Dr. Loo || the Mean Squared Terrors || $200k

A thoughtful smart monitor for personalized health over time



Currently, there is no easy way for people to receive a holistic picture of their daily health. There are Fitbits that can track steps and calories burned, at-home monitors that check blood pressure, and thermometers to check body temperature. Besides the yearly checkup with a primary care physician, however, there are very few ways to gain access to a detailed report about our health. Continuous monitoring of a patient’s health is crucial for both preventative care and management of chronic conditions; however, tests that require people to go out of their way to produce samples (ex: the traditional pee-in-a-cup method and daily diabetic blood glucose testing) are cumbersome at best and often very painful, costly, inconvenient, and difficult to scale. Furthermore, for many of these patients with chronic conditions, there are significant challenges in encouraging adherence to medical regimens, which only exacerbates the aforementioned problems.

The diagnostic and medical labs industry currently has annual revenues of $53 billion. Of that, it’s estimated that $8.5 billion is spent annually on urine testing and screening. Additionally, according to one medical study, the average diabetes patient spends nearly $800 per year on supplies for testing their blood glucose levels, and about $2,100 more on insulin prescriptions and associated supplies. Based on the American Diabetes Association’s most recent estimates, 23.1 million Americans have been diagnosed with diabetes, and there are approximately $327 billion spent on diabetes treatment each year. While diabetes represents just one diagnostic case, these numbers point to tremendous upside and opportunity for a new, disruptive solution to make patients’ lives easier and at a more affordable price.


Solution + Value Prop

Our solution, Dr. Loo, uses IoT sensors, cloud-hosted big data analytics, and machine learning algorithms to give customers dramatically enhanced insight into their current and future health by leveraging day-to-day urine data. While long-term growth and success in this area may require advances in diagnostic technology, there are a number of compelling use cases that are already feasible which we can use to develop our products, while we continue to perform the research and development necessary to achieve our long-term ambitions.

Our MVP is an add-on cartridge to toilets, which will collect samples of users’ urine to monitor their health. These smart toilet cartridges will contain urinalysis test strips with numerous chemical pads (each representing a different test, outlined below, and changing colors when reacting to compounds present in urine) and an optical camera that will capture the results on these strips. Additionally, a smell sensor connected using IoT will be incorporated into the product to provide even more robust results using the odor of a person’s urine. These results will then be digitized, analyzed, and sent in a daily summary to a user’s downloaded app on their phone. 

The strips, optical camera, and smell sensors will incorporate machine learning technologies by feeding the outcomes through the database to come up with a final analysis for the customer based on careful interpretation of colored chemical pads and smell inputs. This will be available to the customer through an app on their mobile device. Additionally, we will anonymize the collected data and run algorithms on verified data from patients with preexisting conditions to assist in the diagnosis of other customers. This incorporation of crowd-sourcing will enable our solution to become even more accurate as more customers use our product.

Samples of a Mock-Up for Our Prototype

While dipsticks for urinalysis have been on the market for decades, the accuracy of those results are heavily dependent on proper sample preparation, correct interpretation of the color scales, and precise readout timing. Our product presents two valuable propositions: a) the testing process requires little to no change in users’ daily behavior, and b) the strips themselves can contain dozens of different tests, customized based on the users’ health needs.

Specifically, we would like to measure the following types of metrics:

  • Nutrition. Urinalyses can identify a person’s nutritional deficiencies by determining whether a person is under or over the daily recommended range of intake on certain vitamins, fats, sugar, protein, etc.
    • Metabolic Analysis (yeast/fungal, vitamin and cellular energy markers)
    • Amino Acid Levels & Oxidative Stress Analysis
  • Hormone Activity. Urinalyses that detect surges in LH (luteinizing hormone) or the presence of hCG (human chorionic gonadotropin hormone) can help women who want to become mothers: (1) plan for their pregnancy by predicting time of ovulation and peak fertility and (2) confirm their pregnancy.
    • LH Hormone Ovulation Test
    • hCG Pregnancy Test
  • General Health. Urinalysis can be indicative of a person’s overall health. In addition to ensuring a person is well hydrated through the color and cloudiness of the urine, dipstick tests can measure acidity, the presence of blood and specific gravity.
    • pH level. Whereas more acidic urine (i.e. lower pH levels) can be associated with stress, inflammation, dehydration and a high-carb diet, a more alkaline urine pH in the range of 5 to 7 is indicative of “calmer physiology, hormone balance, as well as safer and more successful fat loss”. Higher urine acidity can also hint at acidosis, a condition that can lead to kidney stones or be indicative of existing kidney diseases.
    • Urinary specific gravity (concentration of solutes in urine; provides information on kidney’s ability to concentrate urine).
    • Presence of Red & White Blood Cells.
  • Disease-Specific Risks.
    • Glucose level (for diabetics)
    • Protein level (for kidney disease)
    • Presence of bacteria (for urinary tract infections)
    • Prostate cancer

