**Artificial taste perception of tea – Scholarly works and Patent Analysis**
\*\*Double blind review, please do not include authors information in this version \*\*
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**HIGHLIGHTS**
- Tea is widely consumed drink and growth rate of tea industry is very high.
- Tea industry demands unique identity of species for marketing using artificial intelligence.
- Paper gives patent and scholarly work review on the artificial taste perception of tea.
ABSTRACT
***This paper describes scholarly work and patent analysis for an artificial taste perception and tea from the year 2001 to 2022.Tea is the most popular hot beverage in the world and has many health benefits. For marketing nowadays, the taste of tea is identified globally by its geographical origin. It is unique identification done by human tea tasters which decides the taste formula for the respective tea species. According to IMARC (a leading market research company), the global tea market extended a value of US$ 21 Billion in 2020 and expecting a CAGR of 5.1% during 2021-2026.Tea is a much demanded hot beverage, has worldwide consumers and from their outlook, it is important to know about the taste uniformity, safety, and quality of various tea species. To recognize the taste of tea using machines and technology is one more aspect that is currently on-going. The website gauged for this survey is “[www.lens.org](http://www.lens.org)”. This analysis generate explores many important details like research contribution in scholarly work by various authors, institutions, funding agencies, jurisdictions and individual patent documents. Total 3725 patents which are cited by scholarly work records 6742 were found for artificial taste perception of tea.***
*Keywords: Tea, Artificial taste perception, Scholarly work, Patent analysis.*
# INTRODUCTION
Tea is the most widely consumed hot beverage in the world \[7\]. The market of tea is day by day growing with new additives. For brand identification and recognition, it is essential to showcase the unique flavors of tea species. Due to the huge production, processing of tea; it is important to monitor the taste of a tea using machine and artificial intelligence. It will enhance the rate of classification of tea with the display of an automated tea attribute array \[45, 46, 47, 48, 49, 50\].
At the initial stage of any research project, it is a must to carry survey for the available scholarly work and related patents. It will give the actual impact on the economy created by the research, the claims of their research work. For new inventors, it will give the idea with which they can collaborations or acquire research guidance and directions. The funding agencies that sponsor the organizations involved in the same type of research work will boost the discussions and will provide common platform to go together with technology.
So the objective of this paper is to study the available scholarly works and patents on “artificial taste perception of tea” and to identify the research claims and gaps with the database provided on “[www.lens.org](http://www.lens.org)” to finalize the research direction in this area. It will avoid duplication of research stream and may have the potential to generate the patentable idea \[34, 35, 36, 51, 52, 53\].
The Lens is an open and global cyber-infrastructure that will provide the scholarly work and patent database according to the search keyword demanded. It is an open and wide web platform of the patent documents in the world, annotatable digital public goods that are assimilated with scholarly and technical literature along with regulatory and business data. This platform will allow free access for related document collection; provide an online analyzer for mapping various important parameters. It will reinstate the role of the patent system which will aware and inspire entrepreneurs, policymakers, and the public.
# METHODOLOGY
Now day’s world is growing very fast with technology and innovations. The variants of inventions are the leading trends of market. Patent is the intellectual property which gives benefits to first inventors, funding organisations and society as well. Pat-informatics is the scientific way to find relation between patents and growing trends. It also generates macro view of technology current affairs. For researchers who may be common person can file a patent for their inventions with the help of patent analytics and receive benefits for that. Pat-informatics avoids duplication of work and useful in tracking and reporting as well as in big data analytics for commercial purpose.
Artificial perception of tea liquor is a challenging task. The three main attributes of tea are its flavor; its fragrance and its color have to be assessed for the tea quality evaluation. The fusion of tea flavor and fragrance had analyzed by electronic nose and electronic tongue with the help of a fuzzy neural network \[1\]. Two main objectives were studied in the paper \[1\] of which 1st was to show the results of the fusion of e-tongue and e-nose perception were more accurate and 2nd was to develop and analyse various tea grading classifiers.
