WO2019011727A1 - Moteur de recommandation de mise à niveau - Google Patents

Moteur de recommandation de mise à niveau Download PDF

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Publication number
WO2019011727A1
WO2019011727A1 PCT/EP2018/067986 EP2018067986W WO2019011727A1 WO 2019011727 A1 WO2019011727 A1 WO 2019011727A1 EP 2018067986 W EP2018067986 W EP 2018067986W WO 2019011727 A1 WO2019011727 A1 WO 2019011727A1
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WO
WIPO (PCT)
Prior art keywords
upgrade
consumer electronic
electronic device
performance data
electronic devices
Prior art date
Application number
PCT/EP2018/067986
Other languages
English (en)
Inventor
Jessika MALMCRONA
Geir SJURSETH
Thomas Nilsson
Nils UNDÉN
Roger WALLENTIN
Original Assignee
Ebuilder Ab
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ebuilder Ab filed Critical Ebuilder Ab
Publication of WO2019011727A1 publication Critical patent/WO2019011727A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06Q30/0282—Rating or review of business operators or products
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06Q30/01—Customer relationship services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

Definitions

  • the present disclosure relates generally to systems and methods for providing upgrade recommendations for users of consumer electronic devices.
  • US2014/0195297 describes a system that analyzes usage patterns for different functionalities of consumer electronic devices, and based on the usage statistics provides upgrade recommendations. Data is collected from the user device when a functionality of the user device is activated, and the collected data includes e.g. the points of time and duration of the use of the functionality. Using this information, the most used functions and the functions not used at all can be identified, and the ideal device for the user can be recommended based on the usage habits.
  • US2012/0117097 describes a method of utilizing user feedback about a personal computer for providing recommendations on how to upgrade the personal computer with new software and/or hardware based on use pattern data.
  • the present disclosure relates to systems and methods for providing upgrade recommendations for users of consumer electronic devices.
  • Prior art systems do not utilize the possibilities enabled by the collection of device performance data from large amounts of users for providing upgrade recommendations, and they have not addressed the problem that the users will not always appreciate receiving such upgrade recommendations.
  • the system may comprise at least one central processing arrangement and a plurality of consumer electronic devices, each comprising data collecting software for collecting device performance data.
  • the at least one central processing arrangement may be arranged to: receive device performance data collected by the data collecting software from the plurality of consumer electronic devices; analyze the device performance data; generate an upgrade recommendation engine based at least on the device performance data using machine learning; and use the generated upgrade recommendation engine to output an upgrade recommendation for a user of a consumer electronic device, together with an upgrade propensity for the user to upgrade the consumer electronic device.
  • the system comprises a plurality of consumer electronic devices of the same or similar model, wherein information regarding upgrade actions taken by the users of the consumer electronic devices is also transferred to the at least one central processing arrangement, and the upgrade recommendation engine is generated based also on upgrade actions taken by users of consumer electronic devices having the same or similar model experiencing similar device performance data. This improves the upgrade recommendation engine.
  • the above described problems are further addressed by the claimed method for providing upgrade recommendations for users of consumer electronic devices.
  • the method may comprise: collecting device performance data from a plurality of consumer electronic device using data collecting software; transferring device performance data collected by the data collecting software to at least one central processing arrangement; analyzing the device performance data; generating an upgrade recommendation engine based at least on the device performance data using machine learning; and providing, as an output from the generated upgrade recommendation engine, an upgrade recommendation for a user of a consumer electronic device together with an upgrade propensity for the user to upgrade the consumer electronic device.
  • device performance data is collected from a plurality of consumer electronic devices of the same or similar model, and the method further comprises transferring information regarding upgrade actions taken by the users of the consumer electronic devices to the at least one central processing arrangement.
  • the generating of the upgrade recommendation engine may then be based also on upgrade actions taken by users of consumer electronic devices having the same or similar model experiencing similar device performance data. This improves the upgrade recommendation engine.
  • the upgrade recommendation comprises suitable technical specifications and/or suitable model of a new consumer electronic device for a user of a consumer electronic device. This enables a user to find an ideal device that fits the user's needs.
  • the upgrade actions include upgrading the consumer electronic device to a new model, and the upgrade recommendation comprises a suitable model based on the model selected by users of consumer electronic devices having the same or similar model and experiencing similar device performance data. This improves the upgrade recommendation.
  • the data collecting software is installed in each of the plurality of consumer electronic devices by being downloaded as part of an application.
  • the central processing arrangement is arranged to also determine whether to use the generated upgrade recommendation engine to output an upgrade recommendation for a user of a consumer electronic device based on a comparison of the upgrade propensity for the user to upgrade the consumer electronic device with at least one at least one upgrade propensity threshold for the user of the consumer electronic device.
  • the device performance data includes the charging pattern, the average battery life, and/or the state of health (SOH) of a power source such as a battery comprised in the at least one consumer electronic device.
  • SOH state of health
