Attorneys Maria Anderson, Jeremy Carney, Bryan McWhorter, Alex Martinez, Mauricio Uribe hosted a webinar on Strategic Considerations in Patent Subject Matter Eligibility Under Revised USPTO Guidance

1. © 2020
Presented by:Presented by:
Knobbe Martens
June 24, 2020
Trends and Changes in
View of the USPTO’s
Updated Revised
Guidance

2. © 2020 Knobbe Martens
Firm Profile – By the Numbers
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3. © 2020 Knobbe Martens
Meet the Speakers
3
Maria Anderson
Partner - Seattle
Jeremy Carney
Partner - OC
Alex Martinez
Associate - OC
Bryan McWhorter
Partner - Seattle
Mauricio Uribe
Partner - Seattle

4. © 2019
How Did We Get Here? – Patent Subject Matter Eligibility
4
162 Years
1790
Patent
Act
1952
Patent
Act
1998
State
Street
46 Years
2014
Alice
/Mayo
2019
Revised
Guidance
1998
State
Street
2019
Revised
Guidance
2010
Bilski
2014
Alice/
Mayo
2016
Electric
Power
Group
2018
Berkheimer
2016
Enfish
2016
McRO
2014
DDR
2012
Prometheus
2017
Dir Iancu

5. © 2020
Revised Guidance – Step 2A – Prong One
5
Focal Point of Revised Guidance

6. © 2020
Evaluation of Whether a Claim Recites a Judicial Exception
6
• Meaning of “Recites”
⎼ Step 2A – Prong One: Determine whether claim recites a judicial
exception
⎼ Claim “recites” a judicial exception when the judicial exception is “set
forth” or “described” in the claim
o Set Forth – Claims clearly state the judicial exception (e.g., clearly
state a mathematical equation)
o Describe – Claims do not explicitly state the judicial exception but
the concept of the judicial exception can be identified (e.g.,
recitation of the concept of intermediated settlement without
recitation of the terms “intermediated” or “settlement”)

7. © 2020
Revised Guidance – Step 2A – Prong Two
7
Focal Point of Revised Guidance

8. © 2020
Improvements in the Functioning of a Computer or Improvement to Other Technology
8
Step 2A – Prong 2 - Integration into Practical Application
• “[A] n important consideration to evaluate when determining whether the claim as a whole
integrates a judicial exception into a practical application is whether the claimed invention improves
the functioning of a computer or other technology. The courts have not provided an explicit test for
this consideration.”
• Test for Practical Application
• Evaluate the specification to determine if the disclosure provides sufficient details such that
one of ordinary skill in the art would recognize the claimed invention as providing an
improvement.
• Second, if the specification sets forth an improvement in technology, the claim must be
evaluated to ensure that the claim itself reflects the disclosed improvement.
• “The “improvements” analysis in Step 2A determines whether the claim pertains to an improvement
to the functioning of a computer or to another technology without reference to what is well-
understood, routine, conventional activity.”
• “Improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not
an improvement in technology.”

9. © 2020
Section 101 Rejections
– PTO Statistics
• Post-Alice
• 31% increase in 101 rejections after
Alice
• 26% increase in variability of first
Office Action receiving a 101 rejection
(measured across multiple examiners)
• Post Revised Guidance
• 25% decrease in 101 rejections after
Revised Guidance
• 44% decrease in variability of first
Office Action receiving a 101 rejection

10. © 2020 Knobbe Martens
Revised Guidance – Example Claims
10

11. © 2020
Example 43 – Treating Kidney Disease
11
CLAIMS
1. A treatment method comprising:
(a) calculating a ratio of C11 to C13 levels measured in a blood sample from a patient
diagnosed with Nephritic Autoimmune Syndrome Type 3 (NAS-3) to identify the patient
as having a non-responder phenotype;
(b) administering a treatment to the patient having a non-responder phenotype.
2. The method of claim 1, wherein the treatment is a non-steroidal agent capable of treating
NAS-3.
3. The method of claim 1, wherein the treatment is rapamycin.
4. The method of claim 1, wherein the treatment is a course of plasmapheresis.
5. A treatment method comprising administering rapamycin to a patient identified as having
Nephritic Autoimmune Syndrome Type 3 (NAS-3).