In addition to diagnostic capabilities, our product can serve a therapeutic objective by syncing results and making them available to a patient’s doctor. For instance, patients with diabetes would find it particularly beneficial to ensure their blood glucose levels are within normal range and be prompted of appropriate times during the day to take insulin shots. Dr. Loo is also convenient, enabling patients who previously had to go through the discomfort of pricking their finger to simply monitoring their glucose levels through a normal, painless activity. Similarly, certain illnesses require a patient to keep the pH of their urine within specific margins to ensure the efficacy of treatment.

Based on the intended metrics and capabilities described above, we’ve identified two target customer segments:

  1. Users who have no existing conditions or physical symptoms. These users are typically in the 25-40 age group and are very health-conscious. They may have a family history of diabetes, high cholesterol and/or other illnesses, and thus are interested in more frequent monitoring of their personal health.
  2. Users with existing conditions. These users are typically in the 41+ age group and as patients of diabetes, kidney disease and/or other conditions, they need to ensure that certain metrics, such as blood glucose level and urine pH, are within a specific range.


To further validate the market need and opportunity for Dr. Loo, we conducted a survey via Google Forms. Out of the 58 respondents, 73% want health diagnostics more than once a year. In addition, 57% of the respondents would pay more than $10/month for a solution like Dr. Loo.

Desired Report Frequency

Willingness to Pay (per month)


Implementation, Roll-out, & Next Steps

For our first iteration of minimum viable product, we want to sell Dr. Loo directly to a group of end customers. Given Dr. Loo’s ability to help users maintain a healthy track record, as well as detect and prevent the progression of various illnesses, it can be marketed successfully to these customers through healthcare providers and other channels, such as health and fitness magazines and sites. These customers would face an initial cost of $200 for the purchase of the instrument (can be potentially reimbursed through insurance companies) and then a monthly subscription charge of $20 for the mobile app and replaceable cartridges.

For future steps, we want to consider selling to hospitals so that they can install these directly into their toilets and have their patients use them for quicker/more convenient test results. In addition, to make Dr. Loo more accessible to patients and broader user base, we would seek government approval for use of funds from flexible spending accounts and eligibility for reimbursement through insurance companies.

Although our first iteration focuses on urine samples, in the future we will also want to incorporate stool into Dr. Loo’s repertoire so that test results can be even more robust. Samples of stool are able to give more details on conditions that urine can’t analyze. This includes the presence of food poisoning (and gastrointestinal infections), drugs, STDs and other types of diseases. In addition to incorporating stool, we hope to enhance Dr. Loo’s range of usefulness and accuracy by doing the following:

  • Pursue a more granular analysis / detailed results through microscopic exams, gas chromatography/mass spectrometry, etc.
  • Partner with Labcorp and other testing agencies to ensure the latest tests are available
  • Integrate with other fitness applications (i.e. Fitbit)

Another future addition would be to allow for automatic reordering of cartridges based on number of urinalysis strips left in the existing cartridge. This makes it so that users don’t even have to remember when to reorder a new cartridge, paving the way for further automation.


Budget, Cost, & Funding

We would like to request $200k to fund Dr. Loo. We are assuming an upfront cost of $200 per unit to customers for the initial equipment (see below for breakout), plus a monthly subscription fee of $20 (for cartridges and the app), growth rate that starts off at 13% and monthly churn rate that starts off at 4% (with rates stabilizing over the months), and costs for R&D, staff, manufacturing, etc. Please see this sheet for financials surrounding growth and cost hypotheses as well as an in-depth model broken out by month for the first 2 years.

