# US 2021/0342918 A1 · Methods and systems for providing purchase recommendations based on responses to inquiries on product attributes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0342918 A1 - Document ID: US20210342918A1 - Status: Published application - Kind: A1 - Application number: 17/379,863 - Filed: 2021-07-19 - Published: 2021-11-04 - Earliest claimed: 2019-07-09 - Assignees: Capital One Services, LLC - Applicants: Capital One Services, LLC - Inventors: Micah PRICE; Qiaochu TANG; Jason HOOVER; Avid GHAMSARI; Geoffrey DAGLEY - Web page: https://patentgrep.com/patent/US20210342918A1 ## CPC - G06Q 30/0631: {Recommending goods or services} - G06Q 30/0641: {utilising user interfaces specially adapted for shopping} - 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 Systems and methods are disclosed for providing purchase recommendations. According to some examples, a method may include: determining respective frequency distributions of a plurality of vehicle attributes, the frequency distributions being determined based on occurrences of values of the plurality of vehicle attributes in a set of vehicles; selecting a vehicle attribute from the plurality of vehicle attributes based on the frequency distributions of the plurality of vehicle attributes; transmitting, to a user device, an inquiry for user preference regarding the selected vehicle attribute; receiving, from the user device, a response indicating the user preference; and presenting, to the user device, a recommendation of one or more vehicles determined based on the received response. ## Claims ### Claim at position 1 -20. (canceled) ### Claim 21 A computer-implemented method for determining recommendations based on user interaction, the computer-implemented method comprising: determining a candidate set of vehicles; iteratively adjusting the candidate set of vehicles by: determining respective distributional characteristics of a plurality of vehicle attributes, each of the plurality of vehicle attributes occurring in the candidate set of vehicles as adjusted; selecting a vehicle attribute from the plurality of vehicle attributes based on the respective distributional characteristics of the plurality of vehicle attributes; transmitting, to a user device, an inquiry for user preference regarding the vehicle attribute; receiving, from the user device, a response indicating the user preference; adjusting the candidate set of vehicles based on the response; determining whether to perform a next iteration; and responsive to a determination not to perform the next iteration, determining a recommendation of one or more vehicles determined based on the candidate set of vehicles as adjusted. ### Claim 22 (depends on 21) The computer-implemented method of claim 21, wherein selecting the vehicle attribute includes: determining respective estimates of normalized entropy of the plurality of vehicle attributes based on the respective distributional characteristics; and selecting the vehicle attribute from the plurality of vehicle attributes based on the respective estimates of normalized entropy. ### Claim 23 (depends on 22) The computer-implemented method of claim 22, wherein the respective distributional characteristics are percentage distributions, and in determining the respective estimates of normalized entropy, the respective distributional characteristics are used as respective probability mass functions of the plurality of vehicle attributes. ### Claim 24 (depends on 22) The computer-implemented method of claim 22, wherein selecting the vehicle attribute from the plurality of vehicle attributes includes selecting a vehicle attribute having a highest estimate of normalized entropy out of the respective estimates of normalized entropy of the plurality of vehicle attributes. ### Claim 25 (depends on 22) The computer-implemented method of claim 22, wherein the plurality of vehicle attributes includes a categorical attribute, and for the categorical attribute, a respective estimate of normalized entropy is normalized based on a total number of values of the categorical attribute that appear at least once in the candidate set of vehicles. ### Claim 26 (depends on 21) The computer-implemented method of claim 21, wherein the plurality of vehicle attributes includes a quantitative attribute, and determining the respective distributional characteristics includes determining a frequency distribution of the quantitative attribute by grouping values of the quantitative attribute into bins, each of the values of the quantitative attribute corresponding to a respective vehicle in the candidate set of vehicles, and determining a respective frequency of each of the bins, to obtain the frequency distribution of the quantitative attribute. ### Claim 27 (depends on 26) The computer-implemented method of claim 26, wherein determining the frequency distribution of the quantitative attribute includes: prior to grouping the values of the quantitative attribute into bins, processing the values of the quantitative attribute by capping one or more outlier values. ### Claim 28 (depends on 21) The computer-implemented method of claim 21, wherein the user preference indicates a preference for one or more values of the vehicle attribute, adjusting the candidate set of vehicles based on the response includes excluding, from the candidate set of vehicles, vehicles having none of the one or more values for the vehicle attribute, to obtain a reduced set of vehicles as the candidate set of vehicles as adjusted, and the recommendation includes the reduced set of vehicles as the one or more vehicles. ### Claim 29 (depends on 21) The computer-implemented method of claim 21, wherein the plurality of vehicle attributes include one or more of: body style, exterior color, interior color, number of doors, vehicle condition, engine types, transmission type, fuel type, mileage, price, year, or fuel economy. ### Claim 30 (depends on 21) The computer-implemented method of claim 21, wherein in each subsequent iteration, vehicle