Used smartphone degradation prediction and trading system through multiple usage history and battery condition analysis

Abstract

A system for predicting the degradation of used smartphones and trading through multi-use history and battery condition analysis is disclosed, which automatically collects and preprocesses multi-use history data including battery history, physical impact history, usage environment history, software history, appearance condition, and hardware function condition from smartphones to enable objective and quantitative evaluation of the device condition. A used smartphone degradation prediction and trading system through multi-use history and battery condition analysis includes: a data collection unit that collects multi-use history data from used smartphones; a battery analysis unit that generates battery analysis result data by analyzing battery history data among the multi-use history data transmitted from the data collection unit; a degradation calculation unit that calculates a comprehensive degradation score and classifies degradation grades using a pre-trained first artificial intelligence model based on the multi-use history data transmitted from the data collection unit and the battery analysis result data transmitted from the battery analysis unit; a degradation prediction unit that predicts the future degradation progression speed and remaining usable period based on the comprehensive degradation score transmitted from the degradation calculation unit; a price calculation unit that automatically calculates a transaction price based on the comprehensive degradation score transmitted from the degradation calculation unit and future degradation prediction data transmitted from the degradation prediction unit; and a transaction management unit that stores transaction price data transmitted from the price calculation unit, manages distribution history by device, and processes transaction settlements.

Description

Technology Field

[0001] The present invention relates to a system for predicting the degree of degradation of a used smartphone and trading through the analysis of multiple usage history and battery condition. The system collects multiple usage history data from a used smartphone, including battery history, physical impact history, usage environment history, software history, external condition, and hardware function status; precisely analyzes the battery condition; calculates a comprehensive degradation score based on an artificial intelligence model and classifies the degradation grade; predicts the future rate of degradation and the remaining usable period; automatically calculates a transaction price reflecting the prediction results; and performs transaction settlement and distribution history management in a comprehensive manner. Background Technology

[0002] As smartphone penetration rates worldwide reach saturation levels, the used smartphone market is growing rapidly. According to market research, the volume of used smartphone transactions globally amounts to hundreds of millions of units annually, and the market size continues to expand. In Korea as well, the used smartphone distribution industry has grown beyond peer-to-peer trading into a large-scale sector involving specialized buying and selling companies as well as overseas export firms.

[0003] However, the used smartphone market suffers from the lack of objective and quantitative evaluation standards for device condition. Conventional methods for assessing the condition of used smartphones rely primarily on external inspections and simple functional tests, making them highly dependent on the subjective judgment of the evaluator; consequently, it is common for the same device to be assigned different grades depending on the evaluator. This inconsistency in evaluations leads to disputes between trading parties and undermines market credibility.

[0004] In particular, regarding the degradation state of the battery, which is one of the most important factors determining the actual lifespan of a smartphone, current methods are limited to merely checking the State of Health (SoH) value at the current point in time. However, battery degradation cannot be judged solely by the current SoH value; it is influenced by a complex interplay of factors, including past charge / discharge patterns, the ratio of charging methods (fast charging, slow charging, wireless charging), the frequency of use in high and low temperature environments, and a history of overcharging and over-discharging. For example, even among two smartphones exhibiting the same current SoH value, the device that frequently used fast charging has higher internal battery resistance and is more likely to experience faster degradation in the future compared to the device that primarily used slow charging. Conventional technology has not been able to quantitatively analyze the degradation acceleration effect associated with such charging methods.

[0005] Furthermore, the overall degradation of a used smartphone is the result of the complex interplay of various factors, including not only the battery but also history of physical impact, usage conditions, software status, the degree of external damage, and the proper functioning of various hardware features. However, conventional used smartphone evaluation systems fail to comprehensively consider these multiple factors and are limited to fragmentary inspections of individual items, which limits them to an inability to accurately reflect the device's actual residual value.

[0006] Furthermore, predicting not only the current condition of a used smartphone but also the speed at which future degradation will occur is an essential element for determining a reasonable transaction price. The residual value of a device that is expected to degrade rapidly in the future, even if its current condition is good, and a device that exhibits a stable degradation trajectory even if its current condition is somewhat poor, should be evaluated differently. However, conventional technology lacks such a function to predict future degradation, and thus prices are calculated based only on the current condition, leading to frequent returns and claims resulting from faster-than-expected performance degradation of the device after the transaction.

[0007] Furthermore, used smartphone distributors, particularly those engaged in overseas exports, face issues such as confusion during the grade conversion process due to discrepancies between domestic and overseas grading systems, the need to manually manage differing pricing structures across distribution channels (domestic wholesale, overseas export, etc.), and inefficient tracking of device distribution history and transaction settlement. Prior art literature

[0008] KR 10-2018-0098483 The problem to be solved

[0009] The objective of the present invention is to provide a system for predicting the degree of degradation of a used smartphone and for trading through multi-use history and battery condition analysis, which automatically collects and preprocesses multi-use history data including battery history, physical impact history, usage environment history, software history, external condition, and hardware function status from a used smartphone to enable objective and quantitative evaluation of the device condition.

[0010] Furthermore, the objective of the present invention is to provide a system for predicting the degradation of used smartphones and trading through multiple usage history and battery condition analysis, which overcomes the limitations of conventional simple SoH value verification methods by performing a multifaceted battery condition analysis including deriving a battery SoH reduction curve, analyzing internal resistance, calculating the effective cycle count by applying weighting factors according to charging methods, determining whether the battery has been replaced and is genuine, and conducting a comparative analysis against a standard degradation profile. means of solving the problem

[0011] A system for predicting the degree of degradation of a used smartphone and trading through multiple usage history and battery condition analysis according to an embodiment of the present invention for solving the above problem comprises: a data collection unit that collects multiple usage history data from a used smartphone; a battery analysis unit that generates battery analysis result data by analyzing battery history data among the multiple usage history data transmitted from the data collection unit; a degradation calculation unit that calculates a comprehensive degradation score and classifies a degradation grade using a pre-trained first artificial intelligence model based on the multiple usage history data transmitted from the data collection unit and the battery analysis result data transmitted from the battery analysis unit; a degradation prediction unit that predicts the future degradation progression rate and remaining usable period based on the comprehensive degradation score transmitted from the degradation calculation unit; and a price calculation unit that automatically calculates a transaction price based on the comprehensive degradation score transmitted from the degradation calculation unit and future degradation prediction data transmitted from the degradation prediction unit. and includes a transaction management unit that stores transaction price data transmitted from the price calculation unit, manages distribution history by device, and processes transaction settlement.

[0012] The data collection unit comprises: a history extraction unit that automatically extracts battery history data, physical impact history data, usage environment history data, and software history data from the system logs, battery management chip (BMS) data, and sensor logs of the used smartphone through a diagnostic device connected to the used smartphone or diagnostic software installed on the used smartphone; a condition inspection unit that, separately from the history extraction unit, photographs the exterior of the used smartphone using a camera device, inputs the captured image into a pre-trained image analysis model to automatically detect exterior damage items and generate exterior condition data, and generates hardware function test data by performing a hardware function test through diagnostic software installed on the used smartphone; and a preprocessing unit that receives the battery history data, physical impact history data, usage environment history data, and software history data extracted from the history extraction unit, and the exterior condition data and hardware function test data generated from the condition inspection unit, and performs validation, removal of outliers, and normalization processing according to standard values ​​for each device model to generate preprocessed multi-use history data. and a data distribution unit that receives preprocessed multi-use history data transmitted from the preprocessing unit, transmits battery history data included in the preprocessed multi-use history data to the battery analysis unit, and transmits the entire preprocessed multi-use history data to the degradation calculation unit;The battery history data includes the total number of charge / discharge cycles, the number of charges by charging method, the average temperature during charging, the number of overcharge occurrences, the number of over-discharge occurrences, and the trend of change in maximum battery capacity; the physical impact history data includes the number of drop detections based on acceleration sensor logs, impact intensity data, repair history information, and parts replacement history information; the usage environment history data includes cumulative screen usage time, average usage temperature, frequency of exposure to high-temperature environments, and frequency of exposure to low-temperature environments; the software history data includes operating system update history, current operating system version, number of factory resets, and frequency of system errors; the exterior condition data includes scratch detection information, dent detection information, damage detection information, and discoloration detection information; the hardware function test data includes touchscreen responsiveness test results, camera operation status test results, speaker output status test results, microphone input status test results, and biometric recognition function operation status test results; and the battery analysis unit receives data on the trend of change in maximum battery capacity included in the battery history data transmitted from the data distribution unit, and the used smartphone SoH measurement unit that calculates the ratio of the current maximum chargeable capacity to the design capacity and derives a SoH reduction curve relative to the total number of charge / discharge cycles; and internal resistance analysis unit that receives battery history data transmitted from the data distribution unit, calculates the battery internal resistance value, calculates a deviation rate by comparing the calculated internal resistance value with a standard internal resistance value of the same model and same number of cycles stored in the standard profile management unit, and generates an abnormal degradation flag if the calculated deviation rate exceeds a preset abnormal degradation threshold.A charging pattern analysis unit that receives the number of charges by charging method included in the battery history data transmitted from the data distribution unit, applies a weighting coefficient by charging method to each of the number of fast charges, slow charges, and wireless charges, and then sums them to calculate the effective number of cycles; a battery replacement determination unit that analyzes the component replacement history information included in the battery history data transmitted from the data distribution unit to determine whether the battery has been replaced, determines whether the replaced battery is genuine if it has been replaced, and separately classifies usage history data after the replacement point to generate post-replacement battery analysis data; and a standard profile management unit that accumulates a large amount of past diagnostic data for each device model of the used smartphone to generate and manage a standard degradation profile including a curve of SoH decrease relative to the number of cycles, a curve of internal resistance increase relative to the number of cycles, and data on the influence of the charging method by model, and provides comparison reference data to the SoH measurement unit, the internal resistance analysis unit, and the charging pattern analysis unit; wherein the effective number of cycles is calculated by the following [Equation 1];

[0013] [Mathematical Formula 1]

[0014] Ce = Ns × ws + Nf × wf + Nw × ww

[0015] (Here, Ce represents the effective cycle count, Ns represents the number of slow charges, ws represents the slow charge weighting factor, Nf represents the number of fast charges, wf represents the fast charge weighting factor, Nw represents the number of wireless charges, and ww represents the wireless charge weighting factor; ws is 1.0, wf is a value greater than 1.0 and less than or equal to 2.0, and ww is a value greater than 1.0 and less than or equal to 1.5; the specific values ​​of wf and ww are set for each device model based on the model-specific charging method influence data stored in the standard profile management unit mentioned above, and a higher Ce value indicates a higher actual usage intensity of the battery.)

[0016] The battery analysis unit generates battery analysis result data by synthesizing the SoH value and SoH reduction curve calculated from the SoH measurement unit, the internal resistance deviation rate and abnormal degradation flag calculated from the internal resistance analysis unit, the effective cycle count calculated from the charging pattern analysis unit, and the post-replacement battery analysis data generated from the battery replacement determination unit, and transmits the generated battery analysis result data to the degradation degree calculation unit.

[0017] The degradation degree calculation unit comprises: a data integration unit that receives preprocessed multi-use history data transmitted from the data distribution unit and battery analysis result data transmitted from the battery analysis unit, and classifies the received data into six degradation factor categories—battery degradation factor, physical impact degradation factor, usage environment degradation factor, software degradation factor, appearance degradation factor, and hardware function degradation factor—to generate factor-specific data sets; a factor score calculation unit that, for each degradation factor category among the factor-specific data sets transmitted from the data integration unit, normalizes the values ​​of individual data items belonging to the corresponding category to a range of 0 to 100, and calculates category-specific degradation factor scores by weighted summing the normalized individual data item values ​​within the category; and a comprehensive score calculation unit that calculates a comprehensive degradation degree score in a range of 0 to 1000 by applying category-specific weights set by device model and year to the six category-specific degradation factor scores calculated by the factor score calculation unit and performing weighted summing. A grading unit that corresponds the comprehensive deterioration score calculated by the comprehensive score calculation unit to a preset grade standard table and classifies the comprehensive deterioration score into Grade A+ if it is 900 points or more, Grade A if it is 800 points or more but less than 900 points, Grade B+ if it is 700 points or more but less than 800 points, Grade B if it is 600 points or more but less than 700 points, Grade C if it is 400 points or more but less than 600 points, and Grade D if it is less than 400 points; and a contribution analysis unit that generates deterioration cause analysis data by calculating, as a percentage, the ratio in which the deterioration factor score for each deterioration factor category calculated by the factor score calculation unit contributes to the comprehensive deterioration score for the comprehensive deterioration score calculated by the comprehensive score calculation unit;and a weight management unit comprising a model storage member that stores the first artificial intelligence model and provides category-specific weights to the comprehensive score calculation unit, and a learning management member that receives post-transaction feedback data transmitted from the transaction management unit and retrains the category-specific weights of the first artificial intelligence model; wherein the comprehensive degradation score is calculated by the following [Equation 2], and;

[0018] [Mathematical Formula 2]