Some more ideas had picked from other research applications such as three types of sensors as gas for odor analysis, electrochemical sensors for taste analysis, and optical sensors and systems for the color analysis described in the paper \[2\] for the characterization of red wine. The flavor analysis using the SAW device had described in the paper \[4\] where the fusion of e-nose and e-tongue had given. The analysis to find the percentage of the bitterness of Olive oil using the same approach is given in paper \[3\] where the combined and individual sensory reports were generated. With the comparison, it was found the combined sensor perception explores the results accurately rather than individual analysis \[3\].
The combination of fuzzy logic and neural networks will provide the intelligent system which will classify the tea attributes for quality evaluation. The classifier is the algorithm which generates class label to verified object according to its description \[1\].
Figure 1 is the general system block schematic which includes sensors at the input to sense the bitterness and astringency, it was followed by signal conditioning and data acquisition unit where the acquired sample data will be processed for a suitable algorithm frame, this processed data is given as input to algorithm classifier; which will find the attribute quality and classify it accordingly. , In the end, the report is generated digitally for various purposes.
> 
**Figure 1:** System block schematic for artificial taste perception
Generally, the tea is tested by tea tasters for its bitterness, astringency, and briskness, and the marks are assigned on a scale of 1 to 10 \[5\]. The same kind of decision-making algorithm classifier has been established based on - fuzzy logic and neural network. In the training phase, the classifier had trained for certain numbers of tea quality grades as an ideal base. In the testing phase the unknown sample is given as input and according to decisions made in the training phase; the actual decisions are taken for unknown tea samples for grading \[1\]. Classification results show that the combination of E-nose and E-tongue is having a high classification rate \[1\].
Another paper \[5\] is about feature fusion using an artificial neural network (ANN) classifier. In this paper, both data and feature level fusion had performed.
Some adaptive filter algorithms have been given in paper \[32, 33\] for speech enhancement; similar kinds of filters can be developed for artificial taste perception to improve the selectivity of taste attributes.
The objective of this study is to find scholarly works and patent information on “artificial taste perception” and to identify the research gaps that can be explored further \[6, 45\].
**Research area and data source**
The scholarly work and patent survey had been covered the research area “Artificial taste perception of tea”. The scope is limited to artificial taste perception and classification based on flavor and fragrance tea \[45, 46, 47, 48, 49, 50, 51, 52, 53\].
The scholarly work and patent survey had been carried out with the help of the “www.lens.org’ website where the number of data sources are available of major well-known publishers such as Elsevier BV, Wiley, Springer Science, and Business Media LLC, IEEE, (Public Library of Science (PLoS). Total 70,559 publishers are available with this repository and all are available free of cost. Many filters are given on the website for easy handling and finding data such as open access, cited by patent, cited by scholarly works, author, institution, journal, conference, publisher, etc. The terms used in the paper related to patent and scholarly work are derived from two websites IP Australia - [www.ipaustralia.gov.au](https://www.ipaustralia.gov.au/) \[20\] which is the data partner of Lens and https://www.justia.com/intellectual-property/glossary/ \[21\].
The lens offers an application programming interface and data facility for the access of huge scholarly research work and patent documents with the various useful parameters like sort by relevance, scholarly citations (highest), scholarly citations (lowest), citing patent (highest), citing patent (lowest), date published (newest), date published (oldest). The lens labs will reconnoitre and allow the experimentation of related datasets for the improvement of quality and content of the publication resource for the public.
Scholarly works provide additional information features with each publication title like patent, substance, affiliation, no of citing patents, no of citing scholarly works, reference count, and journal article with primary bibliographic information and abstract.
For citing patent tab the following information is given such as published, filed, earliest priority, family, cited work count, cited by, cites with additional information such as cited works, applicants, inventors, and patent application.
**Keywords and search query string**
The specific keywords have been used to generate a research query string. Some keywords are primary and some are secondary \[8\].
Table 1 show the research keywords on artificial taste perception of tea, where the primary and secondary keywords have been mentioned with logical search operators. For the generation of the main research query “artificial taste perception of tea”; filters have been applied for the assessment duration 01 January 2001 to 31 January 2022. Total 3725 patents records were found of which 1414 are of simple families, 1167 are of extended families, and these patents are cited by 2235 patents which were cited by scholarly work records 6742.
