A patent layout achievement marketization benefit calculation and evaluation method and system

Abstract

The application relates to the technical field of patent evaluation, and discloses a patent layout achievement marketization benefit calculation and evaluation method and system, which analyzes the independent claim text of a target patent, forms a technical feature limiting area, a category condition and a logical judgment condition, and determines an engineering feasible domain according to the engineering parameter constraint relationship of a target product; a falling state function or a falling state sequence is generated according to initial technical feature parameters and parameter change data; when the falling state is 1, an optimal parameter offset vector is calculated in the engineering feasible domain, and is converted into an engineering change operation item, and a unit product design cost avoidance is calculated; a unit product patent value reference value is formed in combination with a patent legal state, and an evaluation period cumulative economic value calculation value is generated according to predicted sales data. The application is suitable for patent economic value analysis and license scheme comparison.

Description

Technical Field

[0001] This invention relates to the field of patent evaluation technology, specifically to a method and system for calculating and evaluating the market benefits of patent layout results. Background Technology

[0002] The market value of patent portfolio achievements is typically related to factors such as the scope of patent protection, the technical solution of the target product, the cost of product design changes, the legal status of the patent, and the market size of the product. Existing patent valuation methods mainly rely on the patent's legal status, licensing transaction data, financial data, or market forecast data, lacking structured processing of the correspondence between the patent text and the engineering design parameters of the target product.

[0003] Independent claims typically include various limiting conditions such as numerical ranges, structural relationships, material types, connection methods, and logical combinations. The design parameters of the target product are also subject to engineering constraints such as structural dimensions, circuit connections, thermal conductivity, material properties, manufacturing processes, and assembly conditions. Existing evaluation methods struggle to convert these technical feature limitations into calculable parameter boundaries, and also find it difficult to determine whether parameter adjustments still meet the product design, manufacturing, and operational requirements.

[0004] The technical characteristics of the target product will change with product iteration, process adjustments, and technological route changes. Comparing based on a single point in time makes it difficult to determine whether the target product meets the patent technical characteristic requirements in different time periods, and also makes it difficult to determine the start and end times for calculating the corresponding economic value.

[0005] Furthermore, existing assessment methods typically determine reference amounts directly based on sales revenue, profits, or licensing cases, failing to incorporate feasible solutions for circumventing design parameter adjustments and their engineering costs into a unified calculation process. This results in a lack of correlation between technical feature assessment results, engineering change costs, and market data. Therefore, a calculation and assessment method and system are needed that can integrate patent text, target product engineering design data, parameter time variations, engineering change costs, patent legal status, and projected sales data. Summary of the Invention

[0006] The technical problem solved by this invention is that existing methods for calculating patent value typically process patent legal status, product market data, or engineering cost data separately. They lack technical means to correlate patent technical feature boundaries, target product engineering constraints, changes in product parameters over time, avoidance of design parameter adjustments, and corresponding engineering costs. This makes it difficult to determine whether the target product meets the patent technical feature limitations at different points in time, and it is also difficult to form a verifiable unit product patent value reference value and cumulative economic value calculation value over the evaluation period based on a parameter adjustment scheme with engineering feasibility.

[0007] To address the above problems, the present invention provides the following technical solution:

[0008] The first aspect of this invention provides a method for calculating and evaluating the market benefits of patent layout achievements, comprising the following steps:

[0009] Obtain the independent text of the target patent, as well as the engineering design data, engineering cost data, patent legal status data, and expected sales data of the target product. The engineering design data includes initial technical feature parameters, parameter change data, and engineering parameter constraint relationships.

[0010] The independent text is parsed, and the technical feature constraints that can be represented numerically are converted into numerical intervals corresponding to the technical feature parameters. Based on each numerical interval, a technical feature constraint region is formed, and the technical feature constraints that cannot be directly represented numerically are converted into category conditions or logical judgment conditions.

[0011] Based on the constraints of engineering parameters, the feasible engineering domain corresponding to the combination of target product parameters is determined, and based on the initial technical characteristic parameters and parameter change data, the technical characteristic parameters corresponding to each time point within the effective evaluation period are determined.

[0012] The technical feature parameters corresponding to each time point are compared with the technical feature defined area, and combined with category conditions and logical judgment conditions, a falling state function or falling state sequence is generated.

[0013] For the time point where the value of the falling state function is 1 or the value of the corresponding falling state in the falling state sequence is 1, calculate the parameter adjustment scheme within the engineering feasible region that makes the target product no longer meet all the technical feature constraints and reduces the cost of weighted parameter changes, and obtain the preferred parameter offset vector.

[0014] Based on the parameter-engineering operation mapping relationship, the preferred parameter offset vector is converted into an engineering change operation item. The unit product design avoidance cost is calculated based on the engineering cost data, and the unit product design avoidance cost is adjusted based on the patent legal status data to obtain the unit product patent value reference value.

[0015] Based on the time correspondence, the cumulative economic value of the assessment period is generated according to the reference value of the unit product patent value, the expected sales volume data, and the state function or state sequence.

[0016] This invention transforms the technical feature constraints in independent texts into computationally achievable parameter boundaries, category conditions, and logical judgment conditions, and converts the engineering parameter constraints of the target product during design, manufacturing, and operation into an engineering feasible domain. In the time dimension, the system continuously determines the correspondence between the technical feature parameters of the target product and the technical feature constraint regions. When the target product satisfies all technical feature constraints, it calculates parameter adjustment schemes within the engineering feasible domain that would render at least one technical feature constraint invalid. The parameter adjustment results are then converted into engineering change operation items and corresponding cost data, enabling technical feature judgment, engineering feasibility analysis, and economic value calculation to utilize an interconnected data foundation.

[0017] Preferably, when parsing independent text, the independent text is segmented and the characters are normalized. According to the technical terminology dictionary, unit dictionary, comparison relationship dictionary and sentence pattern matching rules, the technical feature name, parameter value, unit of measurement, comparison relationship and combination relationship between technical features are extracted.

[0018] Technical features that can take values ​​within a continuous numerical range are marked as continuous parameters, while technical features that can only take integer values ​​or preset discrete values ​​are marked as discrete numerical parameters.

[0019] Convert material type, connection method, relative position of components, or other technical features that cannot be directly represented by numerical ranges into category conditions or logical judgment conditions.

[0020] Based on the parallel, selection, inclusion, or conditional relationships between technical features, generate Boolean expressions for combining numerical range judgment results, category condition judgment results, and logical condition judgment results.

[0021] Furthermore, based on the comparison relationships, parameter values, and boundary types of each technical feature parameter, the parameter value range for each technical feature parameter is determined, and a technical feature boundary data table is formed. The technical feature boundary data table includes parameter identifier, parameter name, unit of measurement, lower limit value, upper limit value, lower limit boundary type, and upper limit boundary type.

[0022] For technical feature parameters without upper or lower limits, record the absence of an upper or lower limit identifier in the technical feature boundary data table.

[0023] Perform parameter unit checks, upper and lower limit relationship checks, boundary type integrity checks, and numerical limit conflict checks on the technical feature boundary data table.

[0024] Based on parameter identifiers, parameter names, synonym mapping relationships, and units of measurement, the technical characteristic parameters are mapped to the product design parameters in the engineering design database.

[0025] The engineering parameter constraints are converted into equality conditions, inequality conditions, or parameter linkage conditions to form a set of engineering constraint conditions. The feasible region of the project is then determined based on this set of constraints. For engineering restrictions corresponding to category parameters or logical parameters, they are saved as additional engineering logical conditions.

[0026] Furthermore, for technical characteristic parameters that change continuously over time, an evolution function for the technical characteristic parameters is generated based on the initial technical characteristic parameters and the rate of change of the parameters.

[0027] For technical feature parameters that change according to monthly, quarterly, product version, or process node, a time series of technical feature parameters is generated based on the parameter adjustment amount or parameter change rate corresponding to each time node.

[0028] For a technical feature parameter evolution function that can be analytically determined to identify the boundary intersection point, the time when the technical feature parameter enters and leaves the technical feature defined region can be determined based on the intersection point between the technical feature parameter evolution function and the boundary of the parameter value interval.

[0029] For technical feature parameter evolution functions or technical feature parameter time series that cannot be directly solved analytically, the technical feature parameters at each sampling time point are extracted according to a preset time step, and numerical interval judgment, category condition judgment and logical condition judgment are performed.

[0030] When the judgment results of adjacent sampling time points are inconsistent, the technical feature parameters are close to the boundary of the parameter value range, or the evolution function of the technical feature parameters has extreme points between adjacent sampling time points, the time step of the corresponding time interval is reduced, or the corresponding time interval is recursively subdivided.

[0031] When the result of the numerical range judgment, the category condition judgment, and the logical condition judgment is true after being combined by a Boolean expression, the corresponding state is assigned the value 1; when the result of the combination is false, the corresponding state is assigned the value 0.

[0032] When necessary technical feature parameters are missing and cannot be calculated based on engineering parameter constraints, a missing judgment result flag is generated, but the corresponding time point's fall-in status is not generated.

[0033] Furthermore, when calculating the parameter adjustment scheme, the parameter adjustment amount corresponding to each technical feature parameter is determined, and the adjustment amount of each parameter is normalized according to the allowable adjustment range, design tolerance, current parameter value or preset reference value of each technical feature parameter.

[0034] Based on the design change cost, expected R&D cycle, manufacturing adjustment difficulty, or preset weight table corresponding to each technical feature parameter, determine the parameter change cost weight, and based on the parameter adjustment amount after scale normalization and the parameter change cost weight, determine the weighted parameter change cost corresponding to each parameter adjustment scheme.

[0035] The technical feature exit condition is defined as the adjusted technical feature parameters no longer simultaneously satisfying all numerical range conditions, category conditions, and logical judgment conditions. The engineering feasibility condition is defined as the adjusted technical feature parameters being within the engineering feasible domain and satisfying the additional engineering logical conditions. The parameter adjustment range condition is defined as the adjustment amount of each technical feature parameter being within the corresponding allowable adjustment range.

[0036] In a preferred embodiment of the present invention, for technical feature parameters with closed boundaries, an exit margin greater than zero is set outside the closed boundary, and technical feature exit conditions are formed based on the exit margin.

[0037] For each adjustable technical feature parameter and its exit direction, candidate optimization sub-problems are established. Each candidate optimization sub-problem includes at least one technical feature exit condition, engineering feasibility condition, and parameter adjustment range condition.

[0038] For continuous parameters, sequential quadratic programming or interior point methods are used to solve candidate optimization subproblems; for discrete numerical parameters or category conditions, a combination of candidate value enumeration and continuous parameter optimization is used, or optimization methods with integer constraints are used to solve candidate optimization subproblems.

[0039] Multiple initial parameter offset values ​​are set for the same candidate optimization subproblem, and iterative calculations are performed separately for each. When the constraint residual, the difference in the cost of weighted parameter changes between two adjacent iterations, or the change in parameter offset meets the preset convergence condition, the iteration is terminated and the feasibility of the calculation results is checked.

[0040] The parameter adjustment scheme with the lowest weighted parameter change cost among those that pass the feasibility check is determined as the preferred parameter offset vector. When the difference in weighted parameter change costs among multiple parameter adjustment schemes is not greater than the preset comparison tolerance, the preferred scheme is determined according to the increase in unit product cost, R&D cycle, number of parameter adjustments, or preset priority, or a set of candidate parameter offset vectors is formed.

[0041] Furthermore, when calculating the unit product avoidance design cost estimate, the preferred parameter offset vector is converted into an engineering change operation item based on the dependencies between parameter identifiers, parameter adjustment directions, parameter adjustment ranges, engineering operation identifiers, engineering operation content, related components, cost items, and engineering change operation items.

[0042] Merge duplicate engineering change operation items corresponding to multiple parameter adjustment amounts, and add related engineering change operation items based on the dependencies between the engineering change operation items.

[0043] Based on the engineering operation identifier, associated components, cost items, data effective time, and applicable product model, obtain the R&D change input, material costs, production yield, and expected allocated output from the engineering cost database.

[0044] The R&D change cost allocated per unit product is calculated based on the R&D change investment and the estimated allocated output. The difference in unit product material cost is calculated based on the unit product material cost before and after parameter adjustment. The unit product yield loss cost is calculated based on the production yield and unit material cost before and after parameter adjustment.

[0045] The unit product's R&D change costs, non-negative unit product material cost differences, and unit product yield loss costs are summarized to obtain the unit product design avoidance cost estimate.

[0046] By using cost item identifiers and material item identifiers, multiple engineering change operation items can be combined to address one-time input or material cost changes that are common to each other, thus avoiding the duplicate inclusion of the same cost item.

[0047] Furthermore, a legal status coefficient is determined based on the legal status of the target patent.

