WO2025083853A1 - レコメンド制御装置 - Google Patents

レコメンド制御装置 Download PDF

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Publication number
WO2025083853A1
WO2025083853A1 PCT/JP2023/037903 JP2023037903W WO2025083853A1 WO 2025083853 A1 WO2025083853 A1 WO 2025083853A1 JP 2023037903 W JP2023037903 W JP 2023037903W WO 2025083853 A1 WO2025083853 A1 WO 2025083853A1
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Prior art keywords
recommendation
index
recommendation method
customer
customers
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English (en)
French (fr)
Japanese (ja)
Inventor
愛 早川
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NTT Docomo Inc
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NTT Docomo Inc
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Priority to JP2025552560A priority Critical patent/JPWO2025083853A1/ja
Priority to PCT/JP2023/037903 priority patent/WO2025083853A1/ja
Publication of WO2025083853A1 publication Critical patent/WO2025083853A1/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
    • G06Q30/0204—Market segmentation

Definitions

  • This disclosure relates to a recommendation control device that controls a recommendation system that provides customers with information about various services (broadly including product and content sales, service provision, etc.; details will be described later) as recommendation information.
  • Recommendation systems that provide customers with information about various services as recommended information have been known for some time now, and are used in e-commerce services, video distribution services, and the like.
  • Conventional recommendation systems have typically provided information that customers are likely to like based on their individual hobbies and preferences.
  • the information provided will lack diversity, which can be inconvenient as it reduces opportunities for customers to make new discoveries and gain new realizations.
  • Patent Document 1 discloses one method for realizing serendipity.
  • the recommendation control device includes an index derivation unit that derives an engagement index that indicates the degree of intimacy between a certain service and a customer, and a control unit that switches a recommendation method for the service for the customer to one of a plurality of recommendation methods having different purposes based on the engagement index derived by the index derivation unit.
  • index derivation unit that derives an engagement index that indicates the degree of intimacy between a certain service and a customer
  • control unit that switches a recommendation method for the service for the customer to one of a plurality of recommendation methods having different purposes based on the engagement index derived by the index derivation unit.
  • customer preference information refers to information that broadly includes a customer's hobbies, preferences, tastes, etc.
  • service is a concept that broadly includes the sale of various products and contents, the provision of various services, etc., and below, examples of content recommendations in a video distribution service and examples of recommendations in a virtual space (e.g., the metaverse space) will be described.
  • various services provided by telecommunications carriers i.e., companies that provide telecommunications services such as landlines and mobile phones, so-called communication carriers) are also included.
  • customer means a person who receives or may receive the provision of services from the present to the future, and broadly includes (1) a person who is currently receiving the provision of services, and (2) a person who is not currently receiving the provision of services but may receive the provision of services in the future, and if the scope of the provision of services extends to foreign countries, customers may also include all people (all humanity).
  • the terminal 40 has a basic function of displaying the metaverse space on the display of the terminal 40 in cooperation with a virtual space control server 30 that controls the virtual space (here, the metaverse space) as a whole, and displaying the avatar of the customer (user) operating the terminal 40 in the metaverse space.
  • a virtual space control server 30 controls the virtual space (here, the metaverse space) as a whole, and displaying the avatar of the customer (user) operating the terminal 40 in the metaverse space.
  • the index derivation unit 11 has a function of, for example, generating a learning model M for estimating next purchase intention (hereinafter referred to as "NPI") by machine learning, and deriving an engagement index S for a customer using the learning model M when actually making recommendations to the customer.
  • NPI next purchase intention
  • the NPI here is expressed as two values ("1" or "0"), with "1” indicating a desire to purchase/use the service next time and "0" indicating no desire to purchase/use the service next time.
  • the engagement index S is an index that indicates the degree of intimacy between a certain service and a customer, and takes a continuous value within the range of 0 to 1, for example, with the higher the numerical value (absolute value), the higher the degree of intimacy.
