WO2024166512A1 - 同行判別装置 - Google Patents

同行判別装置 Download PDF

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Publication number
WO2024166512A1
WO2024166512A1 PCT/JP2023/043544 JP2023043544W WO2024166512A1 WO 2024166512 A1 WO2024166512 A1 WO 2024166512A1 JP 2023043544 W JP2023043544 W JP 2023043544W WO 2024166512 A1 WO2024166512 A1 WO 2024166512A1
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Prior art keywords
information
accompanying
mesh
unit
distribution information
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PCT/JP2023/043544
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English (en)
French (fr)
Japanese (ja)
Inventor
和泉 春田
徳浩 勝丸
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株式会社Ｎｔｔドコモ
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Priority to JP2024576140A priority Critical patent/JPWO2024166512A1/ja
Publication of WO2024166512A1 publication Critical patent/WO2024166512A1/ja

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services

Definitions

  • the present invention relates to an accompaniment determination device that determines whether users are accompanying each other.
  • Patent Document 1 describes a method for determining whether multiple users are accompanying each other by looking at the positional relationship with each other's positional information.
  • the location information handled in Patent Document 1 is, for example, latitude and longitude measurement data obtained by GPS or base station positioning, but there are times when the time at which data is acquired varies depending on the device, and some data may be missing, making it difficult to match the data between the two, and in some cases making it impossible to correctly identify accompanying persons.
  • the present invention aims to solve these problems by providing an accompanying person identification device that can accurately identify accompanying persons.
  • the accompanying determination device of the present invention includes a location information acquisition unit that acquires multiple pieces of location information for each of multiple terminals, a distribution information acquisition unit that acquires location distribution information for each of the multiple terminals based on the number of pieces of location information for each specified area, a calculation unit that calculates the similarity between the location distribution information for each of the multiple terminals, and a determination unit that determines that multiple users having each of the terminals are accompanying each other based on the similarity.
  • This invention makes it possible to accurately identify accompanying persons.
  • FIG. 1 is a diagram illustrating the background of the present disclosure.
  • FIG. 13 is a diagram showing an overview of an accompanying discrimination process in the present disclosure.
  • 1 is a diagram showing a system configuration for performing accompanying determination between users in an accompanying determination device 100 according to the present disclosure.
  • FIG. 2 is a diagram illustrating a functional configuration of the accompanying person discrimination device 100 according to the first embodiment of the present disclosure.
  • 4 is a flowchart showing the operation of the accompanying person discrimination device 100.
  • FIG. 11 is a diagram illustrating a detailed process of step S103.
  • FIG. 11 is a diagram showing similarity and its transition over time.
  • FIG. 11 is a block diagram showing a functional configuration of an accompanying person discrimination device 100a according to a second embodiment of the present disclosure.
  • FIG. 13 is an explanatory diagram of generating a probability distribution.
  • FIG. 13 is an explanatory diagram showing each weight when a user is in each mesh.
  • FIG. 13 is a diagram showing histograms when a user is present in each mesh.
  • 4 is a flowchart showing the operation of the accompanying person discrimination device 100a.
  • FIG. 1 is a diagram illustrating an example of a hardware configuration of an accompanying person discrimination device 100 and 100a according to an embodiment of the present disclosure.
  • FIG. 1 is a diagram explaining the background of this disclosure.
  • FIG. 1(a) shows a user U visiting a store T alone.
  • customer analysis was performed based on who was doing what and when.
  • Figure 1(b) shows user U visiting store T together with users U1 and U2. It is known that the presence of accompanying persons can greatly affect a user's behavior, such as their willingness to purchase, and by designing a service that takes into account accompanying person information, it is possible to design a more valuable UX (User Experience). As shown in Figure 1(b), when user A is visiting together with users B and C, user A's behavior may be significantly different compared to when users B, etc. are not present.
  • the purpose of this disclosure is to determine whether user A is acting together with user B, etc.
  • FIG. 2 is a diagram showing an overview of the accompanying determination process in the present disclosure.
  • FIG. 2(a) shows that user A through user C are targets for acquiring location information.