Other costs to keep in mind (also built into the model):

  • $20-25k average to build initial iOS and Android apps
  • Free AWS credits are available for startups to handle our analytics and basic client-server cloud-compute app, and if we could apply for an activate partnership with VC’s help, then we should be able to run for our first year without the need to spend anything on computing infrastructure.
  • Shipment costs of $2 per package

The image below is a build-up of our component costs for the physical product that attaches to the consumer’s toilet.

The components alone total to an estimated $139.07 per unit. With an additional 35% premium for manufacturing and shipping, this brings us to a total cost of $187.75. We then factor in a small buffer to assume an upfront cost of $200 per unit.

The screenshot below is tab 1 of our financials sheet and contains a snapshot of the high-level numbers. We hypothesize we will need $182k; however, we want to factor in an 18k buffer, making our total ask $200k.


We may need to go through FDA approvals for our product since it is a type of medical device. Since our initial product would use existing medical testing procedures and would not be developing them on its own, we do not anticipate needing a high-level type of approval as the risk is very low. Thus if we were to go for approval, we would aim for low-level FDA approval. In the event that the FDA disagrees, we would be able to appeal to be granted permission to do clinical testing with insignificant risk, which requires only IRB approval, or potentially turn to the EU for CE mark approval.

A related risk involves initial medical backing from medical professionals. We will need to ensure we talk to as many doctors to vet any concerns and have them be our biggest advocate in recommending this product to their patients, so that we can ensure greater adoption by end users.

Another risk to keep in mind is the issue of sample dilution. We will be using a significant portion of R&D in order to figure out how to capture the sample. One method is to have a pipette that extends into the toilet bowl and draws up a sample after each bathroom usage. This is easily the least costly method; however, it also means that we would be dealing with diluted samples, as there is already water in the toilet bowl prior to the action. Another method is to come up with some kind of funnel to capture the sample. This would ensure pure samples, however could introduce the risk of bacteria formation, greater manufacturing costs, and minor discomfort of the user.

Lastly, compared to a urinalysis sent directly to labs, dipstick testing lacks precision and is limited in the type of conditions that can be detected. Although the chemical reactions and color changes are reliable in identifying the presence of known abnormalities, dipstick testing does not quantify the seriousness of the abnormalities and their underlying causes. Therefore, in the event that a condition persists, a user would still need to follow up with a healthcare provider for further diagnosis and if necessary, to develop a treatment plan. Nevertheless, dipstick testing is still a much more convenient form of urinalysis that delivers benefits when users have no physical symptoms (and therefore would’ve left a condition undetected) and when patients require ongoing metrics to monitor their illness. As we develop the capability to deliver more granular results (i.e. through stool and microscopic testing), we can mitigate this risk significantly.



There are several types of competitors we will have to keep in mind as we roll-out Dr. Loo. The first is the existing medical laboratory services industry. This industry is relatively concentrated, with the top two companies accounting for about 30% of the overall industry, and with further consolidation expected to take place in the coming years. There have also been a handful of competing efforts from other companies, such as Toto, to make experimental smart toilets, but none of these have made it to mass-market and because they were expensive and designed as entire toilets rather than sensors. Lastly, there are startups, such as Scanadu and S-There, that are trying to come up with technological solutions for analyzing someone’s health off their urine or feces, however these are still in their growth phase and do not have the robustness or ease that our product can deliver to customers.



Siddhant Dube

Eileen Feng

Nathan Stornetta

Tiffany Ho

Christina Xiong




























Viki: Global TV Powered by Fans


Since the 1990s when the Korean Wave (hallyu) spread throughout the world through today, viewership in Korean dramas has been steadily increasing. With a consumer base comprised mostly of millenials and women, viewers turn to these television shows for a number of reasons, from wanting to experience another culture to desiring to binge-watch stories, to simply wanting to experience a better plot than can be found in other shows. There are hundreds of Korean dramas today, and the quality and quantity of these shows is steadily increasing. Because of these show’s wide potential appeal, there are two primary opportunities: 1) a way of getting these shows onto the screens of everyone outside South Korea in a timely fashion and 2) a way to subtitle and localize these in a viewer’s preferred language.