  • the index derivation unit 11 uses data on NPI (the aforementioned value of "1" or "0") acquired from each of multiple customers of the service by questionnaire or the like as the objective variable, and the attribute and behavioral data of each of the multiple customers as explanatory variables to generate a learning model M for estimating NPI by machine learning (learning phase F1 in Figure 2).
  • This learning phase targets customers who have acquired data on NPI by questionnaire or the like.
  • the generated learning model M is stored by the index derivation unit 11.
  • the index derivation unit 11 queries the customer data management server 20 to acquire customer attribute and behavioral data from the customer data management server 20, and as shown in Figures 2 and 3(b), inputs the acquired customer attribute and behavioral data into the learning model M to estimate (derive) the index output from the learning model M as the engagement index S for the customer (estimation phase F2 in Figure 2).
  • the control unit 12 has a function of switching the recommendation method for customer-oriented services to one of multiple recommendation methods having different purposes based on the engagement index derived by the index derivation unit 11. Specifically, the control unit 12 switches to one of multiple recommendation methods based on the value of the engagement index S derived by the index derivation unit 11 (for example, at least one of the change in value and the absolute value). Examples of the "multiple recommendation methods" include a first recommendation method that makes recommendations with the purpose of expanding coverage, and a second recommendation method that makes recommendations based on customer preference information.
  • the recommendation execution unit 13 shown in FIG. 1 has a function of selecting recommendation information (information on services, products, etc.) for the customer using the recommendation method switched (set) by the control unit 12, and providing the selected recommendation information to the customer's terminal 40.
  • the recommendation execution unit 13 cooperates with the virtual space control server 30 to display the recommendation information for the customer in the metaverse space in association with the customer's avatar (for example, near the avatar).
  • the index derivation unit 11 has completed execution of the learning phase F1 in FIG. 2 and is storing the generated learning model M.
  • control unit 12 initially sets the recommendation method to the first recommendation method (step S1 in FIG. 4).
  • the index derivation unit 11 queries the customer data management server 20 to obtain customer attribute and behavioral data from the customer data management server 20 (step S2). The index derivation unit 11 then inputs the obtained customer attribute and behavioral data into the learning model M, and estimates (derives) the index output from the learning model M as the engagement index S for the customer (step S3). The obtained engagement index S is sent to the control unit 12.
  • the control unit 12 judges whether the engagement index S obtained this time is the same as the previous one (maintained) or is showing an increasing trend (step S4). Note that for the first time, since there is no previous engagement index S, it may be judged as "same (maintained)". Here, if it is judged that the engagement index S is maintained or showing an increasing trend (YES in step S4), since it is unlikely that customers will drop out of the service, the control unit 12 sets the recommendation method to the first recommendation method (a method for expanding coverage and achieving serendipity) (step S5).
  • step S6 the control unit 12 sets the recommendation method to the second recommendation method (a method for making recommendations based on customer preference information) (step S6).
  • the information on the recommendation method set in step S5 or S6 above is transmitted from the control unit 12 to the recommendation execution unit 13, and the recommendation execution unit 13 executes the recommendation according to the set recommendation method (step S7).
  • the recommendation execution unit 13 cooperates with the virtual space control server 30 to display recommendation information for the customer in association with the customer's avatar in the metaverse space.
  • FIG. 5 shows an example in which the customers are users A to C, a recommendation method is set independently for each of users A to C, and recommendation information for each user selected by the set recommendation method is displayed near the avatar of each user in the metaverse space.
  • recommendation information of a new genre will be displayed in light of the preference information of the user.
  • a list of multiple pieces of recommended information in new outdoor genres such as hiking and camping may be displayed with a title such as "For those with a high engagement index (familiarity with the service)".
  • recommended information in genres and categories that match the user's preference information may be displayed.
  • a list of multiple pieces of recommended information in indoor genres that match the hobbies and preferences may be displayed with a title such as "We provide information that may suit your hobbies and preferences”.