[0019] D = (α₁ × Fb + α₂ × Fp + α₃ × Fe + α₄ × Fs + α5× Fa + α6× Fh) × 10

[0020] (Here, D represents the overall degradation score (0 to 1000), Fb represents the battery degradation factor score, Fp represents the physical impact degradation factor score, Fe represents the usage environment degradation factor score, Fs represents the software degradation factor score, Fa represents the appearance degradation factor score, and Fh represents the hardware function degradation factor score; Fb, Fp, Fe, Fs, Fa, and Fh are each normalized to values ​​between 0 and 100; α₁, α₂, α₃, α₄, α5, and α6 represent the weighting coefficients for battery, physical impact, usage environment, software, appearance, and hardware function degradation factors, respectively; α₁, α₂, α₃, α₄, α5, and α6 are each values ​​between 0 and 1, α₁ + α₂ + α₃ + α₄ + α5 + α6 = 1, and the larger the value of D, the more the device's condition Indicates good condition)

[0021] The first artificial intelligence model uses as training data post-transaction feedback data including data on the multiple usage history of a previously diagnosed used smartphone, whether a return occurred after the transaction of the device, the reason for the return, and the actual degree of degradation progression measured at the time of repurchase, to continuously update the optimal values ​​of category-specific weights (α₁ to α6); the degradation calculation unit generates degradation diagnosis result data including the comprehensive degradation score calculated by the comprehensive score calculation unit, the degradation grade classified by the grade classification unit, and degradation cause analysis data generated by the contribution analysis unit, and transmits it to the degradation prediction unit and the price calculation unit; the degradation prediction unit receives the comprehensive degradation score and degradation cause analysis data transmitted from the degradation calculation unit and time-series change data of battery SoH included in the preprocessed multiple usage history data transmitted from the data distribution unit, and calculates the slope of SoH reduction during a recently preset period from the received time-series change data to determine whether degradation is accelerating, comprising a trend analysis unit; A prediction operation unit that receives data on the SoH reduction slope and degradation acceleration status transmitted from the trend analysis unit and the overall degradation score transmitted from the degradation calculation unit, calculates individual degradation deviations by comparing them with standard degradation profile data of the same model transmitted from the standard profile management unit of the battery analysis unit, and generates future degradation prediction data by estimating the degradation progression trajectory during a preset prediction period based on the calculated individual degradation deviations and the SoH reduction slope; a remaining lifespan calculation unit that calculates the remaining period until the overall degradation score of the used smartphone decreases below a preset usable lower limit score based on the future degradation prediction data transmitted from the prediction operation unit; and a cohort comparison unit that calculates the percentile rank of the overall degradation score of the used smartphone within the corresponding device group based on device group data of the same model and same release time transmitted from the standard profile management unit of the battery analysis unit.and a reliability calculation unit that calculates a prediction confidence interval based on past prediction accuracy data and the completeness ratio of input data for future degradation prediction data generated from the prediction calculation unit; wherein the degradation prediction unit generates degradation prediction result data including future degradation prediction data generated from the prediction calculation unit, a remaining usable period calculated from the remaining lifespan calculation unit, a percentile rank calculated from the cohort comparison unit, and a prediction confidence interval calculated from the reliability calculation unit, and transmits it to the price calculation unit.

[0022] The above price calculation unit comprises a standard market price management unit that collects standard market price data by device model, storage capacity, color, and carrier in real time by performing API communication with an external market price information system, stores the collected standard market price data in a standard market price database, and updates the standard market price database according to a preset update cycle; A deterioration adjustment unit that receives a comprehensive deterioration score transmitted from the comprehensive score calculation unit of the above deterioration calculation unit, calculates a deterioration adjustment coefficient by correlating the received comprehensive deterioration score with a preset adjustment coefficient table for each deterioration section, wherein if the comprehensive deterioration score is 900 points or more, the deterioration adjustment coefficient is set to a value of 0.95 or more and 1.00 or less; if the score is 800 points or more and less than 900 points, it is set to a value of 0.85 or more and less than 0.95; if the score is 700 points or more and less than 800 points, it is set to a value of 0.75 or more and less than 0.85; if the score is 600 points or more and less than 700 points, it is set to a value of 0.60 or more and less than 0.75; if the score is 400 points or more and less than 600 points, it is set to a value of 0.40 or more and less than 0.60; and if the score is less than 400 points, it is set to a value of less than 0.40. A future degradation correction unit that receives the remaining usable period and the prediction confidence interval included in the degradation prediction result data transmitted from the degradation prediction unit, sets the future degradation correction coefficient to 1.00 if the received remaining usable period is greater than or equal to a preset first reference period, sets the future degradation correction coefficient to a value greater than or equal to 0.90 and less than 1.00 if the remaining usable period is less than the first reference period and greater than or equal to a preset second reference period, sets the future degradation correction coefficient to a value less than 0.90 if the remaining usable period is less than the second reference period, and corrects by multiplying the future degradation correction coefficient by a preset uncertainty correction value if the prediction confidence interval is less than a preset confidence reference value; and receives distribution channel information, and if the received distribution channel is domestic wholesale, sets the distribution channel coefficient to 1.A channel price calculation unit that is set to 00, and in the case of overseas export, sets a distribution channel coefficient by referring to a regional channel coefficient table set for each export region, and in the case of overseas export, retrieves exchange rate information at the relevant time by performing API communication with an external exchange rate information system and reflects the retrieved exchange rate information in the price calculation; and a final price calculation unit that receives the standard market price transmitted from the standard market price management unit, the degradation adjustment coefficient calculated from the degradation adjustment unit, the future degradation correction coefficient calculated from the future degradation correction unit, and the distribution channel coefficient set from the channel price calculation unit to calculate the final transaction price and transmits the calculated final transaction price to the transaction management unit; wherein the final transaction price is calculated by the following [Equation 3], and

[0023] [Mathematical Formula 3]

[0024] P = Pref × Cd × Cf × Cc

[0025] (Here, P represents the final transaction price, Pref represents the standard market price corresponding to the device model, storage capacity, color, and carrier transmitted from the standard market price management unit, Cd represents the degradation adjustment coefficient calculated by the degradation adjustment unit (greater than 0 and less than or equal to 1.00), Cf represents the future degradation correction coefficient calculated by the future degradation correction unit (greater than 0 and less than or equal to 1.00), and Cc represents the distribution channel coefficient set by the channel price calculation unit (a value greater than 0), and a higher value of P indicates a higher transaction price.)

[0026] The final price calculation unit classifies the device as a device unsuitable for transaction and generates a transaction unsuitability flag and transmits it to the transaction management unit when the calculated final transaction price is less than a preset minimum transaction price. The final price calculation unit supports a plurality of price calculation modes, including a purchase price calculation mode and a sales price calculation mode. In the purchase price calculation mode, the purchase price is calculated by applying a preset purchase margin rate to the final transaction price calculated by [Equation 3], and in the sales price calculation mode, the sales price is calculated by applying a preset sales margin rate to the final transaction price.

[0027] The above transaction management unit receives final transaction price data and transaction non-conformity flags transmitted from the above final price calculation unit, automatically aggregates transaction amounts by supplier and seller, generates settlement data by linking the aggregated transaction amount data with a corporate account system, and performs settlement processing according to a preset settlement cycle; a settlement processing unit that sets the IMEI (International Mobile Equipment Identification Number) of the above used smartphone as a reference identifier, maps the comprehensive degradation score, degradation grade, and degradation cause analysis data transmitted from the above degradation calculation unit corresponding to the IMEI, the remaining usable period and percentile rank transmitted from the above degradation prediction unit, and the final transaction price transmitted from the above price calculation unit, stores them in a distribution history database by device, and sequentially records distribution stage history information including the time of purchase, time of receipt, inventory storage period, time of shipment, shipment channel information, and time of final settlement completion in the distribution history database by device. A grade mapping unit that automatically converts the degradation grade transmitted from the grade classification unit of the above degradation grade calculation unit into a grade system used by overseas business partners, and calculates a grade corresponding to the grade system of the corresponding region by referring to a regional grade mapping rule database set for each overseas export region; and a report generation unit that automatically generates a transaction transparency report including the quantity distribution by degradation grade of devices purchased and sold by business partners, the average overall degradation score, the average transaction price, and the transaction volume trend by transaction period, based on the device-specific distribution history data stored in the above history tracking unit, and provides the generated transaction transparency report to the business partner's terminal.and a feedback management unit that collects post-transaction feedback data, including return reason data for devices returned within a preset feedback collection period after sales, claim data received from customers after sales, and comprehensive degradation scores re-measured through the data collection unit for repurchased devices, from among the device-specific distribution history data stored in the history tracking unit; and transmits the collected post-transaction feedback data to a learning management element included in the weight management unit of the degradation calculation unit to be used for re-learning category-specific weights of the first artificial intelligence model;It includes, wherein the grade mapping unit maps the degradation grade transmitted from the grade classification unit to the Working A grade of the overseas grade system if it is Grade A+, maps it to the Working B grade if it is Grade A, maps it to the Working B grade if it is Grade B+, maps it to the Working C grade if it is Grade B, maps it to the Working D grade if it is Grade C, and maps it to the Broken grade if it is Grade D, provided that if a separate mapping rule for the corresponding export region is stored in the regional grade mapping rule database, the separate mapping rule is applied first; the grade mapping unit updates the mapping rules of the regional grade mapping rule database based on grade mismatch feedback data received from overseas business partners; the feedback management unit classifies the collected post-transaction feedback data by device model, degradation grade, and distribution channel and stores it in the feedback analysis database; based on the data accumulated in the feedback analysis database, it calculates the return rate by device model, the claim occurrence rate by degradation grade, and the error rate between the predicted degradation and the actual degradation to generate a system accuracy index; and the generated system accuracy index at a preset period Accordingly, it is provided to the administrator terminal, and the feedback management unit transmits a correction signal to the degradation adjustment unit of the price calculation unit to modify the degradation adjustment coefficient table corresponding to the grade section when a degradation grade section is found in which the return rate included in the system accuracy indicator exceeds a preset return rate threshold, and transmits an update request signal to the standard profile management unit of the battery analysis unit to update the standard degradation profile of the device model when a device model is found in which the error rate between the predicted degradation and the actual degradation included in the system accuracy indicator exceeds a preset error rate allowable threshold. Effects of the invention

[0028] According to the present invention, by automatically collecting multiple usage history data including battery history, physical impact history, usage environment history, software history, appearance condition, and hardware function status from a used smartphone, and by performing validation, outlier removal, and normalization processing by device model, it is possible to enable an objective and quantitative evaluation of the device condition that does not rely on the subjective judgment of an evaluator, thereby improving the reliability of used smartphone transactions.

[0029] In addition, the present invention performs a multifaceted battery condition analysis including deriving an SoH reduction curve, analyzing internal resistance and generating an abnormal degradation flag, calculating the effective cycle count by applying weighting factors according to charging methods, determining whether the battery needs to be replaced and whether it is genuine, and conducting a comparative analysis against a standard degradation profile for each device model; thereby enabling precise diagnosis of the actual degradation state and causes of degradation of a battery that could not be identified by conventional simple SoH value verification methods.

[0030] In addition, the present invention can implement a self-improving degradation calculation system in which the evaluation accuracy automatically improves over time by applying artificial intelligence model-based weights to six degradation factor categories to calculate a comprehensive degradation score of 0 to 1000, classifying grades, and continuously retraining the weights based on feedback data after transactions.

[0031] In addition, the present invention can support rational transaction decision-making by quantitatively predicting changes in the future value of the device as well as its current state through functions such as analyzing degradation trends based on SoH time series change data, predicting future degradation trajectories reflecting individual deviations from standard degradation profiles, calculating remaining usable period, calculating percentile rankings within a cohort, and calculating prediction confidence intervals.

[0032] In addition, the present invention can automate consistent and reasonable price calculation in various distribution channel environments by systematically reflecting a deterioration adjustment coefficient, a future deterioration correction coefficient, and a distribution channel coefficient to the standard market price for the calculation of the final transaction price, automatically classifying unsuitable devices for transaction, and supporting purchase and sales price modes, thereby reducing the time and labor costs required for price calculation and ensuring price consistency. Brief explanation of the drawing

[0033] FIG. 1 is a schematic diagram illustrating the structure of a used smartphone degradation prediction and trading system through multiple usage history and battery condition analysis according to one embodiment of the present invention. FIG. 2 is a flowchart illustrating the flow of a used smartphone degradation prediction and trading system through multiple usage history and battery condition analysis according to one embodiment of the present invention. FIG. 3 is a block diagram of a data collection unit according to one embodiment of the present invention. FIG. 4 is a block diagram of a battery analysis unit according to one embodiment of the present invention. FIG. 5 is a block diagram of a degradation degree calculation unit according to one embodiment of the present invention. FIG. 6 is a block diagram of a degradation prediction unit according to one embodiment of the present invention. FIG. 7 is a block diagram of a price calculation unit according to one embodiment of the present invention. FIG. 8 is a block diagram of a transaction management unit according to one embodiment of the present invention. Specific details for implementing the invention

[0034] Hereinafter, embodiments are described in detail with reference to the attached drawings. However, various modifications may be made to the embodiments, and thus the scope of the patent application is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, and substitutions to the embodiments are included within the scope of the rights.

[0035] Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosed forms, and the scope of this specification includes modifications, equivalents, or substitutions that fall within the technical concept.

