A method for indicating sites using similarity and duration.

Abstract

This invention provides a method for displaying the results of a site search or comparison using site similarities and travel time within a transportation system. [Solution] The method receives a request comprising at least one location included in a traffic system, determines at least two isochronous sites included in a plurality of sites, determines a plurality of similar sites included in at least two isochronous sites, and determines suggestions for the plurality of similar sites, or determines a plurality of similar sites included in at least two isochronous sites, and determines the aforementioned suggestions for the plurality of similar sites, and responds to a request having suggestions.

Description

[Background technology]

[0001] Cross-reference of related applications This application is based on and claims priority date of the following application: [Country] [Application Number] [Application Date] USA 63130693 2020.12.27 United States 63168279 2021.3.31 USA 63237535 2021.8.27 These are incorporated here as references, as they have been sufficiently published.

[0002] This form relates to a system that uses site similarity and travel time within a transportation system to display the results of a site search or comparison.

[0003] When presenting search results to a user, search engines traditionally organize the results to reduce the information overload and increase correlation. This organization typically employs two techniques: clustering and scoring. The goal of clustering is to group similar search results to prevent the user from seeing repeated information. The goal of scoring is to sort search results to limit them to providing the most relevant information to the user.

[0004] The concept of similarity has an intuitive meaning of being sufficiently alike between items. We use the term similarity in a broad sense, with consistency in interpretation by those skilled in the art. Formally, similarity is modeled as a formula that assigns a number ranging from 0 to 1 to a pair of items. The number 0 means that the two items are not similar, while the number 1 means that they are similar. Numbers between 0 and 1 indicate various degrees of similarity, close to dissimilarity. In one form, two items are defined as similar if the number is at least a threshold, for example, at least 0.9. In this disclosure of the invention, any item is considered similar to itself. Similarity is defined in various ways in a specific context. In one form, using text, for example, items are defined as similar if at least 90% of the text matches, for example, in n grams, where n=5. In one form, using numerical values, for example, two real estate assets in a list are defined as similar if they have the same geographical location and a price difference of no more than 5%. In one form, similarity is defined using artificial intelligence software applied to items on a computer system. Examples of artificial intelligence software include neural networks, support vector machines, Markov models, and Bayesian networks. For example, the similarity between two real estate assets in a list is defined using artificial intelligence software applied to images associated with the real estate assets in the list, generating a similarity number in the range of 0 to 1. In another form, similarity is defined using normalized items, for example, the text "San Francisco" contained in an item is transformed into the text "San Francisco, CA", an area of ​​square feet is converted to an area of ​​square meters, and the pixel color of the image is rescaled to achieve an average brightness of 50%. In yet another form, similarity is defined using items represented as mathematical vectors, using the distance between the vectors, for example, Chebyshev distance, Minkowski distance, etc. In yet another form, similarity is defined as cosine similarity, string similarity (e.g., Levenshtein distance), semantic similarity, etc.In one form, vector coordinates are normalized, for example, to achieve meanings of 0 and changes of 1. In one form, similarity is defined by a combination of at least two similarities, for example, using matching text contained in the items, but using artificial intelligence software for images contained in the items, and a combination of the two results, for example, using a weighted sum. In one form, similarity uses only a portion of the items, for example, ignoring mortgage information for real estate in the list. Many other ways of defining similarity are obvious to those skilled in the art and do not deviate from the scope and intent of the embodiments.

[0005] The problems of clustering have been thoroughly studied. For example, there is the prior art survey, "Data Clustering: A Review" by Jain, Murty, and Flynn, ACM Computing Surveys Vol. 31, No. 3, September 1999. To simplify somewhat, given a number of items and the idea of ​​similarity between items, the goal is to assign items into groups of similar items. Several clustering methods have been developed from the prior art, such as connectivity-based clustering, e.g., cohesive hierarchical clustering; center-based clustering, e.g., k-means clustering; distribution-based clustering, e.g., prediction maximization algorithms; density-based clustering, e.g., DBSCAN; grid-based clustering, e.g., STING or CLIQUE; pre-clustering, e.g., canopy clustering; subspace clustering, e.g., CLIQUE or SUBCLU; and project clustering, e.g., PreDeCon. In one form, the clustering method computes clusters that satisfy additional requirements. Examples of requirements include minimum or maximum cluster size, and minimum or maximum cumulative similarity within a cluster. Additional requirements are determined based on the context in which the cluster is used.

[0006] A trivial approach to clustering involves calculating the similarity between all pairs of items and assigning them to the same group if their similarity is at least a threshold. However, the quadratic number of item pairs makes this trivial approach impractical when the number of items is large. To overcome the measurable problem for quadratic pairs, search engines often use heuristic problem learning on prune pairs that are unlikely to be similar. For example, one such heuristic problem learning is described in conventional US 6658423 B1. In this context, each item is a web page. This heuristic problem learning assigns a hash value to each web page. The hash value can be thought of as something like a very short piece of text, derived from the possible very long text of the web page. Web pages are grouped based on their hash values ​​(simply done by sorting hash values, bucketing, etc.), and similarity is calculated only between web pages that have the same hash value. Hash values ​​are constructed in such a way that a matching hash value between two web pages is often equal to two similar web pages. This can be achieved, for example, by using n-grams. As a result, heuristic problem learning can dramatically reduce the number of similarities that need to be computed compared to trivial quadratic approaches.

[0007] Several other heuristic problem learning approaches are advancing to achieve practical clustering in specific application domains. For example, when the item is real estate assets, the heuristic problem learning approaches are US 20150012335 A1, US 9858628 B2, and US 10776888 B1. On the other hand, when the item is job postings, the heuristic problem learning approaches are US 10043157 B2, Burk, Javed, and Balaji, “Apollo: Near-Duplicate Detection for Job Ads in the Online Recruitment Domain” from the International Conference on Data Mining Workshop 2017.

[0008] Many scoring methods have advanced. For example, refer to the prior art US 7058628 B1 for scoring based on page rank in a specific domain of a web search engine, and the prior art US 7974930 B2 for scoring based on characteristics of real estate assets in a specific domain of real estate.

[0009] Recent advancements in navigation technology have made it possible to create an engine for searching or comparing real estate assets using commuting times. Refer to the prior art WO 2019164727. For example, given a user request to identify a workplace, the technology quickly determines the exact travel time between all real estate assets from the workplace within a metropolitan area. This enables a detailed search of real estate assets. However, this prior art method is insufficient to achieve the purpose of showing useful search results. Such suggestions need to address the problems of avoiding repetitive information and improving the relevance of search results in a practical and scalable way. The disclosure of the present invention teaches a method to achieve this purpose.

Summary of the Invention

[0010] We present a simplified summary of the present invention to give the reader some insights into some aspects of the claimed subject matter. This summary is not intended to be an overview of understanding, nor is it intended to fully describe the scope of the present invention or identify important or key configurations of the present invention. The purpose of the summary is to outline some concepts in a form that makes it easy for those skilled in the art to read. The reader should consult the detailed disclosure of the present invention.

[0011] Aspects of the present invention include the following method. 1. A method for determining suggestions for a plurality of sites included in a transportation system using travel length and similarity, the method being characterized by the following: (a) Receiving a request comprising at least one location included in the transportation system, (b) Determine at least two isochronic sites included in the plurality of sites, Here, the length of the journey within the traffic system between each isochrone site and the at least one location is included within the range. (c) Determine the suggestion using one of the following steps: i. Determine a plurality of similar sites included in the at least two isochronic sites, and determine the implications of the plurality of similar sites, or ii. Select at least one first site that is not similar to at least one second site, both of which are included in the at least two isochronous sites, and determine the implications of the at least one first site and the at least one second site, and (d) Respond to the request having the aforementioned suggestion. 2. A method for determining an outline of multiple sites included in a transportation system, using the length and volume of the journey, the method being characterized by: (a) Receiving a request that includes at least one location in the traffic system, (b) Calculate the sequence of two or more sites included in the plurality of sites, where, i. In the sequence, the length of the journey within the traffic system between the first site and the at least one location is at least a range away from the length of the journey within the traffic system between the second site and the at least one location, ii. The amount relating to the third site included in the sequence is at most the amount relating to the fourth site included in the plurality of sites, and it is always the case that the length of the journey in the traffic system between the fourth site and the at least one location is in the same vicinity as the length of the journey in the traffic system between the third site and the at least one location. (c) Determine the summary including the suggestion of the sequence, (d) Respond to the request having the above summary. 3. A method for determining at least two alternative suggestions for multiple points of interest included in a transportation system, the method characterized by: (a) Receiving a request that includes a site in the said transportation system, (b) Determine at least two of the above alternatives, Here, the length of the journey within the traffic system between each alternative and the site is within the shortest threshold, (c) Determine the least two alternative suggestions that are not single and process descriptions, and (d) Respond to the request having the aforementioned suggestion. 4. A method for determining the length of an estimated journey and the indication of at least two sites included in a transportation system, the method characterized by: (a) Receiving a request that includes at least one location in the traffic system, (b) Determine the lengths of at least two estimated journeys, which include the lengths of estimated journeys within the traffic system between the at least two sites and each site included in the at least one location. (c) Select one or more sites included in the at least two sites using the lengths of the at least two estimated paths, Here, the number of sites, which is one or more, is at most a predetermined boundary. (d) Determine the length of at least one journey, which includes the length of the journey within the transportation system between the one or more sites and each site included in the at least one location. (e) Determine the suggestions for the one or more sites using the length of at least one stroke, and (f) Respond to the request having the above suggestion.

[0012] Embodiments of the present invention also include computer systems and apparatus for implementing any of the above methods.