Viki was founded in 2007 to provide internationals access to Korean dramas. Viki solves the problem of translation and localization by using crowdsourcing to subtitle shows. These community contributions are driven by intrinsic motivation, as users are not paid for their work. There are, however, certain recognition tiers for those who hit a threshold (Qualified Contributor status) granting subtitlers special perks, such as access to exclusive content. Viewers can track the completion of subtitles on a newly released show (unsubtitled shows are available on Viki a few hours after being shown in South Korea), and can either choose to watch the show before all the subtitles are complete, or wait until the shows are 100% subtitled in their language (a process that can take <24 hours from its premiere in South Korea). With close to 200 available language subtitles, Viki can provide any of its show to users in nearly any language, provided the community has contributed to produce its subtitles.

For each show, there is a designated team which includes language moderators who ensure the accuracy of subtitles. When the subtitles meet quality standards, the team leader (called the “Channel manager”) locks them to prevent further editing

Additionally, in 2017, Viki launched “Learn Mode” where viewers can watch shows with two sets of subtitles – their own language and that of another. In addition to helping users learn, this also improves the community’s ability to provide future subtitles. They also have the ability to pause the show at any time and highlight a word in the subtitle to learn pronunciation and spelling.


Effectiveness & Competitive Landscape

Significant market adoption (over 35 million monthly active users) has demonstrated Viki’s success in bringing new sources of content and providing suitable translations of that content. Academic research such as that done by researchers at Carnegie Mellon has likewise shown that the crowdsourcing model on which Viki relies for Active Crowd Translation (ACT) can approach the quality level of an expert in the field, giving its translation model further legitimacy.

In sizing the relevant addressable market for Viki, there are two industries of note: the market for translation services, and the market for television and movie entertainment. The total market size for translation services in the US is estimated at $5 billion per year. Furthermore, TV and Movie content production and distribution are estimated at revenues of $100 billion market per year in the US. Viki operates in both of these industries, and specializes in the intersection between the two, as well as how translation of entertainment content in the former opens up greater market opportunities for the latter. Because of the widely recognized wisdom that “content is king” and the race to find timely and unique streaming content first in a competitive market, that intersection has taken on ever-increasing importance.

In terms of revenue, Viki has three main streams: ads, viewer subscriptions, and licensed syndication of subtitled content to other distributors (e.g., Hulu, Yahoo, and Netflix). Viewers have the option of joining Viki for free, however this will force them to watch many ads throughout the course of a show or limit the number of episodes they can see.

Competitors in the television and movie streaming market, such as Netflix (which started streaming Korean dramas in 2015), Dramafever, and Hulu, have the ability to offer similar entertainment. However, their current Korean drama selection is mostly limited to older, “classic” series and they do not seem to have arrangements to onboard every new drama as quickly onto their platform. Meanwhile, competitors in the traditional translation and localization space lack the technology and distribution capabilities that Viki has.


Improvements & Suggestions

We see a number of opportunities for improvement within Viki’s product through a combination of machine learning, new business partnerships, and expansion into adjacent geographic markets and new content formats.

  • Use of machine learning to assist with crowdsourced translation. Viki can start off by building a simple translation system that can detect translation errors and propose suggestions to users based on language-specific rules. For instance, literal translations often ignore grammar, context and the meaning behind idiomatic expressions, such as “letting the cat out of the bag”. Given the number of companies, such as Google and Facebook, that are invested in neural machine translation, we believe that over time, Viki can partner with them to build a more complex solution that uses a combination of speech and image recognition to improve the accuracy of users’ translations. Such a partnership could be deeply beneficial to a technology company such as Google, because while Google has previously deeply indexed text content such as books, websites, and academic articles, its efforts in entertainment content have been more limited, and could help them compete against rival Amazon, who owns imdb.com. Such an arrangement could also further improve the quality and robustness of their data for machine translation and speech-to-text transcription. Additionally, machine learning could supplement easier, common subtitles (for example, the words “hello” or “watch out!”), reducing the need to translate these easier phrases. Greater reliance on assistive machine learning techniques could also be used to: (1) reduce the typical 1-day delay between when shows air and when subtitles are available on Viki and (2) encourage users who can speak and write in less popular subtitle languages to contribute.
  • Use a translate plugin: Both Microsoft & Google have simple speech to text converters with options to translate content into multiple languages. This could be used as a stop gap arrangement to immediately launch shows when they become available with more detailed subtitles coming in < 24 hours.
  • Expand to more shows and countries by partnering with other content providers. Viki can expand on existing partnerships with production companies to offer a wider selection of dramas. By extending, as well as signing on new, long-term contracts (potentially ones where Viki can co-produce dramas), Viki can increase customer retention and acquire new users.