  • steps S2 to S7 are repeated at predetermined intervals. That is, after step S7 is executed, the process waits until the predetermined period has elapsed, and after the predetermined period has elapsed, the process returns to step S2, acquires the latest attribute and behavior data for the customer (step S2), and executes steps S3 and onward.
  • the recommendation method can be appropriately switched between the first recommendation method and the second recommendation method in response to changes in the engagement index S derived for each customer, thereby preventing customers from dropping out of the service while expanding the coverage of recommendations and achieving serendipity.
  • the index derivation unit generates a learning model for estimating NPI by machine learning, and inputs customer attributes and behavioral data into the generated learning model, thereby deriving the value output from the learning model as an engagement index for the customer. In this way, it is possible to derive an appropriate engagement index using the learning model.
  • control unit 12 can switch recommendation methods independently for each customer based on the engagement index S for each customer, thereby switching to a recommendation method that is more appropriate for each customer without being influenced by the attributes and behavioral data of other customers.
  • the configuration of the recommendation control device 10 and peripheral devices in the second embodiment is similar to that shown in FIG. 1 above, so a duplicated description will be omitted here.
  • Steps S1 (initial setting), S2 (obtaining attribute and behavioral data), and S3 (deriving engagement index S) in FIG. 6 are the same as those in the first embodiment (FIG. 4) described above.
  • the recommendation method is set (switched) as follows based on its absolute value. That is, the control unit 12 determines whether the engagement index S is 0.9 or more (step S4A), and if the engagement index S is 0.9 or more, it is unlikely that customers will drop out of the service, so the control unit 12 proceeds to step S5 and sets the recommendation method to the first recommendation method (a method for expanding coverage and achieving serendipity).
  • step S4B determines whether the engagement index S is less than 0.1 (step S4B), and since it is expected that the customer will drop out of the service if the engagement index S is less than 0.1, the control unit 12 proceeds to step S6 and sets the recommendation method to the second recommendation method (a method for making recommendations based on customer preference information).
  • the control unit 12 judges whether the engagement index S obtained this time is the same as the previous one (maintained) or is increasing (step S4C). Note that for the first time, since there is no previous engagement index S, it may be judged as "same (maintained)". Here, if it is judged that the engagement index S is maintained or is increasing (YES in step S4C), it is unlikely that the customer will drop out of the service, so the control unit 12 sets the recommendation method to the first recommendation method (step S5).
  • control unit 12 sets the recommendation method to the second recommendation method (step S6).
  • step S7 the recommendation is executed by the recommendation execution unit 13 according to the set recommendation method (step S7).
  • step S7 the recommendation is executed by the recommendation execution unit 13 according to the set recommendation method.
  • step S7 the recommendation is executed by the recommendation execution unit 13 according to the set recommendation method.
  • the process waits until a predetermined period has elapsed and returns to step S2, whereby the processes of steps S2 to S7 are repeatedly executed at predetermined intervals.
  • the recommendation method can be appropriately switched between the first recommendation method and the second recommendation method based on the absolute value of the engagement index S in addition to the change in the engagement index S derived for each customer. Specifically, if the engagement index S is 0.9 or more, it is unlikely that the customer will drop out of the service, so the recommendation method can be immediately set to the first recommendation method regardless of the change (increase/decrease trend) of the engagement index S. Also, if the engagement index S is less than 0.1, it is expected that the customer will drop out of the service, so the recommendation method can be immediately set to the second recommendation method regardless of the change (increase/decrease trend) of the engagement index S. This makes it possible to expand the coverage of recommendations and realize serendipity while preventing customers from dropping out of the service.
  • the configuration of the recommendation control device 10 and peripheral devices in the third embodiment is similar to that shown in FIG. 1 above, so a duplicated description will be omitted here.
  • Step S1 initial setting in Figure 7 is similar to the processing in the first embodiment described above ( Figure 4), and in the next step S2A, the index derivation unit 11 queries the customer data management server 20 to obtain attribute and behavioral data of multiple customers from the customer data management server 20 (step S2A). Then, the index derivation unit 11 inputs the obtained attribute and behavioral data of each of the multiple customers into the learning model M, and estimates (derives) the index output from the learning model M as an index for each of the multiple customers (step S3A). The obtained index for each customer is sent to the control unit 12.