[0013] The embodiments of the present invention shown in this disclosure are for illustrative purposes only; they are not intended to be exhaustive. Many modifications and variations will become apparent to those skilled in the art within the scope of the embodiments.

[0014] In this explanation, terms such as “first,” “second,” and “the aforementioned” are not used in any restrictive sense, but rather to distinguish them when they would be unclear without them in the text. Singular expressions include plural forms, and their absence would make the text unclear. Terms such as “have,” “include,” and “equip” indicate the existence of a feature or component, and do not exclude other components or features from existing or being added. [Brief explanation of the drawing]

[0015] The drawings included in the disclosure of this invention illustrate various features and advantages of several embodiments of the invention. [Figure 1] Figure 1 shows an illustrative data flow within the engine for searching or comparing real estate assets using commuting periods. [Figure 2] Figure 2 shows an example of a cluster of real estate asset listings contained within an isochrone. [Figure 3] Figure 3 shows an example of a user interface that receives requests from users and displays responses to them. [Figure 4] Figure 4 shows an example of an overview included in the user interface. [Figure 5] Figure 5 shows an example of the steps for determining alternative school suggestions to be included in the user interface. [Figure 6] Figure 6 shows an example of the steps in a two-phase approach for determining the duration of the process.

[0016] The drawings are for illustrative purposes only. Other drawings will be readily recognizable to those skilled in the art and will illustrate the present invention without deviating from the principles of this embodiment. Detailed description of the invention

[0017] This invention relates to a general case in which sites are shown using their similarities and commuting periods. However, for the sake of simplicity in the introduction, we first describe the invention through the form of an engine that searches or compares real estate assets using commuting periods. For brevity, we will call it an engine. This description is not limiting. Later, we will explain how the said method works in the general case. 1. Exemplary Forms

[0018] We describe exemplary embodiments of the present invention. In our description, we use the term "module." Conventionally, a term module refers to a computer system that provides several specific functions (and may therefore be seen as a computer subsystem). In one embodiment, the engine is classified into three modules: (1) acquisition module, (2) indexing module, and (3) serving module. Our choice to classify the engine into specific modules is illustrative and not mandatory. Those skilled in the art will know that, without departing from the scope of this embodiment, the engine could be classified into modules in other manners.

[0019] We describe the following functions of the module. Throughout our description, we refer to the descriptions of Figures 1, 2, 3, 4, 5, and 6, where an apostrophe indicates at least one example of a reference element. We describe the functions as a sequence of steps in a particular order. However, this order is not limited. Those skilled in the art will notice that some steps may be performed in other orders, partially concurrently, or combined or deleted without departing from the scope of this embodiment. 1.1 Acquisition

[0020] The acquisition model (1002) obtains information about real estate asset listings from at least one source (1001). Sources can be classified, but are not limited to, (1) direct sources: corresponding to real estate agents (brokers), landowners, construction companies, etc., who input real estate asset listings into the acquisition module; and (2) indirect sources: corresponding to websites, smartphone apps, etc., that publish the features of real estate asset listings and are crawled by the acquisition module. Acquisitions from first-type sources are performed as traditional call centers, websites, smartphone apps, or other entities with user interfaces where users can input features. Acquisitions from second-type sources are performed as computer systems that use the internet to visit the source, such as websites or smartphone apps that themselves publish features. During the visit, features are scraped. This process is often referred to as crawling. The characteristics of a real estate property listing include, but are not limited to, one of the following: name; address; geographical location; timestamp when the property was entered into or removed from the market; floor level; number of floor levels in the building; geographical orientation of the door; geographical orientation of the window; characteristics of the view from the window; price; rent; security deposit; landlord information; loan information; mortgage information; monthly maintenance fee; description of what is covered by the monthly maintenance fee; move-in date; 1 square meter area; 1 square meter area of ​​the land; price per area unit, e.g., 1 square meter; number of bedrooms; number of bathrooms; structural or layout characteristics; heating or cooling method; elevator description; parking description; swimming pool description; garden or backyard description; children's playground description; gym description; text description of the property written by the landlord or agent; images of the interior, exterior, or view from the window; interior tour video; sound or noise recording with the window open; air quality measurement; agent description; agent rating; or identifier assigned to the real estate property listing by the source.In one form, the acquisition module also obtains information on points of interest related to property listings via routes that are both included in the transportation system. Any point of interest is any location. Information on points of interest, and examples thereof, includes, but is not limited to, at least one of the following: schools (example information: type of school (public, private, daycare, primary school, middle school, high school, university, etc.), school ranking among other schools, tuition fees, school rules, admission possibilities, or geographical area of ​​the school zone), workplaces (example information: job title, or salary information), or amenities (example: public transport stations, highway entrances, parking lots, senior community centers, parks, hospitals, clinics, pharmacies, restaurants, shops, convenience stores, laundry services, banks, ATMs, government offices, crime reports, police stations, or military facilities). In one form, the information obtained by the acquisition module is stored in a non-temporary storage medium (e.g., a database). In one form, the acquisition process is interpreted as an action of measuring the real world, where this measurement obtains information about the physical existence of the entities described above. Physical existence is a form of data about the entities that is stored by the source in a non-temporary storage medium. Many other methods of obtaining real estate asset listings are apparent to those skilled in the art without deviating from the spirit of the above embodiments.

[0021] In one form, the acquisition module operates continuously, and therefore creates a perspective on real estate assets that develop over time. With respect to time, there may be one or more listings related to a physically existing house, for example, because the listing is based on several sources. Many of these have conflicting information, for example, two different square meter areas entered by two confusing real estate agents. A listing generated at one time may be adjusted later, for example, when the landlord edits the property description or uploads new images of the property. A listing may be deleted or restored, for example, when a potential tenant cancels the lease. A listing may have intentionally ambiguous information, for example, when the landlord prefers to cancel private information, for example, by providing a range for the geographical location of the listing, for example, a 100m circle, or for example, a range for floor levels in a building, such as "high floors". A listing may be degraded, for example, when the real estate asset has already been sold, but the sale is not reflected in the source. There are fake listings created to deceive potential tenants, or malicious listings that intentionally distort characteristics. 1.2 Indexing

[0022] The indexing module (1003) standardizes the data obtained by the acquisition module into a form that can be stored in a non-temporary storage medium. In one form, the form represents real estate assets as a feature vector, with each feature associated with a value. The data type of the values ​​is not limited to but may include text, numbers, images, videos, sounds, or smells. The values ​​are appropriately encrypted into a computer-accessible form. For example, price range is represented as two features with numerical values ​​indicating high and fixed. In one form, non-numerical values ​​are mapped to numbers. For example, the feature "elevator" has two possible values, "enabled" and "inactive," which map to values ​​1 and 0, respectively. In one form, the resulting features and their numerical values ​​are added to the feature vector. In one form, we add new features and numerical values ​​for each non-numerical value of a feature. In one form, features and their values ​​are calculated using at least one other feature and its value. For example, unit prices are calculated by dividing the price value by the area value, or the value is calculated by artificial intelligence software performed on features and values ​​by a computer system. In one form, a feature has a value that is a timestamp indicating the moment when the property listing of the feature was obtained by the acquisition module. In one form, the feature vector is sparse, and in that sense, some features are lost for property listings but present for others. For example, not all property listings have images of bathrooms. In one form, the feature vector is represented as a hash map or list. In one form, the indexing module settles conflicting information using, for example, majority voting, averaging, etc. Many other methods for standardizing data are obvious to those skilled in the art and do not fall outside the scope or spirit of the embodiments.

[0023] Standardization is carried out as a module customized for a specific source. The module is created by someone who studies the source in order to properly interpret the meaning of the data obtained from it. Once created, however, the module operates automatically without human supervision.

[0024] In one form, the indexing module creates at least one inverted index (1005) of the property listings. As described later, in the request process, the pre-calculated inverted index allows for the quick identification of property listings where a particular feature has a specific value. For example, the inverted index enables the quick identification of all property listings that have exactly three bedrooms. In one form, the inverted index is implemented as a hash map, sort list, or otherwise. In one form, the indexing module creates an inverted index for each feature that commonly occurs in any feature, such as user requests, e.g., price, number of bedrooms, number of bathrooms, square meter area, etc. Many other methods for creating at least one inverted index are obvious to those skilled in the art, and do not deviate from the scope or spirit of the embodiments.

[0025] In one form, the indexing module creates a clustering (1006) of at least one real estate asset listing. Pre-calculated clusters are useful because they help identify similar listings during the request process. The clustering uses the concept of similarity, one of which is described in the disclosure of this invention. In one form, the module creates a clustering of real estate asset listings using geographical distance. For example, the module reads feature vectors associated with real estate asset listings from a non-temporary storage medium. The geographical locations indicated by these feature vectors are clustered using a greedy approach. For example, if a location is not added to any previously generated cluster that does not exceed a cluster radius set, for example, 10m, the location is processed in any order and the location generates a new singleton cluster. The real estate asset listing is then assigned to a cluster based on the cluster of geographical locations. In one form, the indexing module creates a clustering using similarity defined by any feature, such as each feature that commonly occurs in user requests. In one form, the indexing module creates clusterings that use similarity defined by two or more features, such as only listings that match both price and area, which are considered similar. Many other methods for creating at least one clustering are obvious to those skilled in the art and do not deviate from the scope or spirit of the embodiments.

[0026] In one form, the inverted index or clustering is limited to listings obtained by the acquisition module over a period of time, such as the past 24 hours. In another form, the time period is broad, such as several years, to help identify value trends over time. In yet another form, the inverted index or clustering is limited to listings corresponding to assets in the market.

