Identifying grey-rhino in eminent technologies via patent analysis

doi:10.2478/jdis-2023-0002

Abstract

Abstract:

Purpose: Following the typical features of the grey-rhino event as predictability and profound influence, we attempt to find a special pattern called the grey-rhino in eminent technologies via patent analysis.

Design/methodology/approach: We propose to combine triadic patent families and technology life cycle to define the grey-rhino model. Firstly, we design the indicator rhino-index R_h= ST/SP and descriptor sequence {R_h}, where ST and SP are the accumulative number of triadic patent families and all patent families respectively for a specific technology. Secondly, according to the two typical features of the grey-rhino event, a grey-rhino is defined as a technology that meets both qualitative and quantitative conditions. Qualitatively, this technology has a profound influence. Quantitatively, in the emerging stage, R_h ≥ Rae, where Rae is the average level of the proportion of triadic patent families. Finally, this model is verified in three datasets, namely Encyclopedia Britannica's list for the greatest inventions (EB technologies for short), MIT breakthrough technologies (MIT technologies) and Derwent Manual Code technologies (MAN technologies).

Findings: The result shows that there are 64.71% EB technologies and 50.00% MIT technologies meeting the quantitative standard of the grey-rhino model, but only 14.71% MAN technologies fit the quantitative standard. This falling trend indicates the quantitative standard of the grey-rhino model is reasonable. EB technologies and MIT technologies have profound influence on society, which means they satisfy the qualitative standard of the grey-rhino model. Hence, 64.71% EB technologies and 50.00% MIT technologies are grey-rhinos. In 14.71% MAN technologies meeting the quantitative standard, we make some qualitative judgments and deem U11-A01A, U12-A01A1A, and W01-A01A as grey-rhino technologies. In addition, grey-rhinos and non-grey-rhinos have some differences. R_h values of grey-rhinos have a downward trend, while R_h values of non-grey-rhinos have a contrary trend. R_h values of grey-rhinos are scattered relatively in the early stage and centralize gradually, but non-grey-rhinos do not have this feature.

Research limitations: There are four main limitations. First, if a technology satisfies the quantitative standard of the model, it is likely to be a grey-rhino but expert judgments are necessary. Second, we don't know why it will be eminent, which involves technical contents. Thirdly, we did not consider the China National Intellectual Property Administration (CNIPA) and the German Patent and Trademark Office (DPMA) which also play important roles in worldwide patents, so we hope to expand our study to the CNIPA and the DPMA. Furthermore, we did not compare the rhino-index with other patent indicators.

Practical implications: If a technology meets the quantitative standard, this can be seen as early warning signals and the technology may become a grey-rhino in the future, which can catch people's attention in the emerging stage and make people seize the technical opportunity early.

Originality/value: We define and verify a new pattern called the grey-rhino model in eminent technologies.

Key words: Grey-rhino, Eminent Technologies, Triadic Patent Families, Technology Life Cycle

Shelia X. Wei , Helena H. Zhang , Howell Y. Wang , and Fred Y. Ye .(2023). Identifying grey-rhino in eminent technologies via patent analysis. Journal of Data and Information Science, 8(1), 47-71. DOI: 10.2478/jdis-2023-0002

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Types	EB technologies		MIT technologies		MAN technologies
Types	Number	Ratio (%)	Number	Ratio (%)	Number	Ratio (%)
Y: high	8	23.53	2	5.88	1	2.94
Y: low	14	41.18	15	44.12	4	11.76
Y: total	22	64.71	17	50.00	5	14.71
N: high	4	11.76	5	14.71	16	47.06
N: low	8	23.52	12	35.29	13	38.24
N: total	12	35.29	17	50.00	29	85.29
Total	34	100	34	100	34	100

Types		Min.	Q1	Med.	Q3	Max.	Avg.	Std.	Mann-Whitney U
Types		Min.	Q1	Med.	Q3	Max.	Avg.	Std.	Asymp. Sig.	Exact Sig.
SP	Y: high	24	284	404	1631	11,272	2,417.364	4,263.708	0.236	0.247
	N: high	5	191.500	3,401	8,794.500	25,318	5,655.760	6,864.649	0.236	0.247
	Y: low	32	258	1,605	14,646.500	170,403	13,542.091	30,785.932	0.423	0.427
	N: low	3	148	1,870	7,860.500	271,445	16,956.970	52,020.530	0.423	0.427
ST	Y: high	5	27	51	131	1,913	318.000	626.277	0.354	0.364
	N: high	0	25	231	576.500	2,421	458.440	636.046	0.354	0.364
	Y: low	2	16	96	747.500	5,817	691	1,235.256	0.040	0.040
	N: low	0	2.500	54	174	9,937	651.636	2,063.485	0.040	0.040

Sig.	High-level			Low-level
Sig.	1	2	3	1	2	3	4
Asymp. Sig.	0.000	0.002	0.721	0.000	0.000	0.001	0.026
Exact Sig.	0.000	0.001	0.757	0.000	0.000	0.001	0.027