  • FIG. 2(b) shows a schematic diagram of a storage unit that associates location information for each user with the time of acquisition.
  • FIG. 2(b) further shows that the log information is divided for each analysis section.
  • Figure 2(c) shows the user's movement route in mesh code units.
  • a mesh refers to a regional mesh defined as a longitude and latitude grid on a map in order to obtain various statistical information by digitizing information on the map, and each mesh is assigned code information (mesh code).
  • This mesh code is assigned by the Ministry of Internal Affairs and Communications and is an identifier that indicates a predetermined rectangular area. In this disclosure, it is determined which mesh the location information is contained in, and the location information is converted into a mesh code. In Figure 2(c), the mesh code can be used to determine the user's approximate location and movement route.
  • Figure 2(d) shows a histogram for each mesh code. In other words, it shows the frequency with which a user stayed for each mesh code. Because the area indicated by a mesh code has a certain width, it is possible to determine that a user is in the same area (mesh) even if the user moves. On the other hand, even if part of the user's location information is missing, this can be absorbed overall and the area in which the user stayed can be determined.
  • FIG. 3 is a diagram showing a system configuration for performing accompaniment discrimination between users in the accompaniment discrimination device 100 of the present disclosure.
  • This system includes the accompaniment discrimination device 100, multiple mobile terminals 200, and a location management server 300.
  • the mobile terminals 200 are mobile phones and are devices that can communicate by connecting to a mobile communication network. When the mobile terminals 200 move within a coverage area, and also periodically, they register their own coverage area and the time they are within the coverage area with the location management server 300.
  • the location management server 300 stores the location information obtained by base station positioning in the mobile terminal 200.
  • the mobile terminal 200 registers the location information obtained by base station positioning in the location management server 300.
  • the location management server 300 stores the location information obtained by the mobile terminal 200 using base station positioning, but this is not limited to this.
  • the location information stored may be location information obtained by GPS in the mobile terminal 200, or may be the location information of a base station registered by location registration, or the center position of a sector, or a position calculated based on these.
  • the accompaniment determination device 100 uses the location information stored in this location management server 300 to determine whether the target user to be determined is acting together with other users (accompanying candidates).
  • the target user is designated in advance by the operator of the accompaniment determination device 100 or other service providers that provide services to the target user.
  • the other users may be all users, or may be users who are in the same area as the target user or in a nearby area. They may also be designated in advance by the operator, etc.
  • FIG. 4 is a diagram showing the functional configuration of the accompaniment discrimination device 100 in the first embodiment of the present disclosure.
  • the accompaniment discrimination device 100 includes a radio connection history acquisition unit 101, a position estimation unit 102, a mesh code conversion unit 103, an analysis interval extraction unit 104, a histogram creation unit 105, a similarity calculation unit 106, and an accompaniment discrimination unit 107.
  • the radio wave connection history acquisition unit 101 is a part that acquires and stores radio wave connection history information from the location management server 300.
  • This radio wave connection history information includes location information (information obtained by base station positioning) and the acquisition time of the target user and accompanying candidates for accompanying determination.
  • the target user is designated in advance by an operator of the accompanying determination device 100, etc.
  • Accompanying candidates are selected from other users who are in a specified area during the same time period based on the location information of the target user, but may be all other users.
  • the location estimation unit 102 is a part that extracts the location information of the target user and accompanying candidates based on base station positioning and the acquisition time of the location information from the radio wave connection history information.
  • the mesh code conversion unit 103 is the part that converts the location information into a mesh code.
  • the analysis interval extraction unit 104 is a part that extracts a certain time window (analysis interval) from the location information for a specified period (for example, one day).
  • the analysis interval extraction unit 104 shifts the target for generating the histogram by shifting this window (by a width smaller than the above-mentioned certain time).
  • the histogram creation unit 105 is a part that creates a histogram from the distribution of mesh codes in the window.
  • the similarity calculation unit 106 is a part that calculates the similarity based on the histogram. For example, the similarity calculation unit 106 calculates the similarity using KL divergence or JS divergence.
