Method, apparatus, and non-temporary computer-readable storage medium for determining the strength of identifier records

Abstract

A method, apparatus, and computer-readable storage medium comprising: generating an identifier record group from a database, the identifier record group including an identifier record and at least one other identifier record; generating one or more strength indicators for the generated identifier record group; normalizing a value of each of the one or more strength indicators based on an average value associated with the respective strength indicator; generating a value of a strength metric for the generated identifier record group based on a sum of the normalized values ​​of the one or more strength indicators; ranking the value of the strength metric among values ​​of the strength metric associated with a plurality of identifier record groups in the database; and determining a strength score for the generated identifier record group as a strength score for the identifier record based on the ranking.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit of U.S. Patent Application No. 17 / 680,534, filed Feb. 25, 2022, which claims the benefit of U.S. Provisional Application No. 63 / 154,510, filed Feb. 26, 2021. The entire disclosure of the prior applications is incorporated herein by reference in their entirety for all purposes.

[0002] This disclosure relates to a method for determining the strength of identifier records.

Background Art

[0003] Description of Related Art As the global intellectual property (IP) and innovation landscape changes rapidly, an organization's ability to utilize "smart IP data" has become increasingly important. However, for this purpose, analysis and visualization are hampered by the quality and breadth of the underlying data. Therefore, certain derived metrics of IP data can function as useful analogs that enable concise and efficient data visualization.

[0004] Regarding these derived metrics and other unique benchmarks, little progress has been made with respect to those related to trademarks. Some efforts, such as a so - called top - down approach, have been made to evaluate the value of trademarks, but there is no objective scale to evaluate the value of trademarks from scratch. The zero - to - one approach, in one aspect, considers the foundation of a trademark as a derived metric of the trademark. However, at present, such an approach does not exist.

[0005] The above description of the "Background Art" is intended to present the context of the present disclosure generally. The work of the inventors is not, in the scope described in this "Background Art" section, either expressly or implicitly admitted as prior art to the present invention, in the same way as aspects of the description that may not be admitted as prior art at the time of filing. [Overview of the project] [Means for solving the problem]

[0006] This disclosure includes a method for determining the intensity score of an identifier record. The method generates an identifier record group from a database, which includes an identifier record and at least one other identifier record. Each identifier record in the identifier record group is associated with the same brand. One or more intensity indicators are generated for the generated identifier record group. One or more intensity indicators include at least one of the following: a continuity indicator, a market indicator, a jurisdiction scope indicator, or a distinction indicator. The value of each of the one or more intensity indicators is normalized based on the mean value associated with each intensity indicator. The mean value associated with each intensity indicator is determined based on multiple identifier record groups in the database, which include the generated identifier record group. The intensity metric value for the generated identifier record group is generated based on the sum of the normalized values ​​of one or more intensity indicators in the generated identifier record group. The intensity metric value for the generated identifier record group is ranked among the intensity metric values ​​associated with multiple identifier record groups in the database. Based on the ranking, the intensity score for the generated identifier record group is determined as the intensity score for the identifier record.

[0007] In one embodiment, an improvement recommendation for at least one of the strength indicators of the generated identifier record group is determined based on the fact that the strength score of the generated identifier record group is less than the strength score of the highest-ranked identifier record group among the multiple identifier record groups.

[0008] In one embodiment, improvement suggestions are displayed by a user interface of an electronic device that is connected to a server via a network.

[0009] In one embodiment, the identifier record is determined based on input from an electronic device.

[0010] In one embodiment, the input is a text string or an image.

[0011] In one embodiment, the continuity indicator is the average number of years of trademark registration associated with the generated identifier record group.

[0012] In one embodiment, the market indicator is at least one of the economic footprint of the generated identifier record group or the industry footprint of the generated identifier record group.

[0013] In one embodiment, the jurisdiction scope indicator is the number of registered jurisdictions normalized by the total number of jurisdictions in the database. The trademark associated with the generated identifier record group is registered in that number of registered jurisdictions.

[0014] In one embodiment, the distinguishing indicator is the distinguishability of a representative identifier record in the generated identifier record group.

[0015] In one embodiment, the economic footprint of the generated identifier record group is determined by weighting the number of registered jurisdictions based on the respective gross domestic product of each registered jurisdiction.

[0016] In one embodiment, the industry footprint of the generated identifier record group is determined by calculating the market share of the generated identifier record group (where market share is defined as the percentage of the Nice classification classes occupied by the trademark associated with the generated identifier record group within a given jurisdiction), and further by calculating the number of different Nice classification classes for which the trademark associated with the generated identifier record group is registered within a given jurisdiction.

[0017] In one embodiment, a representative identifier record is associated with a text string. The distinctiveness of the representative identifier record in the generated identifier record group is determined by weighting the representative identifier record by the length of the associated text string, and further by calculating a string similarity measure of the associated text string to other text strings associated with other identifier records in the database, where the other identifier records in the database are identifier records not associated with the generated identifier record group.

[0018] This disclosure includes an apparatus for determining the intensity score of identifier records. The apparatus includes a processing circuit that generates an identifier record group from a database, the identifier record and at least one other identifier record, each identifier record in the identifier record group being associated with the same brand. The processing circuit generates one or more intensity indicators for the generated identifier record group. The one or more intensity indicators include at least one of the following: continuity indicators, market indicators, jurisdiction scope indicators, or distinction indicators. The processing circuit normalizes the value of each of the one or more intensity indicators based on the mean value associated with each intensity indicator. The mean value associated with each intensity indicator is determined based on multiple identifier record groups in the database, which include the generated identifier record group. The processing circuit generates an intensity metric value for the generated identifier record group based on the sum of the normalized values ​​of the one or more intensity indicators for the generated identifier record group. The processing circuit ranks the intensity metric value for the generated identifier record group among the intensity metric values ​​associated with multiple identifier record groups in the database. Based on the ranking, the processing circuit determines the intensity score for the generated identifier record group as the intensity score for the identifier record.

[0019] This disclosure includes a non-temporary computer-readable storage medium for storing computer-readable instructions, which, when executed by a computer to determine the intensity score of an identifier record, cause the computer to: generate an identifier record group from a database, comprising an identifier record and at least one other identifier record, wherein each identifier record in the identifier record group is associated with the same brand; generate one or more intensity indicators for the generated identifier record group, wherein one or more intensity indicators comprises at least one of a continuity indicator, a market indicator, a jurisdiction scope indicator, or a distinction indicator; normalize the value of each of the one or more intensity indicators based on the mean value associated with each intensity indicator, wherein the mean value associated with each intensity indicator is determined based on a plurality of identifier record groups in the database comprising the generated identifier record group; generate an intensity metric value for the generated identifier record group based on the sum of the normalized values ​​of one or more intensity indicators for the generated identifier record group; rank the intensity metric value for the generated identifier record group among the intensity metric values ​​associated with the plurality of identifier record groups in the database; and determine, based on the ranking, the intensity score of the generated identifier record group as the intensity score of the identifier record.