attributes previously selected are omitted from further selections of vehicle attributes, and subsequent distributional characteristics are recalculated for the candidate set of vehicles as adjusted. ### Claim 31 (depends on 21) The computer-implemented method of claim 21, further comprising: initiating a chat bot session enabling a user of the user device to communicate with a chat bot executed by a computer system, wherein the inquiry is transmitted during the chat bot session, and the response is received during the chat bot session. ### Claim 32 (depends on 21) The computer-implemented method of claim 21, further comprising: presenting, to the user device, the recommendation of the one or more vehicles and one or more search filters of a vehicle listing interface, the one or more search filters having been determined in accordance with the user preference. ### Claim 33 A computer system for determining recommendations based on user interaction, the computer system comprising: a memory storing instructions; and one or more processors configured to execute the instructions to perform a process, the process including: determining respective distributional characteristics of a plurality of vehicle attributes, each of the plurality of vehicle attributes occurring in a set of vehicles; selecting a vehicle attribute from the plurality of vehicle attributes based on the respective distributional characteristics of the plurality of vehicle attributes by determining respective estimates of normalized entropy of the plurality of vehicle attributes based on the respective distributional characteristics using an equation η(X), where X is a variable having possible values x1, . . . , xn and corresponds to each of the plurality of vehicle attributes, the equation being: η ⁡ ( X ) = - Σ i n ⁢ P ⁡ ( x i ) ⁢ log b ⁢ P ⁡ ( x i ) log b ⁡ ( n ) , where P(xi) is a probability for X=xi, b is an arbitrary logarithm base, n is a count of the possible values x1, . . . , xn; and selecting the vehicle attribute from the plurality of vehicle attributes based on the respective estimates of normalized entropy; transmitting, to a user device, an inquiry for user preference regarding the vehicle attribute; receiving, from the user device, a response indicating the user preference; and determining a recommendation of one or more vehicles determined based on the response. ### Claim 34 (depends on 33) The computer system of claim 33, wherein, prior to determining the recommendation, the process is iterative, in each subsequent iteration, vehicle attributes previously selected are omitted from further selections of vehicle attributes, and subsequent distributional characteristics are recalculated for a candidate set of vehicles as adjusted based on user responses ### Claim 35 (depends on 34) The computer system of claim 34, wherein the respective distributional characteristics are percentage distributions. ### Claim 36 (depends on 34) The computer system of claim 34, wherein the process further includes, to select the vehicle attribute from the plurality of vehicle attributes, selecting a vehicle attribute having a highest estimate of normalized entropy out of the respective estimates of normalized entropy of the plurality of vehicle attributes. ### Claim 37 (depends on 34) The computer system of claim 34, wherein the plurality of vehicle attributes includes a categorical attribute, and for the categorical attribute, the n is set to a total number of values of the categorical attribute that appear at least once in the set of vehicles. ### Claim 38 (depends on 34) The computer system of claim 34, wherein the plurality of vehicle attributes includes a quantitative attribute, and the process further includes, to determine the respective estimates of normalized entropy, determining a frequency distribution of the quantitative attribute by grouping values of the quantitative attribute into bins, each of the values of the quantitative attribute corresponding to a respective vehicle in the set of vehicles, and determining a respective frequency of each of the bins, to obtain the frequency distribution of the quantitative attribute. ### Claim 39 (depends on 38) The computer system of claim 38, wherein the process further includes, to determine the frequency distribution of the quantitative attribute: prior to grouping the values of the quantitative attribute, processing the values of the quantitative attribute by capping one or more outlier values. ### Claim 40 A computer system for implementing an artificial-intelligence interactive agent for determining recommendations based on user interaction, the computer system comprising: a memory storing instructions; and one or more processors configured to execute the instructions to perform a process, the process including: determining respective distributional characteristics of a plurality of vehicle attributes describing vehicles in a set of vehicles, each of the plurality of vehicle attributes occurring in the set of vehicles; determining respective estimates of normalized entropy of the plurality of vehicle attributes based on the respective distributional characteristics using an equation η(X), where X is a variable having possible values x1, . . . , xn and corresponds to each of the plurality of vehicle attributes, the equation being: η ⁡ ( X ) = - Σ i n ⁢ P ⁡ ( x i ) ⁢ log b ⁢ P ⁡ ( x i ) log b ⁡ ( n ) , where P(xi) is a probability for X=xi, b is an arbitrary logarithm base, n is a count of the possible values x1, . . . , xn; selecting a vehicle attribute from the plurality of vehicle attributes based on the respective estimates of normalized entropy; transmitting, to a user device, an inquiry for user preference regarding the vehicle attribute; receiving, from the user device, a response indicating the user preference; and transmitting, to the user device, a web page including a list of one or more vehicles from the set of vehicles, the list of one or more vehicles having filters set in accordance with the user preference.