[0027] In one form, the indexing module also functions at points of interest, in a manner similar to the manner of manipulating real estate asset listings as described above.

[0028] In one form, standardized data, inverted indexes, or clusterings are stored on a non-temporary storage medium.

[0029] In one configuration, the indexing modules function sequentially, thus maintaining the perspectives of standardized data, inverted indexes, and clustering that evolve over time. 1.3 Serving

[0030] The serving module receives requests, generates suggestions, and responds to requests related to these suggestions. We will now describe several forms of this. 1.3.1 Implications

[0031] The serving module (1008) receives a request (1007) from the user. The request includes the identification of at least one commute destination (2001)(3002). A commute destination is any location, e.g., a geographical location, address, or point of interest. The request also includes parameters that determine the manner in which this at least one commute destination forms at least one commute path. A commute path is a sequence of journeys between pairs of endpoint locations. A commute path includes a commute destination, starting or ending at a real estate asset. A commute path forms one of many shapes, e.g., a round-trip commute path (e.g., home, then work, then home, or home, then the school closest to that home, then home), an open-journey commute path (e.g., home, then a school in the zone containing the home, then a piano lesson, then home), or a disconnected commute path (e.g., home, then work, then another home). Other manners for forming commute paths are obvious to those skilled in the art and do not deviate from the scope or spirit of the embodiments. Other parameters may be included in the request, such as departure time, arrival deadline, likelihood of arriving before the deadline, mode of transport (car, bus, subway, walking, combination, etc.), or frequency of transport. For example, the workplace is visited 5 times a week, and school is visited 3 times a week. Other parameters may be included in filtering limits that restrict the transport, such as what type of transport should be used for the transport, the maximum number of transfers, acceptable types of travel (e.g., subway travel, or bus-subway travel), time window for travel, limit on walking duration, or restrictions on points of interest included in the commuter pass (e.g., only schools in the top 10% of school rankings). Given a specific property, we determine the transport duration for at least one commuter pass. We simply refer to the result as the transport duration between the property and at least one commuter destination. In one form, the transport duration between the property and at least one commuter destination is a numerical value that reflects the aggregate amount of time that all residents of the property extend the transport over the course of time, for example, a day, a week, a month, etc. These residents include families living in the property, roommates, office colleagues, etc.Other parameters include financial information about the user, such as financial asset statements, credit ratings, income information, hourly wage, occupational category, occupational compensation information, mortgage applications; or school information about the user, such as school accreditation qualifications, such as mathematics test scores. The request also includes the identification of desired features or the value of real estate assets (3001), such as an area in the range of 80 and 90 square meters, or desired features of points of interest or their value. Many other ways of making a request are obvious to those skilled in the art and do not deviate from the scope or spirit of the embodiments.

[0032] The serving module then identifies real estate asset listings L(2002) that match the request parameters. For example, if the feature of the request is specified as “an area within the range of 80 and 90 square meters”, then this module identifies all listings that have an area within the range of 80 and 90 square meters. In one form, the identification process is by using an inverted index created by the index module, for example, by traversing sets of listing identifications, each set relating to the feature of the request specified in the request and its value. In one form, identification is limited to listings obtained by the acquisition module within a past time period, such as the past 24 hours. In one form, identification is limited to listings corresponding to assets in the market. In one form, L includes all real estate asset listings. Many other methods for identifying matching real estate asset listings are obvious to those skilled in the art and do not fall outside the scope or spirit of the embodiments.

[0033] The serving module then determines at least one travel distance between each geographical location of listing L and at least one of its commuting destinations, using any manner known to those skilled in the art, such as using a prior art navigation service (1010) as described in this invention. For example, for a specific geographical location H of a real estate asset listing, a specific geographical location of a workplace, and a specific geographical location of a school, the navigation service determines the travel time D from H to the workplace and back to H. w , and the duration of the journey from H to school and back to H D s Calculate the following: Then, the travel time between the real estate asset listing and at least one commuting destination is D w +D s Therefore, the contribution D w and contribution D s This includes. In one form, a process duration is derived from other process durations. For example, a process duration is a weighted sum, and based on, for example, process frequency (5·D w + 3·D s ) / (5+3). In other examples, the journey duration is the difference between journey durations, for example, the journey duration of the current home (included in the request) minus the journey duration of the candidate new home (included in listing L). In one form, the journey duration is the shortest journey duration, calculated, for example, using Dijkstra's algorithm on a graph modeling a traffic system, which is constructed using the prior art described in the disclosure of this invention. In one form, the journey duration is an estimated journey duration, for example, an incrementable factor or an additional number away from the shortest journey duration, for example, a factor of 2, an addendum of 15 minutes or 1000m (such as a characterization of the estimated journey duration corresponding to a journey duration calculated using the method or prior art described in this invention). In one form, the navigation service determines the journey duration using any of the following prior art methods: (a) Representatives, which are locations that frequently occur on the shortest route, are characterized in WO 2019164727 as locations included within a transport system, where the number of representatives is the size of the transport system multiplied by a predetermined proportion, which is at most 1, and examples of representatives include: (i) Sommer's “Shortest-Path Queries in Static Networks”, ACM Computing Surveys, Vol. 46(4) 2014, including landmarks, portals, hubs, beacons, seeds, transit nodes, etc. (ii) Transit stations and global stations in US 8756014 B2, where transits are likely to occur during long connections. (iii) The center of the grid in CN 105975627 A, or (iv) Boundary vertices in US 9222791 B2 (b) Processed graph data with nodes representing pre-filtered map features in US 250075 B2 (c)US 9195953 Reduction load graph in B2 (d)Hierarchy of Polygon Layers in US 7953548 B2 (e) Grid in CN 105975627 A (f) Subgraph obtained by eliminating at least one waypoint in US 8949028 B1 (g) One or more settled nodes in EP 2757504 A1 (h) Forward and backward section paths in EP 1939590 B1 (i) Overlay graph in US 9222791 B2 (j) Intermediate waypoint in US 20110251789 A1; or (k) Movement between a source location or a nearby source station and a target location or a nearby target station in US 8417409 B2, US 8738286 B2, US 8756014 B2, US 10533865 B2, KR 101692501 B1, or CN 104240163 A; Or, by any method of prior art, including: (l) Construction Hierarchies in Geisberger, Sanders, Schultes, and Delling: “Contraction Hierarchies: Faster and Simpler Hierarchical Routing in Road Networks,” Workshop on Experimental and Efficient Algorithms 2008, or Delling, Goldberg, and Warnack: “Faster Batched Shortest Paths in Road Networks,” Workshop on Algorithmic Approaches to Movement Models, Maximization, and Systems 2011 (m) Baum, Buchhold, Dibbelt and Wagner's CRP, GRASP, and PHAST-based technologies: “Fast Exact Computation of Isocontours in Road Networks”, ACM Journal of Experimental Algorithmics, October 2019. (n) Technologies listed in Sommer's survey paper: “Shortest-Path Queries in Static Networks” in ACM Computing Surveys, Vol. 46(4) 2014; or (o) The technology listed in the survey paper by Bast, Delling, Goldberg, Muller-Hannemann, Pajor, Sanders, Wagner, and Werneck is "Route Planning in Transportation Networks" from Algorithm Engineering 2016. In one embodiment, the serving module determines the travel time for the geographical location of the listing before L is identified, stores the travel time in a non-temporary, computer-readable storage medium, and retrieves the travel time from the storage medium after the location in L is identified. In another embodiment, the serving module determines the travel time for a subset of listings, for example, only for listings in a neighborhood within a metropolitan area, for example, within a 500m radius or 1-minute travel time from a preset location. Various methods for selecting a subset are described in the disclosure of the present invention. Many other methods for determining the travel time are obvious to those skilled in the art and do not deviate from the scope or spirit of the embodiments.

[0034] The serving module then groups listings L using the proximity of their journey duration. The group is related to the concept of isochrones, which are lines on a map connecting points with the same journey duration from a given location. However, we use the concept of isochrones (2003) in a broader sense, which carries range. In one form, the range is set to short journey durations, such as 15 minutes. In another form, the range is set to small distances, such as 500m. In yet another form, the range contains at least two values. For example, the serving module considers a continuous range of time with a width of M minutes, which is [0,M), [M,2M), [2M,3M), etc., where M is set to 15, for example. Group i is related to the range [iM,(i+1)M). Then, when D is the geographical location of the listing and the journey duration between at least 1 commute duration, the listing is assigned to group i, which is related to the range containing D. In other examples, a group may have a limited number of consecutive listings within the order of listings sorted by travel duration, for example, a maximum of 1000 listings per group. In this case, the ranges may have different widths. In one form, a group may include real estate listings within a geographical restriction, for example, a house a one-hour drive east of a workplace and a house a one-hour drive west of a workplace. In another form, a group may include real estate listings limited to a metropolitan area neighborhood (2005), for example, within a 500m radius and a one-minute drive from the current location. In one form, several ranges may overlap. In another form, a group may be calculated in relation to an isochrone enclosing a range of travel durations, for example, a predetermined range or a specific range in the request. Many other methods of grouping listings using travel duration proximity are obvious to those skilled in the art and do not deviate from the scope or spirit of the embodiments.

[0035] The serving module then determines clusters of similar listings within any group (2004)(2005). In one embodiment, the serving module uses any concept of similarity as described in the disclosure of the present invention. In one embodiment, clusters are determined using any clustering method as described in the disclosure of the present invention. A cluster may consist of only one listing, for example, when there are no other similar listings, or a cluster may consist of multiple listings. In one embodiment, similarity is influenced by the user, for example, a request may say “ignore multiple agents,” in which case listings that represent different agents, but advertise other similar houses, would be considered similar. In one embodiment, pre-calculated clusters are used to accelerate clustering during the request process, for example, listings are pre-clustered using text features that use pre-calculated clusters, and then each pre-cluster is clustered using user-influenced similarity. Many other methods for determining clusters for a group are obvious to those skilled in the art, and do not fall outside the scope or spirit of the embodiments.