[0048] The patent validity score is determined based on one or more of the following: patent family grant status, scope of protection modifications during the examination process, invalidation proceedings, prior art search results, and patent citations.

[0049] The patent validity risk adjustment coefficient is determined based on the legal status coefficient and the patent validity score. The patent validity risk adjustment coefficient is then used to proportionally adjust the unit product design cost avoidance calculation value to obtain the unit product patent value reference value.

[0050] When the target patent has expired, terminated, or been declared invalid by an effective decision, the legal status coefficient is set to 0.

[0051] When data for determining the validity score of a patent right is lacking, a preset adjustment coefficient or a risk data insufficiency indicator is generated.

[0052] Furthermore, for the projected sales data expressed in continuous time form, the time accumulation calculation is performed based on the unit product patent value reference value, projected sales rate, and the state function corresponding to each time point within the effective evaluation period.

[0053] For projected sales data provided monthly, quarterly, or other assessment periods, discrete cumulative calculations are performed based on the unit product patent value reference value, projected sales, and the sequence of statuses falling within each assessment period.

[0054] When the patent validity risk adjustment coefficient, the unit product avoidance design cost calculation value, or the expected sales volume data change within the effective evaluation period, the effective evaluation period is divided into multiple time intervals according to the time of data change, and the economic value corresponding to each time interval is calculated separately.

[0055] When the status of falling into the assessment changes within the same assessment period, the corresponding assessment period is divided into multiple sub-periods, or the expected sales volume of the corresponding assessment period is allocated according to the proportion of time when the status of falling into the assessment is 1.

[0056] By summarizing the economic value corresponding to each point in time, each time interval, or each assessment period, the cumulative economic value for the assessment period is obtained.

[0057] The second aspect of this invention provides a system for calculating and evaluating the market benefits of patent layout results, including a data acquisition module, a constraint analysis module, a time series evaluation module, an optimization calculation module, and a value accounting module.

[0058] The data acquisition module is used to acquire the independent text of the target patent, as well as the engineering design data, engineering cost data, patent legal status data and expected sales data of the target product. The engineering design data includes initial technical feature parameters, parameter change data and engineering parameter constraint relationships.

[0059] The constraint parsing module is used to parse independent item text, convert technical feature constraints that can be represented numerically into numerical intervals corresponding to technical feature parameters, form technical feature constraint regions based on each numerical interval, convert technical feature constraints that cannot be directly represented numerically into category conditions or logical judgment conditions, and determine the engineering feasible domain corresponding to the combination of target product parameters according to the engineering parameter constraint relationship.

[0060] The time series evaluation module is used to determine the technical feature parameters corresponding to each time point within the effective evaluation period based on the initial technical feature parameters and parameter change data. It compares the technical feature parameters corresponding to each time point with the technical feature defined area, and generates a falling state function or falling state sequence by combining category conditions and logical judgment conditions.

[0061] The optimization calculation module is used to calculate, within the engineering feasible domain, a parameter adjustment scheme that makes the target product no longer meet all technical feature constraints and reduces the cost of weighted parameter changes for time points when the value of the falling state function is 1 or the corresponding falling state in the falling state sequence is 1, and obtains the preferred parameter offset vector.

[0062] The value accounting module is used to convert the preferred parameter offset vector into engineering change operation items according to the parameter-engineering operation mapping relationship, calculate the unit product design avoidance cost estimate based on engineering cost data, adjust the unit product design avoidance cost estimate based on patent legal status data to obtain the unit product patent value reference value, and generate the cumulative economic value estimate for the evaluation period according to the time correspondence relationship, based on the unit product patent value reference value, expected sales data, and the state function or state sequence.

[0063] Through the aforementioned technical solution, the present invention converts the technical feature constraints of the target patent into numerical ranges, category conditions, and logical judgment conditions, and associates them with the engineering parameter constraints of the target product, so that the technical feature judgment is based on the actual design parameters and engineering feasibility conditions of the target product.

[0064] This invention generates a falling state function or falling state sequence based on the changes in the target product parameters within the effective evaluation period, and performs optimization calculations to avoid design parameter constraints when the falling state is 1, so that the parameter adjustment scheme simultaneously meets the technical feature exit conditions, engineering feasibility conditions, and parameter adjustment range conditions.

[0065] This invention converts parameter adjustment schemes into engineering change operation items, generates unit product design cost avoidance calculations based on engineering cost data, and calculates the cumulative economic value over the evaluation period by combining patent legal status data and expected sales data. This forms a continuous data processing process from technical feature analysis, parameter timing judgment, engineering constraint optimization, engineering cost accounting to economic value accumulation.

[0066] This invention provides a method and system for calculating and evaluating the market benefits of patent portfolio development. It has the following beneficial effects:

[0067] 1. This invention converts the numerical limitations, category conditions, and logical judgment conditions in the independent claims into computable data, and combines them with engineering parameter constraints to form a technical feature limitation region and an engineering feasible region, thereby improving the correspondence between the patented technical features and the design parameters of the target product.

[0068] 2. This invention generates a falling state function or falling state sequence based on the changes in the target product parameters over time, which can determine the time interval within which the target product meets the technical feature constraints, reducing the evaluation bias caused by a single point in time.

[0069] 3. This invention calculates parameter adjustment schemes within the engineering feasible domain to make the target product exit the technical feature limitation conditions, and converts the parameter adjustment results into engineering change operation items, so that the unit product has a corresponding engineering data basis for avoiding design costs.

[0070] 4. This invention correlates the unit product's design cost avoidance, patent legal status, expected sales data, and landing status according to time relationship to form a cumulative economic value calculation value for the evaluation period, which facilitates the verification of the calculation process and data source. Attached Figure Description

[0071] Figure 1 This is a schematic diagram of the architecture of a patent layout results marketization benefit calculation and evaluation system according to an embodiment of the present invention.

[0072] Figure 2 A flowchart illustrating a method for calculating and evaluating the market benefits of patent layout results according to an embodiment of the present invention;

[0073] Figure 3 Here is a sequence diagram for constructing the feature and constraint matrix according to an embodiment of the present invention;

[0074] Figure 4 This is a flowchart of timing evaluation and dynamic determination according to an embodiment of the present invention;

[0075] Figure 5 This is a flowchart illustrating the workflow of a design parameter optimization model according to an embodiment of the present invention.

[0076] Figure 6 This is a flowchart illustrating the cost accounting and value benchmark conversion logic of one embodiment of the present invention;

[0077] Figure 7 This is a flowchart illustrating the definite integral and output logic of lifecycle revenue according to an embodiment of the present invention.

[0078] The module includes: 10. Data acquisition module; 20. Constraint analysis module; 30. Time series evaluation module; 40. Optimization calculation module; and 50. Value accounting module. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. The specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the scope of the invention.

[0080] See attached document Figure 1 This invention provides a system for calculating and evaluating the market benefits of patent portfolio achievements, comprising: a data acquisition module 10, a constraint analysis module 20, a time series evaluation module 30, an optimization calculation module 40, and a value accounting module 50. Each module can be deployed on the same computing device or on multiple computing devices connected via a communication network.

[0081] The data acquisition module 10 is used to acquire the independent claim text of the target patent from an external data source or local storage device, and send the independent claim text to the constraint parsing module 20. The independent claim text includes technical features and the combination relationship between technical features, and may also include numerical values, ranges, units, categories or logical limiting conditions corresponding to the technical features.

[0082] The data acquisition module 10 is also used to access the engineering design database through a preset data interface to obtain the technical feature parameters corresponding to the target product and the engineering parameter constraint relationships between the technical feature parameters. The technical feature parameters include at least the parameter name, parameter unit, current parameter value, and allowable adjustment range; the engineering parameter constraint relationships include equality constraints, inequality constraints, and parameter association relationships formed by structural dimensions, circuit connections, heat conduction, material properties, manufacturing processes, or assembly conditions.

[0083] The data acquisition module 10 can also access the engineering cost database, the patent legal status database, and the market information database to obtain engineering change cost data, patent validity status data, and historical or projected sales data of the target product, respectively.

[0084] The constraint parsing module 20 is communicatively connected to the data acquisition module 10 and is used to receive the independent claim text and the constraint relationship of engineering parameters. The constraint parsing module 20 identifies information such as the technical feature name, parameter unit, lower limit of the parameter, upper limit of the parameter, and whether the boundary contains a critical value from the independent claim text according to preset text parsing rules.

[0085] For technical feature constraints that can be represented numerically, the constraint parsing module 20 converts the corresponding constraints into numerical intervals along the technical feature parameter dimension, and constructs a technical feature boundary data table based on each numerical interval. The technical feature boundary data table records at least the parameter identifier, parameter unit, lower limit value, upper limit value, lower limit boundary type, and upper limit boundary type. The numerical intervals corresponding to each technical feature parameter collectively form the technical feature constraint region of the target independent claim.

[0086] For technical feature constraints that cannot be directly represented by continuous numerical values, the constraint parsing module 20 converts them into category values, state values, or logical judgment conditions according to preset coding rules. For example, material type, connection method, or relative position of components can be converted into preset category codes or logical variables. Technical features that cannot be converted into numerical ranges do not participate in range calculations but are used as additional logical conditions in the technical feature inclusion judgment.

[0087] The constraint resolution module 20 is also used to convert engineering parameter constraint relationships into engineering constraint condition sets. These engineering constraint condition sets include equality conditions, inequality conditions, and parameter linkage conditions that each technical feature parameter must satisfy, and based on these, determine the engineering feasible region of the target product parameter combination. The engineering feasible region is used to limit the range of parameters that, after adjustment of the technical feature parameters, can still meet the product design requirements, manufacturing requirements, and physical operation requirements.

[0088] The constraint parsing module 20 sends the technical feature boundary data table and additional logical conditions to the timing evaluation module 30, and sends the engineering constraint condition group to the optimization calculation module 40.

[0089] The time-series evaluation module 30 is connected to both the data acquisition module 10 and the constraint analysis module 20. It is used to acquire the initial technical characteristic parameters of the target product at the start of the evaluation, as well as the rate of change of each technical characteristic parameter during the evaluation period. The rate of change of parameters can be determined from industry technical roadmap data, product iteration plans, historical design version data, or user input data.

[0090] The time-series evaluation module 30 generates an evolution function of the technical characteristic parameters of the target product within the evaluation period based on the initial technical characteristic parameters and parameter change rate data. For discrete parameters that are updated monthly, quarterly, or by product version, the time-series evaluation module 30 generates the corresponding time series of technical characteristic parameters, without requiring the discrete parameters to be converted into continuous functions.

[0091] Within a set effective evaluation period, the time series evaluation module 30 compares the technical feature parameters corresponding to each time point with the technical feature defined region. For continuous parameter evolution functions whose parameter entry and exit times can be analytically determined, the time series evaluation module 30 calculates the time interval for the parameter to enter the technical feature defined region; for parameter evolution functions or discrete parameters that cannot be directly solved analytically, the time series evaluation module 30 performs interval judgment point by point according to a preset time step.

[0092] The interval determination includes at least: determining whether each technical feature parameter is located between its corresponding upper and lower limits, determining whether the critical value satisfies the corresponding open or closed boundary conditions, and determining whether the additional logical conditions are met. Only when the interval conditions for each parameter and the additional logical conditions are met, the timing evaluation module 30 determines that the target product enters the technical feature definition region of the target independent claim at the corresponding time point.

[0093] The time series evaluation module 30 generates a fall-in state function based on the judgment result. When the technical feature parameters of the target product are located within the technical feature defined area and meet the additional logical conditions, the fall-in state function takes the value of 1; otherwise, the fall-in state function takes the value of 0. The time series evaluation module 30 sends the fall-in state function and the technical feature parameters at the corresponding time point to the optimization calculation module 40 and the value accounting module 50.

[0094] The optimization calculation module 40 is communicatively connected to the time series evaluation module 30, the constraint parsing module 20, and the data acquisition module 10. When the value of the state function is 1, the optimization calculation module 40 acquires the technical characteristic parameters, technical characteristic boundary data table, additional logical conditions, engineering constraint condition group, and parameter change cost weight at the corresponding time point.

[0095] The optimization calculation module 40 aims to reduce the cost of parameter changes. Under the premise of satisfying the set of engineering constraints, it calculates the parameter offset that would allow the adjusted technical feature parameter to move out of the technical feature limitation area. The parameter change cost weight is used to represent the relative adjustment cost of different technical feature parameters. The parameter change cost weight can be determined based on engineering change costs, R&D cycle, manufacturing difficulty, or a preset weight table.