[0036] Terms such as "first" or "second" may be used to describe various components, but these terms should be interpreted solely for the purpose of distinguishing one component from another. For example, the first component may be named the second component, and similarly, the second component may be named the first component.

[0037] When it is stated that a component is "connected" to another component, it should be understood that it may be directly connected to or joined to that other component, or that there may be other components in between.

[0038] The terms used in the embodiments are for illustrative purposes only and should not be interpreted as intended to be limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as "comprising" or "having" are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0039] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments pertain. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0040] In addition, when describing with reference to the attached drawings, identical components are assigned the same reference numeral regardless of drawing symbols, and redundant descriptions thereof are omitted. In describing the embodiments, if it is determined that a detailed description of related prior art could unnecessarily obscure the essence of the embodiments, such detailed description is omitted.

[0041] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims.

[0042] In the embodiments of the present invention, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in the embodiments of the present invention.

[0043] The shapes, sizes, ratios, angles, numbers, etc. disclosed in the drawings for explaining embodiments of the present invention are exemplary, and therefore the present invention is not limited to the depicted details. Furthermore, in describing the present invention, if it is determined that a detailed description of related known technology may unnecessarily obscure the essence of the present invention, such detailed description is omitted. Where terms such as "includes," "has," or "is made up" are used in this specification, other parts may be added unless "only" is used. Where a component is expressed in the singular, it includes cases where it includes the plural unless specifically stated otherwise.

[0044] In interpreting the components, they are interpreted to include a margin of error even in the absence of a separate explicit statement.

[0045] In the case of describing a positional relationship, for example, when the positional relationship between two parts is described using expressions such as 'on,' 'upper,' 'lower,' or 'next to,' one or more other parts may be located between the two parts unless 'immediately' or 'directly' is used.

[0046] When elements or layers are referred to as "on" another element or layer, this includes cases where another layer or element is placed directly on top of or in between. Throughout the specification, the same reference numerals refer to the same components.

[0047] The size and thickness of each component shown in the drawings are illustrated for convenience of explanation, and the present invention is not necessarily limited to the size and thickness of the illustrated components.

[0048] The features of each of the various embodiments of the present invention may be combined or combined with one another, either partially or wholly, and as will be fully understood by those skilled in the art, various technical interlocking and operation are possible, and each embodiment may be implemented independently of one another or together in an interlocking relationship.

[0050] FIG. 1 is a schematic diagram illustrating the structure of a used smartphone degradation prediction and trading system through multiple usage history and battery condition analysis according to an embodiment of the present invention. FIG. 2 is a flowchart showing the flow of a used smartphone degradation prediction and trading system through multiple usage history and battery condition analysis according to an embodiment of the present invention. FIG. 3 is a block diagram of a data collection unit according to an embodiment of the present invention. FIG. 4 is a block diagram of a battery analysis unit according to an embodiment of the present invention. FIG. 5 is a block diagram of a degradation calculation unit according to an embodiment of the present invention. FIG. 6 is a block diagram of a degradation prediction unit according to an embodiment of the present invention. FIG. 7 is a block diagram of a price calculation unit according to an embodiment of the present invention. FIG. 8 is a block diagram of a transaction management unit according to an embodiment of the present invention.

[0051] Hereinafter, with reference to FIGS. 1 to 8, a system for predicting the degree of degradation of a used smartphone and trading through multiple usage history and battery condition analysis according to an embodiment of the present invention will be described in detail.

[0052] 1. Overall System Overview

[0053] Referring to FIG. 1, a used smartphone degradation prediction and trading system (100) through multiple usage history and battery condition analysis according to one embodiment of the present invention includes a data collection unit (110), a battery analysis unit (120), a degradation calculation unit (130), a degradation prediction unit (140), a price calculation unit (150), and a trading management unit (160).

[0054] The used smartphone degradation prediction and trading system (100) through multiple usage history and battery condition analysis collects multiple usage history data from a used smartphone (10), analyzes the collected data to calculate a comprehensive degradation score, predicts future degradation progress, automatically calculates a trading price based on the prediction results, and performs transaction settlement and distribution history management in a comprehensive manner. In addition, the used smartphone degradation prediction and trading system (100) through multiple usage history and battery condition analysis performs data communication with an external diagnostic device (20), a camera (30), an external market price information system (40), an external exchange rate information system (50), a corporate account system (60), a business partner's terminal (70), and an administrator terminal (80) to collect necessary external data and provide the processing results externally.

[0055] To examine the data flow of the used smartphone degradation prediction and trading system (100) through multiple usage history and battery condition analysis in detail, the data collection unit (110) collects multiple usage history data from the used smartphone (10) and transmits it to the battery analysis unit (120) and the degradation calculation unit (130). The battery analysis unit (120) analyzes the battery history data among the multiple usage history data transmitted from the data collection unit (110) to generate battery analysis result data, and transmits the generated battery analysis result data to the degradation calculation unit (130). Based on the multiple usage history data transmitted from the data collection unit (110) and the battery analysis result data transmitted from the battery analysis unit (120), the degradation calculation unit (130) calculates a comprehensive degradation score and classifies the degradation grade using a pre-trained first artificial intelligence model. The degradation prediction unit (140) predicts the future degradation rate and remaining usable period based on the comprehensive degradation score transmitted from the degradation calculation unit (130). At this time, the degradation prediction unit (140) directly receives time-series change data of the battery SoH from the data collection unit (110) and receives standard degradation profile data from the standard profile management unit (125) of the battery analysis unit (120) to increase the accuracy of the prediction. The price calculation unit (150) automatically calculates the transaction price based on the comprehensive degradation score transmitted from the degradation calculation unit (130) and the future degradation prediction data transmitted from the degradation prediction unit (140), while reflecting real-time market prices and exchange rates by performing API communication with the external market price information system (40) and the external exchange rate information system (50). The transaction management unit (160) stores transaction price data transmitted from the price calculation unit (150), manages the distribution history for each device, processes transaction settlements in conjunction with the corporate account system (60), and provides reports and system accuracy indicators to the business partner's terminal (70) and the manager's terminal (80).

[0056] In this way, the used smartphone degradation prediction and trading system (100) through multiple usage history and battery condition analysis has six units connected in a pipeline form—data collection, battery analysis, degradation calculation, degradation prediction, price calculation, and trading management—to automate the entire process from the condition diagnosis of the used smartphone (10) to the settlement of the transaction. In particular, the post-transaction feedback data collected from the trading management unit (160) forms a three-way feedback loop structure that is utilized for the artificial intelligence model retraining of the degradation calculation unit (130), the degradation adjustment coefficient correction of the price calculation unit (150), and the standard degradation profile update of the battery analysis unit (120), thereby continuously improving the evaluation accuracy and price calculation rationality of the entire system as trading data accumulates.

[0057] The detailed configuration and operation of each unit are explained below.

[0058] 2. Data collection unit (110)

[0059] Referring to FIG. 2, the data collection unit (110) is a unit that collects multiple usage history data from a used smartphone (10).

[0060] The data collection unit (110) includes a history extraction unit (111), a status inspection unit (112), a preprocessing unit (113), and a data distribution unit (114).

[0061] Looking at the internal structure of the data collection unit (110), the history extraction unit (111) and the condition inspection unit (112) collect data from the used smartphone (10) through independent parallel paths. The history extraction unit (111) collects four types of history data that can be software-extracted from the internal system logs and sensor data of the used smartphone (10), and the condition inspection unit (112) collects external condition and hardware function test data using an external shooting device (30) and diagnostic software. All data collected from the history extraction unit (111) and the condition inspection unit (112) is integratedly preprocessed in the preprocessing unit (113) and then distributed to subsequent units, namely the battery analysis unit (120), the degradation calculation unit (130), and the degradation prediction unit (140), through the data distribution unit (114).

[0062] 2.1 History extraction unit (111)

[0063] The history extraction unit (111) automatically extracts battery history data, physical impact history data, usage environment history data, and software history data from the system logs, battery management chip (BMS) data, and sensor logs of the used smartphone (10) through a diagnostic device (20) connected to the used smartphone (10) or diagnostic software installed on the used smartphone (10).

[0064] Specifically, the history extraction unit (111) performs data communication with a diagnostic device (20) connected to the used smartphone (10) via a USB cable or a wireless communication protocol (e.g., Bluetooth, Wi-Fi Direct), or transmits a data extraction command by executing diagnostic software installed on the operating system of the used smartphone (10). The diagnostic device (20) accesses private log data within the system through a diagnostic interface (e.g., Android Debug Bridge, iOS diagnostic profile) provided by the operating system of the used smartphone (10). The history extraction unit (111) receives raw data responded to from the diagnostic device (20) or the diagnostic software, and classifies the received raw data by data type to structure it into four types of history data: battery history data, physical impact history data, usage environment history data, and software history data.

[0065] The battery history data extracted by the history extraction unit (111) includes the total number of charge / discharge cycles, the number of charges by charging method, the average temperature during charging, the number of overcharge occurrences, the number of over-discharge occurrences, and the trend of changes in the maximum battery capacity. The total number of charge / discharge cycles refers to the cumulative number of times the process of charging the battery of a used smartphone (10) from a completely discharged state to a completely charged state and then discharging it again is counted as one cycle, and the value recorded in the battery management chip (BMS) is read directly. The number of charges by charging method is a value obtained by individually aggregating the number of times charging was performed by each method of fast charging, slow charging, and wireless charging, and is classified based on the charging protocol identification information recorded in the battery management chip (BMS). The average temperature during charging is the time-weighted average of the battery cell temperature measured by the temperature sensor of the Battery Management System (BMS) during charging, and is used to evaluate the degree of deviation from the optimal charging temperature range of lithium-ion batteries (approximately 20°C to 25°C). The number of overcharge occurrences refers to the number of times charging continued while the battery voltage exceeded a preset upper voltage limit (e.g., 4.2V), and the number of over-discharge occurrences refers to the number of times the battery voltage dropped below a preset lower voltage limit (e.g., 3.0V). Since overcharging and over-discharging are major causes of irreversible damage to the electrode structure inside the battery cell, the number of overcharge and over-discharge occurrences serves as a key indicator for determining whether the battery is undergoing abnormal degradation. The trend of maximum battery capacity change is time-series data indicating how the battery's maximum chargeable capacity has changed over time; it reads values ​​periodically measured by the Battery Management System (BMS) and recorded in internal registers.

[0066] The physical impact history data extracted by the history extraction unit (111) includes the number of drop detections based on the acceleration sensor log, impact intensity data, repair history information, and parts replacement history information. The number of drop detections refers to the number of times the acceleration sensor built into the used smartphone (10) detects a preset free-fall acceleration pattern (a pattern in which acceleration of approximately 0G is maintained for a certain period of time), and is extracted from the sensor log. The impact intensity data includes the maximum acceleration value (unit: G) measured by the acceleration sensor at each drop or impact occurrence, and the higher the impact intensity, the higher the possibility of physical damage to the internal circuit board, connector, and display bonding part. The repair history information includes repair records from an authorized service center or an unauthorized repair shop recorded in the operating system of the used smartphone (10), and includes the date and time of repair, the details of the repair, and the type of repair shop (authorized / unauthorized). Parts replacement history information includes whether major components, including displays, batteries, camera modules, and motherboards, have been replaced, the time of replacement, and whether the replaced parts are genuine, and is extracted based on the change history of part serial numbers recorded at the operating system level.

[0067] The usage environment history data extracted by the history extraction unit (111) includes the screen cumulative usage time, average usage temperature, frequency of exposure to high temperature environment, and frequency of exposure to low temperature environment. The screen cumulative usage time refers to the total time the display remains in an active state and is extracted from the usage statistics log of the operating system. The screen cumulative usage time is an indicator that indirectly indicates the overall usage intensity of the device. The average usage temperature refers to the average temperature during use measured by the device's internal temperature sensor, and continuous high-temperature usage accelerates the degradation of not only the battery but also the AP (Application Processor) and memory chips. The frequency of exposure to high temperature environment is the number of times the device's internal temperature exceeds a preset high temperature threshold (e.g., 40°C), and the frequency of exposure to low temperature environment is the number of times the device's internal temperature drops below a preset low temperature threshold (e.g., 0°C). Using the battery in a low-temperature environment reduces the movement speed of lithium ions, thereby temporarily increasing the battery's internal resistance, and frequent exposure to low temperature can cause a lithium plating phenomenon in which lithium metal is deposited on the electrode surface, leading to a permanent reduction in capacity.

[0068] The software history data extracted by the history extraction unit (111) includes the operating system update history, the current operating system version, the number of factory resets, and the frequency of system errors. The operating system update history is a time-series record that includes version information and the application date of the operating system update applied to the used smartphone (10). The current operating system version refers to the exact version number of the operating system installed at the time of extraction and serves as a criterion for determining whether the latest security patch has been applied. The number of factory resets is the total number of times a factory reset has been performed on the used smartphone (10) after it was shipped, and an abnormally high number of factory resets may indicate the repeated occurrence of software defects or attempts to hide data during the used distribution process. The frequency of system errors includes the frequency of operating system kernel panics, forced application terminations, and system reboots, and is extracted from system logs. A high frequency of system errors may be caused by memory chip defects, storage defects, or intermittent contact failures due to soldering defects on the mainboard, and serves as an indirect indicator of hardware defects.

