KR102876979B1 - Customer Asset Management Platform System - Google Patents

Abstract

The present invention relates to a customer asset management platform system, and more specifically, to a customer software asset management platform system for providing post-management, such as extension of use and payment of costs, for software supplied to a customer company.
The present invention has the effect of providing convenience to customers by providing a customer asset management platform system for managing overall matters including extension of use and payment of costs for software assets supplied to customers.

Description

Customer Asset Management Platform System

The present invention relates to a customer asset management platform system, and more specifically, to a customer software asset management platform system for providing post-management, such as extension of use and payment of costs, for software supplied to a customer company.

The word "software" is a combination of the words "soft," meaning "soft," and "ware," meaning "product." It's used as a term corresponding to hardware, the components that make up a computer. Beyond computers, it also encompasses the logical procedures, rules, and related systems that perform functions on devices for specific purposes.

In a computer, software is a set of instructions, or programs, that describe the procedures, rules, and related systems that operate the hardware to perform specific tasks. It encompasses the rules that instruct the computer on the order, procedures, and methods of performing specific tasks, as well as the related systems necessary for operating the computer and executing programs. Among the elements that make up a computer system, software can be defined as the intangible part, like a typical program, excluding the tangible hardware.

Software is divided into system software and application software.

System software: Software that directly controls, integrates, and manages the individual hardware (HW) components of a computer system. It refers to software that helps users use a computer system even if they don't fully understand the physical characteristics or structure of the computer hardware. System software includes operating systems, language processing programs such as linkers, loaders, compilers, and assemblers, and utilities.

Application software: Software designed to perform user-defined functions and used directly by users. Examples include office software such as word processors, spreadsheets, messengers, and web browsers; personal software such as mobile apps; business software such as accounting and enterprise resource planning (ERP); and software for specific technologies such as simulation and computer-aided design (CAD).

Such software is one of the important assets that a company must purchase and manage as an asset, and management of it is necessary, and a system development for this purpose is required.

Korean Patent Publication No. 10-2325430 Korean Patent Publication No. 10-1555247

The purpose of the present invention is to provide a customer asset management platform system for managing overall matters including extension of use and payment of costs for software assets supplied to a customer.

In order to solve the above problem, the present invention includes a customer asset management server (100) for storing contract information of a customer company that has supplied software for use during a set period of time and automatically renewing the period of use according to the stored contract information, and the customer asset management server (100) includes a customer information storage unit (110) for storing customer information transmitted from a customer terminal (200), a software supply unit (120) for supplying software for use during a set period of time to a customer terminal (200) that has entered into a software supply contract, a contract management unit (130) for receiving contract information from the customer terminal (200) and determining whether to renew the period of use of the supplied software according to the transmitted contract information, a usage information collection unit (140) for collecting usage information of the software from the customer terminal (200) to which the software has been supplied, and an artificial intelligence-based analysis of the usage information collected through the usage information collection unit (140) to determine the possibility of extending the use of the software, a cost reduction plan, A customer asset management platform system is provided, which includes a usage information analysis unit (150) for providing usage analysis information including information including budget management, and a usage statistics report generation unit (160) for generating a usage statistics report based on the usage analysis information provided through the usage information analysis unit (150).

The present invention has the effect of providing convenience to customers by providing a customer asset management platform system for managing overall matters including extension of use and payment of costs for software assets supplied to customers.

Figure 1 is a drawing showing the overall configuration of the present invention.
Figure 2 is a block diagram showing the configuration of the customer asset management server (100) of the present invention.

Terms or words used in this specification and claims should not be interpreted as limited to their usual or dictionary meanings, but should be interpreted as meanings and concepts that conform to the technical idea of the present invention, based on the principle that the inventor can appropriately define the concept of the term to explain his or her invention in the best possible way.

Hereinafter, a preferred embodiment of the present invention will be described in detail with reference to the attached drawings.

The present invention relates to a customer asset management platform system, and more specifically, to a customer software asset management platform system for providing post-management, such as extension of use and payment of costs, for software supplied to a customer company.

Referring to Figure 1, the present invention allows a customer asset management server (100) and a customer terminal (200) to communicate via a network.

A network is a network that serves as a transmission path for web pages. It can be a closed network such as a LAN (Local Area Network) or a WAN (Wide Area Network), but it is preferable to have an open network such as the Internet. The Internet refers to a global, open computer network structure that provides various services existing in the TCP/IP protocol and its upper layers, such as HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP (Simple Network Management Protocol), NFS (Network File Service), and NIS (Network Information Service).