[0096] When the optimization calculation module 40 performs constraint optimization calculations, it shall at least meet the following conditions: the adjusted technical feature parameters no longer simultaneously satisfy all parameter interval conditions and additional logical conditions in the technical feature limitation area; the adjusted technical feature parameters are still within the feasible domain of the project; and the parameter change cost is less than the parameter change cost corresponding to other feasible parameter adjustment schemes.

[0097] When multiple feasible solutions exist with the same cost of parameter changes or differences within a preset tolerance range, the optimization calculation module 40 can determine a preferred solution based on the project implementation cycle, the increase in unit product cost, or the user's preset priority, or it can output multiple candidate parameter offset solutions. The optimization calculation module 40 sends the determined preferred parameter offset vector or candidate parameter offset vector set to the value accounting module 50.

[0098] The value accounting module 50 is connected to the optimization calculation module 40, the data acquisition module 10, and the time series evaluation module 30. It is used to receive the preferred parameter offset vector and convert each parameter offset into a corresponding engineering change operation item according to the preset parameter-engineering operation mapping relationship. The engineering change operation item includes one or more of the following: component replacement, circuit parameter adjustment, wiring adjustment, structural dimension modification, material replacement, manufacturing process adjustment, or mold modification.

[0099] The value accounting module 50 retrieves cost data corresponding to each engineering change operation item from the engineering cost database. This cost data includes at least R&D change costs, unit product material cost differences, and unit product loss costs due to process adjustments or yield changes. The value accounting module 50 summarizes these costs to obtain the unit product cost estimate for design avoidance at the corresponding time point.

[0100] The value calculation module 50 can also determine the patent validity risk adjustment coefficient based on patent legal status data, and use this adjustment coefficient to adjust the unit product avoidance design cost calculation value to obtain a unit product patent value reference value. When the patent right has expired, terminated, or been declared invalid by an effective decision, the patent validity risk adjustment coefficient is zero, and the value calculation module 50 no longer accumulates the economic value for the corresponding period.

[0101] The value calculation module 50 acquires the projected sales rate data of the target product within the evaluation period and multiplies the unit product patent value reference value, projected sales rate, and the state function according to the time correspondence. For continuous data, the value calculation module 50 performs time integration within the evaluation period; for discrete data provided by month, quarter, or other time intervals, the value calculation module 50 performs discrete summation according to each evaluation period. This yields the cumulative economic value of the target patent within the evaluation period.

[0102] The value accounting module 50 sends the cumulative economic value calculation value, as well as the corresponding landing status, optimal parameter offset, engineering change operation items, and unit product design cost avoidance calculation value to the display terminal or data storage device at each time point.

[0103] See attached document Figure 2 This invention provides a method for calculating and evaluating the market benefits of patent layout achievements. This method is executed by the aforementioned system for calculating and evaluating the market benefits of patent layout achievements, and specifically includes the following steps:

[0104] S100, the constraint parsing module 20 receives the independent claims text of the target patent and extracts the technical features and corresponding limiting conditions from the independent claims text. For limiting conditions that can be represented numerically, the constraint parsing module 20 converts them into numerical ranges of the corresponding technical feature parameters and forms a technical feature limiting region based on each numerical range; for limiting conditions that cannot be directly represented numerically, the constraint parsing module 20 converts them into category conditions or logical judgment conditions.

[0105] The constraint parsing module 20 also obtains the engineering parameter constraint relationship between various technical feature parameters from the engineering design database, converts the engineering parameter constraint relationship into a set of engineering constraint conditions, and determines the engineering feasible domain of the target product parameter combination accordingly.

[0106] S200, the time series evaluation module 30 obtains the initial technical characteristic parameters and parameter change rate data of the target product at the start time of the evaluation, and generates the technical characteristic parameter evolution function or technical characteristic parameter time series within the evaluation period.

[0107] The time series evaluation module 30 compares the technical feature parameters corresponding to each time point with the technical feature defined region. For parameter evolution functions whose entry and exit times can be analytically determined, the time interval for the technical feature parameter to enter the technical feature defined region is calculated; for parameter evolution functions or discrete parameters that cannot be directly solved analytically, interval judgment is performed point by point according to a preset time step. The time series evaluation module 30 generates a state function based on the judgment results.

[0108] S300, when the value of the state function is 1, the optimization calculation module 40 obtains the technical feature parameters, technical feature limited area, logical judgment conditions and engineering constraint condition group at the corresponding time point, and starts the optimization calculation to avoid design parameter constraints.

[0109] The optimization calculation module 40, while ensuring that the adjusted technical feature parameters are within the engineering feasible region, calculates parameter adjustment schemes that would cause the target product to no longer meet all technical feature constraints. With the goal of minimizing the weighted parameter change cost, it outputs a preferred parameter offset vector. When multiple feasible schemes exist with the same parameter change cost or differences within a preset tolerance range, the optimization calculation module 40 outputs a set of candidate parameter offset vectors, or determines a preferred scheme from these according to a preset priority.

[0110] S400, the value accounting module 50 converts the preferred parameter offset vector into the corresponding engineering change operation item according to the preset parameter-engineering operation mapping relationship, and obtains the cost data corresponding to each engineering change operation item from the engineering cost database.

[0111] The value accounting module 50 calculates the R&D change costs allocated per unit product, the material cost difference per unit product, and the unit product loss costs due to process adjustments or yield changes. It then summarizes these costs to obtain the estimated cost per unit product for design avoidance. Based on patent legal status data, the value accounting module 50 determines a patent validity risk adjustment coefficient and uses this coefficient to adjust the estimated cost per unit product for design avoidance, thus obtaining a reference value for the patent value per unit product.

[0112] S500, the value calculation module 50 obtains the expected sales rate data of the target product within the evaluation period, and calculates the product by multiplying the unit product patent value reference value, the expected sales rate and the state function according to the time correspondence.

[0113] For continuous data, the value accounting module 50 performs time integration within the assessment period; for discrete data provided monthly, quarterly, or other time intervals, the value accounting module 50 performs discrete summation according to each assessment period. Based on the calculation results, the value accounting module 50 generates a cumulative economic value estimate for the assessment period and sends the cumulative economic value estimate, along with the corresponding inclusion status, parameter offset scheme, and engineering cost data, to the display terminal.

[0114] The following provides further explanation of steps S100 to S500, taking into account the data sources, parameter processing methods, and calculation processes involved in each step.

[0115] See attached document Figure 3 In step S100, the constraint parsing module 20 performs structured processing on the technical feature limiting conditions in the independent claim text, and combines them with the engineering parameter constraint relationships in the engineering design database to form the technical feature limiting region and the engineering feasible region. Step S100 includes the following steps:

[0116] S101, the constraint parsing module 20 receives the independent claim text of the target patent through the data acquisition module 10, and identifies the technical features in the independent claim text according to the preset text parsing rules.

[0117] During text parsing, the constraint parsing module 20 first segments and standardizes the characters of the independent claim text, and then extracts the technical feature names, parameter values, units of measurement, comparison relationships, and connection relationships between technical features based on the preset technical terminology dictionary, unit dictionary, comparison relationship dictionary, and sentence matching rules.

[0118] The comparison relation dictionary includes at least the expressions "greater than", "less than", "not less than", "not greater than", "higher than", "lower than", "between", and "range of" and their corresponding boundary types. For the same type of parameter expressed in different units of measurement, the constraint parsing module 20 converts it to a unified unit of measurement according to a preset unit conversion relationship.

[0119] The constraint analysis module 20 identifies technical features that can be represented by continuous or discrete numerical values ​​as technical feature parameters and assigns parameter identifiers to each technical feature parameter. These technical feature parameters together constitute... 3D technical feature parameter space, in which The number of technical feature parameters that can be quantified.

[0120] For technical feature parameters that can take values ​​within a continuous numerical range, the constraint parsing module 20 marks them as continuous parameters; for technical feature parameters that can only take integer values ​​or preset discrete values, the constraint parsing module 20 marks them as discrete numerical parameters. Continuous parameters participate in continuous constraint optimization calculations, while discrete numerical parameters are processed through candidate value enumeration, integer constraints, or comparison of discrete candidate schemes.

[0121] For material types, connection methods, relative positions of components, or other technical features that cannot be directly represented by numerical ranges, the constraint parsing module 20 converts them into category conditions or logical judgment conditions according to preset coding rules. Category conditions and logical judgment conditions do not participate in numerical range calculations; they are judged together with numerical range conditions in subsequent judgments of whether a technical feature falls within a given range.

[0122] The constraint parsing module 20 also identifies parallel, selection, inclusion, and conditional relationships among technical features and converts the corresponding combination relationships into Boolean expressions. The timing evaluation module 30 combines the numerical range judgment results, category condition judgment results, and logical condition judgment results according to these Boolean expressions.

[0123] S102, the constraint analysis module 20 determines the corresponding parameter value range based on the comparison relationship, parameter value and boundary type of each technical feature parameter, and forms a technical feature boundary data table.

[0124] The technical feature boundary data table should include at least the parameter identifier, parameter name, unit of measurement, lower limit value, upper limit value, lower limit boundary type, and upper limit boundary type. Boundary types include open boundary and closed boundary.

[0125] Let the number of quantifiable technical feature parameters be . , No. The lower limit of each technical feature parameter is The upper limit is The corresponding parameter value range is B_i. The parameter value range B_i, according to the definition in the independent claim text, takes one of the following range forms:

[0126] , , or ;

[0127] The parameter value ranges corresponding to each technical feature parameter together form the technical feature constraint region Ω_TF, denoted as:

[0128] ;

[0129] in, Represents a vector of technical feature parameters. The technical feature parameter vector is represented in the first... The values ​​can be taken in each parameter dimension.

[0130] When the text of an independent claim limits a certain technical feature parameter to be greater than a set threshold, the constraint parsing module 20 determines the threshold as the lower limit of the open boundary and records the corresponding interval as an unbounded interval; when the technical feature parameter is limited to be not less than a set threshold, the threshold is determined as the lower limit of the closed boundary.

[0131] When the text of an independent claim limits a certain technical feature parameter to less than a set threshold, the constraint parsing module 20 determines the threshold as the upper limit of the open boundary and records the corresponding interval as an unbounded interval; when the technical feature parameter is limited to not greater than the set threshold, the threshold is determined as the upper limit of the closed boundary.

[0132] For unbounded or unlimited intervals, the technical feature boundary data table records them using an unbounded upper or lower bound identifier, rather than using the maximum or minimum floating-point value supported by the computing device to replace the infinite boundary. This avoids the influence of differences in floating-point ranges in different computing environments on the interval determination results.

[0133] After completing the interval transformation, the constraint parsing module 20 performs a consistency check on the technical feature boundary data table. The consistency check includes at least checking whether parameter units are consistent, whether the lower limit is less than or equal to the upper limit, whether the open and closed boundary identifiers are complete, and whether there are conflicting numerical limits for the same technical feature. If a conflict is found, the constraint parsing module 20 outputs an anomaly flag and suspends further calculations for the corresponding technical feature.

[0134] The technical feature boundary data table, the technical feature defined area, and the category conditions or logical judgment conditions together represent the technical feature definition content in the target independent claim.

[0135] S103, the data acquisition module 10 accesses the engineering design database through a preset data interface.

[0136] Engineering design databases include at least one of the following: electronic design automation system database, computer-aided design system database, product lifecycle management system database, or other databases that store product design data.

[0137] The data acquisition module 10 searches for product design parameters corresponding to the technical feature parameters from the engineering design database based on parameter identifiers, parameter names, synonym mapping relationships, and units of measurement. Product design parameters include at least the current parameter value, allowable adjustment range, parameter unit, and engineering parameter constraints between the parameter and other product design parameters.

[0138] When the product design parameter name in the engineering design database is inconsistent with the technical feature name in the independent claim text, the data acquisition module 10 determines the correspondence between the two according to a preset parameter name mapping table. The parameter name mapping table records at least the technical feature name of the claim, the engineering parameter name, the parameter identifier, and the unit of measurement.

[0139] Engineering parameter constraints include parameter associations formed by circuit connections, heat conduction, structural dimensions, mechanical interference, material properties, manufacturing processes, or assembly requirements. Examples include node current and voltage constraints between circuit parameters, dimensional fit constraints between structural components, and temperature range constraints during product operation.

[0140] S104, the constraint parsing module 20 receives the engineering parameter constraint relationships input from the data acquisition module 10, and converts each engineering parameter constraint relationship into a set of engineering constraint conditions. The set of engineering constraint conditions may include linear or nonlinear equality conditions and inequality conditions.

[0141] For the inequality that can be directly expressed as the first The constraints of each project are represented by the constraint parsing module 20 as follows:

[0142] ;

[0143] For engineering constraints expressed as equations During symbolic computation, the constraint resolution module 20 converts it into... and Two inequality conditions; during numerical calculations, preset calculation tolerances can also be used. Transform the equality conditions into .