  • KL divergence is a measure that indicates the degree to which two probability distributions are similar, and it can be determined that the smaller the value, the more similar the distributions are.
  • JS divergence is also a method for determining the similarity.
  • the similarity calculation unit 106 may use a method other than KL divergence or JS divergence.
  • the similarity calculated here indicates the similarity in the window (analysis interval) extracted by the analysis interval extraction unit 104.
  • the similarity calculation unit 106 calculates the similarity in one window
  • the analysis interval extraction unit 104 shifts the window slightly (while partially overlapping with the previous one window) and further creates a histogram and calculates the similarity.
  • the accompanying discrimination unit 107 is a part that discriminates accompanying based on the degree of similarity.
  • the accompanying discrimination unit 107 discriminates accompanying based on a threshold from the score series calculated for each window, and discriminates the section in which accompanying occurred.
  • the accompanying discrimination unit 107 may discriminate a section as an accompanying section (in which accompanying occurs) when the accompanying score is equal to or less than the threshold for a predetermined number of consecutive times. This threshold and the predetermined number of times may be determined in advance by machine learning, etc., or may be determined in advance based on past data or empirical rules, etc.
  • FIG. 5 is a flowchart showing the operation of the accompanying discrimination device 100.
  • the radio wave connection history acquisition unit 101 acquires the radio wave connection history
  • the position estimation unit 102 acquires the position information of each user and the acquisition time thereof (S101).
  • the mesh code conversion unit 103 converts the position information into a mesh code (S102).
  • the analysis interval extraction unit 104 divides the location information for a specified period into windows of fixed time.
  • the histogram creation unit 105 creates a histogram based on the mesh codes (corresponding to the location information) in the windows (S103).
  • the similarity calculation unit 106 calculates the similarity of the histograms using KL divergence or the like (S104).
  • the accompaniment discrimination unit 107 then extracts accompanying candidates and their sections whose similarity is below a threshold, and discriminates them as accompanying persons and accompanying sections (S105).
  • FIG. 6 is a diagram showing a schematic diagram of the detailed processing of process S103.
  • a correspondence table of time, location information, and mesh codes is stored in a memory unit (not shown).
  • a window w1 is extracted by dividing the mesh codes of the target user by a certain time (e.g., from t1 to t3), and a histogram is created based on the window w1.
  • the mesh codes of window w2 are then extracted after a slight delay of the certain time (e.g., from t2 to t4), and a histogram is created.
  • the mesh codes of window w2 are then extracted after a further slight delay of a certain time (e.g., from t3 to t5), and a histogram is created.
  • the above processing is also performed for accompanying candidates.
  • histograms for similarity determination are created with a certain degree of width.
  • the similarity between histograms for the same time period is then calculated.
  • Figure 7 shows the detailed process of calculating similarity.
  • Figure 7(a) shows the user's movement route for each mesh of the target user A and its histogram. It shows the frequency of stay in each mesh, such as mesh mc1 being 4, mesh mc2 being 3, etc. This frequency of stay indicates the number of location information included in the mesh.
  • Figure 7(b) shows the movement route of User B, a potential accompaniment candidate, for each mesh and its histogram.
  • Figure 7(c) shows the movement route of User C, a potential accompaniment candidate, for each mesh and its histogram. Note that for ease of explanation, some of the mesh codes indicating the meshes have been omitted. All meshes are assigned mesh codes.
  • the KL score is calculated from the histogram for each mesh in a certain time period using the following formula. This formula calculates the well-known KL divergence score.
  • x is the mesh code
  • p(x) is the proportion of frequency when the target user was at x compared to the total
  • q(x) is the proportion of frequency when the accompanying candidate was at x compared to the total.
  • the frequency of mesh codes between the target user and the accompanying candidate is Target user ⁇ mc1:n1, mc2:n2, mc3:n3 ⁇ Accompanying candidate ⁇ mc1:n4, mc2:n5, mc3:n6 ⁇
  • FIG. 8 is a graph showing similarity and its time progression. As shown in the figure, the vertical axis is the score indicating similarity (KL divergence), and the horizontal axis is time. The lower the score based on KL divergence, the higher the similarity.