  • the device performance data includes the free storage space in a storage memory of the consumer electronic device.
  • the consumer electronic device may e.g. be a portable communications device, such as e.g. a smartphone.
  • the plurality of consumer electronic devices may e.g. be hundreds, or thousands, or millions of consumer electronic devices.
  • the at least one central processing arrangement may be one central processing arrangement, or a number of central processing arrangements between which signals are transmitted. Some processing may e.g. take place in one central processing arrangement, and signals may then be transmitted to one or more other central processing arrangements for further processing.
  • Figure 1 schematically illustrates a system for providing upgrade recommendations for users of consumer electronic devices, in accordance with one or more embodiments described herein.
  • Figure 2 is a schematic conceptual overview of a system for providing upgrade recommendations for users of consumer electronic devices, in accordance with one or more embodiments described herein.
  • Figure 3 is a schematic component overview of a system for providing upgrade recommendations for users of consumer electronic devices, in accordance with one or more embodiments described herein.
  • Figure 4 is a schematic overview of an example process and upgrade propensity for an example user of a consumer electronic device, in accordance with one or more embodiments described herein.
  • Figure 5 schematically illustrates a method for providing upgrade recommendations for users of consumer electronic devices, in accordance with one or more embodiments described herein.
  • the data may e.g. be collected by data collecting software in the form of a device inspector, which is a specially developed software for data collection that may be added to any application, regardless of who has created the application.
  • Such applications may be provided by anyone wishing to collect data from consumer devices, and they may be provided directly to users for downloading, without involving any device manufacturers.
  • the present disclosure relates generally to systems and methods for providing upgrade recommendations for users of consumer electronic devices. Embodiments of the disclosed solution are presented in more detail in connection with the figures.
  • FIG. 1 schematically illustrates a system 100 for providing upgrade recommendations for users of consumer electronic devices 110, in accordance with one or more embodiments described herein.
  • the system 100 may comprise at least one central processing arrangement 150 and a plurality of consumer electronic devices 110, each comprising data collecting software 120 for collecting device performance data.
  • the plurality of consumer electronic devices 110 may e.g. be hundreds, or thousands, or millions of consumer electronic devices 110.
  • Device performance data collected by the data collecting software 120 may be received in the at least one central processing arrangement 150, where the device performance data may be analyzed and an upgrade recommendation engine may be generated based at least on the device performance data using machine learning.
  • the data collecting software 120 may e.g. be a device inspector integrated into at least one application that has been downloaded into each of the consumer electronic devices 110.
  • Each of the computer electronic devices may further comprise at least one battery 130 and at least one storage memory 140.
  • FIG. 2 is a schematic conceptual overview of a system 100 for providing upgrade recommendations for users of consumer electronic devices 110, in accordance with one or more embodiments described herein.
  • a central processing arrangement 150 receives device data, e.g. device performance data, from a community 200 of consumer electronic devices 110.
  • the community 200 may e.g. comprise hundreds, or thousands, or millions of consumer electronic devices 110.
  • the central processing arrangement 150 analyses the data and generates an upgrade recommendation engine based at least on the device performance data using machine learning. Machine learning enables the system 100 to handle device performance data from huge amounts of consumer electronic devices 110.
  • the upgrade recommendation engine may output upgrade recommendations to users of consumer electronic devices 110 in the community 200.
  • the upgrade recommendation engine may also output upgrade propensities P u for the users in the community 200 to upgrade their consumer electronic devices 110.
  • the central processing arrangement 150 may also receive other forms of data, such as user data and/or upgrade actions, from the community 200 of consumer electronic devices 110.
  • the upgrade data e.g. device performance data
  • the upgrade recommendation engine
  • recommendation engine may be generated based also on these data, preferably using machine learning.
  • FIG 3 is a schematic component overview of a system for providing upgrade recommendations for users of consumer electronic devices 110, in accordance with one or more embodiments described herein.
  • a device inspector 120 locally running in a consumer electronic device 110 periodically collects device information, such as e.g. device performance data, and transmits this information, e.g. as an "event", to a central processing arrangement 150, where the data can be analyzed.
  • the data may e.g. be device performance data and/or information about upgrade actions.
  • Data may be collected from a whole community 200 of consumer electronic devices 110.
  • the collected data may e.g. be used for generating an upgrade recommendation engine.
  • the upgrade recommendation engine may be arranged to output upgrade recommendations for users of consumer electronic devices 110 in the community 200.
  • the upgrade recommendations may e.g.
  • a new consumer electronic device 110 may comprise suitable technical specifications and/or suitable model of a new consumer electronic device 110.
  • Users of the community 200 of consumer electronic devices 110 may take upgrade actions, such as upgrading their consumer electronic device 110 to a new model. Information about such upgrade actions may be transferred to the central processing arrangement 150, where it may contribute to the generation or training of the upgrade recommendation engine.
  • the central processing arrangement 150 may also be arranged to output an upgrade propensity P u for a user of a consumer electronic device 110.
  • the upgrade propensity P u may be defined as the likelihood that the user upgrades the consumer electronic device 110.
