Agri-Startups India

Federation of Indian Chambers of Commerce and Industry of India (FICCI)-PwC Knowledge Report on Agri-Start-ups: Innovations for boosting the Future of Agriculture in India, was launched by Suresh Prabhu, minister of commerce and industry, Government of India, at the International Conference and Awards for Innovations by Agri Start-ups organised by FICCI in New Delhi. High lights:

• India houses a total of 366 agri based start-ups, of which over 50 per cent came into existence in 2015 and 2016. The combined revenue of all agritech start-ups in India is estimated to be less than $100 million whereas global market is worth $350 billion. Geographically, Karnataka and Maharashtra together account for almost 50 per cent of the total number of agri start-ups opened in the last five years.
• Big data based agri start-ups:Development of farm-specific, data-driven diagnostics to determine soil and crop health has come up as a big opportunity area. Start-ups are leveraging drones or tractor-based solutions to get data (both on weather and agricultural) on field to determine risk. Growing smartphone penetration will enable precise decision making in farming activity, helping farmers to drive increased productivity and revenue while reducing unit costs.
• Start-ups developed around the market linkage model: Innovations must be included to help farmers with timely and accurate estimation of sowing and harvesting in sync with consumer demand patterns. Such linkages operate at the two critical ends of the supply chain: input and output models. These models aim to link producers to remunerative sourcing agencies for procurement and to profitable buyers for output sales.
• Start-ups developed around Farming as a Service (FAAS): Specific farm practices are being identified for provision of technological breakthrough services. Activities such as equipment renting and crop care practices are areas likely to see market traction. FAAS seeks to provide affordable technology solutions for efficient farming. It converts fixed costs into variable costs for farmers, thus making the techniques more affordable for a majority of small farmers. Its services are available on a subscription or payper-use basis in three broad categories, which are crucial across the agriculture value chain.
• IoT enabled technology based agri start-ups: Smart farming, including high-precision crop control, data collection, and automated farming techniques, will remove inefficiencies and bolster productivity. Information on crop yields, rainfall patterns, pest infestation and soil nutrition can be used to improve farming techniques over time. Low capex for predominantly software based solutions is the key feature for such solutions.

Download the report.

Draft National Policy on Electronics 2018 (NPE 2018)- what is new?

In the long wish list, there are few statements/ intentions that stand out.

• Admission:  Replacing M-SIPS scheme with schemes that are easier to implement such as Interest subsidy and Credit default guarantee, etc., in order to encourage new units and expansion of existing units in electronics manufacturing sector.(5.1.4).

• Contradicting: Exempt the import duty on identified capital equipment not being manufactured in the country, to reduce capital expenditure for setting up/ expansion of existing units.(5.1.6). Levy Cess on identified electronic goods to be considered to generate resources for promotion of certain critical sub-sectors of electronics manufacturing such as semiconductor wafer fabrication and display fabrication units.(5.1.9).
• Confusion persists: Promote path-breaking research, grass root level innovations and early stage Start-ups in emerging technology areas such as....having major economic potential, with a special focus on applying the outcomes, including frugal solutions, to solve real-life problems. (5.6.1)
• Daring: ...acquire & pool, Core and Peripheral IPs and make them available to the Industry (5.6.4). Promote investment in mega facilities abroad, such as an existing FAB facility, including support for setting up of R&D units abroad, where eco-system exists for a particular technology.(5.15).

Download document. Comment by 15th November 2018.

EPO guidelines on AI

EPO release guidelines on the patentability of artificial intelligence (AI) and machine learning technologies. Artificial intelligence and machine learning are based on computational models and algorithms for classification, clustering, regression and dimensionality reduction, such as neural networks, genetic algorithms, support vector machines, k-means, kernel regression and discriminant analysis.
Artificial intelligence and machine learning find applications in various fields of technology. For example, the use of a neural network in a heart-monitoring apparatus for the purpose of identifying irregular heartbeats makes a technical contribution. The classification of digital images, videos, audio or speech signals based on low-level features (e.g. edges or pixel attributes for images) are further typical technical applications of classification algorithms. Classifying text documents solely in respect of their textual content is however not regarded to be per se a technical purpose but a linguistic one (T 1358/09). Classifying abstract data records or even "telecommunication network data records" without any indication of a technical use being made of the resulting classification is also not per se a technical purpose, even if the classification algorithm may be considered to have valuable mathematical properties such as robustness (T 1784/06).