  • a time period with a KL score below a predetermined threshold is determined to be when the target user and accompanying person are traveling together.
  • the threshold is set to 0.1, and a time period below this value is determined to be an accompanying period. In the present disclosure, a period from approximately 9:00 to just after 21:00 is determined to be an accompanying period.
  • start time of the window in Figure 6 is associated with each time of the KL score in Figure 8, but this is not limited to this, and it may be determined in advance, such as the end time of the window or an intermediate time.
  • FIG. 9 is a block diagram showing the functional configuration of an accompaniment discrimination device 100a in the second embodiment of the present disclosure.
  • the accompaniment discrimination device 100a includes a radio connection history acquisition unit 101, a position estimation unit 102, a mesh code conversion unit 103, an analysis interval extraction unit 104, a probability distribution creation unit 104a, a histogram creation unit 105, a similarity calculation unit 106, and an accompaniment discrimination unit 107. It differs from the accompaniment discrimination device 100 in that a probability distribution creation unit 104a and a weight information storage unit 104b have been added.
  • the probability distribution creation unit 104a is a part that weights adjacent meshes and creates their probability distribution.
  • the histogram creation unit 105 creates a histogram for each mesh, taking into account the weights.
  • the weight information storage unit 104b is a part that stores the weight coefficient for each mesh. This weight information storage unit 104b stores the weight coefficients of adjacent meshes, centering on the mesh where the user is staying. However, this is not limited to this, and the weight information storage unit 104b may store different weight coefficients for each mesh and weight coefficients for its adjacent meshes. In this case, weight coefficients are set for each mesh and for each adjacent mesh, allowing for accurate similarity determination. It is preferable that the weight coefficient is set to a value that takes into account errors for each mesh, and therefore it is good to calculate the accuracy of the error in advance. For example, it is good to calculate in advance the probability that a user is actually in a certain mesh and the estimated result is estimated in another mesh, and to take this probability into account as well.
  • Figure 10 is an explanatory diagram of how this probability distribution is created.
  • Figure 10(a) shows a diagram that defines the weights of the mesh in which the object is staying and the meshes adjacent to it. As shown in the figure, when the weight of the mesh in which the object is staying is set to 1, the weight of the adjacent meshes is set to 0.5. This makes it possible to reduce the impact of deviations in the estimated position.
  • the weights may be predetermined values, or may be calculated and set in advance based on the accuracy of the error. For example, the probability that the user is actually in mesh code B2 and the estimation result is estimated to be mesh B1 or mesh C2 may be calculated in advance, and the weights may be defined taking this probability into account.
  • Figure 10(b) shows a diagram in which weights are defined for the meshes adjacent to adjacent meshes. As shown in the figure, a smaller weight, such as 0.1, is defined for the adjacent mesh codes.
  • weights of adjacent and nearby meshes are represented by fixed values, but this is not limited thereto, and as described above, the weights may be changed for each mesh.
  • the histogram creation unit 105 uses this weight to generate a histogram. For example, if the user is in mesh B2 and the user's frequency of visit to that mesh B2 is 1, the histogram creation unit 105 creates a histogram of 1 for that mesh B2. Also, for meshes adjacent to mesh B2, such as mesh A1, a histogram of 0.5 is created even if it is not estimated that the user was actually in that mesh.
  • Figure 11 is an explanatory diagram showing the user's movement route and the weights when the user is in each mesh.
  • Figure 11(a) shows the user's movement route in a mesh.
  • FIG. 11(b) shows the weights when the user is in mesh a4.
  • the weight of mesh a4 is 1, and the weights of meshes a3, b3, and b4 are 0.5.
  • FIG. 11(c) shows the weights of each mesh when the user is in mesh b4.
  • the weight of mesh b4 is 1, and the weights of the other adjacent meshes such as a4 are 0.5.
  • FIG. 11(d) shows the weights of each mesh when the user is in mesh b3.
  • the weight of the mesh in which the user is staying is 1, and the weight of the adjacent mesh is 0.5, but this is not limited to this.