[0144] Let the engineering parameter constraint relationship be converted to There are several inequality constraints, among which... A positive integer, a constraint function vector Represented as:

[0145] ;

[0146] in, Indicates the first A set of engineering constraint functions is used to limit the product design conditions, manufacturing conditions, or operating conditions that should be met after adjustments to technical characteristic parameters. 0 indicates a length of [missing information]. A vector of all zeros.

[0147] Constraint Analysis Module 20 determines the feasible region of the project based on the set of engineering constraints. , is represented as:

[0148] ;

[0149] Engineering Feasibility Domain This represents the combination of technical feature parameters that can be adopted under the constraints of product design requirements, manufacturing conditions, and operating conditions. For engineering constraints corresponding to category parameters or logical parameters, the constraint parsing module 20 saves them as additional engineering logical conditions, and uses them as constraints along with the engineering feasible region in subsequent optimization calculations.

[0150] The constraint parsing module 20 stores the function expressions, associated parameters, units of measurement, comparison operators, and calculation tolerances of each engineering constraint function in a structured manner, forming an engineering constraint data table.

[0151] The constraint parsing module 20 sends the technical feature boundary data table, technical feature limited area, category conditions and logical judgment conditions to the time series evaluation module 30; and sends the engineering constraint condition group, engineering feasible region and additional engineering logical conditions to the optimization calculation module 40 for subsequent technical feature fall-in judgment and design parameter constraint avoidance optimization calculation.

[0152] See attached document Figure 4 In step S200, the timing evaluation module 30 determines the changes in each technical feature parameter over time based on the initial technical feature parameters of the target product and the parameter change data within the evaluation period, and judges whether the target product meets the technical feature limitation conditions in the target independent claim at different time points. Step S200 includes the following steps:

[0153] S201, Time Series Evaluation Module 30 obtains the target product at the evaluation start time. The design baseline data is used to extract the corresponding technical feature boundary data table. Given initial technical feature parameter values, construct an initial technical feature parameter vector. ,in:

[0154] ;

[0155] The timing evaluation module 30 maps the product design parameters in the design baseline data to the technical feature parameters in the technical feature boundary data table according to the parameter identifier or parameter name mapping table, and unifies the units of measurement according to the preset unit conversion relationship.

[0156] When a certain technical feature parameter is missing from the design baseline data, the timing evaluation module 30 generates a data missing identifier. For missing parameters that can be calculated from other product design parameters, the timing evaluation module 30 calculates the corresponding parameter value according to the parameter relationship in the engineering constraint group; for missing parameters that cannot be calculated, the technical feature judgment at the corresponding time point is paused, and a prompt for supplemented data is output, without directly assigning the data missing status to the state 0 or 1.

[0157] S202, the time series evaluation module 30 acquires the parameter change data of each technical feature parameter during the evaluation period, and generates the technical feature parameter evolution function or technical feature parameter time series accordingly.

[0158] Parameter change data can originate from historical design versions of the target product, product iteration plans, industry technical roadmap documents, publicly available technical reports, or user input data. Parameter change data from historical design versions is recorded as actual parameter change data; future change data from product iteration plans, industry technical roadmap documents, publicly available technical reports, or user input is recorded as projected parameter change data. Parameter change data must at least record the data source, data creation time, applicable time interval, and an identifier for either actual or projected data. The source and nature of the parameter change data are also recorded in the structured evaluation results.

[0159] For technical characteristic parameters that change continuously over time, the time-series evaluation module 30 calculates the parameter change rate vector. Constructing the evolution function of technical feature parameters ,in The evolution function of the technical characteristic parameters is expressed as:

[0160] ;

[0161] in, For integration variables, Indicates the technical characteristic parameters at time 10:00. The rate of change.

[0162] When a certain technical feature parameter adopts different rates of change in different time intervals, the time series evaluation module 30 divides the parameter rate of change vector into segments according to the start and end times corresponding to each rate of change, and calculates the parameter value in each time interval respectively.

[0163] For discrete technical feature parameters that change monthly, quarterly, product version, or process node, the time series evaluation module 30 does not perform continuous integration, but instead generates a time series of technical feature parameters according to the parameter adjustment amount corresponding to each time node. The parameter vector corresponding to each time node can be represented as:

[0164] ;

[0165] in, Indicates the first Technical feature parameter vectors at each time point Indicates from the first From the [number]th time point to the [number]th The amount of parameter adjustment that occurs at each time point.

[0166] When discrete parameter data is provided in the form of parameter change rate, the time series evaluation module 30 calculates the parameter change according to the length of the corresponding time interval, expressed as:

[0167] ;

[0168] in, Indicates the first The rate of change of parameters over a time interval This indicates the length of the time interval.

[0169] When the same target product contains both continuously changing parameters and discretely changing parameters, the time series evaluation module 30 calculates the two types of parameters respectively, and combines the calculation results into a vector of technical feature parameters corresponding to each time point according to the same time base.

[0170] S203, Time Series Evaluation Module 30 determines the effective evaluation period. ,in, The earliest of the following dates is considered to be: the expiration date of the target patent, the termination date of the patent, the evaluation deadline, the expected cessation of sales of the target product, or the date mentioned above.

[0171] Within the effective evaluation period, the time-series evaluation module 30 will define the technical characteristic parameters and the technical characteristic limitation areas corresponding to each time point. The comparison is performed, and the category conditions and logical judgment conditions are judged simultaneously to determine whether they are true.

[0172] For the evolution function of technical feature parameters that can be solved analytically, the time series evaluation module 30 calculates the time when the parameter enters and leaves the technical feature defined region based on the intersection point between each parameter evolution function and the corresponding interval boundary, and determines the time interval that satisfies all numerical interval conditions.

[0173] For technical feature parameter evolution functions that cannot be solved analytically, the time series evaluation module 30 extracts the technical feature parameters at each sampling time point according to a preset time step, and determines whether each parameter is within its corresponding value range. The preset time step can be set according to day, month, quarter, or product iteration cycle.

[0174] The time step is determined based on the parameter change rate and evaluation accuracy requirements. A smaller time step is used when the parameter changes rapidly; a larger time step can be used when the parameter changes relatively smoothly. When the judgment results at adjacent sampling time points are inconsistent, the time series evaluation module 30 reduces the time step within that time interval, or uses a binary search method to determine the time range of the state change.

[0175] When the parameter values ​​at adjacent sampling time points are close to the technical feature boundary, or when there are extreme points between the parameter evolution function and the two sampling time points, the time series evaluation module 30 can recursively subdivide the corresponding time interval, or calculate the intersection between the parameter evolution function and the technical feature boundary, in order to reduce the possibility of entering the technical feature-defined area in a short time but not being identified.

[0176] For the For each technical feature parameter, the time series evaluation module 30 performs interval judgment based on the boundary type recorded in the technical feature boundary data table. When the lower limit is a closed boundary, a parameter value equal to the lower limit is considered to meet the lower limit condition; when the lower limit is an open boundary, a parameter value equal to the lower limit is considered to not meet the lower limit condition. The upper limit boundary uses the same judgment method.

[0177] When the technical feature parameters have no upper or lower bound identifier, the timing evaluation module 30 only judges the set single-sided boundary and does not use the maximum or minimum floating-point value to replace the unset boundary.

[0178] For non-numerical technical features such as material type, connection method, and relative position of components, the timing evaluation module 30 determines whether the corresponding conditions are met based on category coding or logical judgment rules. When non-numerical technical features change with product versions, the corresponding category value or logical state is updated according to their effective time.

[0179] The timing evaluation module 30 determines a time point as satisfying the technical feature limitation conditions of the target independent claim only when all numerical interval conditions, all category conditions, and logical judgment conditions at the same time point are true. The timing evaluation module 30 also determines a time point as satisfying the technical feature limitation conditions of the target independent claim when the Boolean expression calculation result is true.

[0180] S204, After the timing evaluation module 30 has acquired or calculated all the necessary technical characteristic parameters at the corresponding time points, it generates the state-falling function based on the judgment result. When necessary technical feature parameters are missing and cannot be recalculated, the time series evaluation module 30 generates a missing judgment result flag and does not generate the corresponding fall-in state value for that time point. The fall-in state function is expressed as:

[0181] ;

[0182] For the time series of discrete technical characteristic parameters, the time series evaluation module 30 generates a sequence of the falling state corresponding to each time node. . When the value is 1, it indicates that the first... The target product parameters at each time point satisfy all the technical feature limitations in the target independent claim; When the value is 0, it indicates that at least one technical feature constraint condition is not met.

[0183] The timing evaluation module 30 records the time points when the state changes from 0 to 1 and from 1 to 0, and determines one or more time intervals in which the target product meets the technical feature constraints.

[0184] When falling into the state function The value is 1, or it falls into a discrete state. When the value is 1, the time series evaluation module 30 sends the corresponding time, technical feature parameter vector, technical feature boundary data, and fall-in status to the optimization calculation module 40, and generates an optimization calculation start signal. If the judgment result is missing, no optimization calculation start signal is generated.

[0185] After receiving the optimization calculation start signal, the optimization calculation module 40 performs optimization calculations to avoid design parameter constraints on the technical feature parameters at the corresponding time point.

[0186] See attached document Figure 5 In step S300, the optimization calculation module 40 calculates a parameter adjustment scheme that satisfies the engineering constraints but no longer satisfies all technical feature limitations for the time point when the state function is 1. Step S300 includes the following steps:

[0187] S301, the optimization calculation module 40 receives the optimization calculation start signal sent by the timing evaluation module 30 and obtains the corresponding time point. Technical feature parameter vector Technical feature limited area Category conditions, logical judgment conditions, and feasible engineering domain And additional engineering logic conditions.

[0188] Falling into the state function When the value is 1, it represents a time point. The corresponding technical feature parameters satisfy the numerical range conditions, category conditions, and logical judgment conditions included in the calculation of the target independent claim. Based on this, the optimization calculation module 40 initiates optimization calculations to circumvent design parameter constraints.

[0189] For discrete-time data, the optimization calculation module 40 obtains the landing status. Time nodes with a value of 1 and their corresponding technical feature parameter vectors And perform constraint optimization calculations in the same manner.

[0190] S302, the optimization calculation module 40 determines the adjustment amount and parameter change cost weight of each technical feature parameter.

[0191] Set time point The corresponding parameter offset vector is:

[0192] ;

[0193] in, Indicates the first The adjustment amount of each technical feature parameter. The adjusted vector of technical feature parameters. Represented as:

[0194] ;

[0195] Considering that different technical characteristic parameters may use different units of measurement and numerical ranges, the optimization calculation module 40 first performs scale normalization on the adjustment amounts of each parameter. Let the first parameter be... The reference scale for each technical feature parameter is: Normalized parameter adjustment amount Represented as:

[0196] ;

[0197] in, You can take the first one The allowable adjustment range, design tolerance, current absolute value, or preset reference value for each technical feature parameter. When the reference scale is zero, the preset non-zero minimum scale is used instead. The normalized parameter adjustment vector is represented as follows: .

[0198] The optimization calculation module 40 establishes the parameter change cost weight matrix W, which is represented as:

[0199] ;

[0200] in, Indicates the first The parameter change cost weight corresponding to each technical feature parameter Greater than zero. The weight of parameter change cost can be determined based on the design change cost, expected R&D cycle, manufacturing adjustment difficulty, or a preset weight table in the engineering cost database.

[0201] In one implementation, let the first... The design change cost corresponding to each technical feature parameter is: The expected R&D cycle is The cost weighting ratio is ,and The parameter change cost weight can be calculated as follows:

[0202] ;

[0203] in, This represents the maximum value in the design change cost corresponding to each technical feature parameter. For each technical feature parameter, the maximum value corresponds to the expected R&D cycle. and A positive number is used to prevent the denominator from being zero. If the result is zero, then... Set as the preset minimum positive weight .

[0204] Optimization module 40 constructs a weighted quadratic objective function based on the normalized parameter adjustment vector and the parameter change cost weight matrix:

[0205] ;

[0206] This objective function represents the adjustment amount of different technical feature parameters and their relative change costs. The smaller the value of the objective function, the lower the weighted parameter change cost of the corresponding parameter adjustment scheme.

[0207] S303, the optimization calculation module 40 determines the constraint conditions for constraint optimization calculation based on the technical characteristics of the limited region and the engineering feasible region.

[0208] The first type of constraint is the technical feature exit condition. The adjusted technical feature parameter vector. At least one numerical range condition, category condition, or logical judgment condition must be false, so that all the technical feature limiting conditions included in the calculation in the target independent claim are no longer simultaneously satisfied.