12. © 2020
Example 44 – Denveric Acid
12
CLAIMS
1. A dosage unit comprising denveric acid in a container.
2. The dosage unit of claim 1, wherein the container is a wearable delivery device having a
flexible patch-shaped housing, a needle assembly mounted on one side of the housing, a
reservoir located inside the housing in which the denveric acid is stored, a dosage control
button mounted on the opposite side of the housing from the needle assembly, and a
delivery valve for dispensing a selected dosage of denveric acid from the reservoir to the
needle assembly.
3. The dosage unit of claim 1, wherein the denveric acid is an intermediate-acting denveric
acid.
4. The dosage unit of claim 1, further comprising protamine that is mixed with the denveric
acid in the container in an amount of 0.75 mg to 1.5 mg protamine per every mg of denveric
acid.

13. © 2020
Example 45 – Controller for Injection Mold
13
CLAIMS
1. A controller for an injection molding apparatus having a mold defining a cavity for receiving uncured polyurethane that is heated
to form a molded article during a cycle of operation of the apparatus, the controller configured to:
(a) repeatedly obtain measurements of the temperature of a mold;
(b) calculate an extent of curing completion of polyurethane in the mold using the obtained temperatures and the Arrhenius
equation; and
(c) determine the extent that the polyurethane is cured as a percentage.
2. The controller of claim 1, which is further configured to:
(d) send control signals to the injection molding apparatus once the polyurethane has reached a target percentage, the control
signals instructing the apparatus to open the mold and eject the molded polyurethane from the mold.
3. A system comprising the controller of claim 1 connected to a means for temperature measuring that repeatedly measures the
temperature of the mold.
4. A controller for an injection molding apparatus having a mold defining a cavity for receiving uncured polyurethane that is heated
to form a molded article during a cycle of operation of the apparatus, the controller configured to:
(a) send a control signal to the injection molding apparatus to regulate injection of uncured polyurethane into the mold, and to
heat the mold to a target temperature to cure the polyurethane;
(b) repeatedly obtain temperature measurements of the mold;
(c) compare the obtained temperatures to a target temperature; and
(d) maintain temperature of the mold within two degrees of the target temperature by sending a control signal to the apparatus
to selectively heat or cool the mold when the obtained temperature of the mold is more than two degrees different than the
target temperature.

14. © 2020
Example 46 – Livestock Management
14
CLAIMS
1. A system for monitoring health and activity in dairy livestock animals comprising:
a memory;
a display; and
a processor coupled to the memory programmed with executable instructions, the instructions including
a livestock interface for obtaining animal-specific information, wherein the animal-specific information comprises animal
identification data and at least one of body position data, body temperature data, feeding behavior data, and movement
pattern data; and
a monitoring component for
(a) comparing the obtained animal-specific information with animal information from a herd database to verify an
animal’s identity, and
(b) analyzing the obtained animal-specific information to identify whether the animal is exhibiting an aberrant behavioral
pattern as compared to past behavior of the animal, and
(c) displaying the analysis results for the animal on the display.
2. The system of claim 1, wherein the system further comprises
a feed dispenser that is connected to a feed and supplement supply and is operable to dispense individualized amounts of
feed and optional supplements, and
wherein the monitoring component is further configured for
(d) automatically sending a control signal to the feed dispenser to dispense a therapeutically effective amount of supplemental
salt and minerals mixed with feed when the analysis results for the animal indicate that the animal is exhibiting an aberrant
behavioral pattern indicative of grass tetany.

15. © 2020
Example 46 – Livestock Management
15
CLAIMS
1. A system for monitoring health and activity in dairy livestock animals comprising:
a memory;
a display; and
a processor coupled to the memory programmed with executable instructions, the instructions including
a livestock interface for obtaining animal-specific information, wherein the animal-specific information comprises animal
identification data and at least one of body position data, body temperature data, feeding behavior data, and movement
pattern data; and
a monitoring component for
(a) comparing the obtained animal-specific information with animal information from a herd database to verify an
animal’s identity, and
(b) analyzing the obtained animal-specific information to identify whether the animal is exhibiting an aberrant behavioral
pattern as compared to past behavior of the animal, and
(c) displaying the analysis results for the animal on the display.
2. The system of claim 1, wherein the system further comprises
a feed dispenser that is connected to a feed and supplement supply and is operable to dispense individualized amounts of
feed and optional supplements, and
wherein the monitoring component is further configured for
(d) automatically sending a control signal to the feed dispenser to dispense a therapeutically effective amount of supplemental
salt and minerals mixed with feed when the analysis results for the animal indicate that the animal is exhibiting an aberrant
behavioral pattern indicative of grass tetany.
An abstract “mental process” under the October Guidance:

16. © 2020
Example 46 – Livestock Management
16
CLAIMS
1. A system for monitoring health and activity in dairy livestock animals comprising:
a memory;
a display; and
a processor coupled to the memory programmed with executable instructions, the instructions including
a livestock interface for obtaining animal-specific information, wherein the animal-specific information comprises animal
identification data and at least one of body position data, body temperature data, feeding behavior data, and movement
pattern data; and
a monitoring component for
(a) comparing the obtained animal-specific information with animal information from a herd database to verify an
animal’s identity, and
(b) analyzing the obtained animal-specific information to identify whether the animal is exhibiting an aberrant behavioral
pattern as compared to past behavior of the animal, and
(c) displaying the analysis results for the animal on the display.
2. The system of claim 1, wherein the system further comprises
a feed dispenser that is connected to a feed and supplement supply and is operable to dispense individualized amounts of
feed and optional supplements, and
wherein the monitoring component is further configured for
(d) automatically sending a control signal to the feed dispenser to dispense a therapeutically effective amount of supplemental
salt and minerals mixed with feed when the analysis results for the animal indicate that the animal is exhibiting an aberrant
behavioral pattern indicative of grass tetany.
An abstract “mental process” under the October Guidance:

17. © 2020
Example 46 – Livestock Management
17
CLAIMS
1. A system for monitoring health and activity in dairy livestock animals comprising:
a memory;
a display; and
a processor coupled to the memory programmed with executable instructions, the instructions including
a livestock interface for obtaining animal-specific information, wherein the animal-specific information comprises animal
identification data and at least one of body position data, body temperature data, feeding behavior data, and movement
pattern data; and
a monitoring component for
(a) comparing the obtained animal-specific information with animal information from a herd database to verify an
animal’s identity, and
(b) analyzing the obtained animal-specific information to identify whether the animal is exhibiting an aberrant behavioral
pattern as compared to past behavior of the animal, and
(c) displaying the analysis results for the animal on the display.
2. The system of claim 1, wherein the system further comprises
a feed dispenser that is connected to a feed and supplement supply and is operable to dispense individualized amounts of
feed and optional supplements, and
wherein the monitoring component is further configured for
(d) automatically sending a control signal to the feed dispenser to dispense a therapeutically effective amount of supplemental
salt and minerals mixed with feed when the analysis results for the animal indicate that the animal is exhibiting an aberrant
behavioral pattern indicative of grass tetany.
An abstract “mental process” under the October Guidance:
Additional, non-abstract element

18. © 2020
Example 46 – Livestock Management
18
CLAIMS
1. A system for monitoring health and activity in dairy livestock animals comprising:
a memory;
a display; and
a processor coupled to the memory programmed with executable instructions, the instructions including
a livestock interface for obtaining animal-specific information, wherein the animal-specific information comprises animal
identification data and at least one of body position data, body temperature data, feeding behavior data, and movement
pattern data; and
a monitoring component for
(a) comparing the obtained animal-specific information with animal information from a herd database to verify an
animal’s identity, and
(b) analyzing the obtained animal-specific information to identify whether the animal is exhibiting an aberrant behavioral
pattern as compared to past behavior of the animal, and
(c) displaying the analysis results for the animal on the display.
2. The system of claim 1, wherein the system further comprises
a feed dispenser that is connected to a feed and supplement supply and is operable to dispense individualized amounts of
feed and optional supplements, and
wherein the monitoring component is further configured for
(d) automatically sending a control signal to the feed dispenser to dispense a therapeutically effective amount of supplemental
salt and minerals mixed with feed when the analysis results for the animal indicate that the animal is exhibiting an aberrant
behavioral pattern indicative of grass tetany.
An abstract “mental process” under the October Guidance:
Integration of exception Additional, non-abstract element