  • the weights may be defined differently for each mesh or depending on the adjacent direction.
  • the positioning error may differ between adjacent meshes adjacent in a diagonal direction and adjacent meshes adjacent in the vertical direction, so the weight may be changed in that direction.
  • the weight information storage unit 104b Such rules for defining the weights are stored in the weight information storage unit 104b.
  • Figure 12 shows histograms when a user is present in each mesh in Figure 11.
  • Figure 12(a) shows a histogram when the user is in mesh a4.
  • Meshes a3, b3, b4, c3, and c4, which are adjacent to mesh a4, are assigned a weight of 0.5, and the value reflected in the histogram is the product of this weight multiplied by the frequency of visits to mesh a4 (for example, 1).
  • Figure 12(b) shows the histogram when the user is in mesh b4.
  • a weight of 0.5 is defined for meshes a3, a4, b3, c3, and c4, which are adjacent meshes to mesh b4, and the value obtained by multiplying this weight by the frequency of visits to mesh a4 is reflected in the histogram.
  • Figure 12(c) is a histogram when the user is in mesh b3. The same process as above is carried out. Histograms are similarly created for other destinations, although they are not shown. For ease of explanation, the frequency of visits in each mesh is set to 1, but depending on the user's behavior, the frequency of visits may be 2 or more.
  • FIG. 12(d) is a histogram summing up the frequency of stays reflecting the above weighting in each mesh when moving through each mesh including the mesh mentioned above.
  • FIG. 12(d) shows the histogram for that window.
  • a histogram is created for each window of each user (target user and accompanying candidate), and this is used to calculate the similarity for each window and determine whether or not they are accompanying each other.
  • FIG. 13 is a flowchart showing the operation of the accompanying discrimination device 100a.
  • the radio connection history acquisition unit 101 and the position estimation unit 102 acquire position information (S101), and the mesh code conversion unit 103 converts the position information into a mesh code (S102).
  • the analysis interval extraction unit 104 extracts the mesh code for a certain period of time, and the probability distribution creation unit 104a creates a probability distribution for the mesh in which the user is located and the adjacent meshes (102a). This is based on the method described in Figure 11 above.
  • the histogram creation unit 105 creates a histogram for each mesh where the user stayed according to the probability distribution created by the probability distribution creation unit 104a, and adds them up to create a histogram of the user's movements over a certain period of time (S103). Histograms are created for the target user and accompanying candidates.
  • the similarity calculation unit 106 calculates the similarity between the histograms of the target user and other accompanying candidates (S104), and the accompanying discrimination unit 107 performs accompanying discrimination (S105).
  • a histogram based on the probability distribution can be created, and accompanying discrimination can be performed while taking into account positioning errors.
  • the position estimation unit 102 estimates and acquires multiple pieces of position information for each of the multiple mobile terminals 200.
  • the histogram creation unit 105 acquires a histogram (position distribution information) for each mesh (area) of the multiple mobile terminals 200 based on the number of pieces of position information for each predetermined area for each of the multiple mobile terminals 200.
  • the similarity calculation unit 106 calculates the similarity between the histograms (position distribution information) of each of the multiple mobile terminals 200.
  • the accompanying determination unit 107 determines that the multiple users having the mobile terminals 200 are accompanying each other based on the similarity.
  • position information can be aggregated into meshes (predetermined regions), and a histogram can be created for each mesh based on the number of meshes, and entrainment discrimination can be performed based on the histogram. Therefore, even if some of the position information is missing, the error can be absorbed, enabling accurate entrainment discrimination.
  • the mesh code conversion unit 103 converts the location information into a mesh code (identifier for a specific area), and the histogram creation unit 105 creates a histogram (location distribution information) for each mesh based on the mesh code (identifier).
  • the mesh in the present disclosure refers to a regional mesh defined as a longitude and latitude grid on a map in order to digitize information on the map and collect various statistical information, and each mesh is assigned code information (numerical information). Mesh codes are assigned by the Ministry of Internal Affairs and Communications.
