The patent dataset analyzed is based on search criteria dealing with WEEE recycling, namely focusing on battery recycling, while a ‘control’ sample is produced where the patent data concern a more generical recycling technical context. Such differentiation is essentially achieved by two different selections from the EPO database Patstat (Spring 2022 ed.), each patent search being based on IPC/CPC classification codes selected from a pool of “green” classification codes. While the dataset regarding the batteries is defined by any of the IPC/CPC H01M6/52 or the IPC/CPC H01M10/54 or the CPC Y02W30/84 individually taken or combined together, documents of the ‘control’ should not be assigned with any of the classification codes defining the batteries recycling. The first step of the patent analysis is focusing on applications including either national authorities or supranational authorities, such as the EPO or the WIPO (both addressed through harmonized filing procedures) to evaluate meaningful trends of patent applications filed starting from yr. 1980 to yr. 2022. Such trends can also be assessed in order to normalize the amount of filing events, i.e. by comparing, for each of the most representative patent authorities, the trend of WEEE specific applications with the trend resulting from the combination of the WEEE applications plus the amount of the applications belonging to the respective control sample, for each trend being the applications filed to the same patent authority. A subsequent step entails assessments based on the EPO definition of simple patent families. Essentially, the same strategy has been adopted for both the ‘WEEE/RAEE’ patent families and the ‘control’. The assessment in each case deals with the analysis of the patent families based on the ranking of IPC and CPC codes, respectively, to highlight those most frequently assigned in the field of batteries recycling. Subsequently, qualitative and quantitative information is provided about the most frequently addressed patent authorities, the most representative players and their affiliation, the patent family dimensions, the forward citations (in terms of number of citing families and national/supranational authorities) for each patent family of the dataset. Eventually a ‘proof of concept’ analytic approach deals with the focusing on the filing strategy of the top players.The same analytic approach could be used in case other kinds of electronic waste recycling should be of interest, for example not focusing on batteries. To such aim, the CPC Y02W30/82 classification code, initially present in several patent documents included in the ‘control’ sample if focusing on the batteries recycling, should be then omitted from the control dataset and be replaced with the IPC/CPC classification codes specific for the batteries recycling. Therefore, the CPC Y02W30/82 could define a new dataset, namely that one specifically regarding the WEEE (non-batteries) recycling.

The patent dataset analyzed is based on search criteria dealing with WEEE recycling, namely focusing on battery recycling, while a ‘control’ sample is produced where the patent data concern a more generical recycling technical context. Such differentiation is essentially achieved by two different selections from the EPO database Patstat (Spring 2022 ed.), each patent search being based on IPC/CPC classification codes selected from a pool of “green” classification codes. While the dataset regarding the batteries is defined by any of the IPC/CPC H01M6/52 or the IPC/CPC H01M10/54 or the CPC Y02W30/84 individually taken or combined together, documents of the ‘control’ should not be assigned with any of the classification codes defining the batteries recycling. The first step of the patent analysis is focusing on applications including either national authorities or supranational authorities, such as the EPO or the WIPO (both addressed through harmonized filing procedures) to evaluate meaningful trends of patent applications filed starting from yr. 1980 to yr. 2022. Such trends can also be assessed in order to normalize the amount of filing events, i.e. by comparing, for each of the most representative patent authorities, the trend of WEEE specific applications with the trend resulting from the combination of the WEEE applications plus the amount of the applications belonging to the respective control sample, for each trend being the applications filed to the same patent authority. A subsequent step entails assessments based on the EPO definition of simple patent families. Essentially, the same strategy has been adopted for both the ‘WEEE/RAEE’ patent families and the ‘control’. The assessment in each case deals with the analysis of the patent families based on the ranking of IPC and CPC codes, respectively, to highlight those most frequently assigned in the field of batteries recycling. Subsequently, qualitative and quantitative information is provided about the most frequently addressed patent authorities, the most representative players and their affiliation, the patent family dimensions, the forward citations (in terms of number of citing families and national/supranational authorities) for each patent family of the dataset. Eventually a ‘proof of concept’ analytic approach deals with the focusing on the filing strategy of the top players.The same analytic approach could be used in case other kinds of electronic waste recycling should be of interest, for example not focusing on batteries. To such aim, the CPC Y02W30/82 classification code, initially present in several patent documents included in the ‘control’ sample if focusing on the batteries recycling, should be then omitted from the control dataset and be replaced with the IPC/CPC classification codes specific for the batteries recycling. Therefore, the CPC Y02W30/82 could define a new dataset, namely that one specifically regarding the WEEE (non-batteries) recycling.