[0209] For numerical technical features, the optimization calculation module 40 generates downward exit conditions and upward exit conditions for each technical feature parameter based on the upper and lower limits and open / closed boundary types recorded in the technical feature boundary data table. To prevent parameter values ​​from remaining near the boundary solely due to numerical calculation errors, a positive zero exit margin is set for closed boundaries. .

[0210] Current Limit When the boundary is closed, the downward exit condition is expressed as:

[0211] ;

[0212] Current Limit When the boundary is open, the downward exit condition is expressed as:

[0213] ;

[0214] When the upper limit When the boundary is closed, the upward exit condition is expressed as:

[0215] ;

[0216] When the upper limit When the boundary is open, the upward exit condition is expressed as:

[0217] ;

[0218] When a technical feature parameter has no upper bound marker, no upward exit condition is established; when it has no lower bound marker, no downward exit condition is established. Exit margin. It can be determined based on the parameter measurement accuracy, manufacturing tolerance, or calculated tolerance.

[0219] Since it is only necessary to make at least one technical feature constraint false, the optimization calculation module 40 can establish candidate optimization subproblems for each adjustable parameter and its exit direction. Each candidate optimization subproblem includes a technical feature exit condition and is solved together with the engineering constraints. The optimization calculation module 40 compares the feasible solutions of each candidate optimization subproblem and determines the parameter adjustment scheme with the smaller objective function value.

[0220] For category conditions such as material type, connection method, or relative position of components, the optimization calculation module 40 retrieves the allowed candidate category values ​​from the engineering design database and treats different category values ​​as discrete candidate solutions. Candidate category values ​​must satisfy additional engineering logic conditions. When category adjustments and numerical parameter adjustments coexist, the optimization calculation module 40 performs numerical parameter constraint optimization calculations for each category of candidate solutions.

[0221] The second type of constraint is the engineering feasibility condition. (Adjusted technical feature parameter vector) It needs to be located within the feasible engineering domain. Within, and satisfying additional engineering logic conditions, it is expressed as:

[0222] ;

[0223] When engineering constraints are expressed as constraint function vectors When expressing this, the feasibility conditions of the project can be written as:

[0224] ;

[0225] For engineering constraints expressed by equations, determine whether they are valid by calculating the tolerance according to the equations set in step S104.

[0226] For each technical feature parameter, you can also set allowable adjustment range constraints:

[0227] ;

[0228] in, and They represent the first The minimum and maximum allowable adjustments for each technical feature parameter can be determined based on the parameter allowable range in the engineering design database.

[0229] Based on the aforementioned objective function and constraints, the time point The corresponding parameter-constrained optimization problem is expressed as:

[0230] ;

[0231] The constraints include:

[0232] It no longer meets all the technical feature requirements;

[0233] ;

[0234] .

[0235] This represents the optimal parameter offset vector obtained under the current weights, parameter adjustment range, and engineering constraints. This calculation result is used to represent a low-cost parameter adjustment scheme that allows the target product parameters to escape the technical feature constraints while maintaining engineering feasibility.

[0236] S304, the optimization calculation module 40 solves each candidate optimization sub-problem and determines the preferred parameter offset vector or the candidate parameter offset vector set based on the solution results.

[0237] For candidate optimization subproblems where the objective function and engineering constraint function are continuously differentiable, the optimization calculation module 40 can use sequential quadratic programming or interior point method to solve them; for candidate optimization subproblems containing discrete numerical parameters or categorical variables, a combination of candidate value enumeration and continuous parameter optimization can be used to solve them, or optimization methods capable of handling integer constraints can be used to solve them.

[0238] Before performing iterative calculations, the optimization calculation module 40 sets initial values ​​for parameter offsets based on the exit direction of the corresponding technical features. The initial values ​​for parameter offsets can be determined based on the difference between the current parameter values ​​and the target boundary.

[0239] To reduce the possibility that a single initial value may cause the calculation results to remain in a local solution, the optimization calculation module 40 can set multiple different parameter offset initial values ​​for the same candidate optimization subproblem, perform iterative calculations separately, and compare the feasible parameter adjustment schemes obtained from each calculation.

[0240] During the iteration process, the optimization calculation module 40 synchronously adjusts the relevant technical feature parameters according to the engineering constraint set. When the adjustment of one technical feature parameter causes other engineering constraints to become invalid, the optimization calculation module 40 determines the adjustment amount required for the associated parameters by solving the corresponding engineering constraint functions. The adjustment results of each associated parameter are considered as part of the same parameter offset vector and are not regarded as new avoidance schemes on their own.

[0241] The optimization calculation module 40 can use any of the following conditions as convergence criteria: the constraint residual is not greater than the preset constraint tolerance; the difference between the objective functions of two adjacent iterations is not greater than the preset objective function tolerance; the change in parameter offset is not greater than the preset parameter tolerance.

[0242] When the number of iterations reaches the preset maximum number, but the convergence judgment condition has not yet been met, the optimization calculation module 40 terminates the current iteration and generates a non-converged flag.

[0243] After a candidate optimization subproblem meets the convergence criteria, the optimization calculation module 40 performs a feasibility check on the calculation results. Only when the calculation results meet the technical feature exit criteria, engineering feasibility criteria, and allowable adjustment range constraints are they recorded as feasible parameter adjustment schemes.

[0244] For calculation results that reach the maximum number of iterations but do not converge, they can only be marked as parameter adjustment schemes to be reviewed if the result satisfies all constraints and the constraint residuals are not greater than the preset feasibility tolerance. They are not directly determined as the preferred parameter adjustment schemes.

[0245] If all candidate optimization subproblems have no feasible solutions, the optimization calculation module 40 generates an identifier for an infeasible parameter adjustment scheme and records the constraints that lead to no solution, and does not output the preferred parameter offset vector to the value accounting module 50.

[0246] When a feasible parameter adjustment scheme exists, the optimization calculation module 40 determines that scheme as the preferred parameter offset vector. When multiple feasible parameter adjustment schemes exist, the optimization calculation module 40 compares the objective function values ​​of each scheme and determines the scheme with the smallest objective function value among all the calculated feasible schemes as the preferred parameter offset vector.

[0247] When the difference in the objective function of multiple schemes is no greater than the preset comparison tolerance, the optimization calculation module 40 can perform secondary sorting according to the increase in unit product cost, R&D cycle, number of parameter adjustments or user preset priority, and determine a preferred scheme; or it can retain multiple schemes to form a set of candidate parameter offset vectors.

[0248] The optimization calculation module 40 records the non-zero parameter adjustment amount, the related parameter adjustment amount, the corresponding technical feature exit conditions, the satisfied engineering constraints, and the objective function value in the preferred parameter offset vector, and sends the relevant data to the value accounting module 50 for use in engineering change operation matching and cost calculation.

[0249] See attached document Figure 6 In step S400, the value accounting module 50 converts the parameter adjustment scheme output by the optimization calculation module 40 into engineering change operation items, and calculates the unit product design avoidance cost based on the cost data corresponding to each engineering change operation item. The value accounting module 50 then adjusts the unit product design avoidance cost by considering the legal status and validity risk of the target patent, obtaining a reference value for the unit product patent value. Step S400 includes the following steps:

[0250] S401, the value accounting module 50 receives the preferred parameter offset vector output by the optimization calculation module 40. Extract the non-zero parameter adjustment amount, parameter adjustment direction, adjusted parameter value, and related parameter adjustment amount.

[0251] The value accounting module 50 converts the adjustment amounts of each parameter into corresponding engineering change operation items based on a preset parameter-engineering operation mapping table. The parameter-engineering operation mapping table records at least the parameter identifier, parameter adjustment direction, parameter adjustment range, engineering operation identifier, engineering operation content, related components, cost items, and dependencies between operations.

[0252] For example, when the electrical parameters of components are adjusted, the corresponding engineering change operations may include component model replacement, circuit parameter reconfiguration, and circuit board wiring adjustment; when the structural dimensions are adjusted, the corresponding engineering change operations may include component size modification, assembly position adjustment, and mold modification; when the material parameters are adjusted, the corresponding engineering change operations may include material replacement, process parameter adjustment, and performance verification.

[0253] Let the preferred parameter offset vector correspond to One engineering change operation item, For positive integers, the set of engineering change operations is represented as:

[0254] ;

[0255] in, Indicates a point in time The corresponding number One engineering change operation item.

[0256] When multiple parameter adjustments correspond to the same engineering change operation item, the value accounting module 50 merges duplicate engineering change operation items to avoid double-counting the cost of the same project. When a parameter adjustment needs to be completed by multiple related operations, the value accounting module 50 adds each related operation to the engineering change operation set according to the dependency relationship recorded in the parameter-engineering operation mapping table.

[0257] If there is no engineering change operation item corresponding to a certain parameter adjustment amount in the parameter-engineering operation mapping table, the value accounting module 50 generates a mapping data missing identifier and stops calculating the cost of the corresponding parameter adjustment scheme, or marks the parameter adjustment scheme as needing manual supplementation of engineering operation data.

[0258] When the optimization calculation module 40 outputs a set of candidate parameter offset vectors, the value accounting module 50 performs engineering change operation mapping and cost calculation on each candidate parameter offset vector, and retains the set of engineering change operations and cost calculation results corresponding to each candidate scheme.

[0259] S402, the value accounting module 50 accesses the engineering cost database through the data acquisition module 10, and obtains the cost data corresponding to each engineering change operation item according to the engineering operation identifier, associated components, cost items, data effective time and applicable product model.

[0260] The cost data in the engineering cost database includes at least R&D change investment, estimated R&D cycle, material unit price, material usage quantity, process adjustment costs, mold modification costs, testing and verification costs, production yield, and estimated allocated output. The value accounting module 50 performs currency conversion and price time unification for cost data from different sources according to preset currencies and price benchmark times.

[0261] For the j-th engineering change operation item, the value accounting module 50 calculates the R&D change cost allocated per unit product. Difference in material costs per unit product and unit product yield loss cost .

[0262] In one implementation, the R&D change cost allocated per unit product is expressed as:

[0263] ;

[0264] in, Indicates the first Each engineering change operation item corresponds to a one-time investment in R&D design, sample production, testing and verification, process debugging, or mold modification. This indicates the projected product output during the corresponding cost allocation period. Greater than zero.

[0265] The projected product output can be determined based on the projected output of the target product over its remaining product lifecycle, or based on the projected output during the cost amortization period adopted by the company. If the projected product output is zero or missing, the value accounting module 50 generates a cost allocation anomaly flag and does not perform division calculations.

[0266] The unit product material cost difference represents the difference in unit product material cost before and after parameter adjustment, and can be expressed as:

[0267] ;

[0268] in, Indicates execution of the first Unit product material cost after each engineering change operation item This represents the unit product material cost before the implementation of the engineering change operation item.

[0269] When the unit product material cost difference is negative, it indicates that the material cost after parameter adjustment is lower than the material cost before adjustment. When calculating the unit product design cost avoidance, the value accounting module 50 treats this negative value as zero; the negative amount can be recorded separately as material saving data, but it is not used to increase the unit product patent value reference value.

[0270] The unit product yield loss cost is determined based on the difference in production yield before and after parameter adjustment and the unit product production input. In one implementation, the unit product yield loss cost is expressed as:

[0271] ;

[0272] in, Indicates the first The cost of a single production input or the cost of a unit of materials corresponding to each engineering change operation item. This indicates the expected production yield after executing the engineering change operation. This represents the baseline production yield before implementing the engineering change operation. and The range of values ​​is all greater than zero and not greater than 1.

[0273] When an engineering change operation does not affect production yield, the corresponding unit product yield loss cost is set to zero. When production yield data is missing or exceeds the valid value range, the value accounting module 50 generates a data anomaly flag and does not use the abnormal data for cost calculation.

[0274] The value accounting module 50 summarizes the unit product cost corresponding to each engineering change operation item and obtains the time point. Avoid design costs per unit product , is represented as:

[0275] ;

[0276] Avoiding design costs per unit product This represents the estimated total of the R&D cost allocation, material cost changes, and yield loss costs per unit product after implementing engineering changes according to the current parameter adjustment plan.

[0277] When multiple engineering change operations share the same R&D, testing, mold, or process adjustment input, the value accounting module 50 merges the shared costs according to the cost item identifier, and then includes them in the unit product according to the preset allocation rules to avoid design costs and avoid double inclusion.

[0278] Each R&D, testing, mold, or process adjustment investment is assigned a unique cost item identifier, and the effective date, applicable time interval, and allocation period are recorded. If the same cost item has already been established and included in cost allocation in a previous time interval, the one-time investment will not be established again in subsequent time intervals; instead, the unit product allocation amount will be updated based on the remaining allocated production volume.

[0279] When multiple engineering change operations affect the same material item, the value accounting module 50 merges the material cost changes based on the material item identifier to avoid double-counting the cost difference of the same material.