[0020] The preceding paragraph is provided as a general introduction and is not intended to limit the scope of the following claims. The embodiments described, along with their further advantages, will be best understood by referring to the following detailed description in conjunction with the accompanying drawings. This specification also provides, for example, the following: (Item 1) A device that is communicably coupled to an electronic device via a network, wherein the device is It includes a processing circuit, and the processing circuit is To generate an identifier record group from a database, which includes an identifier record and at least one other identifier record, wherein each identifier record in the identifier record group is associated with the same brand. To generate one or more intensity indicators for the generated identifier record group, wherein the one or more intensity indicators include at least one of a continuity indicator, a market indicator, a jurisdiction scope indicator, or a distinction indicator. Normalizing the value of each of the one or more intensity indicators based on the average value associated with each of the intensity indicators, wherein the average value associated with each of the intensity indicators is determined based on a plurality of identifier record groups in the database, including the generated identifier record group. The intensity metric value of the generated identifier record group is generated based on the sum of the normalized values ​​of one or more intensity indicators in the generated identifier record group, The value of the strength metric of the generated identifier record group is ranked among the strength metric values ​​associated with the plurality of identifier record groups in the database. A device configured to determine, based on the ranking, the strength score of the generated identifier record group as the strength score of the identifier record. (Item 2) The aforementioned processing circuit The apparatus according to item 1, further configured to determine an improvement recommendation for at least one of the one or more strength indicators of the generated identifier record group, based on the fact that the strength score of the generated identifier record group is less than the strength score of the highest-ranked identifier record group among the plurality of identifier record groups. (Item 3) The apparatus described in item 2, wherein the aforementioned improvement suggestions are displayed by the user interface of the electronic device. (Item 4) The apparatus described in item 1, wherein the identifier record is determined based on input from the electronic device. (Item 5) The apparatus described in item 4, wherein the input is a text string or an image. (Item 6) The one or more intensity indicators described above The average number of years of trademark registration associated with the generated identifier record group, as the continuity indicator, The market indicator is at least one of the economic footprint of the generated identifier record group or the industry footprint of the generated identifier record group, The number of registered jurisdictions, normalized by the total number of jurisdictions in the database, as the jurisdictional scope indicator, and the number of registered jurisdictions in which the trademark associated with the generated identifier record group is registered, The apparatus according to item 1, comprising, as a distinguishing indicator, the distinguishability of a representative identifier record of the generated identifier record group. (Item 7) The economic footprint of the generated identifier record group is The apparatus described in item 6, determined by weighting the number of registered jurisdictions based on the respective gross domestic product of each of the registered jurisdictions. (Item 8) The industry footprint of the generated identifier record group is Calculating the market share of the generated identifier record group, wherein the market share is defined as the percentage of the Nice classification class occupied by the trademark associated with the generated identifier record group within a given jurisdiction, and The apparatus according to item 6, wherein the trademark associated with the generated identifier record group is determined by calculating the number of different classes of the Nice classification that are registered within the given jurisdiction. (Item 9) The representative identifier record is associated with a text string, and the distinctiveness of the representative identifier record in the generated identifier record group is The representative identifier record is weighted according to the length of the associated text string, and The apparatus according to item 6, which calculates a string similarity measure of the associated text string to other text strings associated with other identifier records in the database, the other identifier records in the database being identifier records not associated with the generated group of identifier records. (Item 10) A method for determining the strength score of an identifier record, The server's processing circuit and the database generate identifier record groups, which include identifier records and at least one other identifier records, wherein each identifier record in the identifier record group is associated with the same brand. The processing circuit of the server generates one or more intensity indicators for the generated identifier record group, wherein the one or more intensity indicators include at least one of a continuity indicator, a market indicator, a jurisdiction scope indicator, or a distinction indicator. The processing circuit of the server normalizes the value of each of the one or more intensity indicators based on the average value associated with each of the intensity indicators, wherein the average value associated with each of the intensity indicators is determined based on a plurality of identifier record groups in the database, including the generated identifier record group. The processing circuit of the server generates a value for the intensity metric of the generated identifier record group based on the sum of the normalized values ​​of one or more intensity indicators of the generated identifier record group, The processing circuit of the server ranks the value of the strength metric of the generated identifier record group among the strength metric values ​​associated with the plurality of identifier record groups in the database. The processing circuit of the server determines, based on the ranking, the strength score of the generated identifier record group as the strength score of the identifier record. A method that includes the act of doing something. (Item 11) The method according to item 10, further comprising the processing circuit of the server determining an improvement recommendation for at least one of the one or more strength indicators of the generated identifier record group based on the fact that the strength score of the generated identifier record group is less than the strength score of the highest-ranked identifier record group among the plurality of identifier record groups. (Item 12) The method according to item 11, wherein the improvement suggestions are displayed by the user interface of an electronic device that is connected to the server via a network. (Item 13) The method of item 10, wherein the identifier record is determined based on input from an electronic device. (Item 14) The method according to item 13, wherein the input is a text string or an image. (Item 15) The one or more intensity indicators described above The average number of years of trademark registration associated with the generated identifier record group, as the continuity indicator, The market indicator is at least one of the economic footprint of the generated identifier record group or the industry footprint of the generated identifier record group, The number of registered jurisdictions, normalized by the total number of jurisdictions in the database, as the jurisdictional scope indicator, and the number of registered jurisdictions in which the trademark associated with the generated identifier record group is registered, The method of item 10, comprising, as a distinction indicator, the distinction of a representative identifier record of the generated identifier record group. (Item 16) The economic footprint of the generated identifier record group is The method according to item 15, determined by the processing circuit of the server by weighting the number of registered jurisdictions based on the respective gross domestic product of each of the registered jurisdictions. (Item 17) The industry footprint of the generated identifier record group is The processing circuit of the server calculates the market share of the generated identifier record group, wherein the market share is defined as the percentage of the Nice classification class occupied by the trademark associated with the generated identifier record group within a given jurisdiction, and The method of item 15, determined by the processing circuit of the server calculating the number of different classes of the Nice classification in which the trademark associated with the generated identifier record group is registered within the given jurisdiction. (Item 18) The representative identifier record is associated with a text string, and the distinctiveness of the representative identifier record in the generated identifier record group is The processing circuit of the server weights the representative identifier record by the length of the associated text string, and The method of item 15, wherein the processing circuit of the server calculates a string similarity measure of the associated text string to other text strings associated with other identifier records in the database, the other identifier records in the database being identifier records not associated with the generated identifier record group, as determined by this calculation. (Item 19) A non-temporary computer-readable storage medium storing computer-readable instructions, wherein when the instructions are executed by a computer to determine the strength score of an identifier record, the computer... To generate an identifier record group from a database, which includes the identifier record and at least one other identifier record, wherein each identifier record in the identifier record group is associated with the same brand. To generate one or more intensity indicators for the generated identifier record group, wherein the one or more intensity indicators include at least one of a continuity indicator, a market indicator, a jurisdiction scope indicator, or a distinction indicator. Normalizing the value of each of the one or more intensity indicators based on the average value associated with each of the intensity indicators, wherein the average value associated with each of the intensity indicators is determined based on a plurality of identifier record groups in the database, including the generated identifier record group. The intensity metric value of the generated identifier record group is generated based on the sum of the normalized values ​​of one or more intensity indicators in the generated identifier record group, The value of the strength metric of the generated identifier record group is ranked among the strength metric values ​​associated with the plurality of identifier record groups in the database. A non-temporary computer-readable storage medium that performs the following: determining the strength score of the generated identifier record group as the strength score of the identifier record based on the ranking. (Item 20) The stored instruction is sent to the computer. A non-temporary computer-readable storage medium according to item 19, further enabling the determination of an improvement recommendation for at least one of the one or more strength indicators of the generated identifier record group, based on the fact that the strength score of the generated identifier record group is less than the strength score of the highest-ranked identifier record group among the plurality of identifier record groups. [Brief explanation of the drawing]

[0021] A more complete understanding of this disclosure and its many associated advantages will be readily available, as will be better understood by referring to the following detailed description in relation to the attached drawings.