> **Table 1:** Research keywords on artificial taste perception of tea
| **Master keyword** | **Tea** |
| --------------------------- | --------------------------- |
| Secondary keyword using AND | Artificial taste perception |
| Secondary keyword using AND | Artificial perception |
| Secondary keyword using OR | Nil |
For the 2nd query string “Artificial perception of tea” a total of 18554 scholarly work records were found which are citing 6370 patents in the www.lens.org database.
**Query Analysis**
The analysis is of two types qualitative and quantitative analysis. Qualitative analysis is the subjective type of analysis that will dive detailed description of the data by highlighting its performance parameters whereas quantitative is the objective type description of publications that are countable for suitable parameters selected for comparison.
**Qualitative analysis:**
Table 2 is about scholarly work published in “artificial taste perception of tea” of the duration 01 January 2001 to 31 January 2022, it depicts title, publisher, date of publication, fields of study, patent and citation count of publication.
> **Table 2:** Top 10 scholarly works published in “artificial taste perception of tea”
| **Sr. No.** | **Title** | **Date Published** | **Publisher** | **Author/s** | **Fields of Study** | **Citing Patents Count** | **Citing Works Count** |
| ----------- | -------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------ | ---------------------- |
| 1 | ANALYSIS OF RELATIVE GENE EXPRESSION DATA USING REAL-TIME QUANTITATIVE PCR AND THE 2(-DELTA DELTA C(T)) METHOD | 2001 | Academic Press Inc. | Kenneth J. Livak; Thomas D. Schmittgen | Molecular biology; Cell wall organization; MicroRNA 34a; Cell redox homeostasis; Cell wall modification; Lupeol synthase; Protein kinase B signaling; Endosperm cellularization; Floral organ abscission; Bioinformatics; Biology | 555 | 117626 |
| 2 | Deep learning | 27-05-2015 | Nature Publishing Group | Yann LeCun; Yoshua Bengio; Geoffrey E. Hinton | Deep learning; Artificial intelligence; Backpropagation; Object detection; Theano; Representation (systemics); Speech recognition; Computer science; Abstraction (linguistics); Cognitive neuroscience of visual object recognition; Computational model | 226 | 35418 |
| 3 | Fast gapped-read alignment with Bowtie 2 | 04-03-2012 | Nature Publishing Group | Ben Langmead; Steven L. Salzberg | Algorithm; Throughput (business); Alignment-free sequence analysis; Sequence assembly; Sensitivity (control systems); Peak calling; Flexibility (engineering); FASTQ format; Dynamic programming; Computer science; Bioinformatics | 188 | 27127 |
| 4 | Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles | 30-09-2005 | | Aravind Subramanian; Pablo Tamayo; Vamsi K. Mootha; Sayan Mukherjee; Benjamin L. Ebert; Michael A. Gillette; Amanda G. Paulovich; Scott L. Pomeroy; Todd R. Golub; Eric S. Lander; Jill P. Mesirov | Gene; Genome; Gene expression; Cistrome; Set (abstract data type); Gene expression profiling; Integrative bioinformatics; Gene signature; Genetics; Computational biology; Biology | 397 | 26825 |
| 5 | MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets | 22-03-2016 | Oxford University Press | Sudhir Kumar; Glen Stecher; Koichiro Tamura | Operating system; Wizard; Software; Upgrade; Mega-; Datasets as Topic; Graphical user interface; Microsoft Windows; Mac OS; Bioinformatics; Biology | 50 | 25322 |
| 6 | Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. | 10-08-2006 | Cell Press | Kazutoshi Takahashi; Shinya Yamanaka | Induced pluripotent stem cell; Embryoid body; Embryonic stem cell; Molecular biology; Homeobox protein NANOG; Adult stem cell; KOSR; Stem cell; Embryonic Germ Cells; Biology; Cell biology | 1489 | 20423 |
| 7 | Ultrafast and memory-efficient alignment of short DNA sequences to the human genome | 04-03-2009 | BioMed Central | Ben Langmead; Cole Trapnell; Mihai Pop; Steven L. Salzberg | ChIP-exo; Hybrid genome assembly; DNA sequencing theory; Parallel computing; Alignment-free sequence analysis; Peak calling; Integrator complex; FASTQ format; Genetics; Memory footprint; Biology | 398 | 16899 |