[0069] The history extraction unit (111) transmits the extracted four types of history data (battery history data, physical impact history data, usage environment history data, software history data) to the preprocessing unit (113). At this time, the history extraction unit (111) transmits each data item with a timestamp of the extraction time and data source identification information added, thereby enabling the preprocessing unit (113) to track the source and extraction time of the data.

[0070] 2.2 Status inspection unit (112)

[0071] The status inspection unit (112) generates external condition data and hardware function test data of the used smartphone (10) separately from the history extraction unit (111). Unlike the history extraction unit (111), which extracts past usage history from the internal log data of the used smartphone (10), the status inspection unit (112) is in a mutually complementary relationship with the history extraction unit (111) in that it generates data by directly inspecting the current state at the time of diagnosis.

[0072] Specifically, the condition inspection unit (112) captures the exterior of a used smartphone (10) through a shooting device (30) and inputs the captured image into a pre-trained image analysis model to automatically detect exterior damage items and generate exterior condition data. The shooting device (30) captures high-resolution images from multiple angles including the front, back, and sides of the used smartphone (10) and obtains images of consistent quality in a shooting environment where lighting conditions are controlled. The shooting device (30) can obtain images capable of detecting even fine scratches on the display surface by using polarized lighting, and can detect dents and damage with depth three-dimensionally through shooting at various lighting angles. The image analysis model is a deep learning model based on a convolutional neural network (CNN), and is a model that has been pre-trained using a large amount of smartphone exterior image data that is pre-labeled for various types of exterior damage (scratches, dents, cracks, discoloration) as training data. The image analysis model detects the location of damaged areas using bounding boxes based on object detection algorithms, extracts the precise contours of damaged areas through segmentation algorithms, and determines the type and severity of damage through classification algorithms.

[0073] The external condition data generated by the condition inspection unit (112) includes scratch detection information, dent detection information, damage detection information, and discoloration detection information. The scratch detection information includes the number, length, and depth level of scratches present on the display surface and rear cover of the used smartphone (10). The dent detection information includes the number and size of dents present on the frame and rear cover of the used smartphone (10). The damage detection information includes whether there is a crack in the display glass or damage to the rear glass, the extent of damage (the ratio of the damaged area to the total area), and the severity of damage (minor surface crack, penetrating crack, glass detachment). The discoloration detection information includes the burn-in phenomenon of the display, color non-uniformity, and whether the rear cover is discolored.

[0074] Additionally, the status inspection unit (112) generates hardware function test data by performing a hardware function test through diagnostic software installed on the used smartphone (10). The hardware function test consists of a series of diagnostic routines that automatically verify whether the main hardware components of the used smartphone (10) are functioning normally, and each test item is determined in three stages: normal (pass), abnormal (fail), and partially normal (partial pass).

[0075] The hardware function test data generated by the state inspection unit (112) includes touchscreen responsiveness test results, camera operation status test results, speaker output status test results, microphone input status test results, and biometric recognition function operation status test results. The touchscreen responsiveness test results include whether touch input is recognized for multiple points on the display (e.g., grid-shaped test points), response delay time (in ms), and whether multi-touch recognition is normal. The camera operation status test results include whether shooting is possible for the front camera and rear camera respectively, whether autofocus is operating, whether the flash is operating, and whether there is an abnormality in the image quality of the captured image. The speaker output status test results include the speaker output volume, whether there is sound quality distortion, and left / right speaker balance. The microphone input status test results include the microphone input sensitivity, noise level, and whether voice recognition is normal. The biometric recognition function operation status test results include whether the registration and authentication of the fingerprint recognition sensor or facial recognition sensor are operating normally, and the recognition speed.

[0076] The state inspection unit (112) transmits the generated external state data and hardware function test data to the preprocessing unit (113). At this time, the state inspection unit (112) transmits the data to the preprocessing unit (113) using the same data format as the history extraction unit (111), thereby enabling the preprocessing unit (113) to comprehensively preprocess the data received from the history extraction unit (111) and the state inspection unit (112).

[0077] 2.3 Preprocessing unit (113)

[0078] The preprocessing unit (113) receives battery history data, physical impact history data, usage environment history data, software history data extracted from the history extraction unit (111), and appearance state data and hardware function test data generated from the state inspection unit (112), and performs validation, removal of outliers, and normalization processing according to standard values ​​for each device model to generate preprocessed multi-use history data.

[0079] Specifically, the validation of the preprocessing unit (113) includes the process of checking whether each received data item exists within a pre-set valid range for the corresponding data type, and if there are missing data items, displaying the items. If a missing data item occurs among the required data items, the preprocessing unit (113) records the type and number of missing data items to calculate the data completeness ratio, and the calculated data completeness ratio is referenced when calculating the prediction confidence interval in the reliability calculation unit (145) of the deterioration prediction unit (140) described later.

[0080] The removal of outliers by the preprocessing unit (113) includes the process of identifying abnormally high or low data values ​​by applying statistical techniques (e.g., Interquartile Range (IQR)-based outlier detection, Z-score-based outlier detection, Modified Z-Score-based outlier detection), and replacing the identified outliers with a statistical representative value of the corresponding device model (e.g., median) or excluding them from the analysis. The preprocessing unit (113) records the original value and the replaced value of the data item identified as an outlier in a log, thereby ensuring traceability of the outlier processing.

[0081] The normalization processing of the preprocessing unit (113) includes a process of converting each data item into a relative ratio or a standardized scale by referring to a reference value set for each device model, since the battery design capacity, display size, and release time differ depending on the device model. For example, data on the trend of changes in maximum battery capacity is normalized into a ratio value based on the design capacity of the corresponding device model, and the cumulative screen usage time is normalized into a ratio relative to the total number of days elapsed since the release time of the corresponding device model. Through this, normalized data is generated that allows for comparative analysis based on the same criteria even between different device models.

[0082] The preprocessing unit (113) transmits the preprocessed multi-use history data, which has undergone validation, removal of outliers, and normalization processing, to the data distribution unit (114).

[0083] 2.4 Data distribution unit (114)

[0084] The data distribution unit (114) receives preprocessed multi-use history data transmitted from the preprocessing unit (113) and performs a routing role of distributing data suitable for the purpose to subsequent units.

[0085] The data distribution unit (114) transmits the battery history data included in the received preprocessed multi-use history data to the battery analysis unit (120) and transmits the entire preprocessed multi-use history data to the data integration unit (131) of the degradation calculation unit (130). At this time, the data distribution unit (114) transmits the battery history data to the battery analysis unit (120) and simultaneously transmits the entire preprocessed multi-use history data including the battery history data to the degradation calculation unit (130). This data distribution structure enables the battery analysis unit (120) to perform precision analysis specialized for the battery (SoH measurement, internal resistance analysis, charging pattern analysis, battery replacement determination), and enables the degradation calculation unit (130) to comprehensively utilize all usage history data including the battery to evaluate six degradation factor categories.

[0086] Additionally, the data distribution unit (114) also transmits the time series change data of the battery SoH included in the preprocessed multiple usage history data to the trend analysis unit (141) of the degradation prediction unit (140). This is to enable the trend analysis unit (141) to perform SoH time series trend analysis in parallel without waiting for the battery analysis unit (120) to complete its analysis. Through this, the total processing time of the system is reduced.

[0087] As described above, the data collection unit (110) performs the function of systematically collecting multiple usage history data of a used smartphone (10) and supplying it to a subsequent unit through a structure of parallel data collection by the history extraction unit (111) and the status inspection unit (112), integrated preprocessing by the preprocessing unit (113), and purpose-specific data distribution by the data distribution unit (114). Below, the detailed configuration and operation of the battery analysis unit (120), which receives battery history data from the data collection unit (110) and performs precise analysis of the battery condition, will be described.

[0088] 3. Battery analysis unit (120)

[0089] Referring to FIG. 3, the battery analysis unit (120) is a unit that generates battery analysis result data by analyzing battery history data transmitted from the data distribution unit (114) of the data collection unit (110).

[0090] The battery analysis unit (120) includes a SoH measurement unit (121), an internal resistance analysis unit (122), a charging pattern analysis unit (123), a battery replacement determination unit (124), and a standard profile management unit (125).

[0091] Looking at the internal structure of the battery analysis unit (120), four analysis units—SoH measurement unit (121), internal resistance analysis unit (122), charging pattern analysis unit (123), and battery replacement determination unit (124)—each receive battery history data from the data distribution unit (114) and perform analysis in parallel, and the standard profile management unit (125) acts as a hub providing comparison standard data to these four analysis units. Specifically, the standard profile management unit (125) provides a standard curve of SoH reduction relative to the number of cycles to the SoH measurement unit (121), provides a standard internal resistance value of the same model and the same number of cycles to the internal resistance analysis unit (122), and provides data on the influence of the charging method by model (weighting coefficient) to the charging pattern analysis unit (123). Through this structure, each analysis unit can quantitatively calculate the deviation by comparing the measurement value of an individual device with the standard value of the corresponding device model.

[0092] 3.1 SoH measuring unit (121)

[0093] The SoH measurement unit (121) receives battery maximum capacity change trend data included in the battery history data transmitted from the data distribution unit (114).

[0094] The SoH measurement unit (121) calculates the ratio of the current maximum chargeable capacity to the design capacity of the used smartphone (10) based on the received battery maximum capacity change trend data. Here, the design capacity refers to the rated capacity (unit: mAh) designed for the battery of the used smartphone (10) at the time of shipment, and the current maximum chargeable capacity refers to the maximum amount of electric charge that can be stored when the battery is fully charged at the time of diagnosis. The SoH measurement unit (121) calculates the SoH value as a percentage by multiplying the value obtained by dividing the current maximum chargeable capacity by the design capacity by 100. For example, if the current maximum chargeable capacity of a used smartphone (10) with a design capacity of 4,000 mAh is 3,400 mAh, the SoH value is calculated as (3,400 / 4,000) × 100 = 85%.

[0095] Additionally, the SoH measurement unit (121) derives a curve of the decrease in the state of health (SoH) relative to the total number of charge / discharge cycles from the data on the trend of the maximum capacity change of the battery. The SoH decrease curve is a two-dimensional curve with the number of charge / discharge cycles on the horizontal axis and the SoH value on the vertical axis, visually indicating the pattern in which the degradation of the battery has progressed as the number of cycles increases. The SoH measurement unit (121) analyzes the change in the slope of the SoH decrease curve to distinguish between a section where degradation progresses linearly and a section where it progresses acceleratedly. Generally, the SoH decrease curve of a lithium-ion battery shows a pattern of an initial capacity stabilization section during the first few tens of cycles, a linear decrease section over a long period thereafter, an accelerated decrease section after a specific threshold (generally around 80% SoH), and a rapid degradation section. The SoH measurement unit (121) generates basic data that can be used to determine which degradation stage (initial stable stage, linear degradation stage, accelerated degradation stage, rapid degradation stage) the battery of the used smartphone (10) is currently in through this section analysis.

[0096] The SoH measurement unit (121) retains the calculated SoH value, the SoH reduction curve, and the degradation stage determination result to be used for generating the final output of the battery analysis unit (120), and the calculated SoH value is compared with the SoH reduction reference curve relative to the number of cycles of the same model stored in the standard profile management unit (125) to calculate the deviation relative to the standard.

[0097] 3.2 Internal resistance analysis unit (122)

[0098] The internal resistance analysis unit (122) receives battery history data transmitted from the data distribution unit (114) and calculates the battery internal resistance value.

[0099] Battery internal resistance is the resistance generated by electrochemical reactions within the battery cell and includes the interfacial resistance between the electrode and the electrolyte (SEI layer resistance), the ionic conduction resistance of the electrolyte, and the charge transfer resistance of the electrode active material. As battery degradation progresses, the thickness of the SEI layer increases and structural changes occur in the electrode active material, leading to a tendency for internal resistance to increase. Therefore, the internal resistance value is an important indicator of the degree of battery degradation.

[0100] The internal resistance analysis unit (122) calculates the DC internal resistance (DC-IR) based on voltage change data and current data recorded during the charging and discharging process. The DC internal resistance is defined as the value obtained by dividing the voltage drop that occurs when a constant current is applied by the corresponding current value. The internal resistance analysis unit (122) extracts voltage response data at the time of current change from the charging and discharging event recorded in the battery management chip (BMS), and calculates the internal resistance value (R = ΔV / ΔI) by dividing the voltage drop amount (ΔV) by the current change amount (ΔI).

[0101] The internal resistance analysis unit (122) calculates a deviation rate by comparing the calculated internal resistance value with the standard internal resistance value of the same model and same number of cycles stored in the standard profile management unit (125). The deviation rate is a value expressed as a percentage indicating how much the internal resistance value of the used smartphone (10) deviates from the standard internal resistance value under the same conditions. For example, if the standard internal resistance value at the time of the same model and 500 cycles is 100 mΩ and the calculated internal resistance value of the used smartphone (10) is 130 mΩ, the deviation rate is calculated as (130 - 100) / 100 × 100 = 30%.

