Ideal Workflow- Learn how experts use Odin

here you can find a detailed description of a full technology forecasting workflow on the getfocus platform (odin) this is also a typical representation of how our analysts go about a client project for the sake of discussing a workflow with a real world example, we take the example of 'electric vehicle battery chemistries' technology forecasting workflow on odin technology forecasting workflow on odin define the questions and scope (planning) scouting evaluation patent landscape and technology improvement rates deep dive define the questions define the questions typically you start with a broad and high level question with several underlying conditions example we work in electric vehicle powertrains and want to know which technology will be dominant in this field in the future break down this question into smaller questions which electric vehicle powertrain technologies are you interested in all battery chemistries, a specific type of battery chemistry, or transmission technologies? what is the application area? cars, commercial vehicles, marine, aerospace, small vehicles etc what characteristics of the technology are crucial? in the end, you might end up with a question like "which emerging high energy density battery chemistries for next gen electric vehicles are being developed?" this question will serve as the input for our technology scouting features scouting scouting step 1 in creating a comprehensive technology forecast is always to identify which emerging technologies are competing in a given space for this, we need to scout technologies scouting involves finding all related technologies in your field of choice we use the "technology scouting" tab in the chat section for this purpose the "technology scouting" tab gives results for all the technologies in a given area for the search query it outputs list of all the relevant technologies grouped as industry standard technologies, emerging technologies and novel/niche/early stage technologies ensuring coverage from early stage to mature technologies select the reasoning button when accuracy is more important than speed reasoning offer greater accuracy and excel at handling complex tasks at the expense of speed the search query for "technology scouting" and "emerging technology scouting" can be as simple as "battery chemistries" or with added context "battery chemistries for electric vehicles", or with even more added context such as in our example at the top of this article "emerging high energy density battery chemistries for next gen electric vehicles" note it is important to note that when conducting a scouting search, using less context in the query will yield more results this could be beneficial as it includes technologies that may not have been explored for the particular use case, yet hold potential for the future refer to the screenshots below to see the difference evaluation evaluation after scouting, we have a list of competing (emerging) technologies for a certain use case now, we have to evaluate these technologies the primary objective of this step is to assess the suitability of our candidate technologies for the specific use case we have in mind in this particular example, the use case pertains to battery chemistry evaluation entails the assessment of technologies based on various criteria commonly employed for comparing technologies within a specific domain you have the choice to define the desired criteriato evaluate these technologies we use the " ask me anything " tab for this purpose here is an example for technology evaluation using ask me anything give a context you are battery chemistry expert you know everything there is to know about relevant technologies regarding battery chemistry i will give you a list of battery technologies, as an expert evaluate the technologies based on given criterias provide metric the evaluation criterias are cost efficiency, energy density, cycle life, safety and sustainability score definition score all the technologies from 1 to 10 where 10 is the best performing and 1 is the least performing for each metric provide the list of (emerging) technologies ask the tool to output the evaluation results in a table if there are many technologies, we typically select the technologies that have a higher average score to proceed to the next step where we map out the associated patent landscapes note you can further ask the tool for the reasoning for the scores eg "after each criteria add a column with the reasoning for the score" patent landscaping and technology improvement rates patent landscaping and technology improvement rates after scouting and evaluation, we have reduced the list of all possibly interesting (emerging) technologies to those that score the best for our use case next, we need to map out the patent landscapes associated with these technologies to identify their improvement rates in this step, we take the highest scoring technologies/ suitable technologies for the use case to map out their patent landscapes and obtain technology improvement rates patent landscape patent landscape for each technology, we search for the patent landscape using the "smart search" "smart search" button the smart search will reformulate your search query into a longer and more technical description of your input the longer and more technical reformulation aids the ai search model in retrieving the most relevant results after performing a smart search, you will likely find a large number of patents our ai search model helps you get in the right direction, but you still need to filter your results to ensure an accurate dataset this is a crucial step! if you do not filter your dataset, the improvement rate may be calculated using inventions that do not directly relate to your field of interest, and may thus be unreliable the steps below help create a good representative patent landscape to derive a precise improvement rate for the technology including keywords type in the keywords that are generally used to define the technology or relate to the technology (refer to the below picture) excluding keywords type in the keywords that may pollute the landscape for the technology (learn more about how to filter search results in how to filter search results? docid\ wi3kku8jft0fpwd7xnrnz ) 3\ sorting the patent set in the ascending order of relevance we do this to verify whether the patents with the least relevance in the landscape still relate to the given technology if this is the case, you can generally be confident that more similar results also relate to your technology area of interest after sorting according to ascending relevance, sometimes, the title and abstract of the patents may not directly reveal a patent's relevance in such situations, it is recommended to utilize the "ai summary" or "chat with invention" function to determine its relevance if a good portion of the least similar patents are still relevant, the patent landscape can be deemed to be at an acceptable level of accuracy you will never get to 100% accurate results, so this should not be the goal 4\ in the rare case when none of the patents on the first page of ascending relevance relate to the intended technology field, you can filter further with "exclude keywords" that occur in the non relevant patents and/or increase the "similarity" incrementally by 0 5 1% per step until your results get more relevant 5\ once we have a patent landscape that