| 8 | MicroRNAs: Target Recognition and Regulatory Functions | 23-01-2009 | Cell Press | David P. Bartel | Lin-4 microRNA precursor; Argonaute; miR-132; Mirtron; IsomiR; RISC complex; MiRNA binding; Oncomir; Genetics; Computational biology; Biology | 276 | 16038 |
| 9 | Search and clustering orders of magnitude faster than BLAST | 12-08-2010 | Oxford University Press | Robert C. Edgar | Protein methods; Data mining; UniFrac; Orders of magnitude (acceleration); Sensitivity (control systems); Sequence clustering; Sequence; Computer science; Sequence analysis; Cluster analysis | 134 | 14093 |
| 10 | Cutadapt removes adapter sequences from high-throughput sequencing reads | 02-05-2011 | EMBnet Stichting | Marcel Martin | World Wide Web; Ribosome profiling; Adapter (genetics); Small RNA; Color space; Source code; DNA sequencing; Trimming; Biology; Computer hardware | 61 | 12937 |
Table 3 is about Top cited patents on “artificial taste perception of tea” which includes patent publication number, the title of the patent, publication, applications, priority dates of the patent with backward and forward citation patent count and their family sizes.
> **Table 3:** Top cited patents on “artificial taste perception of tea”
| **Display Key** | **Title** | **Publication Date** | **Application Date** | **Earliest Priority Date** | **Backward citation count** | **Forward citation count** | **Simple Family Size** | **Extended Family Size** |
| ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------- | -------------------- | -------------------------- | --------------------------- | -------------------------- | ---------------------- | ------------------------ |
| US 6275806 B1 | System method and article of manufacture for detecting emotion in voice signals by utilizing statistics for voice signal parameters | 14-08-2001 | 31-08-1999 | 31-08-1999 | 7 | 410 | 14 | 18 |
| US 2003/0068371 A1 | Pharmaceutical formulation containing opioid agonist,opioid antagonist and gelling agent | 10-04-2003 | 06-08-2002 | 06-08-2001 | 46 | 271 | 11 | 168 |
| US 7987151 B2 | Apparatus and method for problem solving using intelligent agents | 26-07-2011 | 25-02-2005 | 10-08-2001 | 12 | 225 | 2 | 8 |
| US 7332182 B2 | Pharmaceutical formulation containing opioid agonist, opioid antagonist and irritant | 19-02-2008 | 06-08-2002 | 06-08-2001 | 32 | 204 | 12 | 168 |
| US 9183560 B2 | Reality alternate | 10-11-2015 | 24-05-2011 | 28-05-2010 | 103 | 183 | 5 | 5 |
| US 7144587 B2 | Pharmaceutical formulation containing opioid agonist, opioid antagonist and bittering agent | 05-12-2006 | 06-08-2002 | 06-08-2001 | 17 | 167 | 2 | 168 |
| US 7842307 B2 | Pharmaceutical formulation containing opioid agonist, opioid antagonist and gelling agent | 30-11-2010 | 06-08-2002 | 06-08-2001 | 54 | 160 | 11 | 168 |
| US 2005/0211768 A1 | Interactive vending system(s) featuring product customization, multimedia, education and entertainment, with business opportunities, models, and methods | 29-09-2005 | 16-03-2005 | 16-10-2002 | 9 | 151 | 3 | 3 |
| US 9790490 B2 | CRISPR enzymes and systems | 17-10-2017 | 18-12-2015 | 18-06-2015 | 3 | 147 | 44 | 53 |
| US 2002/0010587 A1 | SYSTEM, METHOD AND ARTICLE OF MANUFACTURE FOR A VOICE ANALYSIS SYSTEM THAT DETECTS NERVOUSNESS FOR PREVENTING FRAUD | 24-01-2002 | 31-08-1999 | 31-08-1999 | 0 | 118 | 2 | 2 |
**Quantitative analysis:**
Research query “artificial taste perception of tea” had generated various graphs for the selected parameters. Those are given below:
> \[CHART\]
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> **Figure 2:** Top ten institutions by scholarly work document count
Figure 2 is the bar chart generated for the top ten institutions according to its scholarly work document count on the given query. The highest 186 documents are available from the Harvard University.