  • Incentivizing multi-lingual members with more diverse rewards. Currently, volunteer translators are granted a free Viki subscription, badges, and some exclusive promotions. Viki can improve upon their rewards system by offering rewards on their partner platforms, such as Netflix, Samsung, and Fuji TV, and could even consider allowing Gold-status qualified contributors early access to content related to the shows they translate. This could further incentivize users to provide more and higher quality translations.
  • Expanding into adjacent markets for translating popular books, manga, and music. In the same way that television and movies require intensive effort to be translated and localized, books, manga, and music are similar forms of media that could likewise be translated through crowdsourcing. Because these are all forms of content that are already made available through subscription-based models (like Viki), the same model could readily be applied to them as well. A ready first candidate for partnership in this area could be Amazon, which already has millions of subscribers to its Kindle Unlimited service for books, and which already has an extensive presence in many parts of the world with unique content that could appeal to users in other countries.










http://clients1.ibisworld.com.proxy.uchicago.edu/reports/us/industry/keystatistics.aspx?entid=1245 http://clients1.ibisworld.com.proxy.uchicago.edu/reports/us/industry/keystatistics.aspx?entid=1246







Siddhant Dube

Eileen Feng

Nathan Stornetta

Tiffany Ho

Christina Xiong

Pitch: Point-to-Point

A smart marketplace for smarter travelers


According to Bond Brand Loyalty, there is $100 billion worth of unclaimed loyalty points. According to another loyalty research firm, Colloquy, about $48 billion worth of loyalty points and rewards are earned each year, of which approximately 33% will never be redeemed.

Many customers are unaware of their points balance or what their points can purchase. Hence points accumulate and expire, and customers lose incentives to stay loyal to brands. For companies, even though unredeemed points are a large source of frequent flyer program profits, research shows that customers who redeem points are more likely to display greater engagement and work harder to earn the next reward. A positive redemption experience can drive members to continue to spend with a brand.

While some customers might be interested in transferring their points or redeeming them on behalf of others, there is currently an institutional void borne out of what economists refer to as the hold-up problem and a lack of trust between any buyers and sellers.



To fix the problem of unused points balances, we create a two-sided marketplace where “sellers” can post how many points they have for which program, and “buyers” can bid on the points they want. Once the auction closes to the highest bidder, funds will be held by the platform in escrow, and the seller will then utilize the points according to the buyer’s wishes (ex: United flight from NY to SF). Upon the buyer’s receipt, funds will flow to the seller.

In this first iteration, we enable sellers to book travel for buyers. Most current loyalty programs do not allow for free transfer of points between individuals; thus, until this program changes, buyers and sellers will need to communicate to ensure that sellers book itineraries correctly. Although there is nothing to prevent customers from doing this already, our platform provides value both by improving discoverability of these opportunities and solving the problem of buyer and seller trust by holding payment in escrow until delivery is verified.

Over time as more loyalty programs enable free transfer of points, our marketplace can accommodate more situations. For example, for sellers with too few points to be of much use, we can allow them to auction their points directly. Buyers can accumulate points from multiple sellers and earn otherwise inaccessible rewards. Eventually, with enough liquidity, our platform can add further value by matching blocks of points to satisfy arbitrary trades, in the same way securities exchanges match bids and asks.

As Point-to-Point grows, it will not only benefit from cross-side network effects, but also open up additional product and business opportunities. As the platform collects data from transactions, we can build a recommendation engine to highlight deals for specific customers. We will also utilize algorithms to educate sellers on points required to cross more valuable thresholds. For example, if a seller had 1489 points with Chase, the algorithm could list shops that the seller would be interested in that could also yield a high return point value, enabling that seller to cross the threshold and sell their 1500+ points for a higher amount. Our platform will also collect relevant data outside the marketplace (ex: new credit card bonuses, 5x points on certain categories, etc.) and distribute this information to members so that they can have a one-stop shop for all loyalty point programs. This will induce stores to participate in our program, as our algorithms can enable higher spending and more efficient advertising.