  • the upgrade propensity P u is therefore a value that changes continuously - the likelihood that a user upgrades the consumer electronic device 110 at any point of time in the future is of course almost 100%, but the likelihood that a user upgrades the consumer electronic device 110 at a given point of time is more difficult to determine.
  • the upgrade propensity P u may be a general propensity for upgrading the consumer electronic device 110, or a propensity for upgrading to a specific model of the consumer electronic device 110.
  • the upgrade propensity P u may e.g. be determined using a predictive model.
  • the predictive model for calculating the upgrade propensity P u may also be determined entirely by machine learning based on data received from the community 200 of consumer electronic devices 110, without using any predetermined algorithm format.
  • Training of the predictive model may include using a training data set which binds specific data points to actual upgrade events in order to perform a regressions analysis.
  • Upgrade events may be automatically tracked by monitoring users and devices in the community 200 of consumer electronic devices 110 and specifically capturing all changes of IMEIs (device identifiers) relative to an individual subscriber identity. Subscribers may e.g. identified by capturing the IMSI (international mobile subscriber identity) from the device.
  • the associated device data from those consumer electronic devices 110 may be used to construct labeled features that train the predictive model and keep it continuously up to date to changing trends in user behavior.
  • the predictive model for calculating the upgrade propensity P u may also represent different measures of experience impediments to device users, since as experience deteriorates the upgrade propensity P u increases.
  • Experience impediments may be derived from continuously sampled device performance data from individual devices on a regular time interval.
  • the upgrade propensity P u may further depend on user data such as e.g. assessed income level, age, gender, personal interests, subscription contract, etc., which may be assessed e.g. from monitoring and analyzing device data in terms of device features or applications actually used by the users in the community 200 of consumer electronic devices 110.
  • Predictive models of the above described kinds may also be used for determining other types of upgrade recommendations, such as e.g. suitable technical specifications and/or suitable model of a new consumer electronic device 110.
  • the central processing arrangement 150 may e.g. continuously scan the data and use machine learning updates, community propensities, and individual propensities for the device type and the specific user respectively, and generate an upgrade recommendation engine based on these data, e.g. using a predictive model.
  • the upgrade recommendation engine may provide an upgrade recommendation for a user of a consumer electronic device 110 based on the combination of community and individual data and propensities.
  • the process may feed the system with individual device data for the consumer electronic device 110 and generate an upgrade recommendation engine, e.g. in the form of a descriptive analytical model. This upgrade recommendation engine may feed predictive analytics that may ultimately make an upgrade recommendation.
  • the upgrade recommendation engine may thus be continuously generated, or trained, based on various types of received data, using machine learning, so that it may provide useful upgrade recommendations to users of the community 200 of consumer electronic devices 110.
  • Figure 4 is a schematic overview of an example process and upgrade propensity for an example user of a consumer electronic device 110, in accordance with one or more embodiments described herein.
  • the upgrade propensity P u is calculated for a user of a consumer electronic device 110 based on data received from the community 200 of consumer electronic devices 110.
  • the upgrade propensity P u is in figure 4 shown in a diagram together with an upgrade propensity threshold Pt determined by the central processing arrangement 150 based on data received from the community 200 of consumer electronic devices 110.
  • the upgrade propensity P u is higher than the upgrade propensity threshold Pt, the user is likely to wish to upgrade the consumer electronic device 110. The user may then appreciate if the upgrade
  • recommendation engine provides an upgrade recommendation, either for suitable technical specifications or for a suitable model of consumer electronic device 1 10.
  • the at least one upgrade propensity threshold may be determined in various ways.
  • the upgrade recommendation engine initially outputs an upgrade recommendation to all users of consumer electronic devices, regardless of their upgrade propensity P u .
  • Data can then be collected regarding whether the upgrade recommendation is appreciated by the users, e.g. by determining which users actually perform an upgrade based on the upgrade recommendation. The result of such a data collection is likely to follow a bell shaped curve, with users having low or very high upgrade propensities P u less likely to upgrade than users having medium high upgrade propensities P u .
  • at least one upgrade propensity threshold Pt may be determined.
  • the at least one upgrade propensity threshold Pt is determined in the form of a lower upgrade propensity threshold Pti and an upper upgrade propensity threshold Ptu, these thresholds may then be used to determine whether the upgrade propensity P u is within the predetermined range between the lower upgrade propensity threshold Pti and the upper upgrade propensity threshold Ptu, and the upgrade recommendation engine may in this case be used to provide upgrade recommendations only to users having upgrade propensities P u within this predetermined range.
  • Different types of device performance data in the form of e.g. CPU load average, internal storage utilization of a storage memory 140, charging pattern of battery 130, average battery life of battery 130, and/or state of health (SOH) of battery 130, may be collected from a consumer electronic device 110 (a method of determining a state of health of a power source of a portable device is e.g. described in US2015/0241515).
  • Other types of device data such as phone type category (e.g. budget/premium), phone age, amount of stored pictures, types of installed applications, NFC status uptime, and/or data and WiFi usage may also be collected.
  • the device performance data is preferably analyzed in at least one central processing arrangement 150, and an upgrade recommendation engine is preferably generated based on different types of device performance data, possibly also in combination with other types of data.
