may not draw the attention of the user 130 c) lower levels of user satisfaction.

The obtained health status of the user 130 can be prioritized in order to i) reduce the number of advertisements ii) personalize the advertisements to the user 130 and iii) increase the number of advertisements that can draw the attention of the user. Based on the prioritized health status of the user 130, the advertisements could be recommended.

As an illustrative example, based on the interpretation of the health record 110 of the user 130, the obtained health status of the user 130 could be as follows:

The obtained health status of the user 130 could be prioritized. Prioritization could be carried out by comparing the obtained health status of the user 130 with a baseline. The baseline could be persons having similar attributes e.g. gender, age, height, weight and similar health status as the user 130. As an illustrative example, after comparing the obtained health status of the user 130 with the baseline, the prioritized health status of the user 130 and the associated treatment could be as follows:

Prioritizing the health status of the user 130 and recommending advertisements based on the prioritized health status of the user 130 may a) result in recommending advertisements that can draw the attention of the user 130 b) reduce the number of unwanted advertisements c) enhance the personalization of advertisements d) increase user satisfaction.

As an illustrative example, based on the prioritized health status of the user 130, advertisements associated with diabetes and obesity could be recommended to the user 130. The advertisements associated with asthma, hair loss and sun burn is of less priority and hence need not be recommended. Alternately, the advertisements associated with diabetes and obesity could be recommended to the user 130 more number of times compared to the advertisements associated with asthma, hair loss and sunburn. This could increase the effectiveness of advertising.

The system disclosed in US20080140445 recommends advertisements based on the health status of the user 130. The number of recommended advertisements may be high and uninteresting to the user. The solution disclosed in the present subject matter prioritizes the health status of the user 130 and recommends advertisements based on prioritized health status of the user 130. Hence, the recommended advertisements could be more interesting and relevant to the user.

Referring now to Fig. 3, in a further embodiment the analysis module 106 includes a logic module 300. The logic module 300 is configured to compare the prioritized health status of the user 130 and the associated treatment with a pre-determined database 320 having healthcare product information and obtain one or more healthcare products.

This embodiment can be advantageous in obtaining one or more healthcare products that match the prioritized health status of the user 130.

The pre-determined database 320 can have information on healthcare products and user reference data. The healthcare products could be logically grouped. As an illustrative example, all the healthcare products related to diabetes e.g. glucometer, insulin pen, activity monitor, sugar free tablets can form one logical group; all the healthcare products related to arthritis can form a second logical group. The pre-determined database 320 could act as a baseline reference. As an illustrative example, the pre-determined database 320 could be as follows:

The healthcare products could be associated with the user reference data. The user reference data could have attributes e.g. age, gender, height, weight of a person and associated healthcare products that the user uses. The logic module compares the prioritized health status of the user 130 with the pre-determined database 320 along with the user reference data and obtains healthcare products that match the user's 130 health status.

The pre-determined database 320 could be regularly updated. This allows the logic module to select new healthcare products that are launched in the market. This could further improve personalization and enhance user satisfaction. As an illustrative example, let us assume the user 130 is a male and his age is

45 years. The logic module compares the prioritized health status of the user 130 with the pre-determined database 320 and based on the comparison obtains the following healthcare products a) insulin pen b) activity monitors and c) glucometer.

Referring now to Fig. 4, in a still further embodiment, the analysis module 106 includes a healthcare product rating module 400. The healthcare product rating module is configured to gather customer ratings for the obtained one or more healthcare products.

Various customers would have used the healthcare products. Based on their experience of using the healthcare products, they would have shared their experience and given their opinion on the healthcare product indicating the merits/demerits of the healthcare products. This information is generally made available in the public domain e.g. web pages, customer survey results and healthcare product survey results. This information can be used to gather customer ratings associated with the obtained healthcare product. Generally, the healthcare product that is accepted by a wide community of customers may have a higher probability of being interest to the user. Hence, instead of selecting the healthcare product based on prioritized health status of the viewer, it would be advantageous to gather rating associated with the healthcare product and recommend the healthcare product based on the gathered customer rating information. This can enhance personalization and user satisfaction.

As an illustrative example, the customer ratings of the obtained healthcare product can be found from web pages. As an example, the gathered customer ratings could be as follows:

Referring now to Fig. 5, in a still further embodiment, the analysis module 106 includes a retrieval and storage module 500. The retrieval and storage module 500 is configured to retrieve an advertisement associated with the obtained one or more healthcare product and store the retrieved advertisement.

Various sources such as television channels, web pages and radio channels generally contain advertisements related to healthcare products. The advertisements associated with the obtained healthcare products can be automatically retrieved from such sources.

This embodiment allows off line collection of all advertisements associated with the obtained healthcare products. As an illustrative example, there could be advertisements related to insulin pen, glucometer and activity monitors. These advertisements can be audio advertisements being broadcast on a radio channel or can be video/multimedia advertisements being broadcast on a television channel. These advertisements could be retrieved and stored. Storing has the advantage that the stored advertisements could be used later when required. Further, storing of the retrieved advertisements allow personalization of the recommendation of the advertisements and hence can enhance user satisfaction.