The above customer terminal may be implemented in various forms. For example, the terminal described in this specification may be a mobile terminal such as a smart phone, a tablet PC, a personal digital assistant (PDA), a portable multimedia player (PMP), an MP3 player, or a stationary terminal such as a smart TV or a desktop computer.

The customer asset management server (100) stores contract information of a customer company that has supplied software for use for a set period of time, and automatically renews the usage period according to the stored contract information.

Referring to FIG. 2, the customer asset management server (100) is configured to include a customer information storage unit (110), a software supply unit (120), a contract management unit (130), a usage information collection unit (140), a usage information analysis unit (150), and a usage statistics report generation unit (160).

Each component is described in detail as follows:

The customer information storage unit (110) is for storing customer information transmitted from the customer terminal (200).

The above customer information includes the customer's company name, location, industry, information on contracted software, and information on expired software.

The software supply department (120) is intended to supply software for use for a set period of time to a customer terminal (200) that has entered into a software supply contract.

The above software can be of various types, such as CAD and vaccines, and a contract for the software that the customer wants to supply is entered into through the software supply department (120), through which the software can be used for a predetermined period of time for each software or for a period set by the customer.

The contract management department (130) receives contract information from the customer terminal (200) and determines whether to renew the usage period of the supplied software based on the transmitted contract information.

The above contract information may include the type of software for which the customer has entered into a supply contract, the start date of use of the software, the remaining period of use, and whether or not automatic renewal is available.

The above contract management unit (130) is composed of a contract information storage unit (131), an automatic renewal unit (132), and a contract management unit (133).

The contract information storage unit (131) is for receiving and storing contract information from the customer terminal (200).

The automatic renewal unit (132) analyzes the contract information stored in the contract information storage unit (131) to determine whether the usage period of the software supplied to the customer terminal (200) is automatically renewed, and automatically renews according to the period set at the time of automatic renewal.

The contract management unit (133) reflects information about software automatically updated by the automatic update unit (132) in the contract information stored in the contract information storage unit (131) and stores it as contract management information.

The usage information collection unit (140) is for collecting software usage information from the customer terminal (200) to which the software is supplied.

The above usage information is information on matters that occur during use, including the amount of usage data, usage patterns, usage time, and error information of each software.

The usage information analysis unit (150) is intended to provide usage analysis information including information on the possibility of extending the use of software, cost reduction measures, and budget management by analyzing the usage information collected through the usage information collection unit (140) based on artificial intelligence.

The above usage information analysis unit (150) includes an AI model unit (151), and the AI model unit (151) collects information including the amount of software used, usage pattern, usage period, and usage cost for each customer to whom the software is supplied, and learns the collected information based on artificial intelligence to create a usage analysis AI model unit.

By analyzing usage information through the usage analysis AI model generated through the above AI model unit (151), usage analysis information including information on the possibility of extending the use of the software, cost reduction measures, and budget management is provided.

The above usage information analysis unit (150) can calculate a usage extension possibility index using mathematical expression 1 based on the amount of software usage data, usage pattern, and usage time collected through the usage information collection unit (140).

(K is the usage extension possibility index, M is the number of extensions, s is the number of software accesses, Gn is the amount of data used when accessing the software, and Tr is the usage time when accessing the software.)

In other words, the more extensions there are, the greater the sum of the amount of data used according to software access, and the greater the sum of the usage time according to software access, the higher the value of the usage extension possibility index.

The sum of the above usage data amount and the sum of the usage time can be adjusted to not have too large values by applying a logarithmic function to each, and the value can be increased in a curved shape by attaching a root function to the number of extensions.

If the above-mentioned usage extension possibility index exceeds a preset value, it can be determined that there is a high possibility of usage extension, and if the extension is not made in the case of non-automatic extension, a guide can be provided to enable extension, such as by sending an extension request message to the customer.

The usage statistics report generation unit (160) is intended to generate a usage statistics report based on the usage analysis information provided through the usage information analysis unit (150). The usage statistics report generation unit is generated according to a preset format and criteria, and includes information including usage analysis information and asset management plans based on the usage analysis information.