  • the control unit 12 determines the value of the index with the lowest absolute value (i.e., the minimum value) among the indices for each of the multiple customers derived in step S3A as the engagement index S for the multiple customers (step S3B).
  • steps S4A to S8 are the same as in the second embodiment described above. That is, based on the change and absolute value of the engagement index S set in step S3B, the recommendation method common to the customers (users A to C) is set (switched) as follows:
  • the control unit 12 sets the recommendation method to the first recommendation method (a method for expanding coverage and achieving serendipity) because it is unlikely that customers will abandon the service (steps S4A and S5).
  • the control unit 12 assumes that the customer will drop out of the service, and therefore sets the recommendation method to the second recommendation method (a method for making recommendations based on customer preference information) (steps S4B and S6).
  • the control unit 12 sets the recommendation method to the first recommendation method or the second recommendation method according to the change in the engagement index S, as in the first embodiment (steps S4C, S5, S6).
  • the control unit 12 sets the recommendation method to the first recommendation method (step S5), and if the engagement index S is on a decreasing trend, it is likely that customers will drop out of the service, so the control unit 12 sets the recommendation method to the second recommendation method (step S6).
  • step S7 the recommendation execution unit 13 executes the recommendation according to the set recommendation method (step S7).
  • a common recommendation method is set for the customers (users A to C), and recommendation information for each user selected by the common recommendation method is displayed near each user's avatar in the metaverse space, for example as shown in FIG. 5.
  • step S7 the process waits until a predetermined period has elapsed and returns to step S2A, whereby the processing of steps S2A to S7 is repeatedly executed at predetermined intervals.
  • a common recommendation method is set for multiple customers, and recommendation information for each user selected by the common recommendation method is provided to each user.
  • the recommendation information for each user is not necessarily the same, but if the attributes and behavioral data of users A to C existing in the metaverse space are similar (for example, if they have common hobbies or similar tastes), it is fully expected that the recommendation information for each user will also be similar, and it is possible to provide highly useful recommendation information that is likely to resonate with (resonates with) multiple customers.
  • NPS Net Promoter Score
  • the recommendation method is set to the first recommendation method when the engagement index S is maintained (same as the previous time), but setting it to the first recommendation method is not essential.
  • the recommendation method may be set to the second recommendation method when the engagement index S is maintained.
  • the recommendation method is initially set to the first recommendation method in step S1 of FIG. 4, but it is not essential to initially set the recommendation method to the first recommendation method.
  • the recommendation method may be initially set to the second recommendation method.
  • the first embodiment described above shows an example of setting (switching) the recommendation method based on the "change" of the engagement index S
  • the second and third embodiments show an example of setting (switching) the recommendation method based on the "change and absolute value" of the engagement index S, but the recommendation method may be set (switched) based only on the "absolute value" of the engagement index S.
  • the recommendation method was set (switched) based on the "change and absolute value" of the engagement index S, as in the second embodiment, but the recommendation method may be set (switched) based on the "change” of the engagement index S, as in the first embodiment, or the recommendation method may be set (switched) based on the "absolute value" of the engagement index S.
  • the value of the index with the lowest absolute value (minimum value) among the indices of each of the multiple customers is set as the engagement index S for the multiple customers, and the recommendation method is set (switched) based on the change and absolute value of the engagement index S, but instead of the minimum value, the arithmetic mean value, median value, etc. of the indices of each of the multiple customers may be set as the engagement index S for the multiple customers.
  • the recommendation method is switched to one of the first recommendation method aimed at expanding coverage and the second recommendation method based on customer preference information, but the candidates for switching may be three or more recommendation methods.
  • examples of "services” in this disclosure include various services provided by telecommunications businesses (so-called communication carriers).