  • the histogram indicates the frequency of visits of the mobile terminal 200 in each mesh based on the number of pieces of location information. This frequency of visits indicates the user's behavior.
  • the histogram creation unit 105 acquires the number of pieces of location information of the mobile device 200 for each mesh (predetermined area) for each predetermined window (analysis section) and creates a histogram based on the information.
  • the similarity calculation unit 106 calculates the similarity based on the histogram in the window (analysis section).
  • a window indicates a time period, and the similarity of user behavior in a certain time period is calculated.
  • the histogram creation unit 105 shifts the window (analysis interval) extracted by the analysis interval extraction unit 104 and creates a histogram for each mesh in that interval.
  • the accompanying discrimination device 100a of the present disclosure further includes a weight information storage unit 104b that stores a weight coefficient (weight information) according to the distance from the position of the mobile terminal 200.
  • the histogram creation unit 105 then creates a histogram for each mesh in a certain window based on the weight coefficient.
  • This weight information storage unit 104b stores the weight coefficients of adjacent or nearby meshes adjacent to a mesh (predetermined area) for each mesh code (identifier) assigned to each mesh (predetermined area). It is preferable that these weight coefficients be set based on the positioning error of the location information.
  • the impact of errors or missing information in the location information logs between accompanying people can be reduced, making it possible to robustly determine whether or not someone is accompanying someone and extract the section in which they were accompanying someone, even when using data with large errors, such as latitude and longitude positioning data obtained by base station positioning.
  • GPS data requires a dedicated app to obtain it, and the quality of the data that can be obtained varies depending on the user's control. In addition, because it places a load on the mobile terminal 200 (battery, etc.), it is necessary to take into consideration the positioning interval.
  • the accompaniment determination device 100 disclosed herein can obtain sufficient accuracy not only with GPS data, but also with latitude and longitude positioning data obtained by base station positioning, making it possible to expand the range of people who can be estimated as accompaniments.
  • the location information handled is not limited to base station positioning, and may of course include GPS data.
  • the accompanying discrimination device 100, 100a disclosed herein has the following configuration.
  • a location information acquisition unit that acquires a plurality of pieces of location information of a plurality of terminals; a distribution information acquisition unit that acquires location distribution information of the plurality of terminals based on the number of pieces of location information for each of the plurality of terminals in a predetermined area; A calculation unit that calculates a similarity between location distribution information of each of the plurality of terminals; a determination unit that determines that a plurality of users each having the terminals are traveling together based on the similarity;
  • An accompanying discrimination device comprising:
  • a conversion unit converts the location information into an identifier of the predetermined area
  • the distribution information acquisition unit acquires the location distribution information based on the identifier.
  • the accompanying discrimination device according to [1].
  • the location distribution information includes a frequency of stay of the terminal based on the number of pieces of location information.
  • the accompanying discrimination device according to [2].
  • the distribution information acquisition unit The number of the plurality of pieces of position information for each of the predetermined regions is acquired for each predetermined analysis section to acquire position distribution information;
  • the calculation unit calculates a similarity based on position distribution information in the analysis section.
  • the accompanying discrimination device according to any one of [1] to [4].
  • a weight information storage unit that stores weight information according to a distance from the position of the terminal, the distribution information acquisition unit acquires the position distribution information based on the weight information.
  • the accompanying discrimination device according to any one of [1] to [4].
  • the weight information storage unit storing weight information of an area adjacent to or near the predetermined area for each identifier assigned to the predetermined area;
  • the accompanying discrimination device according to [6].
  • the weight information is set based on an error in the position information.
  • the accompanying discrimination device according to [6] or [7].
  • each functional block may be realized using one device that is physically or logically coupled, or may be realized using two or more devices that are physically or logically separated and connected directly or indirectly (e.g., using wires, wirelessly, etc.) and these multiple devices.
  • the functional blocks may be realized by combining the one device or the multiple devices with software.
  • Functions include, but are not limited to, judgement, determination, discrimination, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, regarding, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment.
  • a functional block (component) that performs the transmission function is called a transmitting unit or transmitter.