19. © 2020
Example 46 – Livestock Management
19
3. A method for monitoring health and activity in dairy livestock animals comprising:
(a) causing a herd of livestock animals to enter a sorting gate that is automatically operable, wherein each animal in the herd
is equipped with an animal sensor having a radio frequency transponder,
(b) for a particular animal in the herd, obtaining, by a radio frequency reader mounted on or near the sorting gate, animal-
specific information from the animal sensor when the animal sensor is within proximity to the radio frequency reader, the
animal-specific information comprising animal identification data and at least one of body position data, body temperature
data, feeding behavior data, and movement pattern data,
(c) analyzing, by a processor, the obtained animal-specific information from step (ii) with respect to animal information stored
in a herd database to identify the animal and to determine whether the animal is exhibiting an aberrant behavioral pattern as
compared to the past behavior of the animal,
(d) automatically operating the sorting gate, by the processor sending a control signal to the sorting gate to route the animal
into a holding pen when the analysis results from step (iii) for the animal indicate that the animal is exhibiting an aberrant
behavioral pattern, and by the processor sending a control signal to the sorting gate to permit the animal to freely pass
through the sorting gate when the analysis results for the animal indicate that the animal is not exhibiting an aberrant
behavioral pattern, and
(e) repeating steps (b) through (d) for each animal in the herd.
4. A system for monitoring health and activity in a herd of dairy livestock animals comprising:
a memory;
a processor coupled to the memory programmed with executable instructions, the instructions including a livestock interface
for obtaining animal-specific information for a plurality of animals in the herd, wherein the animal-specific information
comprises animal identification data and at least one of body position data, body temperature data, feeding behavior data,
and movement pattern data; and
a herd monitor including (a) a radio frequency reader for collecting the animal-specific information from a plurality of animal
sensors attached to the animals in the herd when the animal sensors are within proximity to the radio frequency reader, each
animal sensor having a radio frequency transponder, and (b) a transmitter for transmitting the collected animal-specific
information to the livestock interface.

20. © 2020
Example 46 – Livestock Management
20
3. A method for monitoring health and activity in dairy livestock animals comprising:
(a) causing a herd of livestock animals to enter a sorting gate that is automatically operable, wherein each animal in the herd
is equipped with an animal sensor having a radio frequency transponder,
(b) for a particular animal in the herd, obtaining, by a radio frequency reader mounted on or near the sorting gate, animal-
specific information from the animal sensor when the animal sensor is within proximity to the radio frequency reader, the
animal-specific information comprising animal identification data and at least one of body position data, body temperature
data, feeding behavior data, and movement pattern data,
(c) analyzing, by a processor, the obtained animal-specific information from step (ii) with respect to animal information stored
in a herd database to identify the animal and to determine whether the animal is exhibiting an aberrant behavioral pattern as
compared to the past behavior of the animal,
(d) automatically operating the sorting gate, by the processor sending a control signal to the sorting gate to route the animal
into a holding pen when the analysis results from step (iii) for the animal indicate that the animal is exhibiting an aberrant
behavioral pattern, and by the processor sending a control signal to the sorting gate to permit the animal to freely pass
through the sorting gate when the analysis results for the animal indicate that the animal is not exhibiting an aberrant
behavioral pattern, and
(e) repeating steps (b) through (d) for each animal in the herd.
4. A system for monitoring health and activity in a herd of dairy livestock animals comprising:
a memory;
a processor coupled to the memory programmed with executable instructions, the instructions including a livestock interface
for obtaining animal-specific information for a plurality of animals in the herd, wherein the animal-specific information
comprises animal identification data and at least one of body position data, body temperature data, feeding behavior data,
and movement pattern data; and
a herd monitor including (a) a radio frequency reader for collecting the animal-specific information from a plurality of animal
sensors attached to the animals in the herd when the animal sensors are within proximity to the radio frequency reader, each
animal sensor having a radio frequency transponder, and (b) a transmitter for transmitting the collected animal-specific
information to the livestock interface.

21. © 2020
Example 46 – Livestock Management
21
3. A method for monitoring health and activity in dairy livestock animals comprising:
(a) causing a herd of livestock animals to enter a sorting gate that is automatically operable, wherein each animal in the herd
is equipped with an animal sensor having a radio frequency transponder,
(b) for a particular animal in the herd, obtaining, by a radio frequency reader mounted on or near the sorting gate, animal-
specific information from the animal sensor when the animal sensor is within proximity to the radio frequency reader, the
animal-specific information comprising animal identification data and at least one of body position data, body temperature
data, feeding behavior data, and movement pattern data,
(c) analyzing, by a processor, the obtained animal-specific information from step (ii) with respect to animal information stored
in a herd database to identify the animal and to determine whether the animal is exhibiting an aberrant behavioral pattern as
compared to the past behavior of the animal,
(d) automatically operating the sorting gate, by the processor sending a control signal to the sorting gate to route the animal
into a holding pen when the analysis results from step (iii) for the animal indicate that the animal is exhibiting an aberrant
behavioral pattern, and by the processor sending a control signal to the sorting gate to permit the animal to freely pass
through the sorting gate when the analysis results for the animal indicate that the animal is not exhibiting an aberrant
behavioral pattern, and
(e) repeating steps (b) through (d) for each animal in the herd.
4. A system for monitoring health and activity in a herd of dairy livestock animals comprising:
a memory;
a processor coupled to the memory programmed with executable instructions, the instructions including a livestock interface
for obtaining animal-specific information for a plurality of animals in the herd, wherein the animal-specific information
comprises animal identification data and at least one of body position data, body temperature data, feeding behavior data,
and movement pattern data; and
a herd monitor including (a) a radio frequency reader for collecting the animal-specific information from a plurality of animal
sensors attached to the animals in the herd when the animal sensors are within proximity to the radio frequency reader, each
animal sensor having a radio frequency transponder, and (b) a transmitter for transmitting the collected animal-specific
information to the livestock interface.