[0022] [Figure 1] This is a schematic diagram of a client device connected to a server via a network, according to an embodiment of the present disclosure. [Figure 2] This is a flowchart illustrating a method for determining a brand strength score according to an exemplary embodiment of the present disclosure. [Figure 3] This is a flowchart of another method for determining a brand strength score according to an exemplary embodiment of the present disclosure. [Figure 4] This is a graphic representation of an intensity indicator according to an exemplary embodiment of the present disclosure. [Figure 5] This is a tabular representation of rankings according to an exemplary embodiment of the present disclosure. [Figure 6] This is a schematic diagram of the hardware configuration of a system for carrying out a method for determining a brand strength score according to an exemplary embodiment of the present disclosure. [Modes for carrying out the invention]

[0023] As used herein, the terms "a" or "an" are defined as one or more. As used herein, the term "plural" is defined as two or more. As used herein, the term "another" is defined as at least the second or subsequent. As used herein, the terms "including" and / or "having" are defined as "comprising" (i.e., open language). Throughout this document, references to "one embodiment," "a particular embodiment," "embodiment," "implementation," "example," or similar terms mean that a particular feature, structure, or characteristic described in relation to an embodiment is included in at least one embodiment of this disclosure. Thus, such phrases or their appearances in various places in this specification do not necessarily all refer to the same embodiment. Furthermore, particular features, structures, or characteristics may be combined in any preferred manner in one or more embodiments, without limitation.

[0024] The scope, strength, context, and future potential of brands belonging to a company and organization are important factors in evaluating the market and competitive landscape. For example, certain brands and trademarks are more valuable than others. Furthermore, because they relate to intangible assets, it is understandable that evaluating their value is a complex yet crucial task. Some companies, considering the increasing importance of these intangible assets, may offer brand valuation not only as insights into commercial strategy but also as a practice for tracking, managing, and verifying transactions. Due to externalities that influence brand perception and relevance (e.g., the competitive position of the company or product, potential customer perception, industry nature), as well as the unpredictability of future business and market performance, such brand valuations often rely on subjective metrics and assumptions or are based purely on business output metrics such as revenue and expenses.

[0025] Recognizing the inadequacy of current subjective brand evaluation tools, trademarks and trademark registration can be considered as means of objectively evaluating brands by assessing the strength and effectiveness of trademark owners protecting their brands.

[0026] Unfortunately, there is currently no practical and objective method for determining the difference between strong and weak trademarks. Therefore, with some related technologies, users seeking insights from global data sources must, where possible, perform their own statistical modeling, data manipulation, data reconstruction, and manual cleaning on data sources spanning multiple jurisdictions from different platforms. For example, if a human were to attempt this approach manually, it would require 80 million calculations performed on multiple independent occasions, totaling over 500 million calculations. Evaluating a single trademark or trademark family would only be possible after over 500 million calculations. Even assuming experienced personnel capable of performing each calculation in 5 minutes, the evaluation would still require 4,656 years of calculations. Given this time requirement, and the fact that trademark evaluations need to be updated daily to incorporate new trademark registrations occurring worldwide every day, the computational burden for this approach is enormous. Consequently, individuals, small businesses, and even large corporations rarely have the resources to build such an objective and analytical tool.

[0027] This disclosure provides a method for determining a brand strength score.

[0028] In one embodiment, an objective tool is provided for analyzing various data sources to determine a brand strength score based on trademark registration. The objective tool includes a method for restructuring the data to enable metrics based on trademark families. The analysis may include an evaluation of intangible assets according to defined metrics that can be scored. For example, the analysis may assess whether the trademark is adequately protected globally, whether the trademark is invested in by the owner, and / or whether the trademark is influential. The scored metrics can then be incorporated into an analytical model to determine the brand strength score.

[0029] In one embodiment, this disclosure enables the generation of a comprehensive report that compares all trademark registrations filed worldwide, compares trademark registrations by trademark family, and does not exclude companies based on their legal form or geographical presence. The objective tools of this disclosure rely on official data, including information on all trademark registrations filed worldwide by any trademark owner, which may be a public company, a private company, or an individual.

[0030] In one embodiment, this disclosure describes an empirical approach for measuring trademark distinctiveness, market footprint, and impact.

[0031] According to one embodiment, the present disclosure describes a method for determining a brand strength score based on trademark registration. The strength score can be determined based on one or more factors of a particular brand having a particular trademark or one or more related trademarks.

[0032] In one embodiment, one or more factors may be one or more intensity indicators, which may include, for example, continuity, market footprint, jurisdictional scope, and distinctiveness and complexity.

[0033] Continuity can be measured by the average number of years a trademark is registered for a particular brand. A higher average number of years may indicate that the brand is well-known and, consequently, likely to be stronger.

[0034] Market footprint includes the economic footprint and / or industry footprint of an entity. Economic footprint may be a measure of the breadth of total investment made in a given brand based on trademark registration. This measure may include a combination of two submetrics. The first submetric, related to investment, includes the total number of determined jurisdictions in which identifier records (or groups of identifier records) associated with the brand are registered. As will be discussed in detail below, each identifier record can be a text string in any character set or an image associated with the brand. An identifier record group can be a collection of identifier records used to define the same brand and therefore a trademark family. For brevity, the remainder of this disclosure will consider identifier records to be text strings. Subsequent references to identifier records may, interchangeably, be references to alphanumeric strings as text strings. The first submetric is related to jurisdiction cost, while the second submetric includes the total number of determined jurisdictions, weighted by gross domestic product (GDP), in which identifier records (or groups of identifier records) are registered. The second submetric correlates with the market footprint based on the jurisdictions in which the trademark is protected. The industry footprint can be a measure of brand penetration and cross-market applicability, for example, based on the Nice Classification, and the two measures may be combined. As those skilled in the art will understand, the Nice Classification is an international classification of goods and services to which trademark registrations apply. The first measure involves determining the percentage of Nice Classification classes occupied by the identifier record within a given jurisdiction. This percentage serves as a measure of market share. The second measure involves determining how many different classes of the Nice Classification the identifier record (e.g., an alphanumeric string) has been successfully registered across in a given jurisdiction.

[0035] Distinctiveness and complexity can be measures of how distinct a given identifier record (or group of identifier records) is from other identifier records (or groups of identifier records) in the database of trademark registrations, where each trademark registration in the database corresponds to an identifier record and / or is associated with an identifier record group. When identifier record groups are considered, trademark registrations from the same brand may be excluded. To determine how unique a brand is, the alphanumeric string associated with the identifier record, or a representative alphanumeric string representing the identifier record group, is first weighted by the length of the alphanumeric string or representative alphanumeric string. Longer strings are inherently more unique than shorter strings. A string similarity algorithm can then be applied to determine how similar a given alphanumeric string is to other alphanumeric strings in the database. In one embodiment, the string similarity algorithm may be an edit distance meter. In one embodiment, the string similarity algorithm may be the Jaro-Winkler edit distance meter or the Sorenson-Dice algorithm, etc. In one embodiment, the string similarity algorithm is a string similarity algorithm performed on an alphanumeric string associated with each identifier record, or a representative alphanumeric string associated with a group of identifier records, and this algorithm includes comparing the alphanumeric string or representative alphanumeric string with a random sample (e.g., 0.15%) of all identifier records (i.e., trademark registrations) in the database.

[0036] In one embodiment, one or more intensity indicators may further include market interest. Market interest can be a measure of how popular a given identifier record (or group of identifier records) is with the general public. For example, the measure may indicate how popular an identifier record is in a particular jurisdiction over an arbitrarily chosen time frame. Such data may be based, for example, on search trends in search engines.

[0037] Next, the strength score of a brand can be determined using the values ​​of one or more strength indicators. The strength score can be determined by first normalizing each value of one or more strength indicators to the global mean of each strength indicator, and then generating a strength score or strength metric value by summing the normalized values ​​associated with one or more strength indicators in an identifier record (or group of identifier records).