We intend to monetize through a commission-based revenue model. For trades in which points are paid for in cash, Point-to-Point would collect a 3-5% fee on the transaction amount. For trades in which points from one program provider is exchanged for those of another, Point-to-Point will not collect any commission. This incentivizes more buyers (and therefore, sellers) to use the platform, creating benefit for all.



Our initial pilot sets up partnerships with loyalty programs that have a wide network of points and already have transfer systems in place, such as Starwood Preferred Guest, Chase Ultimate Rewards, or American Express Membership Rewards. These points can already be redeemed in a variety of settings (hotels, flights, and online retailers) and can sometimes be shared between household members.

We can market Point-to-Point to the popular points forums, including The Points Guy and Reddit, who in general capture a market that is more likely to be motivated by points programs. In line with what Eisenmann, Parker, and Van Alstyne recommend in their article “Strategies for Two-Sided Markets,” we would subsidize early adoption and adoption by price-sensitive users, and work to secure “marquee” users’ exclusive participation with loyalty programs. To incentivize early adopters, we can subsidize transactions for buyers and provide bonuses to sellers.

Furthermore, we will induce further loyalty through discount referrals. To ensure that new entrants do not steal market share, we would form temporary exclusive contracts with participating programs as well as offer our own form of points (earn points to redeem for percent discounts on trades). Over time, we will expand to more loyalty programs, offering an enormous selection for users.



To ensure our solution meets its objective of creating a liquid market for rewards points while benefiting both points programs and users, we would focus on empirical measures of market liquidity, such as those proposed by Gabrielsen, Marzo, and Zagaglia like liquidity ratio, turnover ratio, and variance ratios. We could also measure the following product success metrics:

  • Number of Sign-Ups
  • Retention Rates (e.g., 7-day and 28-day return rates, DAU, WAU, MAU)
  • Usage Per Member (e.g., # of trades and volume executed)
  • # of Referrals
  • Increase in Engagement with Loyalty Programs



Although loyalty programs have become a differentiating feature, allowing companies to win repeat customers and gain market share, the concept of a secondary market where buyers and sellers are matched based on factors, such as  willingness to pay, is still relatively new. Currently, Giift is a loyalty programs and gift card network, allowing consumers to track and exchange points from program issuers (airlines, hotels, retailers, etc.) with one another. However, the “exchange rate” is dictated by Giift (not what the market will bear) and given that only points are exchanging hands, not cash, this limits what a consumer would be able to receive and thus, overall trading activity.

The only other company taking a similar approach of creating a two-sided market platform is Digital Bits. By tokenizing points accumulated with an airline and allowing customers to redeem them for cash or rewards at another rewards provider of their choice, Digital Bits is creating a blockchain-based market and along with it, more flexibility for users. We feel that our solution can successfully compete with Digital Bits because it facilitates communication between buyer and seller without requiring integration with a brand’s existing app or interface. We are also skeptical of the incremental value a blockchain framework provides relative to the additional usage and development complexity it requires. As such, we feel our first-mover advantage and initial user base can perpetuate adoption on both sides of the market.










Eisenmann, Thomas, Geoffrey Parker, and Marshall W. Van Alstyne. “Strategies for two-sided markets.” Harvard Business Review 84, no. 10 (2006): 92.

Gabrielsen, Alexandros, Massimiliano Marzo, and Paolo Zagaglia. “Measuring market liquidity: An introductory survey.” (2011).



Siddhant Dube

Eileen Feng

Nathan Stornetta

Tiffany Ho

Christina Xiong

Photography Fix: Focus Pocus

The NextGen Solution to Your Perfect Photo Needs

The Problem / Opportunity

The US photography market has $10 billion in annual revenue. A number of startups and large technology companies have aimed to improve both professionals and amateurs’ photographs (especially those taken for social media), principally by focusing on post-production services, achieving valuations in the hundreds of millions. To our knowledge no technology company has focused on the pre-production photography component, leaving significant  open space for our company.

According to a recent National Geographic’s 50 Greatest Pictures issue, “a photographer shoots 20,000 to 60,000 images on assignment. Of those, perhaps a dozen will see the published light of day”. Photography is an art that depends on a number of factors – timing, weather, sun exposure, angle, and more – all of which lend to the unfortunately ephemeral nature of the perfect snapshot. This problem creates a great opportunity for a tool that can decrease the amount of time, energy, and planning needed to capture the optimal image.