> **Figure 3:** Scholarly works overtime
The year-wise count of scholarly works is given in figure 3 with a multi-line chart where dark blue color indicates book publications and orange color is given to indicate journal article on the graph. Various publication types with associated color boxes are given at the end of figure 3.
> **Figure 4:** Top 20 Fields of study
The word cloud analysis of the top 20 fields in the said research area is given in figure 4.
\[CHART\]
> **Figure 5:** Top 10 Authors of the fields
The bar chart of figure 5 shows the top 10 authors' name for artificial taste perception of tea. The criterion is the number of scholarly work documents published by authors. The author’s information will help other researchers to collaborate their work, to refer standard published work, or to take guidance for further research direction and it will also help to avoid duplication of work.
> 
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> **Figure 6:** Fields of study covered by the most active Institutions
Figure 6 of the survey shows that the Harvard University had a maximum count of documents on the topic of artificial taste perception of tea in concern with Biology. Some other related fields with document contribution have been shown in figure 6 such as Medicine, Chemistry, internal medicine and biochemistry. Figure 6 highlights the responses from various fields that have proved and recorded the multidisciplinary approach of the research area.
Figure 7 is the scatter plot about open access and non-open access records and patent citing the scholarly works on artificial taste perception of tea. When you click on the circle of the graph of figure 7 on the web it shows the Title of scholarly work, year published, Open access/-non-open access, citing patents, citing scholarly works. You will get all the details for scholarly work at a glance.
> 
>
> **Figure 7:** Scholarly works
>
> **Source:** https://www.lens.org/lens/search/scholar/analysis?patentQueryId=cdfd6597-8239-4e5d-8daf-6b241ccb531c
>
> 
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> **Figure 8:** Citing patents by filing date
Figure 8 is for citing patents by publication date. The document type with the color scheme has been given at the end of figure 8 where US jurisdiction have shown with blue color and yellow color is for WO. In the year 2012, the patent with tile ‘Reality alternate’ of US jurisdiction has got citations 1349. When you click on circle online on the web
() you will get information about the patent such as the title of the patent, publication date, cited by patent count, jurisdiction as mentioned above in the figure.
> 
>
> **Figure 9:** Most active countries/Regions
>
> Using Google sheet the most active regions were found in the artificial taste perception of tea as shown in figure 9. The two-column data obtained from [www.lens.org](http://www.lens.org) about country and Document count for published documents were provided as input to Google sheet for the map creation. As shown in figure 9 the dark green color region is for the United States showing 2580 records on its name; likewise, the contribution of the country for the given research can be found and worldwide research spread is also possible at a glance.
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> 
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> **Figure 10:** Top journals by publisher
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> Figure 10 shows the top five publishers with the source title and document count. With this figure, the user will get the frequency of publications with discipline contribution. Figure 10 on the web also explores the top 10 publishers with a maximum of 20 sources.
>
> Figure 10 is showing that the journal of agriculture and food chemistry of the publisher American Chemical Society has highest document count and is about 124. On 2nd position with document count 118; Nature journal of Nature Publishing Group has recorded their response. In 3rd position source Nature Biotechnology of Nature Publishing Group had been published 94 documents. This record is also useful for new authors to find the correct source and publishers to publish their research work.
>
> Figure 11 is about the top publishers for the research query “Artificial taste perception of tea”. It shows that Elsevier publisher has a maximum count of publications.
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> 
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> **Figure 11:** Top publishers in scholarly works
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> Filed, granted and published patents are given in figure 12. It shows the growing trend in past two decades. Top applicants for citing patents have been listed in the pie chart of Figure 13. Cadbury Adams Usa LLC is the topmost applicant with 17% with 189 patent document count. It shows the patent count in a given area with applicant name.