[0038] In other words, the values ​​of one or more intensity indicators do not directly enter into the brand intensity score. Instead, each value is compared to its respective baseline mean across all data. In one example, each value may be normalized to its respective baseline mean. In another example, each value may be normalized to its respective baseline mean and then assigned points based on a determined distance from its respective baseline mean. The points assigned for each intensity indicator can then be summed to form the intensity score. Strong brands are those in the band with a high intensity score.

[0039] In one embodiment, the objective analysis performed above may take into account different sectors, companies, and countries.

[0040] Referring to the figures, Figure 1 is a schematic diagram of electronic devices, such as a client / user device (first device 101), which is communicatively connected to a second electronic device, such as a server (second device 103), via a network 102, according to an embodiment of the present disclosure. Furthermore, although Figure 1 shows only one client / user device, in one embodiment, additional client / user devices can be communicatively connected to both the first device 101 and the second device 103. For example, a second client / user device can be communicatively connected to both the first device 101 and the second device 103, or multiple client / user devices can be communicatively connected to both the first device 101 and the second device 103.

[0041] The application may be installed on or accessible from the first device 101 in order to perform the methods described herein. The application may also be integrated into the operating system (OS) of the first device 101. The first device 101 may be, but is not limited to, any electronic device such as a personal computer, tablet, smartphone, smartwatch, smart TV, interactive screen, smart projector, or projected platform, or IoT (Internet of Things) device.

[0042] As shown in Figure 1, the first device 101 includes, among other components, a central processing unit (CPU), a graphics processing unit (GPU), main memory, and buffers (frame buffer, audio buffer, etc.) (discussed in more detail in Figure 7). In one embodiment, the first device 101 can call up graphics to be displayed on the display. The graphics of the first device 101 can be processed by the GPU and rendered to a scene stored in a buffer, such as a frame buffer, coupled to the display. In one embodiment, the first device 101 can execute software applications or programs to be displayed on the display. Software applications can be loaded into main memory for execution by the CPU, which may be faster in terms of access time than secondary storage such as a hard disk drive or solid-state drive. The main memory can be, for example, random access memory (RAM), which is the physical memory that is the primary internal memory for the first device 101.

[0043] A CPU may have associated CPU memory, and a GPU may have associated video memory or GPU memory. A frame buffer may be an allocated area of ​​video memory. A GPU may display data relating to a software application. It can be understood that a CPU may have multiple cores, or may itself be one of several processing cores within the first device 101. A CPU may execute commands in a CPU programming language such as C++. A GPU may execute commands in a GPU programming language such as High-Level Shading Language (HLSL). A GPU may also include multiple cores specialized for graphics processing tasks. The above description has been discussed in relation to the first device 101, but it should be understood that the same description applies to the second device 103 in Figure 1. Furthermore, in certain embodiments, the second device 103 may include a database and / or be connected to the database 104 via a network 105.

[0044] Figure 2 is a flowchart of a method for determining a brand strength score based on a trademark registration, according to an embodiment of the present disclosure. In one embodiment, the method can be carried out by the server 103 in Figure 1. In one embodiment, the method can be carried out by a first device 101, or by a combination of the first device 101 and the server 103. The method assumes that the user has provided input to a user device that indicates a brand for determination (e.g., the first device 101 in Figure 1). The input may be a company name, a brand name, or a specific trademark registration.

[0045] Brand instructions may include identifying information for specific identifier records of interest to the user. These specific identifier records may be trademark registrations, such as text strings or images within any character set. In one embodiment, it is assumed that the trademark registration is a text string (e.g., an alphanumeric string), and that multiple trademark registrations are associated with the trademark registration.

[0046] In step 215, identifier record groups can be generated based on specific identifier records. This grouping allows slightly different but related trademark registrations to be associated with the same brand. For example, various trademark registrations may be owned by the same entity, but each of the various trademark registrations, which may be alphanumeric strings, may have minor differences due to typographical errors, mistranslations, and regional differences. For example, company suffixes may differ depending on the region or jurisdiction.

[0047] In one embodiment, step 215 can be carried out by associating identifier records in the database based on the owner name. The association can be carried out using a machine learning algorithm, can be rule-based, or may include manual evaluation. The data in the database may be global trademark registration data. The information may include the number of registration classes in which the trademark is registered in a given jurisdiction, relevant statistics related to the economic output of a given jurisdiction, and market dynamics within a given jurisdiction. Identifier records with similar trademark registrations are grouped to represent the same brand for the purpose of determining brand strength. In other words, the database may include identifier records associated with globally registered unique trademarks. Thus, step 215 enables the grouping of identifier records related to the same brand.

[0048] In step 225, one or more intensity indicators can be calculated for the generated identifier record group. One or more intensity indicators may include continuity indicators, market indicators, jurisdiction indicators, and / or distinction indicators.

[0049] In one embodiment, the continuity indicator may be determined as the average number of years of trademark registration associated with the generated identifier record group. For example, a normalized filing date can be created for each trademark registration associated with the generated identifier record group. The normalized filing date takes into account the rules of individual jurisdictions / regions / countries and allows for the extraction of a useful actual or estimated date on which the trademark registration was filed. The average difference between the normalized filing date and the current date may then be calculated as the average number of years of trademark registration. The calculation may be performed for each identifier record associated with the generated identifier record group. In particular, since the continuity indicator depends on the "current date," the average of the results for trademark registrations associated with the generated identifier record group is valid only on the day the calculation is performed.

[0050] In one embodiment, the average lifespan of a trademark registration can be normalized based on the average lifespan of the remaining identifier record groups in the database. Therefore, after calculating the average lifespan of each of the remaining identifier record groups in the database (i.e., the average lifespan of all other trademark families in the database), the average lifespan of the resulting identifier record group can be normalized based on the average lifespan of the remaining identifier record groups and assigned a score between 0 and 1. Thus, Figure 4 provides a graphical representation of the lifespan of Google trademark registrations.

[0051] In one embodiment, the market indicator may be an economic footprint associated with the generated identifier record group and / or an industry footprint associated with the generated identifier record group. The economic footprint may be calculated by first determining the number of jurisdictions in which the trademark associated with the generated identifier record group is registered, and secondly, by weighting the determined number of jurisdictions in which the trademark is registered according to the respective GDP of each jurisdiction in which the trademark is registered. The industry footprint may be determined by first calculating the market share of the trademark registration associated with the generated identifier record group (the market share is defined as the percentage of the Nice classification class occupied by the trademark registration within a given jurisdiction), and secondly, by calculating the number of different classes of Nice classification in which the trademark is registered within a given jurisdiction.

[0052] In one embodiment, the Nice Classification for each jurisdiction provides the denominator across all global trademark data. Thus, the market indicator can be calculated by dividing the number of trademark registrations associated with the generated identifier record group by the number of existing trademarks registered in a particular class of the Nice Classification in a given jurisdiction. This calculation can be performed for all relevant trademarks registered in the country's Nice Classification. This calculation can then be weighted by GDP, as introduced above.

[0053] In one embodiment, the jurisdiction indicator can be determined as an indicator of trademark scope or brand exposure. For example, for a generated identifier record group, the number of corresponding jurisdictional registrations can be calculated. This calculation includes considering regional trademark registrations and dividing the scope into individual registrations for each member state of the region. The calculated scope can then be considered a normalization of jurisdictional registrations by the total number of jurisdictions that may be considered in the database.