[0036] The Serving Module determines a listing score or a listing cluster. A score is an entity that is compared to other scores, resulting in a ranking. For example, a score is a numerical value that is compared using “greater than or equal to”. In one form, a listing score uses at least one feature and its value, and includes, for example: journey features (e.g., the geographical location of the listing and the journey time between at least one commute destination), point of interest features (e.g., school rank, and at least one point of interest feature and value included on the commute destination), location features (e.g., crime rate, school effectiveness, local services, amenities), monetary features (e.g., market standard value, price increase or decrease), reputation features (e.g., previous lease brokered by a real estate agent, feedback from the previous broker), and transient features (e.g., how many days the listing has been on the market). In one form, a score is defined as a “feature” listed in Conventional Technology US 7974930 B2. In one form, the score is calculated by artificial intelligence software run by a computer system, which is trained on the user's past interactions with the serving module, such as features, values, and clicks on web links associated with requests, which serve as the ground truth for predicting the score. In another form, the score is determined using any mathematical formula, for example, the score is the weighted sum of the values ​​of that number of features. The weights can be positive, negative, or zero. In one form, we use equal weights. In another form, the weights are determined to give each feature an equal chance of influencing the score, for example: we use lower weights for features with high median values ​​across all listings. In another form, we use weights that favor certain features, for example: we use higher weights for monetary features, or higher weights for more recent listings.In one form, the score is influenced by the user, for example, a list might be “ordered by construction date” or “preferred to have a top school rank,” and in any case, the scores of correlated listings can be increased by, for example, appropriately adjusting the weights. In another form, the score of a cluster of listings is a mathematical statistic of the scores of the listings in the cluster, for example: the highest score of any listing in the cluster, or the weighted sum of the values ​​of several features in the cluster. In one form, the score is a vector, whose coordinates are calculated using at least one feature and its value, for example: a two-dimensional vector, whose first coordinate is the invalidated process period, and whose second coordinate is calculated using the aforementioned feature and its value. In one form, such a vector is lexicographically ordered. In another form, the score is text, for example, the names of apartment communities ordered alphabetically. Many other methods for determining the score are obvious to those skilled in the art, and do not deviate from the scope or spirit of the embodiments.

[0037] The serving module selects clusters. In one form, the module selects the number of clusters with the highest score, for example, 20 clusters. In another form, the module selects a number of clusters that satisfy at least one additional requirement. In one form, the additional requirement is to select clusters for a range of journey duration, for example, for range[0;M). In another form, the additional requirement is to select a maximum number of clusters, for example, a maximum of 5 clusters. In another form, the additional requirement is the geographical sparseness of the selected clusters, for example, to select the preset number of the largest clusters in any neighborhood. For example, by processing clusters greedily in order of score, prioritizing the highest score, and preventing clusters from being selected if a threshold or higher of clusters has already been selected in that neighborhood, for example, two or more clusters within 500m or within a 3-minute journey. In another form, the additional requirement is that the selected clusters have different characteristic values, for example, by selecting two-bedroom apartments and three-bedroom apartments. In one embodiment, diversity is ensured by greedily processing clusters and by excluding cluster selection, where the specific characteristics of the cluster have values ​​similar to those of the characteristics of already selected clusters. In one embodiment, additional requests are specified in a request, for example, that the request asks for listings in a particular school zone. In one embodiment, the module selects clusters using any clustering method disclosed in the present invention, where the clustering method operates on items, each of which is a cluster. For example, the clustering method determines an item that is the centroid, and that item becomes a selected cluster. Many other methods of selecting clusters are obvious to those skilled in the art, and do not fall outside the scope or spirit of the embodiments.

[0038] In one configuration, the serving module determines suggestions for at least one listing in a cluster and responds to requests having those suggestions (1009). The cluster is determined using, for example, the similarity between listings and the geographical location of the listings and the travel time between at least one commuter destination, as described above. The suggestions include, but are not limited to, at least one of the following: (a) Listing with the highest score within the cluster (b) The location of the listing within the cluster, related to the score (3007) (c) Process period for listing (3003), (3004) (d) A portion of the itinerary that is included in the itinerary by public transport, such as walking. (e) Listing fragment (3005) which is representative of the listing text. (f) Web links to listings published by the source (3006) (g) At least one feature for listing and its value (3005) (h) Mathematical statistics of the value, e.g., (i) a histogram of the feature values, (ii) frequency statistics, e.g., the most frequent or least frequent value of the feature, (iii) a random sample of the feature values, (iv) the maximum or minimum value of the number, (v) the mean, median, percentile, standard deviation, or variance of the number, or (vi) fractional values ​​of a number within a range, e.g., below a threshold or above a threshold. (i) Mathematical statistics based on a time period, such as price trends over the past five years. (j) Summary of any one listing within the cluster (k) A summary of at least two listings within the cluster, e.g., (i) the number of listings (3007), or (ii) an indication of the differences between the two listings, e.g., one with a lower price or one posted by a more reliable agent. (l) A combined listing constructed by combining the features and values ​​of at least two listings within a cluster, for example, showing the phone numbers of all real estate agents advertising a particular house, but showing only once the number of bedrooms in that particular house. (m) Representative of the cluster graph, or representative of the text. (n) Corresponding to any of the above, but related to a point of interest that is included in at least one commuting destination. In one form, the suggestions for a selected cluster are determined by including suggestions for at least one listing in each of the selected clusters. In one form, the suggestions for any set of real estate listings are determined in the manner in which suggestions for at least one listing in a cluster are determined. For example, by including mathematical statistics of the current market price of the listings, or by mathematical statistics of the price of the listings that match the user request. Many other methods for determining suggestions are obvious to those skilled in the art, and do not deviate from the scope or spirit of the embodiments.

[0039] In one form, we explicitly limit the suggestion of at least one listing (e.g., all listings contained within an isochrone), and as a result, that suggestion contains no information beyond what is explicitly re-quoted by the limitation. The limitation includes anything beyond the suggestion of at least one listing, but uses the additional limitation "only," for example: (a) Only the listing fragment with the highest score among at least one listing (b) Only the two combinations of the suggestion, or (c) Since k≧3, this is only the combination of k that gives the suggestion. This is because the limitation achieves a favorable effect by reducing the information overload placed on the user and increasing correlation. For example, showing just one listing among all listings with a journey duration of between 10 and 20 minutes places a small cognitive load on the user, while providing them with useful pieces of information.

[0040] In one form, the serving module responds to a request that has suggestions for at least two dissimilar clusters (1009). For example, the method selects two clusters whose distance is at least a threshold. The idea of ​​distance between clusters is conventionally known, for example: the shortest distance between any pair of listings from the two clusters, or the distance between the centroids of the two clusters. For example, the threshold is 1000m or a journey of 1 minute. Then the method determines the suggestions for the two clusters, which helps generate a response to a distributed request. The at least two clusters are determined using the listings and the journey distance between the listings' geographical locations and at least one commute destination, for example, by the method described above. For this reason, the first listing in one cluster is considered dissimilar to the second listing in the other cluster. In one form, the journey duration is within a range, but in the other form, the journey duration is not required to be within a range. Many other methods for responding using suggestions of at least two clusters are obvious to those skilled in the art, and do not fall outside the scope or spirit of the embodiments. 1.3.2 Overview

[0041] In one embodiment, the method determines an outline of a real estate property listing using a quantity associated with each listing and a travel period. One advantageous effect of the outline is its ability to help users find listings that strike a personalized balance between the quantity and the travel period. We describe one embodiment, where the quantity is interpreted as its sale price. This interpretation is not limiting. Other interpretations of the quantity are obvious to those skilled in the art and do not deviate from the scope or spirit of the embodiments. To simplify the description of how the outline is determined, we refer to individual listings. However, each such individual listing is understood to correspond to a cluster of similar listings determined according to the method disclosed in this invention. Each travel period described herein is between a real estate property and at least one travel destination.

[0042] In one form, the method calculates a sequence of listings that require short durations, and compares listings with distributed and nearby durations to achieve a lower value. In one form, the calculation begins with listing L that matches the desired characteristics. In another form, the calculation is performed using a greedy method. For example, (a) This method considers listings U that have sale prices and orders listings first by the lowest sale price; (b) Then, this method processes the listing with orders at sale prices; (c) During processing, the method selects the following listing e and excludes any listing from the subsequent being processed, provided that the process duration of this listing falls within the range of the process duration of e; (d) Then, this method repeats step (c) until there are no more listings to process. This calculation yields several numbers k≧1 in the listing. This number k depends on how wide or narrow the range is. For example, the range is set to 15 minutes or 1000m. We sequence this calculated listing in increasing order of travel duration as e1, e2, e3, …, e k For example, e1(4001) has a travel duration of 5 minutes and a sale price of 1 million dollars, e2(4002) has a travel duration of 14 minutes and a sale price of 1.2 million dollars, e3(4003) has a travel duration of 25 minutes and a sale price of 0.7 million dollars, and so on. Due to the manner in which the listing is calculated, we know that for each e i there is no listing with a low sale price, and the travel duration of e i is in the vicinity of the travel duration. This vicinity includes at most the range of the travel duration of e i or at least the range of the travel duration of e i This vicinity includes both ranges. This feature of the vicinity depends on the long monotonic run of the sale price along the travel duration. For example, in the above example, e1 is the cheapest listing of any real estate asset having a travel duration within the vicinity of 5 minutes. In one form, we use a loose requirement for travel duration or quantity to calculate the sequence of listings. For example, we select the maximum of the listing thresholds within the range of the travel duration, such as at most 5 listings. For example, we select the maximum of the listing thresholds having the lowest value of quantity in the vicinity, such as at most 5 listings. In one form, the calculated listings are optionally sequenced (so e1, e2, e3, …, e k do not necessarily follow an increasing order of travel duration; for example, they can follow a decreasing order or a monotonic order of the quantity associated with the calculated listings). Many other methods of calculating the sequence of listings will be apparent to those skilled in the art without departing from the scope and spirit of the embodiments.