accurately reflects the intended technology, save the set (read how this works here searching with odin docid\ a76atae2twqap3j9qm0w5 ) 6\ we repeat these steps for other technologies we want to compare such that we can compare their technology improvement rates technology improvement rates technology improvement rates introduction introduction if we want to make future proof decisions, we have to understand how quickly technologies are getting better, which is what we are measuring with the improvement rate in our research collaboration with mit, we found that the rate of improvement of technologies can be predicted based on metrics in patent data crucially, we also found that the fastest improving technologies based on these metrics typically become the dominant market solution here is how this method works we map out the patent landscapes associated with each technology (as described above) based on the citation network that underlies the identified patents, we can calculate two metrics, cycle time and knowledge flow cycle time tells us how many years it takes for a technology to produce a new generation of itself (calculated from the median age of backward citations) knowledge flow measures the degree of improvement over the previous generation of the technology (calculated from the average number of citations a patent gets within the first 3 years of publication ) these metrics are then used to estimate the improvement rate , which indicates the % of improvement in performance per $ that can be expected from a field of technology each year how to interpret technology improvement rates (tir) how to interpret technology improvement rates (tir) tir is a comparative metric rather than an absolute indicator this means there is no absolute value of improvement rate that can indicate whether a technology is improving fast or not whether a given technology's improvement rate is high or low, therefore always needs to be determined based on its comparison to competing technologies the 'delta' between the improvement rates of two or more technologies in a given domain indicates which of the competing technologies is most likely to become the dominant solution in the future historically speaking, the fastest improving technology out of a set of competitors, always wins note , tir only gives us an overview of how these technologies improve today, but does not tell us when a given will win or dominate in the future to predict when an emerging technology will become dominant, we use the forecasting tool interpreting the graph curves for technology improvement rates interpreting the graph curves for technology improvement rates the 'dip' in improvement rate curves in the last 2 3 years is an artificial drop because of the knowledge flow metric this is because the patents published in the last 3 years have not yet had 3 years to get cited the 'kinks' or sharp rises and drops in a tir curve may occur due to the following reasons (notice the curve for graphene batteries in the example) a small number of patents in the landscape (less than a hundred) a new invention/technology in the domain with a low technology readiness level, usually having a skewed patent citation network forecasting forecasting here we predict when competing technologies will probably dominate in the future please note that this feature requires you to input starting values of current cost and performance associated with the candidate technologies this step can be tricky, as such information is not always readily available the forecasting tool will create charts based on any input, so be careful that you put in, garbage in = garbage out here is how the forecasting tool works we use the current improvement rates of the technologies, which we obtained in previous steps we use the approximate current costs of the technologies for a particular performance metric (in our example, it is $/ kwh) below is an example of a forecasting exercise note please note this is only an example, the improvement rates and costs in the example are arbitrary values selected for a simple explanation interpreting the forecasting curve interpreting the forecasting curve with a current improvement rate of 67 50%, graphene batteries, despite having a very high current cost of $1000 per kwh of energy storage, will overtake solid state lithium batteries by 2033 sodium ion batteries will remain the most cost effective technology for the foreseeable future, despite a lower improvement rate, as their starting costs per kwh are significantly lower deep dive deep dive once you have found the best technology, you can learn quickly and in great detail about the technology by chatting with individual patents or even a set of patents ai summary ai summary summarize any patent with a single click on the "ai summary" button it extracts information from the entire patent text and summarizes it in a simple, easy to read way the following information is displayed patent abstract technologies used in the invention advantages of the invention application areas chat with invention chat with invention the "chat with invention" function lets you chat with a particular patent using natural language through a large language model (llm) the answers are only derived from the given patent text the entire patent text is taken into consideration to answer your questions i e all sections title, abstract, claims, description, publication date/geographical coverage/ assignee, etc here are some examples of the most frequent use cases of "chat with invention" by our analysts here are some examples of the most frequent use cases of "chat with invention" by our analysts open ended questions, to understand the working of the invention in a simple technical language we ask "summarize this invention to an engineer", to find out very specific information we ask for example, "what are the electrode materials mentioned in this invention" "what are the dependent/independent claims in the invention" chat with set chat with set the "chat with set" function lets you analyze an entire set of patents rather than individual ones using natural language through a large language model (llm) you can ask your dataset any question, the llm will analyze it and provide you with answers there are important factors to consider when using the "chat with set" feature for more information, please refer to the following link chat with set docid\ zrio99zefvy z6zb3qtv typical use cases for "chat with set" typical use cases for "chat with set" tracking click on "new this month" to see the latest inventions ask "provide a summary of the inventions related to sodium ion batteries for this month " finding the latest trends you can ask what are the latest trends in this domain? what are the main trends in this domain over the past 5 years? what are the topics being discussed in this dataset and how many inventions belong to each topic? what are the patent numbers that relate to battery electrolytes? list the publication numbers in a comma separated list organization and portfolio analysis what are the top 3 startups in this domain? provide a summary of their most important inventions compare the portfolios of sumitomo industries and yuasa battery inc and explain the main differences comparing inventions what are the differences between patent us 20110052986 a1 and patent cn 117525548 a? how did this technological domain develop? compare 5 years ago with the current year and explain the differences