[0054] In one embodiment, the distinction indicator may be determined as a measure of how distinct an alphanumeric string of an identifier record, or a representative alphanumeric string of a generated identifier record group, is from other identifier records in the database, or other alphanumeric strings associated with other representative alphanumeric strings associated with other identifier record groups. The measure of the distinction indicator may be determined by first weighting the alphanumeric string or representative alphanumeric string by its length, and then calculating a string similarity measure of the string to other strings in the database. The string similarity measure may be a string similarity algorithm, which may be applied to determine how different a given alphanumeric string, and therefore trademark registrations, are from one another. In one embodiment, the string similarity algorithm may be the Jaro-Winkler measurement, among other related techniques. For example, the string similarity algorithm may use edit distance measurement.

[0055] In one embodiment, a distinction indicator may be calculated for each alphanumeric string in the generated identifier record group. The alphanumeric string may be split into two-character strings. Similarly, the alphanumeric string associated with a random representative sample of identifier records in the database may be split into two-character strings. A comparison of the alphanumeric strings from the generated identifier record group with the alphanumeric strings associated with a random representative sample of identifier records in the database yields a similarity score for each record in the identifier record group. The similarity score may then be calculated as the average of the inverse mean scores of the random representative sample scores to provide a uniqueness score for the alphanumeric string compared to all alphanumeric strings associated with the identifier record.

[0056] In step 235, each strength indicator value for the generated identifier record group can be normalized by its respective global mean, defined according to the strength indicator values ​​for the remaining identifier record groups in the database. Normalization can be done by a direct comparison of the corresponding values.

[0057] In one embodiment, normalization can be performed on a scale from 0 to 1. In one embodiment, the normalized values ​​of one or more intensity indicators in the generated identifier record group can then be assigned point values ​​according to the degree of difference between the value and the baseline value. For example, the points may be assigned based on a point scale from 1 to 10. In one embodiment, retrieving the values ​​of one or more intensity indicators in the remaining identifier record groups may include retrieving the points assigned to the intensity indicators in each identifier record group in the database.

[0058] In step 245, the strength metric value for the generated identifier record group can be generated / determined. The strength metric value may be the sum of the normalized values ​​for each of the one or more strength indicators in the generated identifier record group.

[0059] In step 255, the strength metric values ​​of the generated identifier record groups can be ranked among the corresponding strength metric values ​​for each of the remaining identifier record groups in the database. An example ranking is shown in Figure 5.

[0060] In step 265, the relative strength of the brands associated with the generated identifier record group can be determined based on the ranking determined in step 255. The relative strength may be determined by comparing the strength metric value of the generated identifier record group with the highest-ranked identifier record group in the ranking.

[0061] In step 275, based on the ranking and comparison of the strength metric value of the generated identifier record group with the highest-ranked identifier record group, one or more strength indicators for improvement of the generated identifier record group can be recommended. In one embodiment, a recommendation may be made if the comparison of strength metric values ​​results in a value less than 1. In embodiments where the comparison of strength metric values ​​results in a value equal to 1, i.e., the generated identifier record group has the highest-ranked strength metric value, no recommendation is made.

[0062] In embodiments where recommendations are made in step 275, the graphic may be displayed to the user via the user interface of a user device (e.g., device 101 in Figure 1) to inform the user of at least one of one or more strength indicators that can be improved. As previously stated, the excellence of each of the one or more strength indicators is required to be ranked highly. Similarly, the relative weakness of the generated identifier record group can be evaluated by considering the normalized value of each of the one or more strength indicators. Thus, in one embodiment, the recommendations may focus on improving one of the worst-performing strength indicators. In another embodiment, the recommendations may be those requiring an assessment of company-specific capabilities based on which of the one or more strength indicators is likely to see the most improvement in the short term.

[0063] As mentioned above, the recommendations may be provided to the user through the user interface of the user device. In this way, users are provided with immediate feedback regarding the relative strength of the brand.

[0064] Figure 3 is a flowchart of a method for determining a brand strength score based on trademark registration, according to an embodiment of the present disclosure. In one embodiment, the method can be carried out by the server 103 of Figure 1. In one embodiment, the method can be carried out by the first device 101, or by a combination of the first device 101 and the server 103. This method does not make recommendations for improvement regarding the area. Instead, recommendations may be made by another process other than the method described herein, or may not be made at all. However, the remaining steps and subprocesses are substantially the same as those described above with reference to the method of Figure 2, as outlined below.

[0065] In the method shown in Figure 3, it is assumed that the user provides input to a user device that indicates a brand for decision-making purposes (e.g., the first device 101 in Figure 1). The input may be the name of a company, a brand name, or a specific trademark registration.

[0066] Brand instructions may include identifying information for specific identifier records of interest to the user. These specific identifier records may be trademark registrations, such as text strings or images within any character set. In one embodiment, it is assumed that the trademark registration is a text string (e.g., an alphanumeric string), and that multiple trademark registrations are associated with the trademark registration.

[0067] In step 315, identifier record groups can be generated based on specific identifier records. This grouping allows slightly different but related trademark registrations to be associated with the same brand. For example, various trademark registrations may be owned by the same entity, but each of the various trademark registrations, which may be alphanumeric strings, may have minor differences due to typographical errors and mistranslations, as well as regional differences. For example, company suffixes may differ depending on the region or jurisdiction.

[0068] In one embodiment, step 315 can be carried out by associating identifier records in the database based on the owner name. The association can be carried out using a machine learning algorithm, can be rule-based, or may include manual evaluation. The data in the database may be global trademark registration data. The information may include the number of registration classes in which the trademark is registered in a given jurisdiction, relevant statistics related to the economic output of a given jurisdiction, and market dynamics within a given jurisdiction. Identifier records with similar trademark registrations are grouped to represent the same brand for the purpose of determining a brand strength score. In other words, the database may include identifier records associated with globally registered unique trademarks. Thus, step 315 enables the grouping of identifier records related to the same brand.

[0069] In step 325, one or more intensity indicators can be calculated for the generated identifier record group. One or more intensity indicators may include continuity indicators, market indicators, jurisdiction indicators, and / or distinction indicators.

[0070] In one embodiment, the continuity indicator may be determined as the average number of years of trademark registration associated with the generated identifier record group. For example, a normalized filing date can be created for each trademark registration associated with the generated identifier record group. The normalized filing date takes into account the rules of individual jurisdictions / regions / countries and allows for the extraction of a useful actual or estimated date on which the trademark registration was filed. The average difference between the normalized filing date and the current date may then be calculated as the average number of years of trademark registration. The calculation may be performed for each identifier record associated with the generated identifier record group. In particular, since the continuity indicator depends on the "current date," the average of the results for trademark registrations associated with the generated identifier record group is valid only on the day the calculation is performed.

[0071] In one embodiment, the average lifespan of a trademark registration can be normalized based on the average lifespan of the remaining identifier record groups in the database. Therefore, after calculating the average lifespan of each of the remaining identifier record groups in the database (i.e., the average lifespan of all other trademark families in the database), the average lifespan of the resulting identifier record group can be normalized based on the average lifespan of the remaining identifier record groups, and thus assigned a score between 0 and 1. Figure 4 provides a graphical diagram of the lifespan of Google trademark registrations.

[0072] In one embodiment, the market indicator may be an economic footprint associated with the generated identifier record group and / or an industry footprint associated with the generated identifier record group. The economic footprint may be calculated by first determining the number of jurisdictions in which the trademark associated with the generated identifier record group is registered, and secondly, by weighting the determined number of jurisdictions in which the trademark is registered according to the respective GDP of each jurisdiction in which the trademark is registered. The industry footprint may be determined by first calculating the market share of the trademark registration associated with the generated identifier record group (the market share is defined as the percentage of the Nice classification class occupied by the trademark registration within a given jurisdiction), and secondly, by calculating the number of different classes of Nice classification in which the trademark is registered within a given jurisdiction.