[0043] In one form, the method sequences the calculated listings e1, e2, e3, …, ek The implications of the clusters are described in the disclosure of the present invention and are readily apparent to those skilled in the art, and do not deviate from the scope or spirit of the embodiments, e1, e2, e3, ..., e k There are corresponding implications. For example, the implications include sale price and fragments for each calculated listing. In one form, the implications include mathematical statistics on sale price across listings, and the journey period of this listing is e i The process duration is within the range. Examples of mathematical statistics are described in this invention, for example, the 10th percentile, the 50th percentile, and the 90th percentile. This gives the user an idea of ​​what kind of sale price is valid for the listing, and the process duration of this listing is e i The timeframe is comparable. In one form, the insights include mathematical statistics of sale prices across listings. This gives the user an idea of ​​what kinds of sale prices are valid in the general market. For example, the user can see what is considered cheap (e.g., the 10th percentile) (4004) and what is considered expensive (e.g., the 90th percentile) (4005) among currently valid two-bedroom apartments in the metropolitan area real estate market.

[0044] Calculated listings e1, e2, e3, ..., e k These are viewed as “first-level” listings, meaning they provide an overview of the lowest sale price based on the journey duration. In one form, this method calculates “second-level” listings (4006). i For this method, e i and e i+1 , and the duration of the journey d i and d i+1 These are related to the following. Furthermore, this method is a subset of listing U. i Considering this, the duration of those journeys is d i and d i+1 It is between. Then, this method uses any method described in the present invention, such as a method for selecting a cluster, Ui Determine the implications. For example, the implications for U1 include a small number of geographically dispersed listings, such as up to 10, with the listings having a journey duration between 5 and 14 minutes, and the listings having the lowest sale price among the listings within the journey duration range. The implications for U1 include mathematical statistics about U1. Its edge case set is defined as follows: U0 includes all listings within U with a journey duration less than d1, and U k This includes all listings within U that have at least a process duration dk; in one form, its edge case set is further restricted to be within a range of process durations. Set U i If it's empty, for example, when there are no listings that match the characteristics of the request, the process duration d i and d i+1 It has a process period between the two. Therefore, the "second level" is not uniform. In one form, the method calculates the "second level" listing by subdividing the process period and calculating the sequence. In another form, the method calculates the "third level" listing, etc., by further subdividing the process period.

[0045] In one form, the method determines a geographical representation of the relationship between sale price and itinerary. In another form, the geographical representation plots the mathematical statistics of sale price associated with each range of itinerary. For each range of itinerary, the method determines the mathematical statistics of sale price and generates a shape that encrypts these mathematical statistics, such as points, lines, ellipses, triangles, range bars, etc. The geographical representation can take many forms, but is not limited to, plots, histograms, pie charts, and heatmaps. For example, a plot may include a horizontal axis corresponding to the itinerary, a vertical axis corresponding to the sale price, and a rendering of the mathematical statistics of sale price associated with listings within each itinerary range. For example, (4010) represents the 90th percentile of the sale price, (4011) represents the middle of the sale price, and (4012) represents the 10th percentile of the sale price. For example, Figure 4 shows listings with itinerary periods within a range including 30 minutes where the 10th percentile of the sale price is $1 million. Many other methods for determining the geographical representativeness of the relationship between sale price and itinerary are obvious to those skilled in the art and do not fall outside the scope or spirit of the embodiments.

[0046] In one form, the method enables navigation. The method is performed by a device that displays suggestions for “first-level” listings. The user exchanges information using the device. For example, the user can exchange information using user interface elements (4007) (performing gestures such as tapping, moving the mouse, touching, swiping, and touch-and-holding on a touch-sensitive display screen), and exchange information with user interface elements (4008) to respond to the device displaying appropriate “second-level” listing suggestions or to hide suggestions. As a result, the user is shown a compact overview of the lowest sale prices, and the user can iterate through the overview to explore the trade-offs between sale prices and itinerary periods. In one form, the listing presentation is linear (e.g., a list), and the user can scroll the presentation up and down (e.g., circle it), and the user can rotate the presentation, and vice versa. In one form, the locations of “second-level” listings are plotted on a map while those “second-level” listings are displayed. In one configuration, the locations of other listings are hidden from the map while their “second-level” listings are displayed. In another configuration, the user exchanges information using geographical representations. For example, the user taps a user interface element (4013), and the device responds by displaying a listing suggestion, the duration of which is within the range associated with this element. Many other methods for navigation are obvious to those skilled in the art, and do not deviate from the scope or spirit of the embodiments.

[0047] This method uses listings e1, e2, e3, ..., e that use sale prices and itinerary periods. k Other methods for calculating are used. In one embodiment, the method uses any clustering method disclosed in the present invention. Each ei is selected from the clusters, for example, the centroid of the cluster. In one embodiment, at least one additional request to the clusters is set, including: setting a maximum value for k such as 20; e kIt is required to be as low as possible; e i The duration of the journey and e i+1 Setting a minimum range, such as 10 minutes, for the duration of the journey; setting a maximum number, such as 5, of listings to be selected within the vicinity of the journey duration; requiring that only listings with the shortest journey duration be clustered, such as a minimum of 2 hours; requiring that only listings with the lowest sale price be clustered, such as a minimum of 75 percentiles; requiring that e1 be selected from listings with journey durations within a range, such as a minimum of 5 minutes; requiring that e1 have the lowest sale price, such as within 10% of the lowest sale price of any listing. i This involves selecting, and the duration of this listing is e i For example, the process duration is within a range. In one form, a clustering problem with at least one additional request is encrypted as a linear program. In one form, the range of process durations is different from the range of other process durations. For example, the range is narrower for process durations with listings that have relatively lower sale prices. As an effect, listings e1, e2, e3, ..., e k It is not required that the process durations be equally spaced. Many other methods for calculating listings are obvious to those skilled in the art, and do not fall outside the scope or spirit of the embodiments.

[0048] As mentioned above, we used sale price as a form of quantity. Generally, a quantity is an entity that can be compared to other quantities, and as a result, an order is constructed. In one form, this quantity is set to a value of a feature, such as school rank. In another form, a request describes a quantity, for example, a request says "high sale prices are preferred," and in any case, the quantity is the invalidation of the sale price (i.e., the sale price is multiplied by -1). In yet another form, a quantity is derived from a value of a feature using a mathematical formula. For example, a quantity reflecting "floor concentration" is given by the formula (f / b-0.5) 2Calculated for a specific listing using , where b is the number of floors in the building and f is the floor level of the listing within the building. In one form, the device displays derived text related to the derived quantity instead of simply “floor level,” for example, “center of floor.” For example, a quantity reflecting geographical orientation similarity is determined as the absolute value of the difference in orientation along the shortest arc (e.g., the quantity is related to a user request to identify a north window, and 90 degrees for an east window). In one form, the quantity is derived from the values ​​of two or more features, for example, by dividing the sale price by area. In one form, the quantity is determined with respect to features predicted from the requested features. For example, when a user is looking for an apartment of about 100 square meters, then the quantity is set to |x-100|, where x is the square meter area of ​​that apartment. In one form, the prediction is automated based on the request and features, for example, using preset rules; artificial intelligence software learned from past requests, etc. In one form, the quantity is equal to a score. Many other methods for determining the amount are obvious to those skilled in the art, and do not fall outside the scope or spirit of the embodiments.

[0049] In one form, element (4002) is characterized as a suggestion of at least one listing A, and element (4003) is characterized as a suggestion of at least one listing B, both A and B are contained within an isochrone where A is not similar to B. In one form, element (4009) is characterized as a suggestion of at least one listing C, and is contained within the same isochrone E, where A is not similar to C, and B is not similar to C. In one form, isochrone E is broadly determined. For example, given an apartment request, if there are no apartments with a travel period between 20 and 180 minutes, the width is at least 160 minutes. In one form, a suggestion of multiple sites includes an outline of multiple sites.

[0050] Many other methods for determining the details of a real estate asset listing, using quantities related to each listing and process period, are obvious to those skilled in the art and do not fall outside the scope or spirit of the embodiments. 1.3.3 Alternative

[0051] Consider the case of two schools. Each school zone includes a given house. In such a case, the children living in that house will attend one of the two schools. Once attendance is decided, the children commute to the chosen school but not to the other (unchosen) school. Thus, the chosen school contributes to the total commuting time for the house. The other school does not contribute, but it is useful for the house to know that there is an alternative school, for example, because the other school contributes to several other opportunities.

[0052] Therefore, a general problem arises, and here, in order to reduce the information overload on the user and increase correlation, we want to determine the suggestion of alternative points of interest. We present a solution to this problem.