22. © 2020
Example 46 – Livestock Management
22
3. A method for monitoring health and activity in dairy livestock animals comprising:
(a) causing a herd of livestock animals to enter a sorting gate that is automatically operable, wherein each animal in the herd
is equipped with an animal sensor having a radio frequency transponder,
(b) for a particular animal in the herd, obtaining, by a radio frequency reader mounted on or near the sorting gate, animal-
specific information from the animal sensor when the animal sensor is within proximity to the radio frequency reader, the
animal-specific information comprising animal identification data and at least one of body position data, body temperature
data, feeding behavior data, and movement pattern data,
(c) analyzing, by a processor, the obtained animal-specific information from step (ii) with respect to animal information stored
in a herd database to identify the animal and to determine whether the animal is exhibiting an aberrant behavioral pattern as
compared to the past behavior of the animal,
(d) automatically operating the sorting gate, by the processor sending a control signal to the sorting gate to route the animal
into a holding pen when the analysis results from step (iii) for the animal indicate that the animal is exhibiting an aberrant
behavioral pattern, and by the processor sending a control signal to the sorting gate to permit the animal to freely pass
through the sorting gate when the analysis results for the animal indicate that the animal is not exhibiting an aberrant
behavioral pattern, and
(e) repeating steps (b) through (d) for each animal in the herd.
4. A system for monitoring health and activity in a herd of dairy livestock animals comprising:
a memory;
a processor coupled to the memory programmed with executable instructions, the instructions including a livestock interface
for obtaining animal-specific information for a plurality of animals in the herd, wherein the animal-specific information
comprises animal identification data and at least one of body position data, body temperature data, feeding behavior data,
and movement pattern data; and
a herd monitor including (a) a radio frequency reader for collecting the animal-specific information from a plurality of animal
sensors attached to the animals in the herd when the animal sensors are within proximity to the radio frequency reader, each
animal sensor having a radio frequency transponder, and (b) a transmitter for transmitting the collected animal-specific
information to the livestock interface.

23. © 2020
Example 42 – Method for Transmission of Notifications When Medical Records
Are Updated
23
Claim 1:
A method comprising:
(a) storing information in a standardized format about a patient's condition in a plurality of
network-based non-transitory storage devices having a collection of medical records stored
thereon;
(b) providing remote access to users over a network so any one of the users can update the
information about the patient’s condition in the collection of medical records in real time through a
graphical user interface, wherein the one of the users provides the updated information in a non-
standardized format dependent on the hardware and software platform used by the one of the
users;
(c) converting, by a content server, the non-standardized updated information into the
standardized format,
(d) storing the standardized updated information about the patient’s condition in the collection of
medical records in the standardized format;
(e) automatically generating a message containing the updated information about the patient’s
condition by the content server whenever updated information has been stored; and
(f) transmitting the message to all of the users over the computer network in real time, so that
each user has immediate access to up-to-date patient information.

24. © 2020
Example 42 – Method for Transmission of Notifications When Medical Records
Are Updated
24
Claim 1:
A method comprising:
(a) storing information in a standardized format about a patient's condition in a plurality of
network-based non-transitory storage devices having a collection of medical records stored
thereon;
(b) providing remote access to users over a network so any one of the users can update the
information about the patient’s condition in the collection of medical records in real time through a
graphical user interface, wherein the one of the users provides the updated information in a non-
standardized format dependent on the hardware and software platform used by the one of the
users;
(c) converting, by a content server, the non-standardized updated information into the
standardized format,
(d) storing the standardized updated information about the patient’s condition in the collection of
medical records in the standardized format;
(e) automatically generating a message containing the updated information about the patient’s
condition by the content server whenever updated information has been stored; and
(f) transmitting the message to all of the users over the computer network in real time, so that
each user has immediate access to up-to-date patient information.