[0102] The internal resistance analysis unit (122) generates an abnormal degradation flag when the calculated deviation rate exceeds a preset abnormal degradation threshold (e.g., 20%). The abnormal degradation flag is a warning indicator that the battery of the used smartphone (10) is significantly deviating from the normal degradation trajectory of the same model and same number of cycles. Abnormal degradation can occur due to physical damage to the battery cell (deformation of the electrode structure due to drop impact), long-term exposure to a high-temperature environment (promotion of electrolyte decomposition reaction), overcurrent charging due to the use of a non-genuine charger (precipitation of lithium metal on the electrode surface), or accelerated degradation due to a mismatch in specifications after replacement with a non-genuine battery. When an abnormal degradation flag is generated, the internal resistance analysis unit (122) can classify the severity of the abnormal degradation into three stages based on the magnitude of the deviation rate: mild (deviation rate greater than 20% and less than or equal to 35%), normal (deviation rate greater than 35% and less than or equal to 50%), and severe (deviation rate greater than 50%).

[0103] 3.3 Charging pattern analysis unit (123)

[0104] The charging pattern analysis unit (123) receives the number of charges by charging method included in the battery history data transmitted from the data distribution unit (114), applies a weighting factor by charging method to each of the number of fast charges, slow charges, and wireless charges, and then sums them up to calculate the effective number of cycles.

[0105] The effective cycle count is not simply a count of the total number of charge and discharge cycles, but a weighted cycle count calculated by reflecting the different degrees of influence each charging method has on battery degradation. To explain the technical significance of the weighting factors for each charging method, rapid charging is a method that completes charging within a short period by applying a high current (generally 1C or more, up to a maximum of 3C or more). Due to the high current density, it increases physical stress on the lithium ion movement paths within the battery cell, and the Joule heating generated during the charging process promotes electrolyte decomposition reactions, thereby accelerating battery degradation. Wireless charging, which transfers energy via electromagnetic induction, has a lower energy conversion efficiency compared to wired charging (generally at the 80–90% level), resulting in more heat generation during the charging process. Additionally, further heat loss may occur due to alignment errors between the transmitting and receiving coils. On the other hand, slow charging applies a relatively low current (0.5C or less), minimizing stress on the battery cell, and thus has the smallest impact on battery degradation.

[0106] The number of effective cycles is calculated by the following [Equation 1].

[0107] [Mathematical Formula 1]

[0108] Ce = Ns × ws + Nf × wf + Nw × ww

[0109] (Here, Ce represents the effective number of cycles, Ns represents the number of slow charges, ws represents the slow charge weighting factor, Nf represents the number of fast charges, wf represents the fast charge weighting factor, Nw represents the number of wireless charges, and ww represents the wireless charge weighting factor. ws is 1.0, wf is a value greater than 1.0 and less than or equal to 2.0, and ww is a value greater than 1.0 and less than or equal to 1.5. The specific values ​​of wf and ww are set based on model-specific charging method influence data stored in the standard profile management unit (125) for each device model. A higher Ce value indicates a higher actual usage intensity of the battery.)

[0110] For example, if a used smartphone (10) has a history of 200 slow charges, 300 fast charges, and 100 wireless charges, and the fast charging weighting factor wf of the device model is set to 1.5 and the wireless charging weighting factor ww is set to 1.2, the effective number of cycles Ce is calculated as 200 × 1.0 + 300 × 1.5 + 100 × 1.2 = 200 + 450 + 120 = 770. In this case, the effective number of cycles 770 is calculated by comparing it with the simple total number of charges 600, thereby quantitatively reflecting the additional degradation load applied to the battery by fast charging and wireless charging. On the other hand, for a device that performed all 600 total charges as slow charges, the effective number of cycles was calculated as 600 × 1.0 = 600, which quantitatively indicates that the former device, which has a higher fast charging rate, experienced approximately 28% higher usage intensity in terms of battery degradation.

[0111] 3.4 Battery replacement determination unit (124)

[0112] The battery replacement determination unit (124) analyzes the part replacement history information included in the battery history data transmitted from the data distribution unit (114) and determines whether the battery has been replaced.

[0113] The battery replacement determination unit (124) primarily determines whether the battery has been replaced by checking whether the serial number of the battery management chip (BMS) matches the serial number of the used smartphone (10). If the serial number of the battery management chip (BMS) is different from the original serial number registered at the time of the device's shipment, it primarily determines that the battery has been replaced. Additionally, the battery replacement determination unit (124) performs a second determination to estimate the time of battery replacement when a discontinuous increase in capacity (e.g., a point where SoH rises rapidly) is detected in the battery maximum capacity change trend data. Through this two-stage determination, it is possible to indirectly determine whether the battery has been replaced even if the record of the BMS serial number change is lost.

[0114] The battery replacement determination unit (124) determines whether the replaced battery is genuine when it is determined that the battery has been replaced. The determination of genuineness is performed by comparing the manufacturer code, battery cell specification information, and authentication key value recorded in the battery management chip (BMS) with the genuine battery database provided by the manufacturer of the used smartphone (10). If the battery is replaced with a non-genuine battery, the quality variation of the battery cell may be large and the specifications of the protection circuit may differ, so the degradation progression pattern may differ from that of the genuine battery, and there is a risk from a safety perspective.

[0115] When the battery replacement determination unit (124) determines that the battery has been replaced, it classifies the usage history data after the replacement time separately from the usage history data before the replacement time to generate post-replacement battery analysis data. The post-replacement battery analysis data includes the replacement time, whether the replacement battery is genuine, the number of cycles after replacement, and the trend of SoH change after replacement. Through this, the SoH measurement unit (121) and the internal resistance analysis unit (122) can accurately evaluate the current state of the replaced battery without confusion with the history of the battery before replacement.

[0116] 3.5 Standard Profile Management Department (125)

[0117] The standard profile management unit (125) accumulates a large amount of past diagnostic data for each device model of a used smartphone (10) and generates and manages a standard degradation profile.

[0118] The standard degradation profile generated and managed by the standard profile management unit (125) includes a curve of SoH decrease relative to the number of cycles, a curve of internal resistance increase relative to the number of cycles, and data on the impact of charging methods by model. The curve of SoH decrease relative to the number of cycles is a reference curve indicating how the SoH of a specific device model's battery decreases on average as the number of charge and discharge cycles increases. This reference curve is calculated by statistically aggregating data from identical model devices diagnosed through the used smartphone degradation prediction and trading system (100) based on multiple usage history and battery condition analysis, and the standard deviation range (e.g., ±1σ, ±2σ) is managed together with the average curve. The curve of internal resistance increase relative to the number of cycles is a reference curve indicating how the internal resistance of an identical device model's battery increases on average as the number of cycles increases. The data on the impact of charging methods by model is data that quantitatively indicates the magnitude of the impact of fast charging and wireless charging on battery degradation for each device model, and serves as the basis for setting the weighting coefficients wf and ww used by the charging pattern analysis unit (123).

[0119] The standard profile management unit (125) not only provides comparison reference data to the SoH measurement unit (121), internal resistance analysis unit (122), and charging pattern analysis unit (123), but also provides standard degradation profile data and data for a group of devices of the same model and release time to the prediction calculation unit (142) and cohort comparison unit (144) of the degradation prediction unit (140) described later. In this way, the standard profile management unit (125) performs the role of a cross-unit data supply unit that exchanges data not only with the analysis units within the battery analysis unit (120) but also with the degradation prediction unit (140).

[0120] The standard profile management unit (125) continuously updates the standard degradation profile as data of the device diagnosed through the used smartphone degradation prediction and trading system (100) through multiple usage history and battery condition analysis is accumulated, and when it receives an update request signal from the feedback management unit (165) of the trading management unit (160) described later, it regenerates the standard degradation profile of the corresponding device model by reflecting the latest data.

[0121] 3.6 Total output of the battery analysis unit (120)

[0122] The battery analysis unit (120) generates battery analysis result data by combining the SoH value and SoH reduction curve calculated from the SoH measurement unit (121), the internal resistance deviation rate and abnormal degradation flag calculated from the internal resistance analysis unit (122), the effective cycle count calculated from the charging pattern analysis unit (123), and the post-replacement battery analysis data generated from the battery replacement determination unit (124). The battery analysis unit (120) transmits the generated battery analysis result data to the data integration unit (131) of the degradation degree calculation unit (130).

[0123] As described above, the battery analysis unit (120) precisely analyzes the battery history data received from the data collection unit (110) from four perspectives: SoH measurement, internal resistance analysis, charging pattern analysis, and battery replacement determination, and objectively evaluates the battery condition of individual devices through comparison with a standard degradation profile. Below, the detailed configuration and operation of the degradation calculation unit (130), which calculates a comprehensive degradation score by combining the analysis results of the battery analysis unit (120) and the entire multi-use history data of the data collection unit (110), will be explained.

[0124] 4. Deterioration calculation unit (130)

[0125] Referring to FIG. 4, the degradation calculation unit (130) is a unit that calculates a comprehensive degradation score and classifies a degradation grade using a pre-trained first artificial intelligence model based on pre-processed multi-use history data transmitted from the data distribution unit (114) of the data collection unit (110) and battery analysis result data transmitted from the battery analysis unit (120).

[0126] The degradation calculation unit (130) includes a data integration unit (131), a factor score calculation unit (132), a comprehensive score calculation unit (133), a grade classification unit (134), a contribution analysis unit (135), and a weight management unit (136). The weight management unit (136) includes a model storage unit (136a) and a learning management unit (136b).

[0127] Looking at the internal data flow of the degradation calculation unit (130), the data integration unit (131) classifies the received data into six degradation factor categories and transmits them to the factor score calculation unit (132). The factor score calculation unit (132) calculates degradation factor scores for each category and transmits them to the comprehensive score calculation unit (133) and the contribution analysis unit (135). The comprehensive score calculation unit (133) calculates the comprehensive degradation score by applying the category-specific weights provided from the model storage unit (136a) of the weight management unit (136). The calculated comprehensive degradation score is transmitted to the grade classification unit (134) and the contribution analysis unit (135) to be used for degradation grade classification and degradation cause analysis, respectively. Through this sequential processing structure, the final comprehensive degradation score and degradation grade are calculated from the raw data according to a consistent logical system.

[0128] 4.1 Data Integration Department (131)

[0129] The data integration unit (131) receives preprocessed multi-use history data transmitted from the data distribution unit (114) and battery analysis result data transmitted from the battery analysis unit (120).

[0130] The data integration unit (131) classifies the received data into six degradation factor categories: battery degradation factor, physical impact degradation factor, usage environment degradation factor, software degradation factor, appearance degradation factor, and hardware function degradation factor, and generates a data set for each factor.

[0131] Specifically, the battery degradation factor category includes the SoH value, SoH reduction curve, internal resistance deviation rate, abnormal degradation flag, effective cycle count, battery analysis data after replacement, and the number of overcharge occurrences and over-discharge occurrences included in the preprocessed multi-use history data transmitted from the battery analysis unit (120). The physical impact degradation factor category includes the number of drop detections, impact intensity data, repair history information, and parts replacement history information. The usage environment degradation factor category includes the cumulative screen usage time, average usage temperature, frequency of exposure to high-temperature environments, frequency of exposure to low-temperature environments, and average temperature during charging. The software degradation factor category includes the operating system update history, current operating system version, number of factory resets, and frequency of system errors. The appearance degradation factor category includes scratch detection information, dent detection information, damage detection information, and discoloration detection information. The hardware function degradation factor category includes touchscreen responsiveness test results, camera operation status test results, speaker output status test results, microphone input status test results, and biometric recognition function operation status test results.

[0132] The data integration unit (131) transmits the generated six factor-specific data sets to the factor score calculation unit (132).

[0133] 4.2 Factor score calculation unit (132)

[0134] The factor score calculation unit (132) normalizes the value of an individual data item belonging to a category of each deterioration factor category among the factor-specific data sets transmitted from the data integration unit (131) to a range of 0 to 100. During the normalization process, the factor score calculation unit (132) linearly converts the raw value of each data item into a value between 0 and 100 by referring to the measurable range (minimum and maximum values) of the item or a reference range set for each device model, and sets the direction of conversion so that the larger the value, the better the condition of the item. For example, since a higher SoH value indicates better condition, it converts SoH 100% to correspond to a score of 100 and SoH 50% to correspond to a score of 50, and since a lower number of overcharging occurrences indicates better condition, it converts 0 times to correspond to a score of 100 and a preset maximum allowable number to correspond to a score of 0.

[0135] The factor score calculation unit (132) calculates a category-specific degradation factor score by weighting the values ​​of the normalized individual data items within the category. In the category-specific weighting, the weight applied to each data item is set to reflect the relative influence that the data item has on degradation within the category. For example, within the battery degradation factor category, the weights for the SoH value and the number of effective cycles may be set higher than the weights for the number of overcharge occurrences, and if an abnormal degradation flag is generated, additional deductions may be applied to the corresponding battery degradation factor score. Through this, for each of the six categories (battery degradation factor, physical impact degradation factor, usage environment degradation factor, software degradation factor, appearance degradation factor, hardware function degradation factor), category-specific degradation factor scores (Fb, Fp, Fe, Fs, Fa, Fh) ranging from 0 to 100 are calculated.