  • NPI next purchase intention
  • the above-mentioned method can be used to acquire the NPI tendency score for the entire brand for that single communication carrier as an engagement index, and an appropriate recommendation method can be switched to based on the acquired engagement index.
  • the method of this disclosure can be applied to a recommendation service that recommends information on various services provided by the communication carrier (for example, telecommunication line provision services (both landlines and/or mobile phones), services that allow comprehensive payment along with the service fees, etc.).
  • the gist of the present disclosure lies in the following [1] to [6].
  • an index derivation unit that derives an engagement index that indicates an intimacy between a certain service and a customer
  • a control unit that switches a recommendation method for the service for the customer to one of a plurality of recommendation methods having different purposes based on the engagement index derived by the index derivation unit
  • a recommendation control device comprising: [2] The recommendation control device described in [1], wherein the multiple recommendation methods include a first recommendation method that makes recommendations for the purpose of expanding coverage, and a second recommendation method that makes recommendations based on preference information of the customer. [3] The recommendation control device according to [1] or [2], wherein the control unit switches the recommendation method based on a value of the engagement index.
  • the recommendation control device according to [3], wherein the control unit switches the recommendation method based on at least one of a change in the engagement index and an absolute value of the engagement index.
  • the index derivation unit is generating a learning model for estimating the next purchase intention by machine learning using data on the next purchase intention obtained from each of a plurality of customers of the service as a target variable and attribute and behavior data of each of the plurality of customers as explanatory variables;
  • 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 directly or indirectly connected (for example, using wires, wirelessly, etc.).
  • 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, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, 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.
  • a recommendation control device in one embodiment of the present disclosure may function as a computer that executes the processing of the present disclosure.
  • FIG. 9 is a diagram showing an example of a hardware configuration of a recommendation control device 10 according to one embodiment of the present disclosure.
  • the recommendation control device 10 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.
  • the term "apparatus” can be interpreted as a circuit, device, unit, etc.
  • the hardware configuration of the recommendation control device 10 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.
  • Each function of the recommendation control device 10 is 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, runs an operating system to control the entire computer.
  • the processor 1001 may be configured as a central processing unit (CPU) that includes an interface with peripheral devices, a control device, an arithmetic unit, registers, etc.
  • CPU central processing unit
  • 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 some of the operations described in the above-mentioned embodiments. Although it has been described that the various processes are executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001.
  • the processor 1001 may be implemented by one or more chips.
  • the programs may be transmitted from a network via a telecommunications 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 a wireless communication 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 a wireless communication 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 referred to as, for example, a network device, a network controller, a network card, a communication module, etc.
  • the communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., to realize, for example, at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD).
  • FDD Frequency Division Duplex
  • TDD Time Division Duplex
  • 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 recommendation control device 10 may also 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.
  • 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.
  • Each aspect/embodiment described in this disclosure may be a mobile communication system (mobile communications system) for mobile communications over a wide range of networks, including LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (x is, for example, an integer or a decimal number)), FRA (Future Radio Access), and LTE (LTE-Advanced).
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • SUPER 3G IMT-Advanced
  • 4G fourth generation mobile communication system
  • 5G 5th generation mobile communication system
  • 6G 6th generation mobile communication system
  • xG xth generation mobile communication system
  • xG xG (x is, for example, an integer or a decimal number)
  • FRA Full Radio Access
  • the present invention may be applied to at least one of the following systems using appropriate systems: IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, UWB (Ultra-Wide Band), Bluetooth (registered trademark), NR (new Radio Access), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, UWB (Ultra-Wide Band), Bluetooth (registered trademark), and next-generation systems that are expanded, modified, created, or defined based on these.
  • the present invention may be applied to a combination of multiple systems (for example, a combination of at least one of LTE and LTE-A with 5G, 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 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), 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
  • 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.
  • system and “network” are used interchangeably.
  • radio resources may be indicated by an index.
  • the names used for the above-mentioned parameters are not limiting in any respect. 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 therefore the various names assigned to these various channels and information elements are not limiting in any respect.
  • 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
  • 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.”

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