25. © 2020
Example 42 – Method for Transmission of Notifications When Medical Records
Are Updated
25
Reasoning of the 2019 PEG:
Reasoning of the 2019 PEG:

26. © 2020
Example 37 – Relocation of Icons on a Graphical User Interface
26
Claim 1:
A method of rearranging icons on a graphical user interface (GUI) of a computer system, the method comprising:
(a) receiving, via the GUI, a user selection to organize each icon based on a specific criteria, wherein the
specific criteria is an amount of use of each icon;
(b) determining, by a processor, the amount of use of each icon over a predetermined period of time; and
(c) automatically moving the most used icons to a position on the GUI closest to the start icon of the
computer system based on the determined amount of use.
Claim 2:
A method of rearranging icons on a graphical user interface (GUI) of a computer system, the method comprising:
(a) receiving, via the GUI, a user selection to organize each icon based on a specific criteria, wherein the
specific criteria is an amount of use of each icon;
(b) determining the amount of use of each icon using a processor that tracks how much memory has been
allocated to each application associated with each icon over a predetermined period of time; and
(c) automatically moving the most used icons to a position on the GUI closest to the start icon of the
computer system based on the determined amount of use.
An abstract “mental process” under the Jan. Guidance:
“This limitation…is a process that…covers performance of
the limitation in the mind but for the recitation of
generic computer components.”
Additional,
non-abstract
elements
An abstract “mental process” under the Jan. Guidance:
“This limitation…is a process that…covers performance of
the limitation in the mind but for the recitation of
generic computer components.”
NOT an abstract “mental process” under the Jan. Guidance:
This limitation “is not practically performed in the human
mind…because it requires a processor accessing computer
memory indicative of application usage.”

27. © 2020
Example 37 – Relocation of Icons on a Graphical User Interface
27
Claim 3:
A method of ranking icons of a computer system, the method comprising:
(a) determining, by a processor, the amount of use of each icon over a predetermined
period of time; and
(b) ranking the icons, by the processor, based on the determined amount of use.
Both recite an abstract “mental process” under the Jan. Guidance:
Each of these limitations is a process that “covers performance of the
limitation in the mind but for the recitation of generic computer
components.”
“This generic processor limitation is not more than mere instructions to
apply the [judicial] exception using a generic computer component.”
The additional element of the processor does not integrate the
abstract idea into a practical application:
Both recite an abstract “mental process” under the Jan. Guidance:
Each of these limitations is a process that “covers performance of the
limitation in the mind but for the recitation of generic computer
components.”

28. © 2020
Example 38 – Simulating an Analog Audio Mixer
28
Claim:
A method for providing a digital computer simulation of an analog audio
mixer comprising:
(a) initializing a model of an analog circuit in the digital computer, said
model including a location, initial value, and a manufacturing tolerance
range for each of the circuit elements within the analog circuit;
(b) generating a normally distributed first random value for each circuit
element, using a pseudo random number generator, based on a
respective initial value and manufacturing tolerance range; and
(c) simulating a first digital representation of the analog circuit based on
the first random value and the location of each circuit element within the
analog circuit.

29. © 2020
Example 38 – Hypothetical Specification
29
Hypothetical Detailed Description:
• Technical problem: Some existing digital simulations of analog audio
mixers are designed to simulate sounds from analog circuits.
However, the result is poor sound quality.
• Technical solution: Model an analog circuit with pseudo randomly
generated numbers within manufacturing tolerance ranges.

30. © 2020
Example 38 – Hypothetical Figure
30
Random
Number
Generator
Bilinear
Transformation
Digital
Representation

31. © 2020
Example 39 – Method for Training a Neural Network for Facial Detection
31
Claim
A computer-implemented method of training a neural network for facial detection
comprising:
(a) collecting a set of digital facial images from a database;
(b) applying one or more transformations to each digital facial image including
mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital
facial images;
(c) creating a first training set comprising the collected set of digital facial images, the
modified set of digital facial images, and a set of digital non-facial images;
(d) training the neural network in a first stage using the first training set;
(e) set for a second stage of training comprising the first training set and digital non-
facial images that are incorrectly detected as facial images after the first stage of
training; and
(f) training the neural network in a second stage using the second training set.