  • the accompaniment discrimination devices 100 and 100a in one embodiment of the present disclosure may function as a computer that performs processing of the accompaniment discrimination method of the present disclosure.
  • FIG. 14 is a diagram showing an example of the hardware configuration of the accompaniment discrimination devices 100 and 100a in one embodiment of the present disclosure.
  • the above-mentioned accompaniment discrimination devices 100 and 100a may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.
  • the word "apparatus” can be interpreted as a circuit, device, unit, etc.
  • the hardware configuration of the accompaniment discrimination apparatus 100 and 100a may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.
  • the functions of the accompanying discrimination devices 100 and 100a are realized by loading specific software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications via the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.
  • the processor 1001 for example, operates an operating system to control the entire computer.
  • the processor 1001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, registers, etc.
  • CPU central processing unit
  • the mesh code conversion unit 103 described above may be realized by the processor 1001.
  • the processor 1001 also reads out programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to these.
  • the programs used are those that cause a computer to execute at least a part of the operations described in the above-mentioned embodiment.
  • the position estimation unit 102, the mesh code conversion unit 103, the analysis interval extraction unit 104, the histogram creation unit 105, the similarity calculation unit 106, and the accompanying determination unit 107 may be realized by a control program stored in the memory 1002 and operating in the processor 1001, and other functional blocks may be similarly realized.
  • the processor 1001 may be implemented by one or more chips.
  • the programs may be transmitted from a network via a telecommunication line.
  • Memory 1002 is a computer-readable recording medium, and may be composed of at least one of, for example, ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. Memory 1002 may also be called a register, cache, main memory (primary storage device), etc. Memory 1002 can store executable programs (program codes), software modules, etc. for implementing the accompanying discrimination method according to one embodiment of the present disclosure.
  • ROM Read Only Memory
  • EPROM Erasable Programmable ROM
  • EEPROM Electrical Erasable Programmable ROM
  • RAM Random Access Memory
  • Memory 1002 may also be called a register, cache, main memory (primary storage device), etc.
  • Memory 1002 can store executable programs (program codes), software modules, etc. for implementing the accompanying discrimination method according to one embodiment of the present disclosure.
  • Storage 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc.
  • Storage 1003 may also be referred to as an auxiliary storage device.
  • the above-mentioned storage medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.
  • the communication device 1004 is hardware (transmitting/receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.
  • the communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., to realize at least one of, for example, Frequency Division Duplex (FDD) and Time Division Duplex (TDD).
  • FDD Frequency Division Duplex
  • TDD Time Division Duplex
  • the above-mentioned radio wave connection history acquisition unit 101 may be realized by the communication device 1004.
  • This communication device 1004 may be implemented with a transmitting unit and a receiving unit that are physically or logically separated.
  • the input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside.
  • the output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one structure (e.g., a touch panel).
  • each device such as the processor 1001 and memory 1002 is connected by a bus 1007 for communicating information.
  • the bus 1007 may be configured using a single bus, or may be configured using different buses between each device.
  • the accompanying discrimination devices 100 and 100a may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware.
  • the processor 1001 may be implemented using at least one of these pieces of hardware.
  • the notification of information is not limited to the aspects/embodiments described in this disclosure, and may be performed using other methods.
  • the notification of information may be performed by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), higher layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or a combination of these.
  • the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, etc.
  • the input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table.
  • the input and output information may be overwritten, updated, or added to.
  • the output information may be deleted.
  • the input information may be sent to another device.
  • the determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., a comparison with a predetermined value).
  • notification of specific information is not limited to being done explicitly, but may be done implicitly (e.g., not notifying the specific information).
  • Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.
  • Software, instructions, information, etc. may also be transmitted and received via a transmission medium.
  • a transmission medium For example, if the software is transmitted from a website, server, or other remote source using at least one of wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and/or wireless technologies (such as infrared, microwave, etc.), then at least one of these wired and wireless technologies is included within the definition of a transmission medium.
  • wired technologies such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)
  • wireless technologies such as infrared, microwave, etc.
  • the information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies.
  • the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.