As already explained in our previous publication (Analysis of IPC classification codes frequency in patents concerning “in situ” remediation technologies), our Patlib Centre is involved in the context of Horizon2020 funded projects aimed at soil remediation. Therefore, we are especially interested in identifying the trends of the relevant soil remediation technologies that score the highest frequency of citation within the patent dataset. By means of an iterative procedure based on the t-distributed stochastic neighbor embedding (tSNE) algorithm, the patent families can be included in clusters depending on presence/absence of IPC subgroups or main groups or subclasses, chosen among those most abundant in the dataset. Therefore, following three rounds of clustering, about 90% of the initial ‘dataset 1’ (including 1632 simple families - as defined by the European Patent Office) become "unpacked" and clustered. Further assessments based on the patent bibliographic data can be performed, the essential advantage being that the technical content of each cluster is homogeneous, so that the results are aggregated depending on the pattern of specific IPC codes common to each cluster. Within the abovementioned 1632 simple families of the ‘dataset 1’ initially analyzed, there is evidence of 290 simple families, each one being characterized by the assignment of the CPC classification code B09C2101, specifically dealing with “in situ” remediation of soil. Apart from these 290 simple families, a specific search query made on Patstat online, aimed at selecting those patent documents to whom the CPC code B09C2101 has been assigned, has revealed the presence of additional 2521 simple patent families (included in the ‘dataset 2’ and not included in ‘dataset 1’) that underwent a substantially similar analysis focusing on the IPC classification codes’ distribution already described as far as ‘dataset 1’ is concerned. From the observation that in ‘dataset 1’ IPC classification codes such as B09C1/08 and B09C1/10 are among those most frequently assigned to the patent families, further analysis of the patent families of ‘dataset 2’ has been focused on these two IPC classification codes. In fact, these are the most representative also in ‘dataset 2’. Therefore, the analysis based on codes different from the IPC B09C1/00 (reclamation of contaminated soil), which is significantly represented in both datasets although not referring to a specific soil decontamination technique, reveals that for the time being soil decontamination often entails methodologies based on electrochemical remediation as well as microbiological techniques, for example based on the use of enzymes. The information that can be gathered from both datasets is comprehensive being based on data arguable from 4153 patent families.

As already explained in our previous publication (Analysis of IPC classification codes frequency in patents concerning “in situ” remediation technologies), our Patlib Centre is involved in the context of Horizon2020 funded projects aimed at soil remediation. Therefore, we are especially interested in identifying the trends of the relevant soil remediation technologies that score the highest frequency of citation within the patent dataset. By means of an iterative procedure based on the t-distributed stochastic neighbor embedding (tSNE) algorithm, the patent families can be included in clusters depending on presence/absence of IPC subgroups or main groups or subclasses, chosen among those most abundant in the dataset. Therefore, following three rounds of clustering, about 90% of the initial ‘dataset 1’ (including 1632 simple families - as defined by the European Patent Office) become "unpacked" and clustered. Further assessments based on the patent bibliographic data can be performed, the essential advantage being that the technical content of each cluster is homogeneous, so that the results are aggregated depending on the pattern of specific IPC codes common to each cluster. Within the abovementioned 1632 simple families of the ‘dataset 1’ initially analyzed, there is evidence of 290 simple families, each one being characterized by the assignment of the CPC classification code B09C2101, specifically dealing with “in situ” remediation of soil. Apart from these 290 simple families, a specific search query made on Patstat online, aimed at selecting those patent documents to whom the CPC code B09C2101 has been assigned, has revealed the presence of additional 2521 simple patent families (included in the ‘dataset 2’ and not included in ‘dataset 1’) that underwent a substantially similar analysis focusing on the IPC classification codes’ distribution already described as far as ‘dataset 1’ is concerned. From the observation that in ‘dataset 1’ IPC classification codes such as B09C1/08 and B09C1/10 are among those most frequently assigned to the patent families, further analysis of the patent families of ‘dataset 2’ has been focused on these two IPC classification codes. In fact, these are the most representative also in ‘dataset 2’. Therefore, the analysis based on codes different from the IPC B09C1/00 (reclamation of contaminated soil), which is significantly represented in both datasets although not referring to a specific soil decontamination technique, reveals that for the time being soil decontamination often entails methodologies based on electrochemical remediation as well as microbiological techniques, for example based on the use of enzymes. The information that can be gathered from both datasets is comprehensive being based on data arguable from 4153 patent families.