[0053] We begin with a form relating to alternative schools. Our method operates on school S0 (5003) and property asset H (5002). In one form, S0 and H are included in the request. In another form, H is included in the request, and S0 is the school closest to H in terms of journey duration or distance. In another form, S0 is included in the request, and H is determined by a step in our other method. In another form, H and S0 are determined by a step in our other method. We then determine the journey duration D0 (5001) between school S0 and property asset H (e.g., the duration of the round trip between H and S0) using any method disclosed in this invention. Then we determine a set of alternative schools A whose journey duration is not significantly greater than the journey duration D0 of school S0. For this purpose, we select the alternative schools S1, ..., S closest to property asset H with m≧0. m We determine (5004)(5005)(5006). We determine the travel period D between school Si and real estate asset H. iDetermine the following. Then, for each i≧1, set D0 plus the threshold for the process duration, if D i If ≤ t, we evaluate. Examples of thresholds are 20 minutes and 2000m. If the evaluation is successful, then schools Si(5004)(5005) are included in set A. In one form, if the evaluation fails, then school Si(5006) is excluded from set A. For example, when each alternative school is separated from real estate asset H, the resulting set A is empty. In one form, we include school S0 in set A. In another form, we exclude school S0 from set A. In another form, set A contains at least two schools.

[0054] Next, we determine the implications of set A. These implications are not limiting, but include at least one of the following: (a) any of the implications characterized in Section 1.3.1 accordingly; (b) The semantics of set A, such as the text “Average rank of alternative schools”; (c) Information obtained by the acquisition module relating to the school including set A, for example (i) the name of the school, (ii) the type of school, (iii) the rank of the school, (iv) the tuition fees of the school, (v) the rules of the school, or (vi) the possibility of permission to grant permission to the school; (d) Mathematical statistics (5008) of the information obtained by the acquisition module with respect to the schools included in set A, for example: (i) the number of schools, (ii) the maximum, minimum, or average rank of the schools, (iii) the maximum, minimum, or average tuition of the schools, (iv) JPEG2026072105000001.jpg1153, with any possibility of permission from the school, where pi is the possibility of permission from the school Si. Predicted rank values, such as JPEG2026072105000002.jpg1146, where ri is the rank of school Si, or (vi) JPEG2026072105000003.jpg1150, for example, a predicted value of tuition fees, where ui is the tuition fee for school Si; (e) Itinerary period D for schools included in set Ai Mathematical statistics on the following, for example: (i) journey duration D i , or (ii) the maximum, minimum, or average duration of the school; or (f) Any combination of the above, for example: (i) Itinerary period D j fractional part (ii) The total weight of the school rank, such as (1) JPEG2026072105000004.jpg1255, or the total weight of the school course duration, such as (2) weight of the possibility of permission pi.

[0055] In one form, the suggestion of set A includes a description of a journey. In another form, the suggestion of set A includes a suggestion that is not a description of a journey, for example, the number of schools in set A. In another form, it will be obvious to those skilled in the art that the idea of ​​"not a description of a journey" is further restricted in order to exclude prior art.

[0056] In one form, a suggestion of set A has a non-uniformity that depends on set A. A dependence is called uniform when it is limited to at most one school in set A. Any other dependence is non-uniform. For example, the names of schools from set A are uniform, but the names of top-ranked schools from set A containing at least two schools are non-uniform. Non-uniform suggestions are useful because they can summarize information about a large set A. One form of a suggestion that is non-uniform is a mathematical statistic, which is a numerical function for each school from set A containing at least two schools, and in this way the mathematical statistic has a non-zero sub-derivation of each of these numerical values. In one form, the average rank of at least two schools is a non-uniform suggestion. In one form, a suggestion of set A contains a uniform suggestion. In one form, a suggestion of set A contains a non-uniform suggestion. In one form, the idea of ​​“non-uniformity” is further restricted in order to exclude prior art, as is also apparent to those skilled in the art.

[0057] In one form, we explicitly restrict the implications of set A, so that the implications contain no information other than that explicitly re-quoted by the restriction. This restriction includes all of the above implications of set A, but has only additional limitations, for example: (a) Only the names of schools included in set A that have the best ranking among schools (b) Number of schools only (c) Only the average duration of the school year (d) School average rank only (e) Any combination of two suggestions, or (f) Only combinations of k that suggest k ≥ 3. This achieves a favorable effect, as the limitation reduces the information overload on the user and increases correlation. For example, showing only the number of schools in set A as information about set A imposes a small cognitive burden on the user, while providing the user with a useful piece of information.

[0058] In one form, alternative schools S1, ..., S m This is filtered before determining set A. In one form, we use filtering based on real estate assets H located within the school zone of the correlated schools. In another form, this filtering is based on requests. For example, the requests might specify: “Private schools only,” “Schools within a maximum of 20 minutes from home only,” “Schools with a maximum tuition of $500 only,” “Schools ranked in the top 30 percentiles only,” “Schools where my child will be accepted based on school indicators only,” “Schools where there is at least an 80% chance of acceptance given my child’s following characteristics.” In another form, we use itinerary period D i Alternatively, use preset filtering based on a formula that uses values ​​for school characteristics. The example formula represents the filtering specified in the request.

[0059] In one form, we score schools in a manner similar to the scoring listing manner described above. And, accordingly, we use the school scores to select set A.

[0060] In one form, the score of a real estate asset listing is determined using information obtained by an acquisition module for schools included in set A. For example, the score is increased by the mathematical statistics values ​​of set A.

[0061] In one form, suggestions for multiple sites include suggestions for set A. For example, if a request specifies that real estate assets should be ordered by school rank, then suggestions for real estate assets include suggestions for the nearest school and alternative school suggestions for set A. In one form, the calculation of set A includes any method we use. In one form, the serving module responds to requests that have suggestions for set A.

[0062] The above method for suggesting alternative schools can be generalized to suggesting points of interest. However, we have alternative points of interest S1, ..., S m How to make a decision needs to be discussed. Whether or not alternatives are possible for two points of interest depends on the identification of the points of interest, and is therefore arbitrary. Thus, our method automatically determines alternatives in a manner consistent with the viewpoint of a person skilled in the art. For example, if the point of interest is hospitals and the request specifies "orthopedics," then the decision will be based on each hospital that has an orthopedic ward. In one form, we use the similarities between the points of interest to identify alternative points S1, ..., S m To decide.

[0063] Many other methods for determining alternative points of interest are obvious to those skilled in the art, and do not deviate from the scope or spirit of the embodiments. 1.3.4 Two-Phase Approach

[0064] In one embodiment, the serving module uses a two-phase approach: (Phase 1) calculate estimated journey durations and use those journey durations to select some clusters, and (Phase 2) calculate the journey durations of the selected clusters and use those journey durations to determine implications. In one embodiment, the two-phase approach saves resources (Phase 1) and limits the reduction in the amount of journey. For simplicity of explanation, we describe the embodiment by referring to just one commute destination (6001). However, how this embodiment can be generalized to at least one commute destination will be obvious to those skilled in the art, without deviating from the scope or spirit of the embodiment. This embodiment operates on any setting of the listing and comprises: (a) Calculate the estimated travel time between each geographical location and commute destination of the listing, including the following forms: (i) Identify neighborhood representatives (6003) within the threshold of the commute destination (6001), identify neighborhood representatives (6005) within the threshold of the geographical location (6007), extract a pre-calculated journey time (6004) between these two neighborhood representatives, set an estimated journey time to this extracted journey time, and selectively extend it using: the journey time (6002) between the commute destination (6001) and its neighborhood representatives (6003), or the journey time (6006) between its geographical location (6007) and its neighborhood representatives (6005), (ii) Identify neighborhood representatives (6003) within the threshold of the commute destination (6001), extract a pre-calculated travel time (6008) between the neighborhood representatives (6003) and their geographical location (6009), set an estimated travel time to this extracted travel time, and selectively extend it using the travel time (6002) between the commute destination (6001) and its neighborhood representatives (6003), (iii) Identify neighborhood representatives (6011) within a threshold of geographical location (6013), extract a pre-calculated travel time (6010) between the commute destination (6001) and the neighborhood representative (6011), set an estimated travel time to this extracted travel time, selectively extend it using the travel time (6012) between the geographical location (6013) and its neighborhood representative (6011), and (iv) Obtain the travel time (6014) between the commute destination (6001) and the geographical location (6015) from the navigation service, The example threshold is 1000m, or a distance of 1 minute. In one form, we identify at least one neighborhood representative within a threshold of the commute destination or geographical location, and set the estimated journey duration to the minimum of any journey duration between the commute destination and any of the representatives of the restricted location. In one form, we pre-calculate the nearest neighborhood data structure (e.g., a Voronoi diagram for representatives of distance or travel time), and during request processing, we use the nearest neighborhood data structure to determine the representative of the neighborhood. (b) Select one or more clusters of listings using the estimated process duration, but not exceeding a predetermined boundary; the value of the predetermined boundary is set based on at least 1, which does not affect or limit the number of clusters, as the suggestion is determined later in step (d): the number of clusters the suggestion needs to include in the response; the degradation of process quality resulting from the estimated process duration in step (a); the improvement of process quality made by the process duration in step (c); the resource consumption associated with determining the estimated process duration in step (a); or the resource consumption associated with determining the process duration in step (c); for example, the predetermined boundary is set to 1000 (e.g., selecting the cluster with the highest score, e.g., clustering the selected listings by selecting listings that match the desired features, and scoring the clusters); (c) Determine the travel time between each geographical location of the listings included in the selected cluster and their commute destinations, using, for example, a conventional method for calculating the shortest path or a method for calculating travel time disclosed herein; and, (d) Using the process duration to determine the implications of the selected clusters (for example, after updating the clusters and scores using the process duration; by selecting clusters such as the number of highest-scoring clusters, which is the largest preset fraction of a predetermined boundary, for example, the number of highest-scoring clusters, which is up to 20).

