# US 7,412,202 B2 · Method and apparatus for generating recommendations based on user preferences and environmental characteristics > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 7,412,202 B2 - Document ID: US7412202B2 - Status: Granted - Kind: B2 - Application number: 09/825,328 - Filed: 2001-04-03 - Published: 2002-10-03 - Granted: 2008-08-12 - Earliest claimed: 2001-04-03 - Assignees: Koninklijke Philips Electronics N.V. - Inventors: Srinivas Gutta; Mi-Suen Lee; Lalitha Agnihotri - Web page: https://patentgrep.com/patent/US7412202B2 ## CPC - H04H 60/65: for using the result on users' side - H04N 7/163: {by receiver means only} - H04N 21/42202: {environmental sensors, e.g. for detecting temperature, luminosity, pressure, earthquakes} - H04N 21/44222: {Analytics of user selections, e.g. selection of programmes or purchase activity (monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33)} - H04N 21/4524: {involving the geographical location of the client (retrieval from the Internet by querying based on geographical locations G06F16/9537; systems specially adapted for using geographical information in broadcast systems H04H60/70; protocols in which the network application is adapted for the location of the user terminal in communication control or processing H04L67/52; services making use of the location of users or terminals in wireless networks H04W4/02; locating users or terminals in wireless networks H04W64/00)} - H04N 21/4532: {involving end-user characteristics, e.g. viewer profile, preferences (monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18)} - H04N 21/454: Content {or additional data} filtering, e.g. blocking advertisements - H04N 21/4665: {involving classification methods, e.g. Decision trees} - H04N 21/4755: {for defining user preferences, e.g. favourite actors or genre (retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306)} - H04N 21/8106: {involving special audio data, e.g. different tracks for different languages} - H04H 60/40: for identifying broadcast time - H04H 60/51: of receiving stations - H04H: BROADCAST COMMUNICATION (multiplex communication H04J; pictorial communication aspects of broadcast systems H04N) - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract A recommendation system is disclosed that generates recommendations for one or more items based on user preferences and one or more environmental factors. The user's preferences are learned under various environmental conditions using an environmental data collection system. The observed environmental conditions may include, for example, location, characteristics of the location, weather or characteristics of the user's motion, such as a rate of movement. For each positive and negative behavioral example, a number of attributes of the selected item are classified in the user profile together with the prevailing environmental conditions. When recommending an item, the disclosed recommender retrieves the user preferences and evaluates the current environmental conditions. A recommendation score can be generated for each available item based on the user's demonstrated preferences under similar environmental conditions, such as in the same or a similar geographic area or under similar weather conditions. ## Claims ### Claim 1 A method for recommending an item to a user, comprising the steps of: observing one or more environmental characteristics; learning preferences of said user for each item to be recommended while exposed to and under said one or more observed environmental characteristics; and generating a recommendation score for said item based on features of said item and said learned preferences of said user under said one or more observed environmental characteristics, wherein said one or more observed environmental characteristics includes at least one of a weather condition, a characteristic of motion of said user, a location, and one or more characteristics of said location and each of the one or more observed environmental characteristics is associated with a weight assigned by the user. ### Claim 2 (depends on 1) The method of claim 1, wherein said item is a radio station. ### Claim 3 (depends on 1) The method of claim 1, wherein said item is content. ### Claim 4 (depends on 1) The method of claim 1, wherein said item is a product. ### Claim 5 (depends on 1) The method of claim 1, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile. ### Claim 6 (depends on 5) The method of claim 5, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender. ### Claim 7 A method for recommending an item to a user, comprising the steps of: observing one or more environmental characteristics for obtaining information characterizing said one or more environmental characteristics for a given time; learning preferences of said user for each item to be recommended while said user is exposed to and affected by said one or more observed environmental characteristics; and generating a recommendation score for said item based on features of said item, and said learned preferences of said user under said one or more observed environmental characteristics for a given time, wherein said one or more observed environmental characteristics includes at least one of a weather condition and a characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user. ### Claim 8 (depends on 7) The method of claim 7, wherein said item is a radio station. ### Claim 9 (depends on 7) The method of claim 7, wherein said item is content. ### Claim 10 (depends on 7) The method of claim 7, wherein said item is a product. ### Claim 11 (depends on 7) The method of claim 7, wherein said one or more observed environmental characteristics is a user location at said given time. ### Claim 12 (depends on 7) The method of claim 7, wherein said one or more observed environmental characteristics is one or more characteristics of a location. ### Claim 13 (depends on 7) The method of claim 7, wherein said observed preferences of said user under said