[0073] In one embodiment, the Nice Classification for each jurisdiction provides the denominator across all global trademark data. Thus, the market indicator can be calculated by dividing the number of trademark registrations associated with the generated identifier record group by the number of existing trademarks registered in a particular class of the Nice Classification in a given jurisdiction. This calculation can be performed for all relevant trademarks registered in the country's Nice Classification. This calculation can then be weighted by GDP, as introduced above.

[0074] In one embodiment, the jurisdiction indicator can be determined as an indicator of trademark scope or brand exposure. For example, for a generated identifier record group, the number of corresponding jurisdictional registrations can be calculated. This calculation includes considering regional trademark registrations and dividing the scope into individual registrations for each member state of the region. The calculated scope can then be considered a normalization of jurisdictional registrations by the total number of jurisdictions that may be considered in the database.

[0075] In one embodiment, the distinction indicator may be determined as a measure of how distinct an alphanumeric string of an identifier record, or a representative alphanumeric string of a generated identifier record group, is from other identifier records in the database, or other representative alphanumeric strings associated with other identifier record groups. The measure of the distinction indicator may be determined by first weighting the alphanumeric string or representative alphanumeric string by its length, and then calculating a string similarity measure of the string to other strings in the database. The string similarity measure may be a string similarity algorithm, which may be applied to determine how different a given alphanumeric string, and therefore trademark registrations, are from one another. In one embodiment, the string similarity algorithm may be the Jaro-Winkler scale, among other related techniques. For example, the string similarity algorithm may use edit distance measurement.

[0076] In one embodiment, a distinction indicator may be calculated for each alphanumeric string in the generated identifier record group. The alphanumeric string may be split into two-character strings. Similarly, the alphanumeric string associated with a random representative sample of identifier records in the database may be split into two-character strings. A comparison of the alphanumeric strings from the generated identifier record group with the alphanumeric strings associated with a random representative sample of identifier records in the database yields a similarity score for each record in the identifier record group. The similarity score may then be calculated as the average of the inverse mean scores of the random representative sample scores to provide a uniqueness score for the alphanumeric string compared to all alphanumeric strings associated with the identifier record.

[0077] In step 335, each strength indicator value for the generated identifier record group can be normalized by its respective global mean, defined according to the strength indicator values ​​for the remaining identifier record groups in the database. Normalization can be done by a direct comparison of the corresponding values.

[0078] In one embodiment, normalization can be performed on a scale from 0 to 1. In one embodiment, the normalized values ​​of one or more intensity indicators in the generated identifier record group can then be assigned point values ​​according to the degree of difference between the value and the baseline value. For example, the points may be assigned based on a point scale from 1 to 10. In one embodiment, retrieving the values ​​of one or more intensity indicators in the remaining identifier record groups may include retrieving the points assigned to the intensity indicators in each identifier record group in the database.

[0079] In step 345, the strength metric value for the generated identifier record group can be generated / determined. The strength metric value may be the sum of the normalized values ​​for each of the one or more strength indicators in the generated identifier record group.

[0080] In step 355, the strength metric value of the generated identifier record group can be ranked among the corresponding strength metric values ​​for each of the remaining identifier record groups in the database. An example ranking is shown in Figure 5.

[0081] In step 365, the relative strength of the brands associated with the generated identifier record group can be determined based on the ranking determined in step 355. The relative strength may be determined by comparing the strength metric value of the generated identifier record group with the highest-ranked identifier record group in the ranking.

[0082] In one embodiment, following the ranking of strength metric values ​​in step 355, or the determination of the relative strength of the brands in step 365, the graphic may be displayed to the user via the user interface of the user device (e.g., device 101 in Figure 1) to inform the user of the determined strength of the brands.

[0083] In one embodiment, the ranking and determined relative intensity output from the method of Figure 3 can then be used by another process other than that method to generate, provide, and / or display recommendations for one or more intensity indicators for improvement in the generated identifier record group. Recommendations may be made if the comparison of intensity metric values ​​results in a value less than 1. No recommendations are made if the comparison of intensity metric values ​​results in a value equal to 1, i.e., the generated identifier record group is the highest-ranked intensity metric value. In one embodiment, the recommendations may focus on improving the worst-performing one of the one or more intensity indicators. In another embodiment, the recommendations may be company-specific capability assessments based on which of the one or more intensity indicators is likely to see the most improvement in the short term.

[0084] Depending on the aspect of this disclosure, the methods described herein may rely on advanced computation and the ability to rapidly process large datasets. For example, since it involves generating identifier record groups based on user input and for each remaining group in the database, if the identifier records contain text strings, each record in a set of approximately 80 million live records must be reviewed and compared with each remaining record among the approximately 80 million live records in the set. This results in 6,400 trillion computations. If each identifier record contains an image, a comparison of each individual pixel in each image is required, thereby further amplifying the number of computations required. In proportion to the magnitude of this effort, the methods of this disclosure group the 80 million identifier records based on their relevance to other identifier records in the database. This allows subsequent computations, including the determination of one or more intensity indicators, to be applied to a subset of the 80 million identifier records. This reduction in computational demand significantly improves the speed of the method and allows for significant scalability of implementations. In other words, the embodiments discussed herein offer technical improvements in that they reduce computational demand while improving efficiency and accuracy.

[0085] Furthermore, the database itself is constantly being updated, and the calculations described in this disclosure must be performed daily.

[0086] In one embodiment, each of the 80 million identifier records in the database must be judged for each Nice classification (greater than 1) in question, so as to relate to the industry footprint, which would require more than 80 million calculations. Furthermore, the share of the Nice classification can be calculated based on the distribution of shares across all 80 million identifier records. GDP is available for each of the approximately 200 countries and regions worldwide. The industry footprint is normalized and all 80 million identifier records are compared to their distribution on a scale of 0 to 1.

[0087] Overall, the creation of strength scores for each identifier record or group of identifier records can be understood as generating detailed quantitative information for data points that were historically qualitative. The relevant evaluation of names and identifiers has relied on commercial output metrics such as revenue, profit, and stock price. The method described herein reverses that commercial process and instead relies on technical algorithmic analysis of input variables and their quantification and comparison. Since the quantitative values ​​to be compared are provided against historical quantitative information in the back file, fluctuations and proximity of values ​​are emphasized. A large amount of data is used to filter and identify areas of similarity or discrepancy. Furthermore, the method of this disclosure makes it possible to determine the economic activity of a country or region based on the distribution and strength of identifier records (products and companies) compared with all other identifier records in the database.

[0088] The experimental results confirming the usefulness of the methods described in this disclosure are described below.

[0089] The following empirical results can distinguish between good and bad trademarks. This applies when looking at trademarks of a specific company, cohort, or as a whole.

[0090] During verification, all trademarks containing linguistic elements (i.e., marks consisting solely of images are excluded) are analyzed. The strength index described above is calculated for each unique pair of linguistic element and owner. Data visualization software (e.g., Tableau) is used for calculations to enable dynamic scoring and ranking based on selected cohorts.

[0091] The results are reviewed by a panel of internal experts after iteration.

[0092] Regarding the economic footprint, the corresponding metric now includes PTO weighting by GDP, thereby correlating market footprint and trademark registrations. For the industry footprint, since not all nice classifications are equal, the share of a class represented by a single registration is calculated. These shares are then all summed up to a corresponding metric that takes into account both the number of classes and their sizes.

[0093] According to one embodiment, verification is performed by checking current iterations of the method of this disclosure against InterBrand 2019 “Best Global Brands.” This comparison allows for the determination of how a purely empirical approach provides correlation with relevant methods and whether such correlation is provided.