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> 
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> **Figure 12:** Patent documents by Published, Filed and Granted date
>
> \[CHART\]
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> **Figure 13:** Top applicants by patent citing
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> Figure 14 is a heat map of the top 10 Cooperative Patent Classifications by document count.
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> The website is given for detailed patent search-
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> **Figure 14:** Top 10 CPC classification by document count
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> \[CHART\]
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> **Figure 15:** Top 10 inventors by document count
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> Figure 15 is showing the top 10 patent inventors in the field of “artificial taste perception”. The inventor Gebreselassie Petros had published 131 patents and is the highest count.
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> Top Jurisdiction by document count for patent citing is given in the form of the world map of Figure 16 and details are mentioned in table 4 as well. A map is given to check at a glance the worldwide Jurisdiction view for patents.
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> 
**Figure 16:** Top Jurisdiction by document count for patent citing
> **Table 4:** Top Jurisdiction by document count for patent citing
| **Jurisdiction** | **Document Count** |
| ---------------- | ------------------ |
| United States | 2170 |
| WO - WIPO | 1077 |
| European Patents | 478 |
| **Total** | **3725** |
> Table 4 is about jurisdiction and document count. Table 5 is about total patent applications and granted patents for the research query string with the [www.lens.org](http://www.lens.org) repository.
>
> **Table 5:** Top document type
| **Document Type** | **Document Count** |
| ------------------- | ------------------ |
| Patent Application | 2788 |
| Granted Patent | 915 |
| Search Report | 16 |
| Amended Application | 5 |
| Amended Patent | 1 |
| **Total** | **3725** |
## **Case study:** “[Study on the flavor of soybean cultivars by sensory analysis and electronic tongue](https://www.lens.org/lens/scholar/article/110-131-472-966-638/main) (2012)” is the paper citing by following nine papers which are related with artificial taste perception \[31\].
1. ## [Impacto de la grasa y delazúcar en laspropiedadesfísicas y sensoriales de diferentestipos de matrices alimentarias](https://www.lens.org/lens/scholar/article/135-285-383-284-204/main)(2013) \[22\].
2. ## [Tristimulus mathematical treatment application for monitoring fungi infestation evolution in melon using the electrical response of carbon nanostructure-polymer composite based sensors](https://www.lens.org/lens/scholar/article/026-344-597-489-497/main)(2013) \[23\].
3. ## [Combining Cluster Analysis, Surface Response Methodology, and JAR Scales to Increase Consumer Input in Optimizing Acceptability of a High‐Protein Soy Dessert](https://www.lens.org/lens/scholar/article/000-365-499-050-148/main)(2013) \[24\].
4. ## [Electronic tongue system to evaluate flavor of soybean (Glycine Max (L.)Merrill) genotypes](https://www.lens.org/lens/scholar/article/095-367-861-419-725/main) (2014) \[25\].
5. ## [Evaluation of Beef by Electronic Tongue System TS-5000Z: Flavor Assessment, Recognition and Chemical Compositions According to Its Correlation with Flavor](https://www.lens.org/lens/scholar/article/015-857-895-841-246/main)(2015) \[26\].
## 6\. [Effect of hydrocolloid on rheology and microstructure of high-protein soy desserts](https://www.lens.org/lens/scholar/article/092-634-236-330-164/main)(2015) \[27\].
## 7\. [Agronomical Aspects of the Development of Cultivars](https://www.lens.org/lens/scholar/article/023-202-986-896-309/main)(2017) \[28\].
## 8\. [Statistical methods to study adaptability and stability in breeding lines of food-type soybeans](https://www.lens.org/lens/scholar/article/044-140-330-845-083/main)(2018) \[29\].
> 9\. [Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview](https://www.lens.org/lens/scholar/article/024-907-713-375-897/main)(2020) \[30\].