[0280] When the optimization calculation module 40 outputs multiple candidate parameter offset vectors, the value accounting module 50 calculates the unit product avoidance design cost corresponding to each candidate scheme.

[0281] Without specifying other selection rules in advance, the value calculation module 50 sorts the candidate solutions from low to high according to the unit product avoidance design cost, and determines the candidate solution ranked first as the value calculation solution at the current time point.

[0282] When a user pre-specifies the R&D cycle, the number of parameter adjustments, implementation risks, or other priorities as selection criteria, the value calculation module 50 determines the value calculation scheme according to the corresponding selection rules.

[0283] The value accounting module 50 records the final value calculation scheme and its selection basis in the structured evaluation results.

[0284] S403, the valuation module 50 obtains the legal status data of the target patent at the valuation time from the patent legal status database, and determines the patent validity risk adjustment coefficient accordingly. .

[0285] Legal status data includes at least whether the patent right is valid, expired, terminated, or declared entirely invalid, and whether there are any invalidation proceedings for which no effective decision has yet been made. Legal status data is derived from publicly available data from the patent administrative authority or from a patent legal status database that is synchronized with that publicly available data.

[0286] Patent validity risk adjustment coefficient The value range is [0,1]. When the target patent is in a valid state... Determined based on legal status coefficient and patent validity score; when the target patent has expired, terminated, or been declared entirely invalid by an effective decision. Take 0.

[0287] when At that time, unit product avoids design cost reduction without effectiveness risk reduction; when When, the discount will be applied proportionally; when At that time, the corresponding time interval is not included in the cumulative economic value of the assessment period.

[0288] In one implementation, the patent validity risk adjustment coefficient is determined by the legal status coefficient. And patent validity score Determined jointly, and expressed as:

[0289] ;

[0290] When the target patent is in a valid state Take 1; when the target patent has expired, terminated, or been declared invalid in its entirety by an effective decision. Take 0.

[0291] Patent validity score The risk level can be determined based on one or more of the following: the proportion of patents granted within the same patent family, amendments to claims during the examination process, the results of invalidation proceedings, prior art search results, and patent citations. A higher standardized score for each indicator indicates a higher degree of credibility in maintaining the validity of the target patent. Each risk indicator is first converted into a standardized score between 0 and 1, and then weighted according to preset weights.

[0292] Assume to adopt The first risk indicator The standardized score for each risk indicator is The corresponding weight is If all weights are non-negative and the sum of the weights is 1, then the patent validity score is expressed as:

[0293] ;

[0294] The scoring rules and weights for each evaluation indicator are stored in the validity scoring configuration table. The validity scoring configuration table must record at least the indicator name, the original data range, the standardization rules, the indicator weights, and the data update time.

[0295] The number of citations can be used as a supplementary indicator, but not as the sole basis for judging the validity of a patent right. When there is insufficient data to calculate the patent validity score, the valuation module 50 uses a preset adjustment coefficient input by the user or outputs a risk data insufficiency indicator, without automatically presuming that the target patent has high or low validity.

[0296] When the legal status of a patent changes during the evaluation period, the valuation module 50 determines the patent validity risk adjustment coefficient for each time interval according to the time of the change in legal status.

[0297] S404, Value Accounting Module 50 adopts the cost avoidance reference method to avoid design costs per unit product. Adjustment coefficient for patent validity risk Multiply by this to obtain a reference value for the patent value per unit product. , is represented as:

[0298] ;

[0299] When the patent validity risk adjustment coefficient changes over time, the reference value of the patent value per unit product is expressed as follows:

[0300] ;

[0301] Reference value of patents per unit product This reference amount represents the effective risk-adjusted cost per unit of product for design avoidance, under current engineering changes, cost data, and patent legal status. This reference amount is not directly equivalent to actual licensing fees, licensing rates, damages, or realized revenue.

[0302] The value accounting module 50 sends the unit product patent value reference value, unit product design cost avoidance, engineering change operation set, cost details, patent validity risk adjustment coefficient and corresponding data time to step S500 for use in calculating the cumulative economic value during the evaluation period.

[0303] See attached document Figure 7In step S500, the value calculation module 50 calculates the cumulative economic value of the target patent during the evaluation period based on the projected sales data, the reference value of the patent value per unit product, and the patent's status. The module then organizes and outputs the calculation results and related process data. Step S500 includes the following steps:

[0304] S501, the value accounting module 50 accesses the market information database through the data acquisition module 10 to obtain the target product's effective evaluation period. One or more of the following: historical sales data, existing order data, production capacity planning data, and industry market data.

[0305] in, To assess the start time, The earliest of the following dates is considered: the expiration date of the target patent, the termination date of the patent, the evaluation deadline, or the expected date when the target product is no longer available for sale.

[0306] Historical sales data should include at least the product model, statistical time interval, statistical time granularity, sales volume, sales region, and data source. Industry market data may include industry sales change rate, market size change data for the target product's segment, and sales data for similar products.

[0307] The value accounting module 50 unifies the time base and statistical standards for sales data from different sources. When sales data is calculated on a daily, monthly, quarterly, or annual basis, the value accounting module 50 converts it into data corresponding to the time granularity used in the evaluation calculation.

[0308] When the target product has sufficient historical sales data, the value accounting module 50 generates the expected sales data for the evaluation period based on the historical sales data and the preset sales estimation method.

[0309] In one implementation, the value calculation module 50 can use a moving average method, an exponential smoothing method, an autoregressive integral moving average method, or a polynomial curve fitting method to process historical sales data. The specific method used, the input data time range, the calculation parameters, and the output time granularity are recorded in the sales estimation configuration table.

[0310] When using a polynomial curve fitting method, the value calculation module 50 determines the polynomial coefficients based on historical time points and corresponding sales volumes, and extends the fitting results to the evaluation period. The polynomial degree is determined by a preset configuration, or selected from multiple candidate degrees based on the fitting error.

[0311] When using the autoregressive integral moving average method, the value accounting module 50 determines the autoregressive order, difference order, and moving average order based on the historical sales series, and generates the expected sales data for the evaluation period based on the determined parameters.

[0312] When the target product has not yet been sold or historical sales data is insufficient, the value calculation module 50 selects alternative benchmark data from similar products, previous generation products, or products using similar technical solutions according to preset alternative data selection rules.

[0313] The rules for selecting alternative data include at least the similarity of product uses, the similarity of target customer groups, differences in price ranges, differences in sales regions, product life cycle stages, and the similarity of technical parameters. When multiple candidate products exist, the value calculation module 50 selects the candidate product with the highest similarity according to preset scoring rules, or performs weighted processing on the sales data of multiple candidate products.

[0314] When using alternative benchmark data, the value accounting module 50 adjusts the alternative benchmark data proportionally based on differences in market size, price, sales area, or product positioning between the target product and the alternative product, and records the source of the alternative data and the adjustment coefficient.

[0315] For projected sales data expressed in continuous time terms, the value accounting module 50 generates a projected sales rate function. . Indicates time The corresponding projected sales volume per unit time, and Not less than zero.

[0316] For discrete sales data provided on a monthly, quarterly, or other assessment period, the value accounting module 50 generates a projected sales sequence. .in, Indicates the first Projected sales volume for each evaluation period. Discrete sales data does not need to be converted to a continuous function.

[0317] When the sales estimate result is negative, the value calculation module 50 will adjust the corresponding negative value to zero, or output a sales estimate anomaly flag. When sales data is missing, it can be processed by interpolating adjacent time intervals, continuing the previous period's value, or using user-supplemented data. The processing method used is recorded in the calculation result.

[0318] S502, Value Calculation Module 50 obtains the reference value of the unit product patent value obtained in step S400. The falling state function obtained in step S200 And calculate the cumulative economic value for the assessment period by combining the projected sales data. .

[0319] For cases where the unit product patent value reference value, expected sales rate, and inclusion status are all expressed as continuous-time functions, the cumulative economic value calculated over the assessment period is expressed as follows:

[0320] ;

[0321] in, Indicates time The corresponding reference value of patent value per unit product. This indicates the estimated sales volume per unit of time. This indicates the state function that has fallen into the state.

[0322] when When the target product parameters at the corresponding time point do not simultaneously meet all the technical feature limitations included in the calculation of the target independent claim, the cumulative economic value increment at that time point is zero.

[0323] when At that time, the value accounting module 50 multiplies the reference value of the patent value per unit product by the expected sales volume per unit time, and includes the product in the cumulative economic value of the corresponding time interval.

[0324] When the patent validity risk adjustment coefficient, unit product avoidance design cost, or expected sales rate change during the assessment period, the valuation module 50 divides the assessment period into multiple time intervals according to the time of data change and calculates the economic value of each time interval separately. The calculation results of each time interval are added together to obtain the cumulative economic value of the assessment period.

[0325] For discrete data provided monthly, quarterly, or other assessment periods, the value accounting module 50 performs discrete summation according to each assessment period. The discrete calculation formula is expressed as:

[0326] ;

[0327] in, This indicates the number of assessment periods included in an effective assessment cycle. Indicates the first Reference value of unit product patent value for each evaluation period. Indicates the first Projected sales volume for the assessment period Indicates the first The corresponding state of each assessment period.

[0328] when When representing the sales rate per unit time rather than the total sales volume for the corresponding evaluation period, discrete calculations also include the length of the evaluation period. , is represented as:

[0329] ;

[0330] in, Indicates the first The duration of each assessment period, and its time unit is... The unit of time used remains consistent.

[0331] When the status changes within the same assessment period, the value calculation module 50 further divides the assessment period into multiple sub-periods according to the time of the status change, or allocates the expected sales volume of the assessment period according to the proportion of time when the status is 1.

[0332] When any data point is missing for a given evaluation period, such as the unit product patent value reference value, projected sales volume, or any status that falls within the specified range, the value calculation module 50 generates a data missing indicator. The value calculation module 50 can pause the calculation for that evaluation period or supplement the corresponding data according to preset data completion rules, but it needs to record the data completion method in the output results.

[0333] The cumulative economic value estimate for the assessment period represents the undiscounted nominal cumulative reference amount formed under the current technical feature assessment results, parameter adjustment plans, engineering cost data, patent legal status, and projected sales data. This value is not directly equivalent to actual licensing revenue, product sales profit, damages, or realized investment income.

[0334] S503, the value accounting module 50 summarizes the cumulative economic value calculation value during the assessment period and associates the time data, parameter data and cost data generated during the calculation process according to a unified time identifier.

[0335] The value accounting module 50 shall record at least the following data: the status of each assessment point or assessment period, the vector of technical feature parameters, the vector of preferred parameter offset, the engineering change operation items, the unit product design avoidance cost, the patent validity risk adjustment coefficient, the unit product patent value reference value, the expected sales volume, and the cumulative economic value.

[0336] When multiple candidate parameter adjustment schemes exist at the same time point, the value accounting module 50 records the parameter offset, engineering change operation items and unit product design cost avoidance corresponding to each candidate scheme, records the basis for determining the value calculation scheme, and marks the scheme selected for the cumulative economic value calculation during the evaluation period.

[0337] The value accounting module 50 organizes the aforementioned data in chronological order to form a structured evaluation result. The structured evaluation result includes at least the evaluation task identifier, target patent identifier, target product identifier, evaluation period, data source, calculation parameters, anomaly identifier, and calculation results at each time point or evaluation period.

[0338] To facilitate the verification of the calculation process, the value accounting module 50 can also record the sales data processing method, cost allocation method, risk indicator weight, time step, exit margin, and candidate solution selection rules.

[0339] S504, the value accounting module 50 sends the structured evaluation results to the display terminal, data storage device or external business system.

[0340] The display terminal can show the cumulative economic value calculated during the assessment period, the economic value of each assessment period, the changes in cumulative economic value over time, and the cost data corresponding to each parameter adjustment plan.

[0341] In one implementation, the display terminal includes a result display area, a cumulative economic value curve, a unit product design cost avoidance curve, and a parameter offset display area.

[0342] The cumulative economic value curve is plotted on the horizontal axis as time, and on the vertical axis as the cumulative economic value calculated up to the corresponding time. The unit product design cost avoidance curve is plotted on the horizontal axis as time, and on the vertical axis as the unit product design cost avoidance corresponding to each time point or evaluation period.

[0343] The parameter offset display area can use a parameter offset radar chart, a parameter change curve, or a parameter adjustment detail table. A parameter offset radar chart is used to display the adjustment amount of multiple technical feature parameters at the same point in time; a parameter change curve is used to display the change of the same technical feature parameter over time.

[0344] The display terminal can also display data missing indicators, mapping data missing indicators, cost allocation abnormality indicators, sales estimation abnormality indicators, and indicators of no feasible parameter adjustment schemes, so that users can determine whether there is data in the evaluation results that needs to be supplemented or reviewed.