Solution & Data Strategy

Focus Pocus creates a solution that can track, identify, and predict the best locations for a photo, enabling casual and professional photographers to see where they should navigate to in order to capture their ideal shot. This solution will be made available as a downloadable app on the user’s phone. In future iterations, Focus Pocus may be installed natively into wifi-enabled cameras.

Focus Pocus solves the problem of finding and taking the ideal shot by crowdsourcing the best possible photograph locations and conditions, relying on large amounts of publicly available data combined with sensor data (from users’ cameras/input) and guiding the user through the photo setup process.  

First, Focus Pocus will integrate with photo-sharing platforms like Instagram, Flickr, Google Photos, and 500px to identify publicly available photographs that are either (1) highly popular or (2) of a high quality for the area you are located. Highly popular photographs on social media can be measured by how frequently each photo is clicked, shared, or liked. High quality photos can be identified using deep learning photo-scoring algorithms that can identify good photographs based on characteristics such as clarity, uniqueness, color, etc.

Next, Focus Pocus will identify which ideal shots are available to you and what settings or angles you need to use in order to achieve them, based on your camera type, time of day, lighting conditions, and other user-specific data.

Most of this data is available; photographs taken using non-phone cameras will typically contain large amounts of technical metadata under the Exchangeable Image File Format which includes the following:

  • Date and time taken
  • Image name, size, and resolution
  • Camera name, aperture, exposure time, focal length, and ISO
  • Location data, lat/long, weather conditions, and map

Over time, after initial training and being provided with photo datasets, Focus Pocus can continue to map out the entire city, with the goal of handling everything from recommending tourists photo spots to internally setting up the camera with the right specs and using AR to position the camera at the right height and distance from the subject. Tracking sunlight and weather conditions by utilizing training data to identify the best locations using current time and conditions can also be developed (by using integrations like LinkedIn and Rapportive).


Pilot & Prototype

The project lends itself to piloting at trivial cost in a single city before scaling to other cities. As a pilot, we would set up sensors (light and weather sensors, cameras, etc.) at a handful of highly-trafficked photo locations in Chicago, determined by assessing geospatial photo density using a service like TwiMap or InstMap. Early candidates would be the Bean, Navy Pier, Millennium Park, and Willis Tower. Focusing on these locations initially would also make it easier to market the product with concentrated advertising or founding employees giving demonstrations on-site.

From there we would develop a simple mobile photography app for users that would be used to recommend ideal photo locations and also enforce ideal camera settings within the app for a location based not only on the phone’s sensors, but also from our more refined on-site sensors.



To ensure that our solution meets the objectives of identifying the best locations and conditions for photographs, we could evaluate the predictive power of sensors by benchmarking photographs taken using Focus Pocus against those without. We could also measure the following success metrics:

  • Number of Downloads/Installations, Retention Rates, Usage Per Member

In validating market need, we’ve also done research on applications similar to Focus Pocus and have found that businesses, such as Yelp and Flickr have already used deep learning to build photo-scoring models. For instance, by assessing factors, such as depth of field, focus and alignment, Yelp is able to select the best photos for its partner restaurants. However, this use case is after-the-fact (i.e. after the photo has already been taken), whereas the solution we propose allows for actions to be taken to optimize photo quality before it’s taken.





Jin, Xin, et al. “Deep image aesthetics classification using inception modules and fine-tuning connected layer.” Wireless Communications & Signal Processing (WCSP), 2016 8th International Conference on. IEEE, 2016.

Aiello, Luca Maria, Rossano Schifanella, Miriam Redi, Stacey Svetlichnaya, Frank Liu, and Simon Osindero. “Beautiful and damned. Combined effect of content quality and social ties on user engagement.” IEEE Transactions on Knowledge and Data Engineering 29, no. 12 (2017): 2682-2695.

Datta, Ritendra, and James Z. Wang. “ACQUINE: aesthetic quality inference engine-real-time automatic rating of photo aesthetics.” In Proceedings of the international conference on Multimedia information retrieval, pp. 421-424. ACM, 2010.







Team Members

Siddhant Dube

Eileen Feng

Nathan Stornetta

Tiffany Ho

Christina Xiong