[0094] The results showed that 27% of the InterBrand 2019 Top 100 were also within the Top 100 as determined by the method of disclosure. 43% of the InterBrand 2019 Top 200 were also within the Top 200 as determined by the method of disclosure. 50% of the InterBrand 2019 Top 500 were also within the Top 500 as determined by the method of disclosure. 66% of the InterBrand 2019 Top 1000 were also within the Top 1000 as determined by the method of disclosure. 81% of the InterBrand 2019 Top 2000 were also within the Top 2000 as determined by the method of disclosure.

[0095] Please note that various variations of the method disclosed herein may be included in this disclosure. In the relevant field, brand valuation methodologies tend to be top-down. In contrast, the method disclosed herein is independent and bottom-up, and therefore based on a much broader information base. Also note that while InterBrand-style ranking is biased towards in-house brands, the method disclosed herein can also be used for product brands. Accordingly, the metrics of the method disclosed herein include a much higher number of individual word trademarks.

[0096] The embodiments and functional operations of the subject matter described herein can be implemented in digital electronic circuits, in clearly embodied computer software or firmware, in computer hardware including the structures disclosed herein and their structural equivalents, or in one or more combinations thereof. The embodiments of the subject matter described herein can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a specific, non-transient program carrier for execution by a data processing device or for controlling the operation of a data processing device. Alternatively or additionally, the program instructions can be encoded on artificially generated propagating signals, such as machine-generated electrical, optical, or electromagnetic signals generated to encode information for transmission to a suitable receiving device for execution by a data processing device. The computer storage medium can be a machine-readable storage device, a machine-readable storage board, a random-access or serial-access memory device, or one or more combinations thereof.

[0097] The term "data processing device" refers to data processing hardware and encompasses all kinds of devices, equipment, and machines for processing data, including, for example, programmable processors, computers, or multiple processors or computers. Furthermore, a device may be or may further include special-purpose logic circuits, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits). In addition to hardware, a device may optionally include code that constitutes the execution environment for computer programs, such as processor firmware, protocol stacks, database management systems, operating systems, or one or more combinations thereof.

[0098] Computer programs, also called or written as programs, software, software applications, modules, software modules, scripts, or code, may be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and may be deployed in any form, including as standalone programs or as modules, components, subroutines, or other units suitable for use in a computing environment. Computer programs may, though not required, correspond to files in a file system. A program may be stored in part of a file that holds other programs or data, such as one or more scripts stored in a markup language document, a single file dedicated to the program in question, or multiple coordinated files, such as one or more modules, subprograms, or parts of code. Computer programs may be deployed to run on one or more computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0099] The processes and logic flows described herein can be implemented by one or more programmable computers that execute one or more computer programs to perform their functions by operating on input data and generating outputs. Alternatively, the processes and logic flows can be implemented by special-purpose logic circuits, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the devices can also be implemented in this manner.

[0100] Computers suitable for running computer programs include, as an example, general-purpose or special-purpose microprocessors or both, or any other type of central processing unit. Generally, a central processing unit receives instructions and data from read-only memory, random-access memory, or both. The most important elements of a computer are the central processing unit for executing or generating instructions, and one or more memory devices for storing instructions and data. Generally, a computer also includes or is operablely connected to one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, for receiving data, transferring data, or both. However, a computer is not required to have such devices. Furthermore, a computer can be incorporated into other devices, such as, to name a few, mobile phones, personal digital assistants (PDAs), portable audio or video players, game consoles, Global Positioning System (GPS) receivers, or portable storage devices, such as Universal Serial Bus (USB) flash drives. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into special-purpose logic circuits.

[0101] To provide user interaction, embodiments of the subject matter described herein can be implemented on a computer having a display device for displaying information to the user, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, and a keyboard and pointing device, such as a mouse or trackball, on which the user can provide input to the computer. User interaction can also be provided using other types of devices, for example, the feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, including acoustic, voice, or tactile input. In addition, the computer can interact with the user by sending and receiving documents on the device the user is using, for example, by sending a web page to a web browser on the user's device in response to a request received from a web browser.

[0102] Embodiments of the subject matter described herein can be implemented in a computing system that includes, for example, a data server as a backend component, a middleware server as a middleware server, or a client computer having, for example, a graphical user interface or web browser on which a user can interact with the implementation of the subject matter described herein, or any combination of one or more such backend, middleware, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication, such as a communication network. Examples of communication networks include local area networks (LANs) and wide area networks (WANs), such as the Internet.

[0103] A computing system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises from computer programs running on each computer that have a client-server relationship with each other. In some embodiments, the server sends data, such as an HTML page, to a user device, for the purpose of displaying data to a user interacting with the user device (which acts as a client), and receiving user input. Data generated on the user device, such as the results of user interaction, may be received from the user device by the server.

[0104] An example of such a computer is shown in Figure 6, which illustrates a schematic diagram of computer system 600. System 600 can be used for the operations described in relation to any of the computer implementation methods described above, according to one implementation form. In one embodiment, system 600 may correspond to device 101 in Figure 1. In one embodiment, system 600 may correspond to device 103 in Figure 1.

[0105] System 600 includes a processor 610, memory 620, storage device 630, and input / output device 640. Each of components 610, 620, 630, and 640 is interconnected using a system bus 650. The processor 610 can process instructions to be executed within system 600. In one implementation, the processor 610 is a single-threaded processor. In another implementation, the processor 610 is a multi-threaded processor. The processor 610 can process instructions stored in memory 620 or storage device 630 to display graphical information of a user interface on the input / output device 640.

[0106] Memory 620 stores information within the system 600. In one implementation, memory 620 is a computer-readable medium. In another implementation, memory 620 is a volatile memory unit. In yet another implementation, memory 620 is a non-volatile memory unit.

[0107] The storage device 630 can provide mass storage to the system 600. In one implementation, the storage device 630 is a computer-readable medium. In various different implementations, the storage device 630 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device.

[0108] The input / output device 640 provides input / output operation to the system 600. In one implementation, the input / output device 640 includes a keyboard and / or a pointing device. In another implementation, the input / output device 640 includes a display unit for displaying a graphical user interface. In yet another implementation, the input / output device 640 is a user device as described herein.

[0109] This specification includes many specific details of implementation, but these should not be interpreted as limitations on the claims, but rather as descriptions of features that may be specific to particular embodiments.

[0110] Certain features described herein in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented separately in multiple embodiments or in any suitable subcombination. Furthermore, although features have been described above as acting in a particular combination and initially further claimed in this manner, one or more features from a claimed combination may, in some cases, be removed from the combination, and the claimed combination may be moved to a subcombination or a variation of a subcombination.

[0111] Similarly, although the drawings depict operations in a specific order, this should not be understood as requiring that such operations be performed in the specific order shown, or sequentially, or that all illustrated operations be performed in order to obtain the desired results. In certain situations, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and moreover, the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0112] Specific embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions enumerated in the claims can be performed in a different order and still yield the desired results. As an example, the process depicted in the attached diagram does not necessarily require the specific order shown, i.e., sequential order, to obtain the desired results. In some cases, multitasking and parallel processing may be advantageous.

[0113] Clearly, numerous modifications and variations are possible in light of the above teachings. Therefore, it should be understood that the present invention may be implemented in ways other than those specifically described herein, within the scope of the appended claims.

[0114] Accordingly, the foregoing description merely discloses and explains exemplary embodiments of the present invention. As will be understood by those skilled in the art, the present invention can be embodied in other specific forms without departing from its spirit or essential characterization. Accordingly, the disclosure of the present invention is intended to be illustrative, but like other claims, it does not limit the scope of the invention. Including any readily recognizable variations of the teachings herein, the disclosure, in part, defines the scope of the terms of the foregoing claims in such a way that the subject matter of the invention is not dedicated to the public.