  • the device performance data for simplicity relates solely to internal storage utilization of a storage memory 140 of the consumer electronic device 110.
  • Data may e.g. be collected from a consumer electronic device 110 using a locally running device inspector 120.
  • CarrierO "Turk Industries ⁇ 3"
  • CPU Architecture ARMv8-A
  • Chipset 'SAMSUNGEXYNOS8890
  • CPU Features 'fp asimd evtstrm aes pmuii shal sha2 crc32"
  • Kernel Version '3.18.14-11104523
  • Kernel Architecture : 'aarch64
  • the internal storage utilization (freelntStorage) of the consumer electronic device 110 isolated from the rest of the data and spread over time may e.g. be as follows:
  • the events may also be processed and stored for community weighting.
  • the community data shows trends and may be used to dynamically assign weights in the predictive model.
  • the central processing arrangement 150 may e.g. have assigned a weight of 6 to any device with less than 100 MB of free space.
  • the system can track upgrade trends for users and apply weights accordingly.
  • Last month the calculated weight applied to a space below a given threshold may have been 5, but today's data may make that a 7.
  • the thresholds themselves follow their own trends and are calculated as part of the community data weighting.
  • Additional weighting may come in the form of user provided feedback. This data may describe for example why a user chose to upgrade, or even their own inclination toward an upgrade now and over time.
  • the predictive model may recognize that those users who both have a high score with regard to their upgrade propensity and have actively expressed an intention to upgrade are even more likely to perform an upgrade. This data over time is also interesting from a community trend perspective with regard to the descriptive analytical model. Upgrade propensity
  • the central processing arrangement 150 may apply weights in an ongoing fashion. Assuming that the upgrade recommendation engine is queried right after D6, the system may have the following data points and weights:
  • the upgrade recommendation engine may based on this data output an upgrade recommendation and an upgrade propensity P u , e.g. by the predictive model predicting both when an individual is most inclined to upgrade to a new consumer electronic device 110, and which model best suits the user's needs.
  • the descriptive analytical data may e.g. show that this user has already made several upgrades within the Samsung family in the past, and that community trend data follows this same trend.
  • additional user feedback may also indicate inclination for an upgrade.
  • the upgrade recommendation engine may e.g. output a 6 out of 10 upgrade propensity P u that this user will upgrade to a Samsung Galaxy S8 (immediately following D6 from the data above).
  • the predictive model may also show that the upgrade propensity P u will likely increase to an 8 in ten days based on an analysis of internal storage utilization over time, plus community trend analysis.
  • This upgrade propensity P u represented one example based on a single vector: internal storage utilization. Obviously, the upgrade propensity P u based on combining a number of different attributes from community device trends with individual device attributes creates an aggregated score. Using machine learning, enormous amounts of data may be combined in order to generate a very accurate upgrade recommendation engine.
  • the algorithm may be updated based on machine learning.
  • FIG. 5 schematically illustrates a method 500 for providing upgrade recommendations for users of consumer electronic devices, in accordance with one or more embodiments described herein.
  • the method 500 may comprise: Step 520: Collecting device performance data from a plurality of consumer electronic devices 110 using data collecting software 120.
  • Step 530 Transferring device performance data collected by the data collecting software 120 to at least one central processing arrangement 150.
  • Step 550 Analyzing the device performance data.
  • Step 560 Generating an upgrade recommendation engine based at least on the device performance data using machine learning.
  • Step 580 Providing, as an output from the upgrade recommendation engine, an upgrade recommendation for a user of a consumer electronic device 110 and an upgrade propensity P u for the user to upgrade the consumer electronic device 110.
  • the upgrade recommendation comprises suitable technical specifications and/or suitable model of a new consumer electronic device 110 for a user of a consumer electronic device 110
  • the device performance data may be collected from a plurality of consumer electronic devices 110 of the same or similar model.
  • the method 500 may then further comprise:
  • Step 540 Transferring information regarding upgrade actions taken by the users of the consumer electronic devices 110 to the at least one central processing arrangement 150.
  • the generating 560 of the upgrade recommendation may then based also on upgrade actions taken by users of consumer electronic devices 110 having the same or similar model experiencing similar device performance data.
  • the upgrade actions include upgrading the consumer electronic device 110 to a new model.
  • the upgrade recommendation may then comprise a suitable model based on the model selected by users of consumer electronic devices 110 having the same or similar model and experiencing similar device performance data.
  • the method 500 further comprises at least one of the following: Step 510: installing the data collecting software 120 in each of the plurality of consumer electronic devices 110 by downloading it as part of an application.
  • Step 570 determining whether to provide 580, as an output from the upgrade recommendation engine, an upgrade recommendation for a user of a consumer electronic device 110, based comparing the upgrade propensity P u for the user to upgrade the consumer electronic device 110 with at least one at least one upgrade propensity threshold for the user of the consumer electronic device 110.
  • the device performance data includes the charging pattern, the average battery life, and/or the state of health (SOH) of a power source such as a battery 130, comprised in the at least one consumer electronic device 110.
  • the device performance data includes the free storage space in a storage memory of the at least one consumer electronic device 110.
  • the consumer electronic device 110 is a portable communications device, such as e.g. a smartphone.