[0065] In one form, the method performs smoothing of a process close to a representative. Smoothing prevents the process from having an unnatural shape near a location, where a pre-calculated process joins the extended process. For example, we take a pre-calculated sub-process of a process that starts at a representative close to the source location, and extend this sub-process to have a process between this source location and locations included in the sub-process (which do not need to be representative). Additional information on smoothing can be found in prior art WO 2021222046.

[0066] In one form, this method calculates the outline using a two-phase approach. In one form, this method calculates the “first level” listings e'1, e'2, ..., e' as described above. k’ This method calculates the estimated journey duration instead of the actual journey duration, and uses a narrow range, such as 1 minute. This is because the range is narrow and the value k' is often large. Then, this method lists e'1, e'2, ..., e' k’ Determine the duration of the process. Then, this method uses a wider range, such as 15 minutes, and this duration to determine the “first level” listings e1, e2, e3, ..., e k Calculate and list e'1, e'2, ..., e' k’ This is done by means of reshuffling and pruning. For example, this method is by listing e1, e2, e3, ..., ek Calculate, where this calculation is a listing e'1, e'2, ..., e' k’ The process begins with (above, we show the form, where the calculation begins with listing L). In one form, the method calculates the geographical location using the estimated journey duration. Many other methods for calculating the outline using a two-phase approach are obvious to those skilled in the art and do not deviate from the scope or spirit of the embodiments.

[0067] In one embodiment, the method employs a two-phase approach within our method. In one embodiment, the method uses a two-phase approach to compute at least two alternative suggestions, where set A is determined using estimated path durations, and the suggestions are determined using path durations. In one embodiment, the method computes isochrones using estimated path durations, and then suggestions using path durations. Many other methods for employing a two-phase approach are apparent to those skilled in the art, and do not deviate from the scope or spirit of the embodiments. 1.3.5 Adjustment Steps

[0068] In one embodiment, the serving module performs a comparative adjustment step compared to the steps disclosed in the present invention. For example, the serving module performs the following steps: other orders, partially concurrent processing, or a combination or omission of several steps. In one embodiment, the two steps of grouping and clustering are combined into one. For example, we expand the feature vector. We take the feature vector of a listing and add: a value representing the geographical location of the listing and the journey time between at least one commute destination, and also a value representing the correlation point between the feature and interest. The feature vector expanded in this way is clustered. In one embodiment, the clusters are determined using any clustering method disclosed in the present invention. In one embodiment, the clusters satisfy additional requirements. For example, we restrict any cluster to span to the maximum range of minutes along the axis of the additional feature, for example, a maximum of 15 minutes. In one embodiment, pre-calculated clusters are used to accelerate clustering during request processing, for example, by means of a clustering algorithm that starts its execution with the pre-calculated clusters. In one embodiment, the step of selecting a cluster is not similar. In one embodiment, the step identifies a cluster of at least one site. In one embodiment, the step identifies at least one site using any information contained in the request described in the disclosure of the present invention, such as filtering restrictions or feature of the request. Many other methods for performing the adjustment step are obvious to those skilled in the art and do not deviate from the scope or spirit of the embodiments. 2 General Cases

[0069] We use the term "journey" in a broad sense and ensure consistency in its interpretation among those skilled in the art. This term includes meanings that include moving objects or data. A journey description is whatever a person skilled in the art would name it. Here are some examples of journey descriptions: (1) "Hi, you need to go one block north and then turn a little to the left" and (2) "5 dollars." The length of a journey is a numerical value that a person skilled in the art would consider relevant to the journey, such as: the monetary cost of the journey; the distance in meters; fuel consumption; or specific features or attributes of the journey description, such as the number of transfers or walking distance. As another example, we use the term journey duration when we mean the length of a journey in terms of time. In one form, the length of a journey is derived using any endpoint of the journey, such as real estate assets, commuting destinations, or whatever their characteristics or values ​​may be. For example, the journey length is derived using a weighted sum of values, such as the sum of two values: (1) the distance in meters from the company's exit to the building's entrance door, and (2) the value of a feature indicating the floor level of the real estate asset within the building. In one form, the journey length uses a request. For example, this request involves an arbitrary conversion between two values. For example, the journey length is fuel consumption multiplied by the conversion rate from fuel units to monetary amounts, thus converting a maximization objective using fuel consumption to a maximization objective using monetary cost. In one form, the journey length uses an arbitrary preset conversion. In one form, the journey length uses two or more maximization objectives combined with one maximization objective, using an arbitrary mathematical formula such as a weighted sum. In one form, the maximization objective is combined with a multi-objective maximization search based on multi-dimensional cost. For example, the method is to find real estate assets that minimize the journey duration penalized by the journey's monetary cost and the journey duration permitted by filtering restrictions. The length of a journey is, in itself, a description of the journey. A description of a journey may or may not include the length of the journey, may include only the length of the journey, or may include some other data.The present invention provides instructions on how to calculate the description of a journey, for example, any method for calculating a journey disclosed in the present invention, or any prior art method for calculating a journey described in the present invention, such as using Dijkstra's algorithm.

[0070] We use the term "transportation system" in a broad sense, with consistency in its interpretation among those skilled in the art. In some forms, this includes: road and vehicle systems; public transport systems comprising buses and subways; airports, airplanes and aircraft lanes; or ships and sea lanes. A transportation system does not necessarily require the physical movement of objects. The methods disclosed in this invention simply need to be able to determine the description of the journey between elements of a transportation system. For this reason, a transportation system that moves data is an example of a transportation system. For example, computer networks comprising these transportation elements: wireless / wired (similar to roads), and hubs / switches (similar to stations / turns). Any combination of transportation systems permitted for movement between them is a transportation system. Many other examples of transportation systems are obvious to those skilled in the art, without falling outside the scope or spirit of the embodiments.

[0071] In one embodiment, the present invention considers real estate forms other than search or compare. In one embodiment, the method indicates job postings using the similarity between job postings and the travel time between the location where the job is performed and the home location. For search and compare forms, see Prior Art WO 2021222046.

[0072] Generally, the methods disclosed in this invention use both any site (a site is referred to as a real estate asset in the section above) and any place (a place is referred to as a commute destination above), both of which are included in a transportation system, and the methods determine the indication of at least one site using a description of at least one journey between at least one site and at least one place (the journey duration used above is generalized to the journey description). A site is any location. It is a real estate asset such as, for example, an apartment, a rental house, a house with a garden, a lunch, or a hotel. It can also be a place where people work, a restaurant, a shop, etc. A place can also be any location. It can be a school, a grandparent's house, a weekend golf course, a favorite restaurant, a doctor's office, a place of worship, etc. It can also be a place where people live. In one form, a point of interest is interpreted as a site. In another form, a point of interest is interpreted as a place.

[0073] In one form, a method for searching or comparing at least one site using a description of at least one route in a transport system between at least one site and at least one location, the method comprising: (a) receiving a request comprising the at least one location; and (b) responding to the request with the result of a search or comparison obtained using the description of the at least one route. In one form, the result of the search or comparison is in the form of an indication of at least one site.

[0074] In one embodiment, the method disclosed herein performs a variety of the functions or steps described above. In one embodiment, some functions and steps are performed in other orders, partially concurrently, or some functions or steps are combined or omitted. For example, the method performs serving, but not acquisition or indexing. In other embodiments, the indexing module (1003) does not generate any inverted index (1005) and does not generate any clustering (1006). In other embodiments, the request does not include the identification of at least one commuter destination. In one embodiment, the method performs clustering or scoring without using the journey time between real estate assets and at least one commuter destination. In one embodiment, multiple listings are determined using one of the following, but not both: (i) similarity between listings, or (ii) the geographical location of the listings and the journey time between at least one commuter destination. Many other examples of performing a variety of functions or steps are obvious to those skilled in the art, and do not deviate from the scope or spirit of the embodiments.

[0075] Aspects of the present invention may take the form of hardware, software, or a combination of both. Steps of the present invention, such as blocks in a flowchart, may be executed outside of the rules depending on the function or optimization, and may be partially simultaneous or saved from a cache. Aspects may take the form of a sequential system or a parallel / distributed system, where each component embodies several aspects in overlap with other components, and the components are communicated using, for example, any kind of network. The present invention is not written with reference to any particular programming language. Computer programs performing operations for aspects of the present invention may be written in any programming language, such as C++, Java, or JavaScript. Any program may run on any hardware platform, such as a central processing unit (CPU) or graphics processing unit (GPU), and associated with memory or storage devices. Programs may perform aspects of the present invention on one or more software platforms, including, but not limited to, the Android or iOS operating system, or web browsers such as Firefox, Chrome, Internet Explorer, or Safari. 3 ways