[0136] The factor score calculation unit (132) transmits the calculated deterioration factor scores for each of the six categories to the comprehensive score calculation unit (133) and the contribution analysis unit (135).

[0137] 4.3 Comprehensive Score Calculation Department (133)

[0138] The comprehensive score calculation unit (133) calculates a comprehensive deterioration score in the range of 0 to 1000 by applying category-specific weights set for each device model and year to the deterioration factor scores for each of the six categories calculated by the factor score calculation unit (132) and weighting them.

[0139] The overall deterioration score is calculated by the following [Equation 2].

[0140] [Mathematical Formula 2]

[0141] D = (α₁ × Fb + α₂ × Fp + α₃ × Fe + α₄ × Fs + α5× Fa + α6× Fh) × 10

[0142] Here, D represents the overall degradation score (0 or greater and 1000 or less), Fb represents the battery degradation factor score, Fp represents the physical impact degradation factor score, Fe represents the usage environment degradation factor score, Fs represents the software degradation factor score, Fa represents the appearance degradation factor score, and Fh represents the hardware function degradation factor score. Fb, Fp, Fe, Fs, Fa, and Fh are each normalized to values ​​between 0 and 100. α₁, α₂, α₃, α₄, α5, and α6 represent the weighting factors for battery, physical impact, usage environment, software, appearance, and hardware function degradation, respectively. α₁, α₂, α₃, α₄, α5, and α6 are each values ​​between 0 and 1, and α₁ + α₂ + α₃ + α₄ + α5 + α6 = 1. The larger the D value, the better the condition of the device.

[0143] For example, for a used smartphone (10), Fb = 82, Fp = 90, Fe = 75, Fs = 88, Fa = 70, Fh = 95 are calculated from the factor score calculation unit (132), and for the corresponding device model, α₁ = 0.30, α₂ = 0.15, α₃ = 0.10, α₄ = 0.10, α5 = 0.20, α6 = 0.15 are set, then the total degradation score D is (0.30 × 82 + 0.15 × 90 + 0.10 × 75 + 0.10 × 88 + 0.20 × 70 + 0.15 × 95) × 10 = (24.6 + 13.5 + 7.5 + 8.8 + 14.0 + 14.25) × 10 = 82.65 × 10 = 826.5. In this case, the total deterioration score is 826.5 points, so it is classified as Grade A by the grade classification unit (134) described later.

[0144] The category-specific weights (α₁ to α6) applied in the comprehensive score calculation unit (133) are provided from the model storage unit (136a) of the weight management unit (136). The weights are set differently for each device model and year of manufacture, because the relative importance of factors affecting degradation varies depending on the device model and year of manufacture. For example, for a device model that has been released for more than 3 years, the weight (α₁) of the battery degradation factor may be set relatively high, and for a recently released device model, the weights of the appearance degradation factor (α5) and the hardware function degradation factor (α6) may be set relatively high.

[0145] The comprehensive score calculation unit (133) transmits the calculated comprehensive deterioration score to the grade classification unit (134), the contribution analysis unit (135), the trend analysis unit (141) and prediction calculation unit (142) of the deterioration prediction unit (140), and the deterioration adjustment unit (152) of the price calculation unit (150).

[0146] 4.4 Classification section (134)

[0147] The grade classification unit (134) classifies the deterioration grade by matching the total deterioration score calculated from the total score calculation unit (133) to a pre-set grade standard table.

[0148] Specifically, the grade classification unit (134) classifies the total deterioration score as Grade A+ if it is 900 points or more, as Grade A if it is 800 points or more or less than 900 points, as Grade B+ if it is 700 points or more or less than 800 points, as Grade B if it is 600 points or more or less than 700 points, as Grade C if it is 400 points or more or less than 600 points, and as Grade D if it is less than 400 points.

[0149] Grade A+ indicates that the device is in top condition, virtually equivalent to a new product, while Grade A indicates that the device is in excellent condition and poses no impediments to general use. Grade B+ indicates that the device is in good condition but some degradation has been detected, and Grade B indicates that moderate degradation has occurred. Grade C indicates that significant degradation has taken place, limiting the service life, and Grade D indicates that severe degradation has occurred, making normal use difficult.

[0150] The grade classification unit (134) transmits the classified deterioration grade to the history tracking unit (162) and grade mapping unit (163) of the transaction management unit (160).

[0151] 4.5 Contribution Analysis Department (135)

[0152] The contribution analysis unit (135) generates deterioration cause analysis data by calculating the percentage of the deterioration factor score for each deterioration factor category calculated by the factor score calculation unit (132) that contributed to the overall deterioration score calculated by the overall score calculation unit (133).

[0153] Specifically, the contribution analysis unit (135) calculates the ratio that the weighted score (αi × Fi) of each category occupies within the weighted sum of the total degradation score (α₁ × Fb + α₂ × Fp + α₃ × Fe + α₄ × Fs + α5 × Fa + α6 × Fh) as a percentage. Continuing with the aforementioned numerical example, the contribution of the battery degradation factor is 24.6 / 82.65 × 100 29.8%, the contribution of physical shock degradation factors is 13.5 / 82.65 × 100 16.3%, the contribution of environmental degradation factors is 7.5 / 82.65 × 100 9.1%, the contribution of software degradation factors is 8.8 / 82.65 × 100 10.6%, the contribution of appearance deterioration factors is 14.0 / 82.65 × 100 16.9%, the contribution of hardware functional degradation factors is 14.25 / 82.65 × 100 It is calculated as 17.2%. Through this, it can be quantitatively determined that the main cause of the decrease in the overall degradation score of the used smartphone (10) is the battery degradation factor (29.8%), and that the appearance degradation factor (16.9%) has the next largest impact. The degradation cause analysis data is used to provide transparent information about the condition of the device to transaction participants and to increase the precision of future degradation prediction and price calculation.

[0154] 4.6 Weight Management Unit (136)

[0155] The weight management unit (136) includes a model storage unit (136a) and a learning management unit (136b).

[0156] The model storage unit (136a) stores the first artificial intelligence model and provides category-specific weights (α₁ to α6) to the comprehensive score calculation unit (133). The model storage unit (136a) stores a set of category-specific weights optimized for each device model and year of manufacture, and when the comprehensive score calculation unit (133) calculates the comprehensive deterioration score of a specific device, it retrieves and provides a set of weights corresponding to the model and year of manufacture of the device.

[0157] The learning management unit (136b) receives post-transaction feedback data transmitted from the feedback management unit (165) of the transaction management unit (160) and retrains the category-specific weights of the first artificial intelligence model. The learning management unit (136b) may perform retraining in real time whenever post-transaction feedback data is received, or may perform batch retraining when feedback data of a preset batch size (e.g., 100 cases) is accumulated.

[0158] The first artificial intelligence model continuously updates the optimal values ​​of category-specific weights (α₁ to α6) by using post-transaction feedback data as training data, which includes data on the multiple usage history of a previously diagnosed used smartphone (10), whether a return occurred after the transaction of the device, the reason for the return, and the actual degree of deterioration measured at the time of repurchase. The learning objective of the first artificial intelligence model is to adjust the weights in a direction that minimizes the discrepancy between the calculated overall deterioration score and the actual device condition after the transaction (whether it was returned, actual degree of deterioration). Specifically, the first artificial intelligence model sets the mean absolute error (MAE) or mean squared error (MSE) between the predicted overall deterioration score and the actual overall deterioration score remeasured at the time of repurchase as the loss function, and updates the weights by applying an optimization algorithm based on gradient descent. Through this feedback-based relearning mechanism, the prediction accuracy of the first artificial intelligence model is continuously improved as transaction data accumulates.

[0159] 4.7 Comprehensive output of the degradation calculation unit (130)

[0160] The deterioration degree calculation unit (130) generates deterioration diagnosis result data including a comprehensive deterioration degree score calculated from the comprehensive score calculation unit (133), a deterioration grade classified from the grade classification unit (134), and deterioration cause analysis data generated from the contribution analysis unit (135), and transmits it to the deterioration prediction unit (140) and the price calculation unit (150).

[0161] As described above, the degradation level calculation unit (130) classifies the data received from the data collection unit (110) and the battery analysis unit (120) into six degradation factor categories, calculates a comprehensive degradation level score by applying weights based on an artificial intelligence model, and generates degradation grade and degradation cause analysis data. Below, the detailed configuration and operation of the degradation prediction unit (140), which predicts future degradation progress based on the calculation results of the degradation level calculation unit (130), will be explained.

[0162] 5. Deterioration prediction unit (140)

[0163] Referring to FIG. 5, the degradation prediction unit (140) is a unit that predicts the future degradation rate and remaining usable period based on the comprehensive degradation score transmitted from the degradation calculation unit (130).

[0164] The deterioration prediction unit (140) includes a trend analysis unit (141), a prediction calculation unit (142), a remaining lifespan calculation unit (143), a cohort comparison unit (144), and a reliability calculation unit (145).

[0165] Looking at the internal data flow of the degradation prediction unit (140), the trend analysis unit (141) analyzes the slope of the decrease in the SoH time series data and transmits it to the prediction calculation unit (142), and the prediction calculation unit (142) estimates the future degradation trajectory by comparing it with the standard degradation profile of the standard profile management unit (125). The estimated future degradation prediction data is transmitted to the remaining lifespan calculation unit (143) and the reliability calculation unit (145), respectively, and the cohort comparison unit (144) independently calculates percentile rankings using the device group data of the standard profile management unit (125). In this way, the degradation prediction unit (140) does not stop at static evaluation of the degradation state at the current point in time, but dynamically predicts the future degradation progression trajectory through multifaceted analysis including time series change analysis, reflection of deviations compared to the standard profile, comparison with the same model device group, and calculation of prediction reliability.

[0166] 5.1 Trend Analysis Department (141)

[0167] The trend analysis unit (141) receives the comprehensive degradation score and degradation cause analysis data transmitted from the degradation calculation unit (130), and the time series change data of the battery SoH included in the preprocessed multiple usage history data transmitted from the data distribution unit (114).

[0168] The trend analysis unit (141) calculates the slope of SoH reduction during a recently preset period from the received time series change data to determine whether deterioration is accelerating. The slope of SoH reduction represents the rate of change of the SoH value over time and is expressed as the amount of SoH reduction per unit time (e.g., % / month or % / 100 cycles). The trend analysis unit (141) calculates the slope of SoH reduction during a recently preset period (e.g., the last 3 months, the last 100 cycles) through linear regression analysis based on the least squares method and compares the calculated recent slope with the average slope of SoH reduction over the entire usage period. If the recent slope is greater than or equal to the preset acceleration judgment threshold multiplier (e.g., 1.5 times) relative to the average slope, it is determined that deterioration is accelerating, and if the recent slope is similar to or smaller than the average slope, it is determined that deterioration is proceeding stably.

[0169] The trend analysis unit (141) transmits the calculated SoH reduction slope and deterioration acceleration data to the prediction calculation unit (142).

[0170] 5.2 Prediction operation unit (142)

[0171] The prediction calculation unit (142) receives data on the SoH reduction slope and whether deterioration is accelerated transmitted from the trend analysis unit (141), and the overall deterioration score transmitted from the deterioration calculation unit (130).

[0172] The prediction calculation unit (142) calculates an individual degradation deviation by comparing it with standard degradation profile data of the same model transmitted from the standard profile management unit (125) of the battery analysis unit (120). The individual degradation deviation is an indicator showing how far ahead or behind the current degradation state of the used smartphone (10) is compared to the standard degradation trajectory of the same model and the same usage period. For example, if the standard SoH at the 500-cycle point of the same model is 88% and the SoH of the device is 82%, the individual degradation deviation is -6 percentage points, indicating that the degradation has progressed further compared to the standard.

[0173] The prediction operation unit (142) generates future deterioration prediction data by estimating the trajectory of deterioration progression during a future preset prediction period (e.g., 6 months, 12 months, 24 months) based on the calculated individual deterioration deviation and the SoH reduction slope transmitted from the trend analysis unit (141). The estimation of the deterioration progression trajectory is performed by taking the overall deterioration score at the current point in time as a starting point, reflecting the trend of the recent SoH reduction slope, and applying the individual deviation relative to the standard deterioration profile as a correction factor to calculate the expected overall deterioration score at each future point in time. If it is determined that deterioration is progressing stably, the prediction operation unit (142) applies a linear extrapolation method that maintains the current SoH reduction slope constant. If deterioration acceleration is detected, the prediction operation unit (142) estimates the future deterioration trajectory reflecting the acceleration trend by applying a non-linear prediction model based on an exponential function or a polynomial function.

[0174] The prediction calculation unit (142) transmits the generated future deterioration prediction data to the remaining lifespan calculation unit (143) and the reliability calculation unit (145), and finally provides it to the price calculation unit (150).

[0175] 5.3 Remaining lifespan calculation unit (143)

[0176] The remaining lifespan calculation unit (143) calculates the remaining period until the total degradation score of the used smartphone (10) decreases to a level below a preset usable lower limit score, based on future degradation prediction data transmitted from the prediction calculation unit (142).