[0345] The valuation module 50 can store or export structured evaluation results in a preset file format. The output results can serve as reference data for patent economic value analysis and licensing scheme comparison.

[0346] The following describes the processing procedure of the patent layout results market revenue assessment system, using electronic device heat dissipation components as the target product. The claim text, product parameters, engineering costs, legal status data, and sales data used in this application embodiment are all example data, used to illustrate the data processing relationship between steps S100 and S500.

[0347] The text of the independent claims of the target patent received by the data acquisition module 10 is as follows:

[0348] A heat dissipation component for an electronic device includes a substrate and a plurality of heat sinks disposed on the substrate. The spacing between the heat sinks is 1.8 mm to 2.4 mm, the height of the heat sinks is 18 mm to 24 mm, the airflow opening ratio formed by adjacent heat sinks is not less than 0.60, the heat sinks are made of aluminum alloy, and the heat sinks are integrally formed with the substrate.

[0349] The constraint parsing module 20 segments, normalizes, and identifies technical features of the independent claim text. The technical features obtained from the parsing are shown in Table 1.

[0350] Table 1:

[0351] Feature identifier Technical features Data types Qualifications unit P1 Heatsink spacing d Continuous parameters 1.8≤d≤2.4 mm P2 Heatsink height h Continuous parameters 18≤h≤24 mm P3 Duct opening ratio r Continuous parameters r≥0.60 Dimensionless C1 Heat sink material Category conditions aluminum alloy — C2 Connection method between heat sink and substrate Logical conditions One-piece molding —

[0352] The technical feature parameter vector corresponding to the numerical technical feature is represented as follows: ;

[0353] The defined regions for the three numerical technical features are as follows:

[0354] ;

[0355] Category condition C1 and logical condition C2 do not participate in numerical interval operations. The constraint parsing module 20 combines the three numerical interval judgment results, the material condition judgment result, and the connection method judgment result according to a logical "AND" relationship to form a Boolean expression:

[0356] B = A1∧A2∧A3∧C1∧C2;

[0357] Where A1, A2, and A3 represent the interval judgment results corresponding to the heat sink spacing, heat sink height, and air duct opening ratio, respectively. When the calculation result of Boolean expression B is true, the target product parameters satisfy all the technical feature limitations of this independent claim.

[0358] The data acquisition module 10 retrieves the product design parameters and engineering parameter constraints corresponding to the heat sink spacing, heat sink height, and air duct opening ratio from the engineering design database based on the parameter name mapping table. The allowable range of parameters recorded in the engineering design database is: heat sink spacing d is 1.5 mm to 2.8 mm, heat sink height h is 16 mm to 23 mm, and air duct opening ratio r is 0.55 to 0.70.

[0359] Based on the structural space and thermal performance requirements of the target product, the constraint analysis module 20 generates the following engineering constraints:

[0360] ;

[0361] ;

[0362] ;

[0363] The first engineering constraint limits the structural space occupied by the heat dissipation components; the second engineering constraint limits the combination of heat sink height, heat sink spacing, and airflow opening ratio to achieve the preset thermal performance requirements; the third engineering constraint limits the lower limit of the allowable airflow opening ratio. Each constraint coefficient is determined based on the structural design data and thermal simulation results of the target product.

[0364] The corresponding feasible region is represented as:

[0365] ;

[0366] The evaluation in this application embodiment begins on January 1, 2026, and ends on December 31, 2031. The data acquisition module 10 obtains the corresponding parameter values ​​for each year from the product design version data and product iteration plan. The parameter changes are shown in Table 2.

[0367] Table 2:

[0368] Time point Heatsink spacing d / mm Heatsink height h / mm Duct opening ratio r Material Connection method January 1, 2026 2.6 17 0.58 copper brazing January 1, 2027 2.5 18 0.6 aluminum alloy One-piece molding January 1, 2028 2.4 19 0.62 aluminum alloy One-piece molding January 1, 2029 2.3 20 0.63 aluminum alloy One-piece molding January 1, 2030 2.2 21 0.64 aluminum alloy One-piece molding January 1, 2031 2.1 22 0.65 aluminum alloy One-piece molding

[0369] The data corresponding to January 1, 2026, comes from the current design baseline of the target product; the data after January 1, 2027, comes from the product iteration plan and is recorded as expected parameter change data.

[0370] The time series evaluation module 30 compares the technical characteristic parameters of each time point with the technical characteristic defined area according to the annual time step, and combines the judgment results according to Boolean expressions.

[0371] On January 1, 2026, the heatsink spacing, heatsink height, airflow opening ratio, materials, and connection methods all failed to meet the corresponding limiting conditions simultaneously, therefore the state was 0.

[0372] On January 1, 2027, the heatsink height, airflow opening ratio, materials, and connection methods meet the corresponding constraints, but the heatsink spacing is 2.50 mm, which exceeds the upper limit of the closed boundary of 2.40 mm, so the state of falling into the limit is still 0.

[0373] On January 1, 2028, the heatsink spacing was 2.40 mm, the heatsink height was 19 mm, the airflow opening ratio was 0.62, the heatsink material was aluminum alloy, and the heatsink and the substrate were integrally molded. All the technical feature constraints were met, so the state changed from 0 to 1.

[0374] All time points from 2029 to 2031 also meet all the technical feature constraints, and the corresponding state is 1.

[0375] The time series evaluation module 30 records January 1, 2028 as the time of the state change and sends the corresponding technical feature parameter vector to the optimization calculation module 40. This parameter vector is represented as:

[0376] ;

[0377] The optimization calculation module 40 uses the technical feature parameter vector corresponding to January 1, 2028 as the initial parameters for the constraint optimization calculation. The reference scales for the heat sink spacing, heat sink height, and air duct opening ratio are 0.60 mm, 6.00 mm, and 0.15, respectively, and the corresponding parameter change cost weights are 0.40, 0.35, and 0.25, respectively.

[0378] The weight corresponding to the spacing of the heat sinks is determined based on the cost of mold adjustment, the weight corresponding to the height of the heat sinks is determined based on the structural modification and verification cycle, and the weight corresponding to the air duct opening ratio is determined based on the difficulty of adjusting the air duct structure.

[0379] For closed boundaries, the optimization calculation module 40 sets the heat sink spacing margin to 0.05 mm, the heat sink height margin to 0.10 mm, and the air duct opening ratio margin to 0.01, and establishes the adjustment schemes for the three candidate parameters in Table 5 below.

[0380] Table 3:

[0381] Candidate solutions Adjusted parameters Parameter offset Cost of weighted parameter change Project feasibility Option A d=2.45mm Δd = +0.05mm 0.0028 satisfy Option B h=17.90mm Δh = −1.10 mm 0.0118 satisfy Option C r=0.59 Δr = −0.03 0.01 satisfy

[0382] Option A adjusts the heatsink spacing from 2.40 mm to 2.45 mm. The adjusted heatsink spacing exceeds the upper limit of the closed boundary and reaches the exit margin, therefore the technical feature constraint conditions corresponding to the heatsink spacing no longer apply.

[0383] The adjusted parameter vector corresponding to scheme A is:

[0384] ;

[0385] Substituting scheme A into the engineering constraints, we get:

[0386] =19+4×2.45−31.5=−2.7;

[0387] =14.0−(0.8×19+10×0.62−2×2.45)=−2.5;

[0388] =0.55−0.62=−0.07;

[0389] Since the calculated results of all engineering constraint functions are not greater than 0, scheme A lies within the feasible region of the project. The normalized parameter adjustment for scheme A is:

[0390] ;

[0391] The corresponding cost of changing the weighted parameters is:

[0392] =0.40×(0.0833) 2 ≈0.0028;

[0393] The optimization calculation module 40 calculates the weighted parameter change costs for schemes B and C in the same way. All three candidate schemes meet the technical feature exit conditions and engineering feasibility conditions. Scheme A has the lowest weighted parameter change cost, so scheme A is determined as the preferred parameter adjustment scheme.

[0394] The preferred parameter offset vector is represented as:

[0395] ;

[0396] Based on the parameter-engineering operation mapping table, the value accounting module 50 converts the increase of 0.05 mm in heat sink spacing into the following engineering change operation item:

[0397] The first engineering change operation item is to adjust the spacing between adjacent forming teeth in the heat sink extrusion die and complete the trial molding and dimensional verification; the second engineering change operation item is to update the product structure drawings and re-execute the air duct simulation and thermal performance verification.

[0398] The cost data corresponding to each engineering change operation item is shown in Table 4.

[0399] Table 4:

[0400] Engineering Change Operation Items One-time investment Expected production allocation Unit R&D cost allocation Unit additional material cost Unit yield loss cost Mold Adjustment and Verification 180,000 yuan 120,000 units 1.50 yuan / set 0.35 yuan / set 0.61 yuan / set Drawing updates and thermal performance verification 60,000 yuan 120,000 units 0.50 yuan / set 0 yuan / set 0 yuan / set

[0401] The baseline production yield corresponding to mold adjustment and verification is 0.97, and the adjusted expected production yield is 0.95, with a unit material cost of 28 yuan. The unit yield loss cost is calculated as follows:

[0402] ;

[0403] Drawing updates and thermal performance verification do not change the material composition or production yield, and the unit cost of additional materials and the unit cost of yield loss are both taken as 0.

[0404] The unit product cost avoidance design is calculated as follows:

[0405] D = 1.50 + 0.35 + 0.61 + 0.50 = 2.96 yuan / unit;

[0406] The value accounting module 50 sets cost item identifiers for mold adjustment input and verification input, and records the cost effective time, applicable time interval and expected allocated output to avoid the same one-time input being repeatedly included in subsequent evaluation periods.

[0407] The data acquisition module 10 retrieves the legal status data of the target patent from the patent legal status database. Within the effective evaluation period of this application embodiment, the target patent is in a valid state; therefore, the legal status coefficient... Take 1.

[0408] The value accounting module 50 selects three effectiveness evaluation indicators: patent family authorization status, examination and claim amendment status, and prior art search results. The scores and weights of each indicator are shown in Table 5.

[0409] Table 5:

[0410] Evaluation indicators Standardized scoring Weight Patent family authorization status 0.9 0.35 Examination and amendments to claims 0.75 0.35 Existing technology search results 0.8 0.3

[0411] The patent validity score is calculated as follows:

[0412] =0.90×0.35+0.75×0.35+0.80×0.30=0.8175;

[0413] The risk adjustment factor for the validity of the patent right is:

[0414] = · =1×0.8175=0.8175;

[0415] The reference value for patents per unit product is:

[0416] = D = 0.8175 × 2.96 = 2.4198 yuan / unit;

[0417] The value calculation module 50 retains the unrounded unit product patent value reference value for subsequent cumulative calculations, and can retain two decimal places when displaying it externally.

[0418] This application example uses annual discrete sales data. The inclusion status for 2026 and 2027 is 0, and is not included in the cumulative economic value for the assessment period; the inclusion status for 2028 to 2031 is 1. The projected sales volume and corresponding economic value for each year are shown in Table 6.

[0419] Evaluation period <![CDATA[Fall into state I n > <![CDATA[Expected sales volume Q n / unit]]> Reference value of patents per unit product / yuan Economic value per period / yuan 2028 1 80000 2.4198 193584 2029 1 100000 2.4198 241980 2030 1 120000 2.4198 290376 2031 1 90000 2.4198 217782

[0420] The cumulative economic value over the assessment period is calculated using a discrete summation method:

[0421] ;

[0422] Substituting the data from each year, we get:

[0423] =193584+241980+290376+217782=943722 yuan;

[0424] Therefore, the cumulative economic value calculated for the assessment period obtained in this application embodiment is RMB 943,722. This value is an undiscounted nominal cumulative reference amount formed based on the judgment results of the example technical features, parameter adjustment scheme, engineering costs, patent validity risk adjustment coefficient, and expected sales data.

[0425] The value accounting module 50 will generate a structured evaluation result by combining the cumulative economic value of the evaluation period, the corresponding entry status, the offset vector of the preferred parameters, the engineering change operation items, the unit product avoidance design cost, and the unit product patent value reference value for each evaluation period, and send it to the display terminal, data storage device, or external business system.

[0426] The electronic device heat dissipation components, number of technical features, parameter values, engineering constraint functions, parameter change cost weights, cost data, effectiveness evaluation indicators, sales data, and time granularity used in this application embodiment are all used to illustrate the system's processing from data input to result output, and do not constitute a limitation on other product types, parameter forms, calculation methods, or data sources.

