"Snippet ( 6) - Inter Parte Review - Patent owner response"

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"One such example is Sense Networks ,a recent US – based start up company, which uses millions of GPS estimates sourced from mobile phones within a city to predict , for instance, which sub-population for tribe might be interested in a specific type of night club or bar (e.g., a jazz club)." September 2010 ,e.Digital Corporation
Exhibit 2017 - Page 3

"It is now feasible to distribute and run experiments with a large number of participants from all around the world rather than in Laboratory controlled conditions using a smalluser study." IEEE communications Magazine. September 2010 , e.DIGITAL Corporation Exhibit 2017 – Page 3 LINK

"Figure 2 shows carbon monoxide readings captured in Ghana using mobile sensors attached to a taxicabs as part of the participatory Urbanism project, as an example of a community sensing application." September 2010 ,e.Digital Corporation Exhibit 2017 - Page 5

"Alternatively , an effective approach for some systems have been sensor sampling routines with admission control stages that do not process data that is low quality , saving resources ,and reducing errors (e.g., Sound Sense)" September 2010, e.Digital Corporation Exhibit 2017 - Page 7

"Researchers recognize the strength of leveraging social media outlets such as Face-book , Tweeter , and flicker as ways to not only disseminate information but build community awareness (e.g., citizen science)." September 2010, e.Digital Corporation
Exhibit 2017 - Page 9

"The combination of these advances opens the door for new innovative research and will lead to the development of sensing applications that are likely to revolutionize a large number of existing business sectors and ultimately significantly impact our everyday lives. Many questions remain to make this vision a reality. For example, how much intelligence can we push to the phone without jeopardizing the phone experience? What breakthroughs are needed in order to perform robust and accurate classification of activities and context out in the wild? How do we scale a sensing application from an individual to a target community or even the general population? How do we use these new forms of largescale application delivery systems (e.g., Apple AppStore, Google Market) to best drive data collection, analysis and validation? How can we exploit the availability of big data shared by applications but build watertight systems that protect personal privacy? While this new research field can leverage results and insights from wireless sensor networks, pervasive computing, machine learning, and data mining, it presents new challenges not addressed by these communities.

The Dartmouth College gang, that published this article Sept 2010, an article with more questions than answers, released an app based on their invented issues 2008 on the Apple store....

Keep in mind the comments are Sept 2010...

"SOCIAL NETWORKING

Millions of people participate regularly within online social networks. The Dartmouth CenceMe project [2] is investigating the use of sensors in the phone to automatically classify events in people’s lives, called sensing presence, and selectively share this presence using online social networks such as Twitter, Facebook, and MySpace, replacing manual actions people now perform daily."

see reference above...as it relates to the full context of the reference...

"For example, an early use of app store distribution by researchers in academia is the CenceMe application for iPhone [2], which was made available on the App Store when it opened in 2008."(a comment made well after 2008) It is now feasible to distribute and run experiments with a large number of participants from all around the world rather than in laboratory controlled conditions using a small user study. For example, researchers interested in statistical models that interpret human behavior from sensor data have long dreamed of ways to collect such large-scale real-world data. These app stores represent a game changer for these types of research. However, many challenges remain with this new approach to experimentation via app stores. For example, what is the best way to collect ground-truth data to assess the accuracy of algorithms that interpret sensor data? How do we validate experiments? How do we select a good study group? How do we deal with the potentially massive amount of data made available? How do we protect the privacy of users? What is the impact on getting approval for human subject studies from university institutional review boards (IRBs)? How do researchers scale to run such large-scale studies? For example, researchers used to supporting small numbers of users (e.g., 50 users with mobile phones) now have to construct cloud services to potentially deal with 10,000 needy users. This is fine if you are a startup, but are academic research laboratories geared to deal with this?"

The comment of 2010 "It is now feasible" does not relate to the released Dartmouth College gang issues of 2008...with many questions to be dealt with as of Sept 2010.

e.Digitals patent application 12/891,875 was filed Sept-28-2010...and published as patent 8,311,522

sman, that's my consideration of just one reference listed...though I would venture to say, that the leaning of exhibit 2017.....too many questions exhibited in the article.

With that, IMVHO, the Miluzzo / Dartmouth College issue is a non issue.

doni