The case study “[Study on the flavor of soybean cultivars by sensory analysis and electronic tongue](https://www.lens.org/lens/scholar/article/110-131-472-966-638/main) (2012)” has referred to 30 papers and the compilation had done by www.lens.org and the references are available with the sources with their further individual citation. This case deflects after 2013 the study of artificial taste perception for the number of applications has increased tremendously. Table 6 is about the summary of top-cited patents.
**Table 6:** Summary of top-cited key patents
| **Sr. No.** | **Patent number** | **Summary of the patent** | **Ref.** |
| ----------- | ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- |
| 1 | US 6423363 B1 | The invention is about Aqueous dispersions of plant sterols and other high melting lipids. The process is explained about dispersion which will provide even structure to spreads and food products. | \[37\] |
| 2 | WO 2016/205749 A1 | The patent is about systems, methods, and compositions for targeting nucleic acids and specifically on novel DNA or RNA-targeting CRISPR effector protein. | \[38\] |
| 3 | US 9364005 B2 | The invention provides materials such as formulation comprising an exogenous endophytic bacterial population and methods for conferring improved plant traits or benefits on plants. | \[39\] |
| 4 | WO 2010/099062 A1 | This invention is about oral care compositions with natural ingredients, for stimulating salivary flow which will help to treat and preventxerostomia. | \[40\] |
| 5 | US 2018/0320163 A1 | The patent is about systems, methods, and compositions for targeting nucleic acids and specifically on novel DNA or RNA-targeting CRISPR effector protein. | \[41\] |
| 6 | WO 2016/149685 A1 | This patent is about Cosmetic and dietary supplement compositions having silymarin and sulfoalkyl ether cyclodextrin, particularly sulfobutyl ether cyclodextrin, are described. The compositions and methods described here are useful skin treatments. | \[42\] |
| 7 | US 2014/0371322 A1 | In this patent, aphenoxybenzamine transdermal composition for the treatment of neuropathic pain is revealed. Permeation enhancer composition within disclosed phenoxybenzamine transdermal composition may improve penetration of phenoxybenzamine in a patient's tissue or skin. | \[43\] |
| 8 | WO 2014/201083 A2 | In this patent, aphenoxybenzamine transdermal composition for the treatment of neuropathic pain is revealed. Permeation enhancer composition within disclosed phenoxybenzamine transdermal composition may improve penetration of phenoxybenzamine in a patient's tissue or skin. | \[44\] |
**FINDINGS AND DISCUSSIONS**
Authors had been reviewed the scholarly work and patent record of the single [www.lens.org](http://www.lens.org) repository for research query “artificial taste perception”. The survey had been carried for the duration from 01 January 2001 to 31 January 2022. All the graphs and tables belong to the [www.lens.org](http://www.lens.org) database for the given search queries.
**CONCLUSION AND RECOMMENDATIONS**
The standard website named “[www.lens.org](http://www.lens.org)” had been used to find related patents and scholarly work publications. The research query “artificial taste perception of tea” had been generated on the above-mentioned website resulted in a total of 6742 scholarly work publications were found which are citing 3725 patents. A total of 18554 scholarly work publications were found of which 6370 citing patents on “Artificial perception of tea”. This survey shows the growing trend in artificial taste perception after 2004-05. The year 2017 is the peak year showing for filed patents on artificial taste perception of tea. This survey is very useful to get the research done in artificial perception and Tea industry. Tea producing countries like India need to plan market strategies according to interest of people for the particular tea species. The current trends, research and funding organisations and researchers involved in the topic will be helpful for agricultural commodity.
**ACKNOWLEDGMENTS**
**
**You can acknowledge help from colleagues, other institutions or disclose any funding from governmental organization or third-party organization here.