The above usage statistics report generation unit (160) may, based on usage analysis information provided by the usage information analysis unit (150), recommend alternative software to replace the currently used software if it determines that the currently used software is inappropriate. It is preferable to recommend alternative software that can be used in conjunction with the currently used software or can be used solely as a replacement for the alternative software.

The above usage statistics report generation unit (160) calculates a substitutability index through mathematical expression 2 based on how many teams of other customers are using the alternative software among all customer companies and how many teams of customers are using the software currently being used by the customer company. If the calculated substitutability index is within a preset standard range, it can be determined that the alternative software can replace the software currently being used and can include information about this in the usage statistics report.

In other words, it is possible to determine whether alternative software can be substituted based on how many teams among similar customer companies are using alternative software based on the software in use and how many teams are using the software the customer company is using.

(DS is the substitutability index, NL is the number of customers among all customers who are using the software including the software being used by the customer, NO is the number of customers among all customers who are using only the software being used by the customer, AD is the number of customers among all customers who are using at least one of the remaining software excluding the software being used by the customer and alternative software, UD is the number of customers among all customers who are using both the software being used by the customer and alternative software)

The substitutability index calculated through the above mathematical formula 2 can be derived higher as the number of customers who use the alternative software together with the software currently being used by the customer company increases, and the value can be derived by judging that the correlation between the alternative software and other software excluding the software currently being used by the customer company is low as the number of customers who use the alternative software together with the software currently being used by the customer company increases compared to the number of customers who use the alternative software together with at least one of the remaining software excluding the software currently being used by the customer company among all customers.

The customer terminal (200) is a terminal owned by the customer and can communicate with the customer asset management server (100) via a network.

The customer asset management server (100) further includes an evaluation unit, and the evaluation unit evaluates the customer asset management server based on review data received from the customer terminal.

The above evaluation department collects information on the satisfaction level of software supplied to consumers and calculates a rating for each supplier based on this information.

The above rating is characterized in that it is calculated in proportion to the ratio of the number of words corresponding to pre-learned positive keywords among all words constituting the response data to the number of words corresponding to pre-learned positive keywords among all words constituting the response data.

To this end, the analysis unit calculates the rating using the following mathematical formula 3.

Here, q is the rating, w is the first weight that is set differentially according to the type of software, a is the second weight that is set differentially according to the consumer's usage environment, pkj is the number of words corresponding to pre-learned positive keywords among all words constituting the response data, nkj is the number of words corresponding to pre-learned negative keywords among all words constituting the response data, and m is the total number of survey response data.

For example, if w is 5, a is 8, pk is 15, and nm is 4, the rating q can be calculated to be approximately 12.5.

In this way, the system according to the present invention can provide a more satisfactory service by evaluating the quality of the service provided through the system based on the results of a survey of users.

In some other embodiments, the customer asset management platform system according to the present invention further includes an encryption unit that encrypts and manages purchase request information and ratings of software.

The above encryption unit calculates a security score based on the characteristics of the purchase request information or the rating, and selectively encrypts only data for which the calculated security score is higher than a preset reference value.

The above security score is characterized in that it is calculated by calculating set variables using as variables the feature index calculated at the time of generation, the size value of the embedding vector for the pre-learned reference word, the size value of the embedding vector for the keyword extracted from the purchase desired information or the rating, and the number of keywords extracted from the purchase desired information or the rating.

Specifically, the security score is calculated in proportion to a first operation value, which is a ratio of the number of keywords to the difference between the size value of the embedding vector for the pre-learned reference word and the size value of the embedding vector for the keyword extracted from the purchase desired information or the rating, and is characterized in that the first operation value is calculated in proportion to a second operation value in which the feature index is reflected.

In some other embodiments, the security unit calculates the security score using the following mathematical expression 4.

Here, S_i is the encryption index calculated for experimental data i, f_i is the feature index calculated at the time of generation of data i, e_i is the size value of the embedding vector of the keyword set for experimental data i, e_a is the average size value of the embedding vector of the keyword set for each of the other experimental data uploaded to the same chat room as experimental data i, s is a setting value set in proportion to the capacity of the data, and c is a weight value set for each type of chat.

To this end, the system according to the present invention can build a neural network that extracts contextual information for input data by learning learning data using the Word2Vec algorithm to understand or estimate the meaning of the data.

The Word2Vec algorithm can incorporate a Neural Network Language Model (NNLM). A NNLM is essentially a neural network consisting of an input layer, a projection layer, a hidden layer, and an output layer. NNLM is used to vectorize words. Because NNLM is a well-known technology, a detailed description will be omitted.