Referring now to Fig. 6, in a still further embodiment, the analysis module 106 includes a decision module 600. The decision module 600 is configured to select the healthcare product and the frequency with which the advertisement associated with the healthcare product is to be played based on the gathered customer rating. Further, the decision module is configured to access the retrieval and storage module and retrieve the advertisement associated with the selected healthcare product. Furthermore, the decision module is configured to transmit the retrieved advertisement to the home network along with the frequency with which the retrieved advertisement is to be played.

The gathered customer ratings could be effectively used to recommend advertisements to the user 130. The decision module could be a rule base system. As an illustrative example, the rule base system could be as follows: If the priority of the obtained health status of the user (e.g. diabetes) is very high and the gathered customer rating of the associated healthcare product (e.g. insulin pen) is greater than a pre-determined threshold (e.g. > 6), recommend the advertisement associated with the healthcare product (e.g. insulin pen) to be played 4 times.

Further, the frequency with which the advertisement is to be played could be associated with the gathered customer rating of the healthcare product. The higher the rating, the selected healthcare product can have a higher chance of being accepted by the user. The advertisements associated with healthcare product that have been rated higher could be played more number of times. This can improve the effectiveness of advertising and can improve personalization. This can further enhance user satisfaction. As an alternative, the user 130 feedback on the recommended advertisements could be associated with the customer rating of the healthcare product. Based on the user's 130 rating of the healthcare product, the recommendation of the advertisements could be suitably adapted.

As an illustrative example, advertisement related to activity monitor that has a customer rating of 7 could be played to the user. The user may give his feedback rating for the activity monitor as 5. In such a scenario, the advertisement recommender will not recommend the activity monitor. On the other hand, it could recommend insulin pen which has a customer rating of 6.

Referring now to Fig. 7, in a still further embodiment the advertisement recommender includes a display mode selection module 700. The display mode selection module 700 is configured to select the display mode based on user 130 input. The display mode can be one of mode 1 : display the recommended advertisement in a virtual channel mode 2: substitute the broadcast advertisement with the recommended advertisement and display the recommended advertisement. Display mode selection has several advantages. While the user 130 is watching a program, during the commercial breaks the recommended advertisements could substitute the commercial break advertisements. Alternately, the user 130 can opt to view all the recommended advertisements by selecting a virtual channel. By providing the display mode selection, the recommended advertisements can be displayed in a personalized manner. This can improve personalization and enhance user satisfaction.

Referring now to Fig. 8, the method 800 for recommending advertisements to the user 130 includes the following steps: a step 820 of receiving information associated with the user 130, a step 840 of accessing a health record 110 associated with the user 130, a step 860 of analyzing contents of the health record 110 associated with the user 130 and recommending one or more advertisements to a home network 140 based on the analysis of the health record 110 associated with the user 130 and a step 880 of receiving user 130 feedback on the recommended one or more advertisements and, based on the user feedback, adapting the recommendation of the one or more advertisements to the home network 140.

The present subject matter discloses an advertisement recommender which can collect the advertisements related to the health status of the user and play them between telecasted programs or between recorded programs. Further, the subject matter discloses a prioritization module that can prioritize the health status of the user. Furthermore, the subject matter discloses a healthcare product rating module that could gather the customer rating of the prioritized healthcare products and recommend advertisements based on the gathered customer rating of the prioritized healthcare products. Furthermore, the subject matter discloses a learning module that initially recommends advertisements related to the healthcare product based on the healthcare product rating available in the public domain and subsequently learns the advertisements to be recommended based on the user's feedback. The disclosed advertisement recommender is useful in a home network 140 environment and can recommend personalized advertisements to the user.

In summary, the present subject matter discloses an advertisement recommender for recommending advertisements to a user. The advertisement recommender comprises an input module configured to receive information associated with the user; an access module configured to access a health record associated with the user (130); an analysis module configured to analyze contents of the health record associated with the user and recommend one or more advertisements to a home network based on the analysis of the health record associated with the user; and a learning module configured to receive user feedback on the recommended one or more advertisements and, based on the user feedback, adapt the recommendation of the one or more advertisements to the home network.

While the subject matter has been illustrated in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the subject matter is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art of practicing the claimed subject matter, from a study of the drawings, the disclosure and the appended claims. Use of the verb "comprise" and its conjugates does not exclude the presence of elements other than those stated in a claim or in the description. Use of the indefinite article "a" or "an" preceding an element or step does not exclude the presence of a plurality of such elements or steps. A single unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependant claims does not indicate that a combination of these measures cannot be used to advantage. The figures and description are to be regarded as illustrative only and do not limit the subject matter. Any reference sign in the claims should not be construed as limiting the scope.