## 3 Methodology
### Data Preparation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Topic** & **SME Description** \\ \hline Computing Resource & Communications traffic to exchange sensor data and a wide range of other information, including the need for low latency to meet real-time demands, place additional burden on available computational resources. Objectives target optimal resource allocation and usage of onboard and cloud-based computing resources and optimize communications across multiple network interfaces and servers. \\ \hline Cybersecurity & Growing wireless connectivity between vehicles and other things, including other vehicles, expands the vulnerability surface for cyber-attacks. Objectives address enhanced cybersecurity, including encryption, authentication, and intrusion detection methods. \\ \hline Driving Safety & Objectives utilize vehicle-to-everything connectivity and sensors on other vehicles to enhance visibility and situational awareness, safely navigating in complex environments, including through intersections and among pedestrians, and avoiding collisions. \\ \hline Information Management & Demand for efficient management of information across software applications and services scales with increased vehicle connections. Objectives ensure that systems present relevant information to vehicle operating and in-cabin infotainment systems to prevent data overload and prioritize information that is essential for vehicle operation, safety, and user experience. \\ \hline Multi-vehicle Networking & Vehicle clusters can form and maintain microvehicular clouds to efficiently share and exchange information. Objectives address the efficient use of resources among vehicles to enable capabilities such as distributed data storage, collaborative computing, reliable communications, and service provisioning. \\ \hline Platooning & The streamlined aerodynamics resulting from vehicles following each other more closely than normal (platooning) results in better fuel efficiency and improved traffic flow. Objectives address various ways to utilize wireless, sensors, and real-time control mechanisms to enable safer and more cost-efficient platooning and to alert law enforcement. \\ \hline Smart Parking & Locating parking spaces in crowded and complex environments can be challenging and contribute to congestion. Objectives facilitate cooperative parking space searches, including charging for the “ego” vehicle by using sensors and microvehicular clouds or centralized services. \\ \hline Traffic Signaling & Suboptimal traffic signal timing can exacerbate congestion. Objectives leverage wireless communications and sensors among vehicles to assess conditions and predict arrival times while dynamically optimizing traffic signaling for overall traffic impact. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SME classification of general patent objectives. The topic modeling algorithms require cleaned text to provide the best results. Hence, the text cleaning procedure removed noisy text, including characters and words with little or no meaning. These included punctuation marks, numbers, and short tokens such as those containing at most three characters. The field of NLP defines stop words as those that contribute little or no meaning towards understanding a sentence. Common English stop words are _they_, _which_, _that_, _were_, _having_, _doing_, _while_, and _from_. Initially removing short tokens such as _the_, \(a\), _an_, _in_, _on_, _at_, or _and_ reduces the size of the corpus to speed up the removal of longer stop words, including words that are specific to patent documentation. For example, words commonly used in patent documents are _system_, _method_, _embodiment_, _invention_, _disclosure_, _claim_, _provide_, _describe_, and _include_. In this analysis, the words _connected_, _vehicle_, and _vehicles_ were stop words because they must appear in all documents, by definition of the search keywords.