one or more environmental conditions are recorded in a profile. ### Claim 14 (depends on 13) The method of claim 13, wherein said observed preferences of said user under said one or more environmental conditions are recorded in a profile as a set of rules by a decision tree recommender. ### Claim 15 A system for recommending an item to a user, comprising: a memory for storing computer readable code; and a processor operatively coupled to said memory, said processor configured to: observe one or more environmental characteristics for obtaining information characterizing said one or more environmental characteristics for a given time learn preferences of said user for each item to be recommended while exposed to and under said one or more observed environmental characteristics; and generate a recommendation score for said item based on features of said item and said learned preferences of said user under said one or more observed environmental characteristics, wherein said one or more observed environmental characteristics includes at least one of a weather condition and a characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user. ### Claim 16 (depends on 15) The system of claim 15, wherein said item is a radio station. ### Claim 17 (depends on 15) The system of claim 15, wherein said item is content. ### Claim 18 (depends on 15) The system of claim 15, wherein said item is a product. ### Claim 19 (depends on 15) The system of claim 15, wherein said one or more observed environmental characteristics is a location. ### Claim 20 (depends on 15) The system of claim 15, wherein said one or more observed environmental characteristics is one or more characteristics of location. ### Claim 21 (depends on 15) The system of claim 15, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile. ### Claim 22 (depends on 21) The system of claim 21, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender. ### Claim 23 A system for recommending an item to a user, comprising: a memory for storing computer readable code; and a processor operatively coupled to said memory, said processor configured to: observe at least one motion characteristic for obtaining information characterizing said one or more environmental characteristics for a given time; learn preferences of said user for each item to be recommended while exposed to and under said characteristic of motion; obtain information characterizing said characteristic of motion of said user for a given time; and generate a recommendation score for said item based on features of said item, and said learned preferences of said user under said characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user. ### Claim 24 (depends on 23) The system of claim 23, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile. ### Claim 25 (depends on 24) The system of claim 24, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender. ### Claim 26 A computer readable medium having computer readable code means embodied thereon for recommending an item to a user, said computer readable program code means comprising: a step to learn preferences of said user for each item to be recommended while exposed to and under one or more observed environmental characteristics; and a step to generate a recommendation score for said item based on features of said item and said learned preferences of said user under said one or more observed environmental characteristics, wherein said one or more environmental characteristics includes at least one of a weather condition and a characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user. ### Claim 27 (depends on 26) The article of manufacture of claim 26, wherein said item includes at least one of a radio station, content and product. ### Claim 28 (depends on 26) The article of manufacture of claim 26, wherein said one or more observed environmental characteristics further includes at least one of a location, and one or more characteristics of said location. ### Claim 29 (depends on 26) The article of manufacture of claim 26, wherein said learneded preferences of said user under said one or more environmental characteristics are recorded in a profile. ### Claim 30 (depends on 26) The article of manufacture of claim 26, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender. ### Claim 31 A computer readable medium having computer readable code means embodied thereon for recommending an item to a user, said computer readable program code means comprising: a step to obtain information characterizing one or more observed environmental characteristics for a given time; a step to learn preferences of said user for each item to be recommended by observing said user's personal exposure to one or more observed environmental characteristics; and a step to generate a recommendation score for said item based on features of said item, and said learned preferences of said user under said one or more observed environmental characteristics for a given time, wherein said one or more observed environmental characteristics includes at least one of a weather condition and a characteristic of motion of said user and each of the one or more observed environmental characteristics is associated with a weight assigned by the user. ### Claim 32 (depends on 31) The article of manufacture of claim 31, wherein said item includes at least one of a radio station, content and product. ### Claim 33 (depends on 31) The article of manufacture of claim 31, wherein said one or more observed environmental characteristics further includes at least one of a location, and one or more characteristics of said location. ### Claim 34 (depends on 31) The article of manufacture of claim 31, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile. ### Claim 35 (depends on 31) The article of manufacture of claim 31, wherein said learned preferences of said user under said one or more environmental characteristics are recorded in a profile as a set of rules by a decision tree recommender.