32. © 2020
Example 39 – Hypothetical Specification
32
Hypothetical Detailed Description:
• Technical problem: Some existing neural networks are used for facial
detection. However, existing neural networks fail to detect human
faces where there are shifts, distortions, and variations in the faces in
the images.
• Technical solution:
o First, expanded training set is used by applying mathematical
transformations to an initial set of facial images. E.g., smoothing or
contrast reduction applied to images. But, this can introduce false
positives.
o Second, retrain with processed non-facial images to reduce false
positives.

33. © 2020
Example 38 – Hypothetical Figure
33
Transformation
Functions
Train Neural Network
Retrain with non-facial images

34. © 2020
Examples 38 & 39 – Practice Tips
34
• Describe/point to the technical problem/solution.
• Include figures to help tell the technical
problem/solution story.
• Where possible address technical deficiency in the
prior art system.
o E.g., poor sound quality, false positives, slower,
network bandwidth, etc.

35. © 2020
Example 41 Discussion – First, a brief return to Example 39
1. A computer-implemented method of training a neural network for facial detection comprising:
collecting a set of digital facial images from a database;
applying one or more transformations to each digital facial image including mirroring, rotating,
smoothing, or contrast reduction to create a modified set of digital facial images;
creating a first training set comprising the collected set of digital facial images, the modified set of
digital facial images, and a set of digital non-facial images;
training the neural network in a first stage using the first training set;
creating a second training set for a second stage of training comprising the first training set and
digital non-facial images that are incorrectly detected as facial images after the first stage of training; and
training the neural network in a second stage using the second training set.
35

36. © 2020
Example 41
1. A method for establishing cryptographic communications between a first computer terminal and a second computer terminal
comprising:
receiving a plaintext word signal at the first computer terminal;
transforming the plaintext word signal to one or more message block word signals MA;
encoding each of the message block word signals MA to produce a ciphertext word signal CA, whereby CA=MAe (mod
n);
where CA is a number representative of an encoded form of message word MA;
where MA corresponds to a number representative of a message and 0 ≤ MA ≤ n-1;
where n is a composite number of the form n=p*q;
where p and q are prime numbers;
where e is a number relatively prime to (p-1)*(q-1); and
transmitting the ciphertext word signal CA to the second computer terminal over a communication channel.
36

37. © 2020
Example 41
37
PTO Analysis:
Well-understood, routine, conventional subject matter can
integrate an abstract idea into a practical application. Thus, even
though receiving a signal at a first computer, transforming it, and
transmitting the transformed signal to a second computer are
described in the background as being conventional, we do not
consider in Step 2A – Prong 2 whether the additional elements are
conventional when determining whether the abstract idea is
integrated into a practical application.

38. © 2020
Example 41 – Eligibility Reasoning
• Background:
⎼ Various cryptographic encoding and decoding methods are available to assist with these security and
authentication needs. However, many of them require expensive encoding and decoding hardware
as well as a secure way of sharing the private key used to encrypt and decrypt the message.
⎼ To solve these problems, applicants have invented a method for establishing cryptographic
communications using an algorithm to encrypt a plaintext into a ciphertext. … The invention improves upon
prior methods for establishing cryptographic communications because by using only the variables n and e
(which are publicly known), a plaintext can be encrypted by anyone.
• PTO Analysis:
⎼ The combination of additional elements use the mathematical formulas and calculations in a specific
manner that sufficiently limits the use of the mathematical concepts to the practical application of
transmitting the ciphertext word signal to a computer terminal over a communication channel.
⎼ Thus, the mathematical concepts are integrated into a process that secures private network
communications….
38

39. © 2020
Example 41
1. A method for establishing cryptographic communications between a first computer terminal and a second computer terminal
comprising:
receiving a plaintext word signal at the first computer terminal;
transforming the plaintext word signal to one or more message block word signals MA;
encoding each of the message block word signals MA to produce a ciphertext word signal CA, whereby CA=MAe (mod
n);
where CA is a number representative of an encoded form of message word MA;
where MA corresponds to a number representative of a message and 0 ≤ MA ≤ n-1;
where n is a composite number of the form n=p*q;
where p and q are prime numbers;
where e is a number relatively prime to (p-1)*(q-1); and
transmitting the ciphertext word signal CA to the second computer terminal over a communication channel.
39
Eligible