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Abstract

Selon un ou plusieurs modes de réalisation, l'invention concerne un système (100) pour fournir des recommandations de mise à niveau pour des utilisateurs de dispositifs électroniques grand public. Le système (100) comprend au moins un agencement de traitement central (150) et une pluralité de dispositifs électroniques grand public (110), chacun comprenant un logiciel de collecte de données pour collecter des données de performance de dispositif. Le ou les processeurs centraux (agencement 150) sont agencés pour recevoir des données de performance de dispositif collectées par le logiciel de collecte de données (120), analyser les données de performance de dispositif, générer un moteur de recommandation de mise à niveau sur la base au moins des données de performance de dispositif à l'aide d'un apprentissage automatique, et utiliser le moteur de recommandation de mise à niveau généré pour délivrer une recommandation de mise à niveau pour un utilisateur d'un dispositif électronique grand public (110) conjointement avec une propension à la mise à niveau Pu pour l'utilisateur pour mettre à niveau le dispositif électronique grand public (110).
PCT/EP2018/067986 2017-07-14 2018-07-03 Moteur de recommandation de mise à niveau WO2019011727A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112508718A (zh) * 2020-12-03 2021-03-16 中国人寿保险股份有限公司 一种保单的续费提醒方法及装置

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120117097A1 (en) 2010-11-10 2012-05-10 Sony Corporation System and method for recommending user devices based on use pattern data
US20140195297A1 (en) 2013-01-04 2014-07-10 International Business Machines Corporation Analysis of usage patterns and upgrade recommendations
US20150241515A1 (en) 2014-02-24 2015-08-27 Cellebrite Mobile Synchronization Ltd. System and method for determining a state of health of a power source of a portable device

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120117097A1 (en) 2010-11-10 2012-05-10 Sony Corporation System and method for recommending user devices based on use pattern data
US20140195297A1 (en) 2013-01-04 2014-07-10 International Business Machines Corporation Analysis of usage patterns and upgrade recommendations
US20150241515A1 (en) 2014-02-24 2015-08-27 Cellebrite Mobile Synchronization Ltd. System and method for determining a state of health of a power source of a portable device

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112508718A (zh) * 2020-12-03 2021-03-16 中国人寿保险股份有限公司 一种保单的续费提醒方法及装置

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