**REFERENCES**
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\[37\] Traska Alexander Wolodymyr , Patrick Matthew. [Aqueous Dispersion](https://www.lens.org/lens/patent/189-769-418-662-843) \[internet\] . Available from:
\[38\] Koon Eugene , Zhang Feng , Wolf Yuri I , Shmakov Sergey , Severinov Konstantin , Semenova Ekaterina , Minakhin Leonid , MakarovaKira S , KonermannSilvana , Joung Julia , Gootenberg Jonathan S , Abudayyeh Omar O. [Novel Crispr Enzymes And Systems](https://www.lens.org/lens/patent/004-858-408-856-017) \[internet\]. Available from:
\[39\] MitterBirgit , PastarMilica , Sessitsch Angela. [Plant-endophyte Combinations And Uses Therefor](https://www.lens.org/lens/patent/134-886-720-306-580) \[internet\]. Available from: https://www.lens.org/lens/patent/134-886-720-306-580
\[40\] Hsu Stephen. [Oral Care Compositions For Treating Xerostomia](https://www.lens.org/lens/patent/026-390-050-485-289) \[internet\]. Available from:
\[41\] Koonin Eugene , Zhang Feng , Wolf Yuri I , Shmakov Sergey , Severinov Konstantin , Semenova Ekaterina , Minakhin Leonid , MakarovaKira S , KonermannSilvana , Joung Julia , Gootenberg Jonathan S , Abudayyeh Omar O. [Novel Crispr Enzymes And Systems](https://www.lens.org/lens/patent/162-763-710-883-46X) \[internet\]. Available from:
\[42\] Pipkin James D , Rajewski Roger , MainousBeau.[Compositions Containing Silymarin And Sulfoalkyl Ether Cyclodextrin And Methods Of Using The Same](https://www.lens.org/lens/patent/127-573-590-311-136) \[internet\]. Available from:
\[43\] Glasnapp Andrew B. [Phenoxybenzamine Transdermal Composition](https://www.lens.org/lens/patent/004-741-575-579-257) \[internet\]. Available from:
\[44\] Glasnapp Andrew B.[Phenoxybenzamine Transdermal Composition](https://www.lens.org/lens/patent/036-855-550-508-766) \[internet\]. Available from:
\[45\] Patil, A. B., & Bachute, M. (2021). A Bibliometric Analysis of the Tea Quality Evaluation using Artificial Intelligence. Library Philosophy and Practice, 1-21.
\[46\] Patil, A. B., Bachute, M., & Kotecha, K. (2021). Artificial Perception of the Beverages: An indepth Review of the Tea sample. IEEE Access.
\[47\] Patil, A., Bachute, M., & Kotecha, K. (2021). Identificationand Classification of the Tea Samples by Using Sensory Mechanism and Arduino UNO. Inventions, 6(4), 94.
\[48\] Kaushal Puri, Devasheesh Tripathi, Yashvi Sudan, Prof. A.B Patil, -2020/6’ ‘Feature Extraction Technique for Emotion Detection using Machine Learning, SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) ISSN: 2348 – 8549,Volume 7,Issue5, pp. 41-46,May202
\[49\] Prof. Amruta Patil Siddharth Ojha, Akshay Kapoor, number 01, 2017 ,“Soil Moisture and Sunlight Monitoring-Controlling using Raspberry Pi for Greenhouse” international journal of innovative trends in engineering (ijite) issn: 2395-2946 issue: 43, volume 27
\[50\] Singh, G., Srivastsva, S., Gupta, G., & Patil, A. B., May2020 , “Arduino Uno based Smart Cane for Osteoarthritis patients”, International Journal of Scientific Research and Engineering Development, ISSN : 2581-7175, volume 3, issue 2,pp. 1150-1155
\[51\] Amruta Patil,Prof. R.M.Khaire, January 2014, “Establishment of evaluation metric and quality analysis of enamel coating thickness and thermal resistivity of copper wire using arm7 processor” International Journal of Application or Innovation in Engineering & Management (IJAIEM),Volume 3, Issue 1, , ISSN 2319 – 4847
\[52\] Amruta Patil,Prof. R.M.Khaire, March – April 2014, “ Automatic Resistance detection and Abrasion testing of copper wire used in transformer or motor windings by ARM 7 processor”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 3, Issue 2, ISSN 2278-6856
\[53\] Amruta Patil, Shalvi Patel, Mayank Monga, MuKul Pandey, March 2014 , “GPS based friend tracker and online /offline SMART reminder for android systems” , Volume 2, Issue 3, ISSN 2302-208