**3. Methodology**

The proposed methodology aims at creating a model capable of evaluating the complexity and volume of patent applications, in addition to a new fair and efficient manner of distributing patent applications to patent examiners. For this, it is necessary to obtain the application data with its variables of interest, evaluate patent applications according to the selected variables, create a specific logic for this distribution, and, finally, evaluate the new proposed logic compared to the original distribution. Thus, the proposed method can be divided into four main parts, which complement each other and will be detailed below.

## **3.1 Evaluation of patent applications as to volume of data and complexity: initial tests**

We obtained data from applications that already went through the first examination step during two years, in the area of electricity, more precisely from May 2015 to May 2017, month in which the research was initiated. By identifying and selecting the variables, the proposal was to tabulate data from all patent applications analyzed, including all relevant variables selected, and identify the patent examiner who received the application for analysis. These data were defined as the Initial Test Sample and, based on it, the IPC for the sample patent applications and, consequently, the specific area of expertise (ZAE) of each examiner were then identified.

As this is a problem with multiple variables of interest, we attempted to find a multivariate analysis method to solve it. The bibliographic review was made to choose a method that meets the following criteria:


*A Methodology for Evaluation and Distribution of Patent Applications to INPI-BR Patent… DOI: http://dx.doi.org/10.5772/intechopen.98400*

• was mathematically and statistically robust and scientifically tested in many different fields of knowledge.

Given the established criteria, the method of principal component analysis (PCA) was selected as the basic tool for evaluation of patent applications. Such method allows for determination of such principal components of the specific problem according to the share of general variance explained by each of the components. Following identification of these new components, a General Complexity Ratio (IGC) is proposed for the patent applications, which is the ratio of the weighted sum of the most significant components plus their eigenvalues to the sum of the eigenvalues themselves, which were obtained from the correlation matrix of the original variables of the problem. Based on these ratios for each of the applications, these were classified into up to five classes (Very Light, Light, Moderate, Heavy, and Very Heavy) according to the average and the standard deviation of the general ratios obtained. It is important to highlight that the eigenvalues and eigenvectors were determined both manually and with the assistance of the software Matlab and of the Matrix Calculator (Available at https://matrixcalc.org/pt/vectors. html), and the other steps were executed using Excel electronic spreadsheets (Microsoft Office 2010).

#### **3.2 Evaluation of patent applications as to volume of data and complexity: validation tests with time**

After choosing the method, determining the ratios and the classification of the applications into classes, the next step is a sensitivity analysis/validation of the ratios and classification of the patent applications. For this step, an experimental/ empirical research is proposed, aiming to establish a correlation between the ratios and classifications found and the time/effort to exam the patent applications. First, the substantive process of patent examination by INPI and the standard examination report were analyzed in order to identify the main examination steps and directly or indirectly related variables. Based on these main examination steps, a form was prepared to survey the time for examination, to be filled up by the examiners, in which information about the time to execute each step is inserted. Thus, a list of applications is determined, hereinafter referred to as Standard Sample, with tabulation of data, including all the variables of interest in addition to the time for examination. The PCA method will then be applied to this new sample, and the IGC ratios for each patent application will be calculated, in addition to their classification into classes. In this context, the correlation between the ratios obtained and the time for examination will be verified, and the PCA method will be applied, including several simulations with variations in the sample size and in the number of variables. This procedure aims at showing the variables with direct impact on the time for examination and the representativeness of the IGC regarding these variables, evaluating the minimum necessary sample size, and also testing the applicability of the PCA method.

#### **3.3 Distribution of patent applications**

In this 3rd part of the proposed method, a specific logic for distribution of applications was built based on the previously obtained classification into the five classes, and also on the classification of the application according to the IPC. Data obtained from the patent applications were separated by main IPC subclasses, and so, the main subclasses examined by each examiner were identified, as well as their ZAE was determined. This area was obtained considering the subclasses of patent

applications with occurrences above 5% of the total examinations by each examiner evaluated based on the largest sample obtained, the Initial Test Sample. Thus, a new logic of distribution of patent applications is proposed according to the classification of the general ratios and to the ZAE, considering the following criteria:


#### **3.4 Evaluation of the new distribution logic**

In the fourth and last part of the proposed method, first a new sample of more recent patent applications was obtained, hereinafter referred to as the Final Redistribution Sample. Patent application data were obtained from the same examiners in the field of electricity that made up the Initial Test Sample, however, with the first examinations carried out between May and July 2020. It is important to note that, after the backlog combat plan was implemented by INPI, the examination process has somewhat changed for most of the patent applications in the area of electricity. Hence, obtaining this redistribution sample was necessary to harmonize the examination process carried out by the examiners evaluated therein with the process implemented by the examiner, from which the Standard Sample with time was obtained. This harmonization was made using standard samples with time and redistribution samples containing the same type of patent applications, in other words, patent applications that may be examined using data from previous searches by international offices. It is important to note that this type of application covers an average of 88% of the total stock of patent applications filed until 2016 in the field of electricity.

Based on this new sample, we apply the proposed model and logic for distribution, and then calculate a Distribution Balancing Ratio (IBD) both for the original distribution and for the new distribution, a ratio that ranges from zero to one, and considers the differences between the medians of the variables of each examiner's samples and the general medians of the division's variables. Within this context, it should be noted that the closer the medians of the variables of the examiners' individual samples are to the general medians of the division's variables, the larger the amount of the IDB is and, consequently, the better balanced the distribution is. The breakdown of the IBD, including explanations and analyses about its formula, maximum and minimum limits, will be presented in item 4 – Development.