[0177] The usable lower limit score refers to an overall degradation score corresponding to the minimum condition level at which a used smartphone (10) can be utilized for normal use purposes, and is pre-set by the device model or the system manager. For example, if the usable lower limit score is set to 400 points, the remaining lifespan calculation unit (143) searches for a point in time when the score reaches 400 points in the trajectory of the expected overall degradation score at each future point in time included in the future degradation prediction data, and calculates the period from the current point in time to that point in time as the remaining usable period. The remaining usable period can be expressed in units of days, months, or the number of expected cycles.

[0178] 5.4 Cohort Comparison Section (144)

[0179] The cohort comparison unit (144) calculates the percentile rank of the total degradation score of the used smartphone (10) within the corresponding device group based on data of the same model and same release time of the device group transmitted from the standard profile management unit (125) of the battery analysis unit (120).

[0180] The percentile rank calculated by the cohort comparison unit (144) is an indicator of where the used smartphone (10) is located relative to within a group of devices (cohort) under the same conditions. The cohort comparison unit (144) retrieves the distribution of overall degradation scores of devices of the same model and same release date accumulated in the standard profile management unit (125) and calculates the percentile where the current device's overall degradation score is located within the distribution. For example, if the percentile rank is 85, it means that the device is in good condition, corresponding to the top 15% of devices of the same model and same release date. Through cohort comparison, even if devices have the same overall degradation score, the 85th percentile in older models can be interpreted as indicating a relatively better state of management than the 85th percentile in recently released models.

[0181] 5.5 Reliability Calculation Unit (145)

[0182] The reliability calculation unit (145) calculates a prediction confidence interval for future deterioration prediction data generated from the prediction calculation unit (142) based on past prediction accuracy data and the completeness ratio of the input data.

[0183] The past prediction accuracy data is statistical data representing the degree of agreement between the future degradation prediction and the actual degradation progress performed by the used smartphone degradation prediction and trading system (100) through the analysis of multiple usage history and battery condition. The input data completeness ratio refers to the ratio of data items that are normally collected and preprocessed by the preprocessing unit (113) among the multiple usage history data items collected for the used smartphone (10), and the more missing data items there are, the lower the prediction reliability.

[0184] The reliability calculation unit (145) calculates a narrow confidence interval (i.e., high prediction reliability) when the past prediction accuracy is high and the completeness ratio of the input data is high, and calculates a wide confidence interval (i.e., low prediction reliability) when the past prediction accuracy is low or the completeness ratio of the input data is low.

[0185] 5.6 Comprehensive output of the degradation prediction unit (140)

[0186] The deterioration prediction unit (140) generates deterioration prediction result data including future deterioration prediction data generated from the prediction calculation unit (142), the remaining usable period calculated from the remaining lifespan calculation unit (143), the percentile rank calculated from the cohort comparison unit (144), and the prediction confidence interval calculated from the reliability calculation unit (145), and transmits it to the future deterioration correction unit (153) of the price calculation unit (150).

[0187] As described above, the deterioration prediction unit (140) supports the price calculation unit (150) in reflecting the future decrease in value in the price by dynamically predicting the future deterioration trajectory beyond the static deterioration evaluation at the current time. Below, the detailed configuration and operation of the price calculation unit (150), which calculates the final transaction price by synthesizing the results of the deterioration degree calculation unit (130) and the deterioration prediction unit (140), will be explained.

[0188] 6. Price calculation unit (150)

[0189] Referring to FIG. 6, the price calculation unit (150) is a unit that automatically calculates the transaction price based on the comprehensive deterioration score transmitted from the deterioration calculation unit (130) and future deterioration prediction data transmitted from the deterioration prediction unit (140).

[0190] The price calculation unit (150) includes a standard market price management unit (151), a deterioration degree adjustment unit (152), a future deterioration correction unit (153), a channel price calculation unit (154), and a final price calculation unit (155).

[0191] Looking at the internal structure of the price calculation unit (150), the standard market price management unit (151) collects standard market prices from the external market price information system (40) and provides them to the final price calculation unit (155), the deterioration degree adjustment unit (152) receives the comprehensive deterioration score from the comprehensive score calculation unit (133) of the deterioration degree calculation unit (130) and calculates the deterioration degree adjustment coefficient, the future deterioration correction unit (153) receives deterioration prediction result data from the deterioration prediction unit (140) and calculates the future deterioration correction coefficient, and the channel price calculation unit (154) sets the distribution channel coefficient. The final price calculation unit (155) receives the standard market price, deterioration degree adjustment coefficient, future deterioration correction coefficient, and distribution channel coefficient transmitted from these four units and calculates the final transaction price. Through this structure, the final transaction price is calculated by sequentially applying three-stage adjustments—deterioration adjustment, future deterioration correction, and price differentiation by distribution channel—starting from the reference market price.

[0192] 6.1 Standard Market Price Management Department (151)

[0193] The standard market price management unit (151) collects standard market price data by device model, storage capacity, color, and carrier in real time by performing API communication with an external market price information system (40). The standard market price management unit (151) stores the collected standard market price data in a standard market price database and updates the standard market price database according to a preset update cycle.

[0194] The standard market price data refers to the average transaction price formed in the trading market of used smartphones (10), and is a reference price based on model and specifications that does not reflect the deterioration state of the device. The standard market price management unit (151) secures the reliability of the market price data by referencing multiple market price information sources from an external market price information system (40), and if the market price of a specific model fluctuates rapidly by exceeding a preset fluctuation threshold rate, it is indicated by a separate abnormal fluctuation flag so that it can be referenced when calculating the price.

[0195] 6.2 Heatwave Adjustment Department (152)

[0196] The deterioration degree adjustment unit (152) receives the comprehensive deterioration degree score transmitted from the comprehensive score calculation unit (133) of the deterioration degree calculation unit (130), and calculates the deterioration degree adjustment coefficient by correlating the received comprehensive deterioration degree score with a pre-set adjustment coefficient table for each deterioration degree section.

[0197] Specifically, the deterioration adjustment unit (152) sets the deterioration adjustment coefficient to a value of 0.95 or higher and 1.00 or lower when the total deterioration score is 900 points or higher, sets it to a value of 0.85 or higher and less than 0.95 when the score is 800 points or higher and less than 900 points, sets it to a value of 0.75 or higher and less than 0.85 when the score is 700 points or higher and less than 800 points, sets it to a value of 0.60 or higher and less than 0.75 when the score is 600 points or higher and less than 700 points, sets it to a value of 0.40 or higher and less than 0.60 when the score is 400 points or higher and less than 600 points, and sets it to a value of less than 0.40 when the score is less than 400 points.

[0198] The deterioration adjustment coefficient is a coefficient that determines the price reduction ratio based on the deterioration state relative to the standard market price. A value closer to 1.00 is set as the overall deterioration score is higher (i.e., the better the condition of the equipment), and a smaller value is set as the overall deterioration score is lower, thereby increasing the range of price reduction. The deterioration adjustment unit (152) continuously calculates the deterioration adjustment coefficient in proportion to the overall deterioration score within each section, thereby preventing sudden price fluctuations at the section boundaries. When the deterioration adjustment unit (152) receives a correction signal from the feedback management unit (165) of the transaction management unit (160) described later, it modifies the deterioration adjustment coefficient table corresponding to the grade section.

[0199] 6.3 Future degradation correction unit (153)

[0200] The future degradation correction unit (153) receives the remaining usable period and the prediction confidence interval included in the degradation prediction result data transmitted from the degradation prediction unit (140).

[0201] The future degradation correction unit (153) sets the future degradation correction factor to 1.00 if the received remaining usable period is greater than or equal to the preset first reference period. The future degradation correction unit (153) sets the future degradation correction factor to a value greater than or equal to 0.90 and less than 1.00 if the remaining usable period is less than the first reference period and greater than or equal to the preset second reference period. The future degradation correction unit (153) sets the future degradation correction factor to a value less than 0.90 if the remaining usable period is less than the second reference period.

[0202] The future degradation correction unit (153) corrects the future degradation correction coefficient by multiplying it by a preset uncertainty correction value when the prediction confidence interval is less than a preset confidence threshold value. The uncertainty correction value is set to a value greater than 0 and less than or equal to 1.00, and the future degradation correction coefficient is further lowered as the uncertainty of the prediction increases, thereby preemptively reflecting the trading risk caused by the prediction error in the price.

[0203] 6.4 Channel price calculation unit (154)

[0204] The channel price calculation unit (154) receives distribution channel information and, if the received distribution channel is domestic wholesale, sets the distribution channel coefficient to 1.00, and if it is overseas export, sets the distribution channel coefficient by referring to the regional channel coefficient table set for each export region. The regional channel coefficient is a value set by reflecting the demand for used smartphones in the overseas market, competition intensity, logistics costs, and customs duties. If it is overseas export, the channel price calculation unit (154) retrieves exchange rate information at that time by performing API communication with the external exchange rate information system (50) and reflects the retrieved exchange rate information in the price calculation.

[0205] 6.5 Final price calculation unit (155)

[0206] The final price calculation unit (155) receives the standard market price transmitted from the standard market price management unit (151), the deterioration degree adjustment coefficient calculated from the deterioration degree adjustment unit (152), the future deterioration correction coefficient calculated from the future deterioration correction unit (153), and the distribution channel coefficient set from the channel price calculation unit (154) to calculate the final transaction price.

[0207] The final transaction price is calculated by the following [Equation 3].

[0208] [Mathematical Formula 3]

[0209] P = Pref × Cd × Cf × Cc

[0210] Here, P is the final transaction price, Pref is the standard market price corresponding to the device model, storage capacity, color, and carrier transmitted from the standard market price management unit (151), Cd is the degradation adjustment coefficient (greater than 0 and less than or equal to 1.00) calculated from the degradation adjustment unit (152), Cf is the future degradation correction coefficient (greater than 0 and less than or equal to 1.00) calculated from the future degradation correction unit (153), and Cc is the distribution channel coefficient (greater than 0) set by the channel price calculation unit (154). The larger the value of P, the higher the transaction price.

[0211] For example, if the standard market price (Pref) of a used smartphone (10) is 500,000 won, the degradation adjustment coefficient (Cd) corresponding to a total degradation score of 826.5 points is 0.90, the future degradation correction coefficient (Cf) based on the remaining usable period is 0.95, and the distribution channel coefficient (Cc) for domestic wholesale distribution is 1.00, the final transaction price P is calculated as 500,000 × 0.90 × 0.95 × 1.00 = 427,500 won. If the same device is exported overseas, and the distribution channel coefficient of the export region is set to 1.10, the final transaction price is calculated as 500,000 × 0.90 × 0.95 × 1.10 = 470,250 won.

[0212] If the calculated final transaction price is less than the preset minimum transaction price, the final price calculation unit (155) classifies the device as a device unsuitable for transaction and generates a transaction unsuitability flag and transmits it to the transaction management unit (160). Devices classified as unsuitable for transaction are excluded from trading and may be classified into a parts recycling or disposal path.

[0213] The final price calculation unit (155) supports multiple price calculation modes, including a purchase price calculation mode and a sales price calculation mode. In the purchase price calculation mode, the purchase price is calculated by applying a pre-set purchase margin rate to the final transaction price calculated by [Equation 3]. In the sales price calculation mode, the sales price is calculated by applying a pre-set sales margin rate to the final transaction price.

[0214] The final price calculation unit (155) transmits the calculated final transaction price data to the settlement processing unit (161) and the history tracking unit (162) of the transaction management unit (160).

[0215] As described above, the price calculation unit (150) calculates a reasonable final transaction price by applying three-stage corrections of deterioration, future deterioration, and distribution channels to the standard market price. Below, the detailed configuration and operation of the transaction management unit (160), which performs settlement, history tracking, grade conversion, report generation, and feedback management based on the calculated transaction price, will be explained.

[0216] 7. Transaction Management Unit (160)

[0217] Referring to FIG. 7, the transaction management unit (160) is a unit that stores transaction price data transmitted from the price calculation unit (150), manages the distribution history for each device, and processes transaction settlements.

[0218] The transaction management unit (160) includes a settlement processing unit (161), a history tracking unit (162), a grade mapping unit (163), a report generation unit (164), and a feedback management unit (165).

[0219] Looking at the internal structure of the transaction management unit (160), the settlement processing unit (161) receives transaction price data from the final price calculation unit (155) and processes the settlement linked to the corporate account system (60); the history tracking unit (162) manages the data transmitted from the degradation level calculation unit (130), the degradation prediction unit (140), and the price calculation unit (150) in an integrated manner based on IMEI; the grade mapping unit (163) converts the degradation grade transmitted from the grade classification unit (134) into an overseas grade system; and the report generation unit (164) generates a transaction transparency report based on the data from the history tracking unit (162) and provides it to the terminal (70) of the trading partner. The feedback management unit (165) collects feedback data after a transaction and acts as the starting point of a three-way feedback loop that transmits feedback signals to the learning management unit (136b) of the degradation calculation unit (130), the degradation adjustment unit (152) of the price calculation unit (150), and the standard profile management unit (125) of the battery analysis unit (120), respectively.