The patent dataset analysed is based on search criteria aimed at retrieving patent documents dealing with "in situ" remediation technologies. The dataset has been created in the context of the Horizon2020 funded project "Posidon" (https://www.posidonproject.eu/). According to the European Environment Information and Observation Network for soil (EIONET-SOIL), the number of estimated potential soil contaminated sites is more than 2.5 million , of which about 14 % (340 000 sites) are highly likely to be contaminated, and hence in need of remediation measures. In terms of budget, the management of contaminated sites is estimated to cost around 6 billion Euros (€) annually. The aim of the project is to foster the development of innovative technical solutions through pre-commercial procurement selection procedures. The initial elucidation of the prior art, based on an extensive analysis of patent documents is fundamental. As Patlib centre staff members, also enrolled in the "monitoring board" of Posidon, we produce evidence that there is a considerable amount of predivulgation of decontamination technologies applicable for "in situ" reclamation of contaminated soil and/or water emerging from patent documents. Since we are especially interested in identifying the trends of the technologies that score the highest frequency of citation within the patent dataset, we illustrate one way of "unpacking" the patent dataset by identifying recurrent patterns of IPC classification codes. To this purpose, the IPC classification codes characteristic of each patent family of the dataset are analysed by isolating and clustering through subsequent stages the patent documents sharing specific IPC subgroups, main groups and subclasses patterns. During each phase the t-distributed stochastic neighbor embedding (tSNE) algorithm is applied to an array of patent families depending on presence/absence of IPC subgroups or main groups or subclasses, chosen among those most frequent in the dataset. Therefore, following the first round of clustering, those patent documents sharing specific IPC subgroups patterns are isolated and ready for additional investigation. The remaining patent documents undergo the second analytic phase by means of tSNE, therefore those patent documents sharing specific IPC main groups patterns are isolated and ready for additional investigation. The remaining patent documents undergo the final clustering by means of tSNE in order to separate the patent documents depending on specific patterns of IPC subclasses. By means of this procedure about 90% of the initial dataset (1632 simple families - as defined by the European Patent Office) become "unpacked" and clustered. Further assessments based on the patent bibliographic data can be performed, the essential advantages being that the technical content of each cluster is homogeneous and the results of different clusters can be subsequently aggregated, when a specific IPC code is common to such clusters.

The patent dataset analysed is based on search criteria aimed at retrieving patent documents dealing with "in situ" remediation technologies. The dataset has been created in the context of the Horizon2020 funded project "Posidon" (https://www.posidonproject.eu/). According to the European Environment Information and Observation Network for soil (EIONET-SOIL), the number of estimated potential soil contaminated sites is more than 2.5 million , of which about 14 % (340 000 sites) are highly likely to be contaminated, and hence in need of remediation measures. In terms of budget, the management of contaminated sites is estimated to cost around 6 billion Euros (€) annually. The aim of the project is to foster the development of innovative technical solutions through pre-commercial procurement selection procedures. The initial elucidation of the prior art, based on an extensive analysis of patent documents is fundamental. As Patlib centre staff members, also enrolled in the "monitoring board" of Posidon, we produce evidence that there is a considerable amount of predivulgation of decontamination technologies applicable for "in situ" reclamation of contaminated soil and/or water emerging from patent documents. Since we are especially interested in identifying the trends of the technologies that score the highest frequency of citation within the patent dataset, we illustrate one way of "unpacking" the patent dataset by identifying recurrent patterns of IPC classification codes. To this purpose, the IPC classification codes characteristic of each patent family of the dataset are analysed by isolating and clustering through subsequent stages the patent documents sharing specific IPC subgroups, main groups and subclasses patterns. During each phase the t-distributed stochastic neighbor embedding (tSNE) algorithm is applied to an array of patent families depending on presence/absence of IPC subgroups or main groups or subclasses, chosen among those most frequent in the dataset. Therefore, following the first round of clustering, those patent documents sharing specific IPC subgroups patterns are isolated and ready for additional investigation. The remaining patent documents undergo the second analytic phase by means of tSNE, therefore those patent documents sharing specific IPC main groups patterns are isolated and ready for additional investigation. The remaining patent documents undergo the final clustering by means of tSNE in order to separate the patent documents depending on specific patterns of IPC subclasses. By means of this procedure about 90% of the initial dataset (1632 simple families - as defined by the European Patent Office) become "unpacked" and clustered. Further assessments based on the patent bibliographic data can be performed, the essential advantages being that the technical content of each cluster is homogeneous and the results of different clusters can be subsequently aggregated, when a specific IPC code is common to such clusters.