The Word2vec algorithm, designed for text mining, determines proximity by examining the preceding and following relationships between words. The Word2vec algorithm is an unsupervised learning algorithm. As its name suggests, the Word2vec algorithm is a metric technique that represents the meaning of words in vector form. The Word2vec algorithm can represent each word as a vector in a space of approximately 200 dimensions. Using the Word2vec algorithm, a vector corresponding to each word can be derived.

The Word2vec algorithm can dramatically improve accuracy in natural language processing compared to other conventional algorithms. Word2vec can learn the meaning of words by leveraging the relationships between words and adjacent words in sentences within an input corpus. The Word2vec algorithm is based on artificial neural networks and assumes that words with similar contexts have similar meanings. The Word2vec algorithm trains on text documents, and it trains the artificial neural network on words that appear nearby (5 to 10 words before or after a word) as related words. Because words with related meanings are more likely to appear close together in a document, the two words can gradually have similar vectors through repeated training.

The Word2vec algorithm's learning methods include the Continuous Bag of Words (CBOW) method and the skip-gram method. The CBOW method predicts a target word using the context created by surrounding words. The skip-gram method predicts potential surrounding words based on a single word. The skip-gram method is known to be more accurate for large-scale datasets.

Therefore, in embodiments of the present invention, the Word2vec algorithm utilizing the skip-gram method is used. For example, if training is successfully completed using the Word2vec algorithm, similar words can be located nearby in a high-dimensional space. According to the Word2vec algorithm described above, the closer the distribution of surrounding words in a learning document is to a word, the more similar the resulting vector values can be. In addition, words with similar resulting vector values can be considered similar. Since the Word2vec algorithm is a well-known technology, a detailed description of the vector value calculation will be omitted.

The server can input the collected data into the neural network and extract the evaluation result vector value representing contextual information.

The server can calculate the similarity between the evaluation result vector value and each of the multiple reference vector values, and extract the reference vector value with the highest similarity to the evaluation result vector value among the multiple reference vector values. At this time, the similarity calculation method may be Euclidean distance, cosine similarity, Tanimoto coefficient, etc.

The management server can extract the word corresponding to the reference vector value with the highest similarity to the evaluation result vector value as the word corresponding to the recognized text.

Additionally, the management server can train an artificial neural network and utilize the trained artificial neural network. The processor can train or execute an artificial neural network stored in memory, and the memory can store the trained artificial neural network. The electronic device that trains the artificial neural network and the electronic device that uses the artificial neural network can be the same, but they can also be separate. Artificial intelligence is a computer system that partially implements the functions of the human brain and can learn, guess, and make decisions on its own. As learning progresses, the probability of extracting an answer can increase. Artificial intelligence can be composed of learning and component technologies that utilize it. AI learning is an algorithmic technology that classifies and learns features based on input data, and the component technologies can be technologies that partially implement the functions of the human brain using learning algorithms.

AI is a technology that easily approaches problems with multiple probabilistic answers, enabling it to logically and probabilistically infer optimal cycles, methods, and plans based on input data. AI inference techniques can include evaluating input data, making optimal predictions, knowledge- and probability-based inferences, and preference-based planning.

An artificial neural network (ANN) is a learning algorithm in the field of machine learning. It implements the connections between neurons and synapses in the brain through a program. An ANN can be programmed to create a neural network structure and then train it to achieve a desired function. While errors may exist, it can learn from massive amounts of data, producing appropriate output data based on input data. Its advantages include the ability to obtain output data that yields statistically positive results and its resemblance to human reasoning.

The server can build a query/metric dataset required for learning using an artificial intelligence algorithm built on big data, and for this purpose, it can include multiple pre-trained artificial neural networks.

The system according to the present invention may include multiple pre-trained artificial neural networks for performing machine learning algorithms. Machine learning allows the system to output data based on input data and utilize the results to learn independently, thereby enhancing its data processing capabilities. The artificial neural network extracts features from input data, infers patterns, and outputs result data. As these processes accumulate, the reliability of the result data increases.

In this embodiment, the artificial neural network may be an algorithm that outputs text data from at least one feature data item, including the shape, length, number, and height difference of objects recognized as text. The artificial neural network can infer optimal output data by using big data as input data directly or after processing it to remove unnecessary data.