Claims

1. A device that is communicably coupled to an electronic device via a network, wherein the device is It includes a processing circuit, and the processing circuit is To generate an identifier record group from a database, which includes an identifier record and at least one other identifier record, wherein each identifier record in the identifier record group is associated with the same brand. To generate one or more intensity indicators for the generated identifier record group, wherein the one or more intensity indicators include at least one of a continuity indicator, a market indicator, a jurisdiction scope indicator, or a distinction indicator. Normalizing the value of one or more intensity indicators based on the average value associated with each of the intensity indicators, wherein the average value associated with each of the intensity indicators is determined based on a plurality of identifier record groups in the database, including the generated identifier record group. To generate a value for the intensity metric of the generated identifier record group based on the sum of the normalized values ​​of one or more intensity indicators in the generated identifier record group, The value of the strength metric of the generated identifier record group is ranked among the strength metric values ​​associated with the plurality of identifier record groups in the database. An apparatus configured to determine the strength score of the generated identifier record group based on the aforementioned ranking.

2. The aforementioned processing circuit The apparatus according to claim 1, further configured to determine an improvement recommendation for at least one of the one or more strength indicators of the generated identifier record group, based on the fact that the strength score of the generated identifier record group is less than the strength score of the highest-ranked identifier record group among the plurality of identifier record groups.

3. The apparatus according to claim 2, wherein the improvement suggestions are displayed by the user interface of the electronic device.

4. The apparatus according to claim 1, wherein the identifier record is determined based on input from the electronic device.

5. The apparatus according to claim 4, wherein the input is a text string or an image.

6. The one or more intensity indicators described above The average number of years of trademark registration associated with the generated identifier record group, as the continuity indicator, As the market indicator, at least one of the economic footprint of the generated identifier record group or the industry footprint of the generated identifier record group, The number of registered jurisdictions, normalized by the total number of jurisdictions in the database, as the jurisdictional scope indicator, and the number of registered jurisdictions in which the trademark associated with the generated identifier record group is registered, The apparatus according to claim 1, further comprising, as a distinction indicator, the distinguishability of a representative identifier record of the generated identifier record group.

7. The economic footprint of the generated identifier record group is The apparatus according to claim 6, determined by weighting the number of registered jurisdictions based on the respective gross domestic product of each of the registered jurisdictions.

8. The industry footprint of the generated identifier record group is Calculating the market share of the generated identifier record group, wherein the market share is defined as the percentage of the Nice classification class occupied by the trademark associated with the generated identifier record group within a given jurisdiction, and The apparatus according to claim 6, wherein the trademark associated with the generated identifier record group is determined by calculating the number of different classes of the Nice classification that are registered in the given jurisdiction.

9. The representative identifier record is associated with a text string, and the distinctiveness of the representative identifier record in the generated identifier record group is The representative identifier record is weighted according to the length of the associated text string, and The apparatus according to claim 6, comprising calculating a string similarity measure of the associated text string to other text strings associated with other identifier records in the database, wherein the other identifier records in the database are identifier records not associated with the generated identifier record group.

10. A method for determining the strength score of an identifier record group, The process involves generating an identifier record group from the server's processing circuitry and the database, which includes an identifier record and at least one other identifier record, wherein each identifier record in the identifier record group is associated with the same brand. The processing circuit of the server generates one or more intensity indicators for the generated identifier record group, wherein the one or more intensity indicators include at least one of a continuity indicator, a market indicator, a jurisdiction scope indicator, or a distinction indicator. The processing circuit of the server normalizes the value of each of the one or more intensity indicators based on the average value associated with each of the intensity indicators, wherein the average value associated with each of the intensity indicators is determined based on a plurality of identifier record groups in the database, including the generated identifier record group. The processing circuit of the server generates a value for the intensity metric of the generated identifier record group based on the sum of the normalized values ​​of one or more intensity indicators of the generated identifier record group, The processing circuit of the server ranks the value of the strength metric of the generated identifier record group among the strength metric values ​​associated with the plurality of identifier record groups in the database. A method comprising determining the strength score of the generated identifier record group based on the ranking by the processing circuit of the server.

11. The method according to claim 10, further comprising the processing circuit of the server determining an improvement recommendation for at least one of the one or more strength indicators of the generated identifier record group based on the fact that the strength score of the generated identifier record group is less than the strength score of the highest-ranked identifier record group among the plurality of identifier record groups.

12. The method according to claim 11, wherein the improvement suggestions are displayed by a user interface of an electronic device that is communicably connected to the server via a network.

13. The method according to claim 10, wherein the identifier record is determined based on input from an electronic device.

14. The method according to claim 13, wherein the input is a text string or an image.

15. The one or more intensity indicators described above The average number of years of trademark registration associated with the generated identifier record group, as the continuity indicator, As the market indicator, at least one of the economic footprint of the generated identifier record group or the industry footprint of the generated identifier record group, The number of registered jurisdictions, normalized by the total number of jurisdictions in the database, as the jurisdictional scope indicator, and the number of registered jurisdictions in which the trademark associated with the generated identifier record group is registered, The method according to claim 10, further comprising, as a distinction indicator, the distinction of a representative identifier record of the generated identifier record group.

16. The economic footprint of the generated identifier record group is The method according to claim 15, wherein the number of registered jurisdictions is determined by the processing circuit of the server, weighting the number of registered jurisdictions based on the respective gross domestic product of each of the registered jurisdictions.

17. The industry footprint of the generated identifier record group is The processing circuit of the server calculates the market share of the generated identifier record group, wherein the market share is defined as the percentage of the Nice classification class occupied by the trademark associated with the generated identifier record group within a given jurisdiction, and The method according to claim 15, wherein the processing circuit of the server determines the number of different classes of the Nice classification in which the trademark associated with the generated identifier record group is registered within the given jurisdiction.

18. The representative identifier record is associated with a text string, and the distinctiveness of the representative identifier record in the generated identifier record group is The processing circuit of the server weights the representative identifier record by the length of the associated text string, and The method according to claim 15, wherein the processing circuit of the server calculates a string similarity measure of the associated text string to other text strings associated with other identifier records in the database, by calculating that the other identifier records in the database are identifier records not associated with the generated identifier record group.

19. A non-temporary computer-readable storage medium storing computer-readable instructions, wherein when the instructions are executed by a computer to determine the strength score of an identifier record group, the computer... To generate an identifier record group from a database, which includes an identifier record and at least one other identifier record, wherein each identifier record in the identifier record group is associated with the same brand. To generate one or more intensity indicators for the generated identifier record group, wherein the one or more intensity indicators include at least one of a continuity indicator, a market indicator, a jurisdiction scope indicator, or a distinction indicator. Normalizing the value of one or more intensity indicators based on the average value associated with each of the intensity indicators, wherein the average value associated with each of the intensity indicators is determined based on a plurality of identifier record groups in the database, including the generated identifier record group. To generate a value for the intensity metric of the generated identifier record group based on the sum of the normalized values ​​of one or more intensity indicators in the generated identifier record group, The value of the strength metric of the generated identifier record group is ranked among the strength metric values ​​associated with the plurality of identifier record groups in the database. A non-temporary computer-readable storage medium that performs the following: determining the strength score of the generated identifier record group based on the aforementioned ranking.

20. The stored instruction is sent to the computer. The non-temporary computer-readable storage medium according to claim 19, further comprising determining an improvement recommendation for at least one of the one or more strength indicators of the generated identifier record group based on the fact that the strength score of the generated identifier record group is less than the strength score of the highest-ranked identifier record group among the plurality of identifier record groups.

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