[0076] Embodiments of the present invention include the following methods. 1. A method for determining the implications of multiple sites included in a transportation system, using the length and similarity of the routes, the method being characterized by: (a) Receiving a request that includes at least one location in the traffic system, (b) Determine at least two isochronic sites included in the plurality of sites, Here, the length of the journey within the traffic system between each isochrone site and the at least one location is included within the range. (c) Determine the suggestion using one of the following steps: i. Determine a plurality of similar sites included in the at least two isochronic sites, and determine the implications of the plurality of similar sites, or ii. Select at least one first site that is not similar to at least one second site, both of which are included in the at least two isochronous sites, and determine the implications of the at least one first site and the at least one second site, and (d) Respond to the request having the aforementioned suggestion. 2. A method for determining an outline of multiple sites included in a transportation system, using the length and volume of the journey, the method being characterized by: (a) Receiving a request that includes at least one location in the traffic system, (b) Calculate the sequence of two or more sites included in the plurality of sites, where, i. In the sequence, the length of the journey within the traffic system between the first site and the at least one location is at least a range away from the length of the journey within the traffic system between the second site and the at least one location, ii. The amount relating to the third site included in the sequence is at most the amount relating to the fourth site included in the plurality of sites, and it is always the case that the length of the journey in the traffic system between the fourth site and the at least one location is in the same vicinity as the length of the journey in the traffic system between the third site and the at least one location. (c) Determine the summary including the suggestion of the sequence, (d) Respond to the request having the above summary. 3. A method for determining at least two alternative suggestions for multiple points of interest included in a transportation system, the method characterized by: (a) Receiving a request that includes a site in the said transportation system, (b) Determine at least two of the above alternatives, Here, the length of the journey within the traffic system between each alternative and the site is within the shortest threshold, (c) Determine the least two alternative suggestions that are not single and process descriptions, and (d) Respond to the request having the aforementioned suggestion. 4. A method for determining the length of an estimated journey and the indication of at least two sites included in a transportation system, the method characterized by: (a) Receiving a request that includes at least one location in the traffic system, (b) Determine the lengths of at least two estimated journeys, which include the lengths of estimated journeys within the traffic system between the at least two sites and each site included in the at least one location. (c) Select one or more sites included in the at least two sites using the lengths of the at least two estimated paths, Here, the number of sites, which is one or more, is at most a predetermined boundary. (d) Determine the length of at least one journey, which includes the length of the journey within the transportation system between the one or more sites and each site included in the at least one location. (e) Determine the suggestions for the one or more sites using the length of at least one stroke, and (f) Respond to the request having the above suggestion. 4. Computer systems and devices

[0077] One embodiment of the present invention is a computer system (illustration shown in Figure 1). The computer system may be in the form of hardware, software, or a combination of both. The computer system includes at least one processor, such as a CPU or GPU. The computer system includes a non-temporary computer system-readable storage medium that stores one or more programs to be executed by at least one processor. The forms of non-temporary computer system-readable storage mediums are known in the prior art and therefore do not need to be described here. One or more programs comprise instructions to be executed by at least one processor to perform at least one step of the method disclosed in the present invention. In one embodiment, the instructions are expressed in any programming language, such as C++, Java, or JavaScript. Each method gives rise to a computer system. Any such computer system is considered a general-purpose computer specifically programmed to perform a particular method described in the disclosure of the present invention. Thus, as an effect, the computer system is a specific-purpose computer and is programmed to perform a particular step of the method in accordance with instructions from software (one or more programs) that encrypts the method. Many other forms of computer systems are obvious to those skilled in the art, and do not fall outside the scope or spirit of the embodiments.

[0078] One embodiment of the present invention is a device, also referred to as an apparatus. Illustrations are shown in Figures 3, 4 and 5. It will be apparent to those skilled in the art that the apparatus in our figures can be modified (e.g., rearranged, resized, colored, shaped, added or removed) without deviating from the scope and spirit of the present embodiment. The apparatus has a physical form, such as a smartphone application or a web page. The apparatus receives a request from a user via a “receiver,” which is, for example, the user interface of a smartphone application, where, for example, the user can type their work address into a search box, tap a map displayed by the smartphone application to locate their work location, speak to a conversational recognition engine describing their work location, and provide GPS readings to encrypt their work location. In one embodiment, the receiver receives any information contained in a request as described in the disclosure of the present invention. The apparatus then generates a suggestion to use the method described in the disclosure of the present invention. In one embodiment, this generation is performed by executing one or more suitable programs suitable for at least one processor. The device then responds to the user by showing the suggestion via a “transmitter,” for example: a display on a smartphone app (for example, by displaying as shown in Figure 3 (3005)), a conversational synthesizer (for example, by speaking to the user), augmented reality lenses contained in glasses worn on the user's head, a Jedi Knight 3D projector, etc. It will be obvious to those skilled in the art that the present invention is not limited to one type of device, nor to one type of receiver or transmitter. Many other forms of the device will be obvious to those skilled in the art, and will not deviate from the scope or spirit of the embodiments. 5. Final Chapter

[0079] Those skilled in the art will know that various modifications can be made, and that substitutions can be made with essentially equivalents without deviating from the scope and spirit of the invention. Moreover, certain circumstances apply to the teachings of the invention without deviating from the scope and spirit of its forms. Therefore, regardless of the fact that the invention is stated with reference to the disclosed forms, the invention should not be limited to these inventions. Rather, the invention includes all forms that fall within the scope of the accompanying claims.

[0080] Each claimed method does not involve a “mental process” (i.e., no steps of the claimed method performed in the mind of a person). Each claimed method is automatic. Section 4 describes examples of automatic methods. The scope of each claimed method excludes any form that is ineligible for patentability in a particular jurisdiction, where the patent application is filed during the PCT national / regional stage. For example, a Canadian patent application would explicitly state that each claimed method is limited to forms applicable to Canadian patents. Each particular jurisdiction excludes certain forms from its jurisdiction (different jurisdictions exclude different sets of forms).

[0081] In one form, any claimed method is to be performed on a computer system (i.e., computer execution) and to achieve the purpose of the device (e.g., the purpose of searching, comparing, and determining suggestions). Section 4 describes examples. In one form, any claimed method is limited to a form that falls within the meaning of “manual of manufacture” in the sense of the exclusive rights statute used in New Zealand, for this is obvious to one person skilled in the art. In one form, any claimed method is limited to a form that falls within the meaning of “technical features” in the sense of the European Patent Agreement, for this is obvious to one person skilled in the art.

[0082] Prior art referenced in the disclosure of this invention is understood to be general knowledge of the art, and those skilled in the art possess such knowledge.

[0083] In claims, prior grounds are sometimes enclosed in boxes: boxed terms in claims are used later as dotted-line boxed terms.

[0084] We include a glossary of selected phrases as they appear in the claims and provide references in the specification as examples. These references are not intended to be exhaustive and other references may exist. The sequence of phrases in the table above is intended to follow the order in which the terms first appear in the claims. JPEG2026072105000005.jpg225166JPEG2026072105000006.jpg51166

Claims

1. A method for determining an outline of multiple sites included in a transportation system, using the length and volume of the journey, the method being characterized by: (a) Receiving a request that includes at least one location in the traffic system, (b) Calculate the sequence of two or more sites included in the plurality of sites, where, i. In the first and second sites included in the sequence, the lengths of the following two journeys: the length of the journey within the traffic system between the first site and the at least one location, and the length of the journey within the traffic system between the second site and the at least one location are separated by at least a range, and ii. The quantity related to the third site included in the sequence is at most the quantity related to the fourth site included in the plurality of sites, and this is always the case when the stroke length L4 is in the vicinity of the stroke length L3. Here, L4 is the length of the journey within the traffic system between the fourth site and the at least one location, and L3 is the length of the journey within the traffic system between the third site and the at least one location. (c) Determine the summary including the suggestion of the sequence, (d) In response to the request, the response includes the summary.

2. A method for navigating an overview of multiple sites included in a transport system, using the length of the journey, the method being characterized by: (a) Receiving a request that includes at least one location in the traffic system, (b) Using the length of the first path in the traffic system between the site and the at least one location, calculate a sequence of at least one cluster of sites included in the plurality of sites, wherein the sequence includes a first cluster including at least two sites, (c) Determine the outline, which includes the sequence suggestion and also includes the first suggestion of the first cluster, wherein the first suggestion depends on the length of the first stroke, and display the outline on the apparatus. (d) Determine at least one second-level site included in the plurality of sites, Here, the length of the second stroke in the traffic system between each second level site and the at least one location is: i. The length of the second stroke is shorter than the length of the stroke in the traffic system between the clusters included in the sequence and the at least one location. ii. The length of the second stroke is at least the length of the stroke in the traffic system between the cluster included in the sequence and the at least one location, or iii. The length of the second stroke is the length of the stroke in the traffic system between the cluster included in the sequence and the at least one location, and the length of the stroke in the traffic system between the next cluster included in the sequence and the at least one location, and (e) Navigate the above overview as follows: i. Information exchange using the user interface elements of the device, receiving information from the user, ii. Responding to the information exchange by displaying the second suggestion of at least one second-level site on the device, or by concealing the second suggestion from the device.

3. A method for navigating an overview of multiple sites included in a transportation system, using quantity, the method being characterized by: (a) Receiving a request that includes at least one location in the traffic system, (b) Using the first quantity, calculate the sequence of at least one cluster of sites included in the plurality of sites, wherein the sequence includes the first cluster containing at least two sites, (c) Determine the above summary, which includes the suggestion of the sequence and also includes the first suggestion of the first cluster, where the first suggestion depends on the first quantity, and display the summary on the apparatus. (d) Determine at least one second-level site included in the plurality of sites, Here, the second quantity contained within each second-level site is i. The second quantity is shorter than the quantity contained within the cluster contained within the sequence. ii. The second quantity is at least the quantity contained within the cluster contained within the sequence, or iii. The second quantity is between the quantity contained in the cluster contained within the sequence and the quantity contained in the next cluster contained within the sequence, and (e) Navigate the above overview as follows: i. Information exchange using the user interface elements of the device, receiving information from the user, ii. Responding to the information exchange by displaying the second suggestion of at least one second-level site on the device, or by concealing the second suggestion from the device.

4. A computer program for causing a computer to perform the method described in claim 1.

5. A computer program for causing a computer to perform the method described in claim 2.

6. A computer program for causing a computer to perform the method described in claim 3.

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