Artificial intelligence machine learning models can be categorized into Supervised Learning, Unsupervised Learning, Semisupervised Learning, and Reinforcement Learning, depending on the learning type. Machine learning algorithms that can be used include Decision Trees, K-Nearest Neighbor, Artificial Neural Networks, Support Vector Machines, Ensemble Learning, Gradient Descent, Naive Bayes Classifiers, Hidden Markov Models, and K-Means Clustering.

An artificial neural network may be pretrained on various input values that may be included in the input data. An artificial neural network may be trained using reinforcement learning, a learning method. Reinforcement learning is a method that gradually increases the probability of obtaining a correct result by setting rewards and constraints. An artificial neural network may also be modeled based on a convelutional neural network (CNN) or a recurrent neural network (RNN).

In this way, the system according to the present invention can estimate the meaning of text data using big data and an artificial neural network.

The technology according to the present invention may be implemented as an application or in the form of program commands that can be executed by various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, etc., either singly or in combination.

The program commands recorded on the above computer-readable recording medium are those specifically designed and configured for the present invention, and may also be known and available to those skilled in the art of computer software.

Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, and flash memory.

Examples of program instructions include not only machine language codes, such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform processing according to the present invention, and vice versa.

The embodiments described in this specification and the configurations illustrated in the drawings are merely the most preferred embodiments of the present invention and do not represent all of the technical ideas of the present invention. Therefore, it should be understood that there may be various equivalents and modified examples that can replace them.

100 Customer Asset Management Server
110 Customer Information Storage
120 Software Supply Department
130 Contract Management Department
131 Contract Information Storage
132 Automatic Renewal Department
133 Contract Management Department
140 Usage Information Collection Department
150 Usage Information Analysis Department
151 AI Model Department
160 Usage Statistics Report Generation Department
200 customer terminals

Claims (6)

Includes a customer asset management server (100) to store contract information of a customer company that has supplied software for use for a set period of time and to automatically renew the period of use according to the stored contract information.
The above customer asset management server (100)
A customer information storage unit (110) for storing customer information transmitted from a customer terminal (200),
A software supply department (120) for supplying software to be used for a set period of time for a customer terminal (200) that has entered into a software supply contract,
A contract management department (130) that receives contract information from a customer terminal (200) and determines whether to renew the usage period of the supplied software based on the transmitted contract information,
A usage information collection unit (140) for collecting software usage information from a customer terminal (200) to which software is supplied,
A usage information analysis unit (150) for analyzing the usage information collected through the usage information collection unit (140) based on artificial intelligence to provide usage analysis information including information on the possibility of extending the use of the software, cost reduction measures, and budget management, and
A usage statistics report generation unit (160) for generating a usage statistics report based on the usage analysis information provided through the above usage information analysis unit (150),
Includes an evaluation unit for evaluating the customer asset management server based on review data received from the customer terminal,

The above contract management department (130)
A contract information storage unit (131) for receiving and storing contract information from a customer terminal (200),
It includes an automatic renewal unit (132) for analyzing the contract information stored in the contract information storage unit (131) to determine whether the usage period of the software supplied to the customer terminal (200) is automatically renewed, and for automatically renewing according to the set period when automatic renewal is performed.
The above usage information analysis unit (150)
It includes an AI model unit (151) for collecting information including the amount of software used, usage pattern, usage period, and usage cost for each customer to whom the software is supplied, and for learning the collected information based on artificial intelligence to create a usage analysis AI model unit.
The usage extension possibility index of the above usage information analysis unit (150) is proportional to the number of extensions of the software, the total sum of the usage data amount per software connection, and the total sum of the usage time per software connection.

The above usage statistics report generation unit (160)
If the software being used is determined to be inappropriate based on the usage analysis information provided through the usage information analysis department (150), other alternative software is recommended to replace it.
Among all customer companies, the number of teams using the alternative software and the number of customer teams using the software used by the customer company are used to calculate the substitutability index. If the calculated substitutability index is within the preset standard range, the alternative software is judged to be able to replace the software being used and the alternative software is recommended.
The above substitutable index is
A customer asset management platform system characterized by being calculated through the number of customers including the software being used by the customer among all customers, the number of customers using only the software being used by the customer among all customers, the number of customers using at least one of the remaining software excluding the software being used by the customer and alternative software among all customers, and the number of customers using both the software being used by the customer and alternative software among all customers. delete delete delete delete delete