## 4 Discussion  
In the previous studies reviewed and analyzed, key studies include [PERSON]'s CHA cache storage framework, [PERSON]'s memory reduction proposal, [PERSON]'s VA system, and [PERSON]'s edge learning architecture for privacy enhancement [29; 30; 44; 45]. These solutions focus on automated systems like home automation, where interconnected devices respond to various events, commands, or voice orders. However, while the first two studies implement a cache memory, they do not store data or responses obtained from the cloud associated with the different user queries. On the other hand, research on energy optimization through the implementation of VAs primarily focuses on energy management in homes or some industrial settings. Notable studies include those by [PERSON] et al., 2023, and [PERSON], 2022 [27; 46], and the minimization of energy consumption in smart buildings through real-time data processing presented by [PERSON], 2024 [23]. Regarding energy measurement and optimization in sensor networks or systems involving IoT devices, some notable studies include those by [PERSON], 2014, and [PERSON] et al., 2012 [21; 22]. However, these studies do not involve VAs in their applications. Additionally, it is possible to affirm that the metrics found, such as those by [PERSON] et al., 2020, and [PERSON] et al., 2017 [36; 37], are not aligned with the same conditions associated with the system proposed in this work. It is important to highlight that none of the studies listed in Table 1 comprehensively address all the research areas identified in this study, such as energy savings, voice recognition, cache memory management, and metric development. Additionally, we did not find models that implement cache usage in VAs with a similar purpose to ours. Some cited studies that consider cache usage [29; 30] do so with different objectives, such as task automation in homes or specific locations, memory management, or improving processing efficiency. However, they do not focus on reducing energy consumption in VAs performing any type of query to a local server or the cloud. In our case, we propose an approach that, to the best of our knowledge, is novel, specifically aimed at optimizing energy consumption through the use of cache for voice assistants. Since the evaluation objectives of these studies differ from ours, making a direct comparison with these models would not be appropriate or representative. In terms of consumption behavior for cloud queries, it can be determined that communication technology influences the energy consumption of VAs, as connection bandwidth directly impacts the response time for queries, which, in turn, generates variations in energy consumption. The tests conducted on our model were performed using a fiber optic network with a 700 Mbps internet connection channel. The results of these tests are presented in the measurement tables for cloud query times. In this context, and considering related studies [47], if tests were performed using a 4 G mobile network with a bandwidth of 100 Mbps, nominal response times would be approximately seven times slower. In contrast, using a 5 G network with a nominal capacity of 10 Gbps, response times could be up to 14 times faster. Based on the results obtained and in line with the proposal of this work, the primary reason for the significant difference in the metrics is the use of cache memory, which significantly reduces response times. Specifically, cloud responses are influenced by factors such as the type of language model used [48] and the conditions of the cloud connection, which directly affect response times and, consequently, the differences in metrics. The results show that the time spent on cache operations is negligible or has minimal impact compared to the time required to obtain a response from the cloud. This suggests that this solution could be implemented in most systems without incurring significant costs compared to systems without this optimization, and it could potentially help reduce energy consumption.