[0427] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for calculating and evaluating the market benefits of patent layout achievements, characterized in that, Includes the following steps: Obtain the independent claims text of the target patent, as well as the engineering design data, engineering cost data, patent legal status data, and expected sales data of the target product. The engineering design data includes initial technical feature parameters, parameter change data, and engineering parameter constraint relationships. The independent claim text is analyzed, and the technical feature limiting conditions that can be expressed numerically are converted into numerical intervals corresponding to the technical feature parameters. Based on each numerical interval, a technical feature limiting region is formed, and the technical feature limiting conditions that cannot be directly expressed numerically are converted into category conditions or logical judgment conditions. Based on the constraints of engineering parameters, determine the engineering feasible domain corresponding to the combination of target product parameters, and based on the initial technical characteristic parameters and parameter change data, determine the technical characteristic parameters corresponding to each time point within the effective evaluation period; The technical feature parameters corresponding to each time point are compared with the technical feature defined area, and combined with category conditions and logical judgment conditions, a falling state function or falling state sequence is generated. For the time point where the value of the falling state function is 1 or the value of the corresponding falling state in the falling state sequence is 1, calculate the parameter adjustment scheme within the engineering feasible region that makes the target product no longer meet all technical feature constraints and reduces the cost of weighted parameter changes, and obtain the preferred parameter offset vector. According to the parameter-engineering operation mapping relationship, the preferred parameter offset vector is converted into an engineering change operation item. The unit product design avoidance cost is calculated based on the engineering cost data. The unit product design avoidance cost is adjusted based on the patent legal status data to obtain the unit product patent value reference value. Based on the time correspondence, the cumulative economic value of the assessment period is generated according to the reference value of the unit product patent value, the expected sales volume data, and the state function or state sequence.

2. The method for calculating and evaluating the market benefits of patent layout results according to claim 1, characterized in that, The analytical independent claim text includes: The independent claims text is segmented and its characters are standardized. Based on the technical terminology dictionary, unit dictionary, comparison relation dictionary and sentence pattern matching rules, extract the technical feature name, parameter value, unit of measurement, comparison relation and the combination relationship between technical features; Technical features that can take values ​​within a continuous numerical range are marked as continuous parameters, while technical features that can only take integer values ​​or preset discrete values ​​are marked as discrete numerical parameters. Convert material type, connection method, relative position of components, or other technical features that cannot be directly represented by numerical ranges into category conditions or logical judgment conditions; Based on the parallel, selection, inclusion, or conditional relationships between technical features, generate Boolean expressions for combining numerical range judgment results, category condition judgment results, and logical condition judgment results.

3. The method for calculating and evaluating the market benefits of patent layout results according to claim 1, characterized in that, Forming the defined region of the technical features and determining the engineering feasible domain includes: Based on the comparison relationship, parameter value and boundary type of each technical feature parameter, determine the parameter value range corresponding to each technical feature parameter and form a technical feature boundary data table. The technical feature boundary data table includes parameter identifier, parameter name, unit of measurement, lower limit value, upper limit value, lower limit boundary type and upper limit boundary type. For technical feature parameters without upper or lower limits, record the absence of upper or lower limit identifiers in the technical feature boundary data table. Perform parameter unit checks, upper and lower limit relationship checks, boundary type integrity checks, and numerical limit conflict checks on the technical feature boundary data table; Based on parameter identifiers, parameter names, synonym mapping relationships, and units of measurement, the technical feature parameters are mapped to the product design parameters in the engineering design database; The engineering parameter constraints are converted into equality conditions, inequality conditions, or parameter linkage conditions to form a set of engineering constraint conditions, and the feasible region of the project is determined based on the set of engineering constraint conditions. Save the project constraints corresponding to the category parameters or logical parameters as additional project logical conditions.

4. The method for calculating and evaluating the market benefits of patent layout results according to claim 1, characterized in that, Determining the technical characteristic parameters corresponding to each time point and generating the falling state function or falling state sequence includes: For technical characteristic parameters that change continuously over time, an evolution function for the technical characteristic parameters is generated based on the initial technical characteristic parameters and the rate of change of the parameters; For technical feature parameters that change according to monthly, quarterly, product version or process node, a time series of technical feature parameters is generated based on the parameter adjustment amount or parameter change rate corresponding to each time node; For the evolution function of technical feature parameters that can be analytically determined to identify the boundary intersection points, the time when the technical feature parameters enter and leave the defined region of the technical feature is determined based on the intersection points between the evolution function of the technical feature parameters and the boundary of the parameter value range. For technical feature parameter evolution functions or technical feature parameter time series that cannot be directly solved analytically, extract the technical feature parameters at each sampling time point according to the preset time step, and perform numerical interval judgment, category condition judgment and logical condition judgment. When the judgment results of adjacent sampling time points are inconsistent, the technical feature parameters are close to the boundary of the parameter value range, or the evolution function of the technical feature parameters has extreme points between adjacent sampling time points, the time step of the corresponding time interval is reduced, or the corresponding time interval is recursively subdivided. When the result of the numerical range judgment, the category condition judgment, and the logical condition judgment is true after being combined by a Boolean expression, the corresponding state is assigned the value 1; when the result of the combination is false, the corresponding state is assigned the value 0. When necessary technical feature parameters are missing and cannot be calculated based on engineering parameter constraints, a missing judgment result flag is generated, but the corresponding time point's fall-in status is not generated.

5. The method for calculating and evaluating the market benefits of patent layout results according to claim 1, characterized in that, The calculation of the parameter adjustment scheme includes: Determine the parameter adjustment amount corresponding to each technical feature parameter; Based on the allowable adjustment range, design tolerance, current parameter value, or preset reference value of each technical feature parameter, the adjustment amount of each parameter is normalized. Determine the weight of parameter change cost based on the design change cost, expected R&D cycle, manufacturing adjustment difficulty, or preset weight table corresponding to each technical feature parameter. Based on the parameter adjustment amount after scale normalization and the parameter change cost weight, determine the weighted parameter change cost corresponding to each parameter adjustment scheme; The adjusted technical feature parameters no longer simultaneously satisfy all numerical range conditions, category conditions, and logical judgment conditions, and are used as the technical feature exit condition. The adjusted technical feature parameters are located within the engineering feasible domain and satisfy the additional engineering logic conditions, and are used as engineering feasible conditions. The adjustment amount of each technical feature parameter is within the corresponding allowable adjustment range, which is used as the parameter adjustment range condition.

6. The method for calculating and evaluating the market benefits of patent layout results according to claim 1, characterized in that, The obtained preferred parameter offset vector includes For technical feature parameters with closed boundaries, a zero exit margin is set outside the closed boundary, and the exit conditions for the technical feature are formed based on the exit margin. For each adjustable technical feature parameter and its exit direction, candidate optimization sub-problems are established. Each candidate optimization sub-problem includes at least one technical feature exit condition, engineering feasibility condition, and parameter adjustment range condition. For continuous parameters, sequential quadratic programming or interior point methods are used to solve candidate optimization subproblems; for discrete numerical parameters or category conditions, a combination of candidate value enumeration and continuous parameter optimization is used, or optimization methods with integer constraints are used to solve candidate optimization subproblems. For the same candidate optimization subproblem, set multiple initial values ​​for parameter offsets and perform iterative calculations for each; When the constraint residual, the difference in the cost of weighted parameter change between two adjacent iterations, or the change in parameter offset meets the preset convergence condition, the iteration is terminated and the feasibility of the calculation results is checked. The parameter offset vector with the lowest weighted parameter change cost among the parameter adjustment schemes that have passed the feasibility check is determined as the preferred parameter offset vector. When the weighted parameter change cost difference among multiple parameter adjustment schemes is not greater than the preset comparison tolerance, the preferred scheme is determined according to the unit product cost increase, R&D cycle, number of parameter adjustments, or preset priority, or a set of candidate parameter offset vectors is formed.

7. The method for calculating and evaluating the market benefits of patent layout results according to claim 1, characterized in that, The calculated unit product cost avoidance design cost estimate includes: Based on the dependencies between parameter identifiers, parameter adjustment directions, parameter adjustment ranges, engineering operation identifiers, engineering operation content, related components, cost items, and engineering change operation items, the preferred parameter offset vector is converted into an engineering change operation item. Merge duplicate engineering change operation items corresponding to multiple parameter adjustment amounts, and add related engineering change operation items based on the dependencies between the engineering change operation items; Based on the engineering operation identifier, associated components, cost items, data effective time and applicable product model, obtain the R&D change input, material cost, production yield and expected allocated output from the engineering cost database; Calculate the R&D change cost allocated per unit product based on the R&D change investment and the expected allocated output. Calculate the difference in unit product material cost based on the unit product material cost before and after parameter adjustment; Calculate the unit product yield loss cost based on the production yield and unit material cost before and after parameter adjustment; The unit product's R&D change costs, non-negative unit product material cost differences, and unit product yield loss costs are summarized to obtain the unit product design avoidance cost estimate. By using cost item identifiers and material item identifiers, multiple engineering change operation items can be combined to represent changes in one-time input or material costs.

8. The method for calculating and evaluating the market benefits of patent layout results according to claim 7, characterized in that, The adjustment of the unit product design avoidance cost calculation value based on patent legal status data includes: Determine the legal status coefficient based on the legal status of the target patent; The patent validity score is determined based on one or more of the following: patent family grant status, claim amendments during the examination process, invalidation proceedings, prior art search results, and patent citations. The patent validity risk adjustment coefficient is determined based on the legal status coefficient and the patent validity score. By using the patent validity risk adjustment coefficient to proportionally adjust the unit product design cost avoidance calculation value, a reference value of the unit product patent value is obtained. When the target patent has expired, terminated, or been declared invalid by an effective decision, the legal status coefficient is set to 0. When data for determining the validity score of a patent right is lacking, a preset adjustment coefficient or a risk data insufficiency indicator is generated.

9. The method for calculating and evaluating the market benefits of patent layout results according to claim 8, characterized in that, The cumulative economic value calculated during the assessment period includes: For projected sales data expressed in continuous time form, time accumulation calculation is performed based on the unit product patent value reference value, projected sales rate, and falling into the state function corresponding to each time point within the effective evaluation period. For projected sales data provided on a monthly, quarterly or other assessment period basis, discrete cumulative calculation is performed based on the unit product patent value reference value, projected sales and the sequence of statuses for each assessment period. When the patent validity risk adjustment coefficient, the unit product avoidance design cost calculation value, or the expected sales volume data change within the effective evaluation period, the effective evaluation period is divided into multiple time intervals according to the time of data change, and the economic value corresponding to each time interval is calculated separately. When the status of falling into the assessment changes within the same assessment period, the corresponding assessment period is divided into multiple sub-periods, or the expected sales volume of the corresponding assessment period is allocated according to the proportion of time when the status of falling into the assessment is 1. By summarizing the economic value corresponding to each point in time, each time interval, or each assessment period, the cumulative economic value for the assessment period is obtained.

10. A system for calculating and evaluating the market benefits of patent layout achievements, characterized in that, It includes a data acquisition module, a constraint parsing module, a time series evaluation module, an optimization calculation module, and a value accounting module; The data acquisition module is used to acquire the independent claim text of the target patent, as well as the engineering design data, engineering cost data, patent legal status data and expected sales data of the target product. The engineering design data includes initial technical feature parameters, parameter change data and engineering parameter constraint relationships. The constraint parsing module is used to parse the text of the independent claims, convert the technical feature limiting conditions that can be expressed numerically into numerical intervals corresponding to the technical feature parameters, form a technical feature limiting region based on each numerical interval, convert the technical feature limiting conditions that cannot be directly expressed numerically into category conditions or logical judgment conditions, and determine the engineering feasible domain corresponding to the combination of target product parameters according to the engineering parameter constraint relationship. The time series evaluation module is used to determine the technical feature parameters corresponding to each time point within the effective evaluation period based on the initial technical feature parameters and parameter change data. It compares the technical feature parameters corresponding to each time point with the technical feature defined area, and generates a falling state function or falling state sequence by combining category conditions and logical judgment conditions. The optimization calculation module is used to calculate, within the engineering feasible domain, the parameter adjustment scheme that makes the target product no longer meet all technical feature constraints and reduces the cost of weighted parameter changes for the time point when the value of the falling state function is 1 or the value of the corresponding falling state in the falling state sequence is 1, and obtain the preferred parameter offset vector. The value accounting module is used to convert the preferred parameter offset vector into engineering change operation items according to the parameter-engineering operation mapping relationship, calculate the unit product design avoidance cost estimate based on engineering cost data, adjust the unit product design avoidance cost estimate based on patent legal status data to obtain the unit product patent value reference value, and generate the cumulative economic value estimate for the evaluation period according to the time correspondence relationship, based on the unit product patent value reference value, expected sales data, and the state function or state sequence.

Images