[0220] 7.1 Settlement processing unit (161)

[0221] The settlement processing unit (161) receives the final transaction price data and transaction non-conformity flags transmitted from the final price calculation unit (155) and automatically aggregates the transaction amount by supplier and by seller. The settlement processing unit (161) generates settlement data by linking the aggregated transaction amount data with the corporate account system (60) and performs settlement processing according to a preset settlement cycle (e.g., daily, weekly, monthly). Settlement processing includes payment of purchase proceeds, billing of sales proceeds, deduction of transaction fees, and generation of basic data for tax calculation. The settlement processing unit (161) automatically excludes devices assigned a transaction non-conformity flag from the settlement target and classifies them into a separate management path.

[0222] 7.2 History Tracking Unit (162)

[0223] The history tracking unit (162) sets the IMEI (International Mobile Equipment Identification Number) of the used smartphone (10) as the reference identifier. The IMEI is a 15-digit identification number uniquely assigned to each smartphone worldwide and is an immutable identifier that does not change from the production to disposal of the device.

[0224] The history tracking unit (162) maps the comprehensive degradation score, degradation grade, and degradation cause analysis data transmitted from the degradation calculation unit (130) in correspondence with the IMEI, the remaining usable period and percentile rank transmitted from the degradation prediction unit (140), and the final transaction price transmitted from the price calculation unit (150), and stores them in the distribution history database for each device. The history tracking unit (162) sequentially records distribution stage history information, including the time of purchase, time of receipt, inventory storage period, time of shipment, shipment channel information, and time of final settlement completion, in the distribution history database for each device.

[0225] 7.3 Grade Mapping Section (163)

[0226] The grade mapping unit (163) automatically converts the degradation grade transmitted from the grade classification unit (134) of the degradation calculation unit (130) into a grade system used by overseas trading partners. The grade mapping unit (163) calculates a grade corresponding to the grade system of the corresponding region by referring to a regional grade mapping rule database set for each overseas export region.

[0227] Specifically, the grade mapping unit (163) maps the deterioration grade transmitted from the grade classification unit (134) to the Working A grade of the overseas grade system if it is Grade A+, maps it to the Working B grade if it is Grade A, maps it to the Working B grade if it is Grade B+, maps it to the Working C grade if it is Grade B, maps it to the Working D grade if it is Grade C, and maps it to the Broken grade if it is Grade D. If a separate mapping rule for the corresponding export region is stored in the regional grade mapping rule database, the grade mapping unit (163) applies that separate mapping rule first.

[0228] The grade mapping unit (163) updates the mapping rules of the regional grade mapping rule database based on grade mismatch feedback data received from overseas business partners. Through this, it continuously secures adaptability to changes in the grade standards of overseas business partners.

[0229] 7.4 Report generation unit (164)

[0230] The report generation unit (164) automatically generates a transaction transparency report based on the distribution history data for each device stored in the history tracking unit (162), including the quantity distribution by degradation grade of devices purchased and sold by each trading partner, the average overall degradation score, the average transaction price, and the transaction volume trend by transaction period. The report generation unit (164) provides the generated transaction transparency report to the terminal (70) of the trading partner.

[0231] 7.5 Feedback Management Department (165)

[0232] The feedback management unit (165) collects post-transaction feedback data from among the distribution history data for each device stored in the history tracking unit (162). Specifically, the post-transaction feedback data collected by the feedback management unit (165) includes data on the reason for return of a device that was returned within a pre-set feedback collection period after the sale, claim data received from a customer after the sale, and a comprehensive deterioration score re-measured through the data collection unit (110) for the repurchased device.

[0233] The feedback management unit (165) transmits the collected post-transaction feedback data to the learning management unit (136b) included in the weight management unit (136) of the degradation calculation unit (130) to be used for re-learning the category-specific weights of the first artificial intelligence model.

[0234] The feedback management unit (165) classifies the collected post-transaction feedback data by device model, degradation grade, and distribution channel and stores it in the feedback analysis database. Based on the data accumulated in the feedback analysis database, the feedback management unit (165) calculates the return rate by device model, the claim occurrence rate by degradation grade, and the error rate between the predicted degradation and the actual degradation to generate a system accuracy index. The feedback management unit (165) provides the generated system accuracy index to the administrator terminal (80) according to a preset period.

[0235] The feedback management unit (165) transmits a correction signal to the degradation adjustment unit (152) of the price calculation unit (150) to modify the degradation adjustment coefficient table corresponding to the grade range when a degradation grade range is found in which the return rate included in the system accuracy indicator exceeds a preset return rate threshold.

[0236] The feedback management unit (165) transmits a request for update to the standard profile management unit (125) of the battery analysis unit (120) to update the standard degradation profile of the device model when a device model is found in which the error rate between the predicted degradation and the actual degradation included in the system accuracy indicator exceeds a preset error rate allowable threshold.

[0237] In this way, the feedback management unit (165) systematically collects and analyzes feedback data generated after a transaction and performs a self-correction function that continuously improves the accuracy and reliability of the entire used smartphone degradation prediction and trading system (100) through multiple usage history and battery condition analysis through three paths: retraining the artificial intelligence model of the degradation calculation unit (130) (feedback path to the learning management unit (136b)), correcting the degradation adjustment coefficient of the price calculation unit (150) (feedback path to the degradation adjustment unit (152)), and updating the standard degradation profile of the battery analysis unit (120) (feedback path to the standard profile management unit (125)).

[0238] A used smartphone degradation prediction and trading system (100) through multiple usage history and battery condition analysis according to one embodiment of the present invention comprises a systematic data collection unit (110), a precise battery analysis unit (120), an artificial intelligence-based comprehensive degradation calculation unit (130), a degradation prediction unit (140), a multi-stage correction-based automatic price calculation unit (150), and a trading history management and feedback-based system self-correction unit (160) that are organically connected to form an integrated system. Through this, objectivity, accuracy, transparency, and efficiency in used smartphone trading can be achieved simultaneously.

[0240] Although embodiments of the present invention have been described in more detail with reference to the attached drawings, the present invention is not necessarily limited to these embodiments and may be modified in various ways within the scope of the technical spirit of the present invention. Accordingly, the embodiments disclosed in the present invention are intended to explain, not limit, the technical spirit of the present invention, and the scope of the technical spirit of the present invention is not limited by these embodiments. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. The scope of protection of the present invention shall be interpreted by the claims below, and all technical spirits within an equivalent scope shall be interpreted as being included within the scope of rights of the present invention.

[0241] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below. Explanation of the symbols

[0242] 10: Used smartphones 20: Diagnostic device 30: Recording device 40: External Market Price Information System 50: External Exchange Rate Information System 60: Corporate Account System 70: Client's terminal 80: Administrator Terminal Composition of the present invention 100: Used Smartphone Degradation Prediction and Trading System through Multiple Usage History and Battery Condition Analysis 110: Data Collection Unit 111: History Extraction Unit 112: Status Inspection Department 113: Preprocessing section 114: Data Distribution Unit 120: Battery Analysis Unit 121: SoH measurement unit 122: Internal Resistance Analysis Section 123: Charging Pattern Analysis Department 124: Battery Replacement Determination Unit 125: Standard Profile Management Department 130: Deterioration Calculation Unit 131: Data Integration Department 132: Factor Score Calculation Section 133: Comprehensive Score Calculation Department 134: Classification Department 135: Contribution Analysis Department 136: Weight Management Department 136a: Model storage component 136b: Lack of learning management 140: Degradation Prediction Unit 141: Trend Analysis Department 142: Prediction Operation Unit 143: Remaining Life Calculation Unit 144: Cohort Comparison Section 145: Reliability Calculation Unit 150: Price Calculation Unit 151: Standard Market Price Management Department 152: Burning Island Control Department 153: Future Degradation Correction Unit 154: Channel Pricing Department 155: Final Price Calculation Unit 160: Transaction Management Unit 161: Settlement Processing Department 162: Traceability Department 163: Grade Mapping Section 164: Report Generation Department 165: Feedback Management Department

Claims

Claim 1 A data collection unit that collects multi-use history data from a used smartphone; a battery analysis unit that generates battery analysis result data by analyzing battery history data among the multi-use history data transmitted from the data collection unit; a degradation calculation unit that calculates a comprehensive degradation score and classifies a degradation grade using a pre-trained first artificial intelligence model based on the multi-use history data transmitted from the data collection unit and the battery analysis result data transmitted from the battery analysis unit; a degradation prediction unit that predicts the future degradation progression rate and remaining usable period based on the comprehensive degradation score transmitted from the degradation calculation unit; and a price calculation unit that automatically calculates a transaction price based on the comprehensive degradation score transmitted from the degradation calculation unit and the future degradation prediction data transmitted from the degradation prediction unit. and a transaction management unit that stores transaction price data transmitted from the price calculation unit, manages distribution history by device, and processes transaction settlement; wherein the data collection unit includes: a history extraction unit that automatically extracts battery history data, physical impact history data, usage environment history data, and software history data from the system logs, battery management chip (BMS) data, and sensor logs of the used smartphone through a diagnostic device connected to the used smartphone or diagnostic software installed on the used smartphone; and a status inspection unit that, separately from the history extraction unit, photographs the exterior of the used smartphone through a camera device and inputs the captured image into a pre-trained image analysis model to automatically detect exterior damage items and generate exterior condition data, and performs a hardware function test through diagnostic software installed on the used smartphone to generate hardware function test data.A preprocessing unit that receives battery history data, physical impact history data, usage environment history data, software history data extracted from the history extraction unit, and appearance condition data and hardware function test data generated from the condition inspection unit, and performs validation, removal of outliers, and normalization processing according to standard values ​​for each device model to generate preprocessed multi-use history data; and a data distribution unit that receives the preprocessed multi-use history data transmitted from the preprocessing unit, transmits battery history data included in the preprocessed multi-use history data to the battery analysis unit, and transmits the entire preprocessed multi-use history data to the degradation calculation unit.The battery history data includes the total number of charge / discharge cycles, the number of charges by charging method, the average temperature during charging, the number of overcharge occurrences, the number of over-discharge occurrences, and the trend of change in maximum battery capacity; the physical impact history data includes the number of drop detections based on acceleration sensor logs, impact intensity data, repair history information, and parts replacement history information; the usage environment history data includes cumulative screen usage time, average usage temperature, frequency of exposure to high-temperature environments, and frequency of exposure to low-temperature environments; the software history data includes operating system update history, current operating system version, number of factory resets, and frequency of system errors; the exterior condition data includes scratch detection information, dent detection information, damage detection information, and discoloration detection information; the hardware function test data includes touchscreen responsiveness test results, camera operation status test results, speaker output status test results, microphone input status test results, and biometric recognition function operation status test results; and the battery analysis unit receives data on the trend of change in maximum battery capacity included in the battery history data transmitted from the data distribution unit, and the used smartphone SoH measurement unit that calculates the ratio of the current maximum chargeable capacity to the design capacity and derives a SoH reduction curve relative to the total number of charge / discharge cycles; and internal resistance analysis unit that receives battery history data transmitted from the data distribution unit, calculates the battery internal resistance value, calculates a deviation rate by comparing the calculated internal resistance value with a standard internal resistance value of the same model and same number of cycles stored in the standard profile management unit, and generates an abnormal degradation flag if the calculated deviation rate exceeds a preset abnormal degradation threshold.A charging pattern analysis unit that receives the number of charges by charging method included in the battery history data transmitted from the data distribution unit, applies a weighting coefficient by charging method to each of the fast charging count, slow charging count, and wireless charging count, and then sums them to calculate the effective number of cycles; a battery replacement determination unit that analyzes the component replacement history information included in the battery history data transmitted from the data distribution unit to determine whether the battery has been replaced, determines whether the replaced battery is genuine if it has been replaced, and separately classifies usage history data after the replacement point to generate post-replacement battery analysis data; and a standard profile management unit that accumulates a large amount of past diagnostic data for each device model of the used smartphone, generates and manages a standard degradation profile including a curve of SoH decrease relative to the number of cycles, a curve of internal resistance increase relative to the number of cycles, and data on the influence of the charging method by model, and provides comparison reference data to the SoH measurement unit, the internal resistance analysis unit, and the charging pattern analysis unit....including, and the above effective cycle number is calculated by the following [Equation 1], [Equation 1] Ce = Ns × ws + Nf × wf + Nw × ww (wherein Ce represents the effective cycle number, Ns represents the number of slow charges, ws represents the slow charge weighting factor, Nf represents the number of fast charges, wf represents the fast charge weighting factor, Nw represents the number of wireless charges, and ww represents the wireless charge weighting factor; ws is 1.0, wf is a value greater than 1.0 and less than or equal to 2.0, and ww is a value greater than 1.0 and less than or equal to 1.5; the specific values ​​of wf and ww are set based on the model-specific charging method influence data stored in the above standard profile management unit for each device model, and a higher Ce value indicates a higher actual usage intensity of the battery). The above battery analysis unit [complies with] the SoH value and SoH reduction curve calculated from the above SoH measurement unit, the internal resistance deviation rate and abnormality calculated from the above internal resistance analysis unit A system for predicting and trading the degradation level of a used smartphone through multiple usage history and battery condition analysis, which generates battery analysis result data by synthesizing a degradation flag, the effective number of cycles calculated from the charging pattern analysis unit, and post-replacement battery analysis data generated from the battery replacement determination unit, and transmits the generated battery analysis result data to the degradation level calculation unit. Claim 2 delete Claim 3 delete

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