  • At least one of the channel and the symbol may be a signal (signaling).
  • the signal may be a message.
  • a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.
  • a radio resource may be indicated by an index.
  • the names used for the parameters described above are not intended to be limiting in any way. Furthermore, the formulas etc. using these parameters may differ from those explicitly disclosed in this disclosure.
  • the various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and the various names assigned to these various channels and information elements are not intended to be limiting in any way.
  • MS Mobile Station
  • UE User Equipment
  • a mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.
  • determining may encompass a wide variety of actions.
  • Determining and “determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., searching in a table, database, or other data structure), and considering ascertaining as “judging” or “determining.”
  • determining and “determining” may include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and considering ascertaining as “judging” or “determining.”
  • judgment” and “decision” can include considering resolving, selecting, choosing, establishing, comparing, etc., to have been “judged” or “decided.” In other words, “judgment” and “decision” can include considering some action to have been “judged” or “decided.” Additionally, “judgment (decision)” can be interpreted as “assuming,” “ex
  • connection refers to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” to one another.
  • the coupling or connection between elements may be physical, logical, or a combination thereof.
  • “connected” may be read as "access.”
  • two elements may be considered to be “connected” or “coupled” to one another using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.
  • the phrase “based on” does not mean “based only on,” unless expressly stated otherwise. In other words, the phrase “based on” means both “based only on” and “based at least on.”
  • any reference to an element using a designation such as "first,” “second,” etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must precede the second element in some way.
  • a and B are different may mean “A and B are different from each other.”
  • the term may also mean “A and B are each different from C.”
  • Terms such as “separate” and “combined” may also be interpreted in the same way as “different.”
  • 100...accompanying discrimination device 200...mobile terminal, 300...location management server, 101...radio wave connection history acquisition unit, 102...location estimation unit, 103...mesh code conversion unit, 104...analysis section extraction unit, 105...histogram creation unit, 106...similarity calculation unit, 107...accompanying discrimination unit.

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JP2021012697A (ja) * 2019-07-08 2021-02-04 ライン プラス コーポレーションＬＩＮＥ Ｐｌｕｓ Ｃｏｒｐｏｒａｔｉｏｎ ユーザの位置情報とイベント会場別のイベント情報を利用してユーザの関心事を分析する方法、システム、および非一時的なコンピュータ読み取り可能な記録媒体に記録されるコンピュータプログラム
JP2022145450A (ja) * 2021-03-19 2022-10-04 株式会社ピース企画 クラスタ生成装置、クラスタ生成方法及びクラスタ生成プログラム

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* Cited by examiner, † Cited by third party
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JP2011154484A (ja) * 2010-01-26 2011-08-11 Nippon Telegr & Teleph Corp <Ntt> 行動状況を判定するための方法、装置及びプログラム
JP2012014357A (ja) * 2010-06-30 2012-01-19 Nippon Telegr & Teleph Corp <Ntt> 移動体特徴記述装置、移動体特徴記述方法、移動体特徴記述プログラム及び記録媒体
JP2015011107A (ja) * 2013-06-27 2015-01-19 株式会社Ｎｔｔドコモ 人口変化判断装置、及び人口変化判断方法
JP2015049681A (ja) * 2013-08-30 2015-03-16 Kddi株式会社 類似移動経路をとるユーザを検索する装置、プログラム及び方法
JP2018073043A (ja) * 2016-10-27 2018-05-10 Kddi株式会社 移動開始終了情報に基づき通行量を推定する装置、プログラム及び方法
JP2021012697A (ja) * 2019-07-08 2021-02-04 ライン プラス コーポレーションＬＩＮＥ Ｐｌｕｓ Ｃｏｒｐｏｒａｔｉｏｎ ユーザの位置情報とイベント会場別のイベント情報を利用してユーザの関心事を分析する方法、システム、および非一時的なコンピュータ読み取り可能な記録媒体に記録されるコンピュータプログラム
JP2022145450A (ja) * 2021-03-19 2022-10-04 株式会社ピース企画 クラスタ生成装置、クラスタ生成方法及びクラスタ生成プログラム

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