40. © 2020
Example 40 – Claim 1
1. A method for adaptive monitoring of traffic data through a network appliance connected
between computing devices in a network, the method comprising:
collecting, by the network appliance, traffic data relating to the network traffic
passing through the network appliance, the traffic data comprising at least one of
network delay, packet loss, or jitter;
comparing, by the network appliance, at least one of the collected traffic data to a
predefined threshold; and
collecting additional traffic data relating to the network traffic when the collected
traffic data is greater than the predefined threshold, the additional traffic data comprising
Netflow protocol data.
40
PTO Analysis:
But for the “by the network appliance” language, the claim
encompasses a user simply comparing the collected packet loss
data to a predetermined acceptable quality percentage in his/her
mind.
PTO Analysis:
Each of the collecting steps analyzed individually may be viewed
as mere pre- or post-solution activity.

41. © 2020
Example 40 – Eligibility Reasoning
• Background:
⎼ Because NetFlow records are very large, the continual generation and export of NetFlow
records in such a setup substantially increases the traffic volume on the network, which
hinders network performance. Moreover, continual analysis of the network is not always
necessary when the network is performing under normal conditions.
⎼ Applicant’s invention addresses this issue by varying the amount of network data collected based
on monitored events in the network.
• PTO Analysis:
⎼ The method limits collection of additional Netflow protocol data to when the initially collected data
reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of
network performance.
⎼ This provides a specific improvement over prior systems, resulting in improved network monitoring.
41

42. © 2020
Example 40 – Claims 1 and 2 Compared
1. A method for adaptive monitoring of traffic data through a network appliance connected between computing
devices in a network, the method comprising:
collecting, by the network appliance, traffic data relating to the network traffic passing through the
network appliance, the traffic data comprising at least one of network delay, packet loss, or jitter;
comparing, by the network appliance, at least one of the collected traffic data to a predefined threshold;
and
collecting additional traffic data relating to the network traffic when the collected traffic data is greater
than the predefined threshold, the additional traffic data comprising Netflow protocol data.
2. A method for monitoring of traffic data through a network appliance connected between computing devices in
a network, the method comprising :
collecting, by the network appliance, traffic data relating to the network traffic passing through the
network appliance, the traffic data comprising at least one of network delay, packet loss, or jitter; and
comparing, by the network appliance, at least one of the collected traffic data to a predefined threshold.
42

43. © 2020
Example 40 – Claim 2
2. A method for monitoring of traffic data through a network appliance connected between
computing devices in a network, the method comprising:
collecting, by the network appliance, traffic data relating to the network traffic
passing through the network appliance, the traffic data comprising at least one of
network delay, packet loss, or jitter; and
comparing, by the network appliance, at least one of the collected traffic data to a
predefined threshold.
43
PTO Analysis:
The collecting step is recited at a high level of generality (i.e., as a
general means of gathering network traffic data for use in the
comparison step), and amounts to mere data gathering, which is a
form of insignificant extra-solution activity.

44. © 2020
Example 40 – Claims 1 and 2 Compared
1. A method for adaptive monitoring of traffic data through a network appliance connected between computing
devices in a network, the method comprising:
collecting, by the network appliance, traffic data relating to the network traffic passing through the
network appliance, the traffic data comprising at least one of network delay, packet loss, or jitter;
comparing, by the network appliance, at least one of the collected traffic data to a predefined threshold;
and
collecting additional traffic data relating to the network traffic when the collected traffic data is greater
than the predefined threshold, the additional traffic data comprising Netflow protocol data.
2. A method for monitoring of traffic data through a network appliance connected between computing devices in
a network, the method comprising :
collecting, by the network appliance, traffic data relating to the network traffic passing through the
network appliance, the traffic data comprising at least one of network delay, packet loss, or jitter; and
comparing, by the network appliance, at least one of the collected traffic data to a predefined threshold.
44
Eligible
Ineligible

45. © 2020
Tips
45
• Continue to review new disclosures with a critical eye (this advice has been consistent since
Bilski)
• Continue to use technical problem/technical solution approach
• Look to include additional language/discussion helping support the “practical application”
• The “practical application” should dovetail nicely with the “technical solution”
Drafting Tips
• Interview every 101 rejection as many Examiners are indicating that they will withdraw the
101 rejection without the need for further written argument
• Be prepared to walk through the entire Revised Guidance analysis
• Include arguments for why the claims recite patent eligible subject matter under BOTH the
revised guidelines and the case law
• Consider strategic amendment and arguments that take the claims outside the scope of
Section 101 arguments
Prosecution Tips

46. Thank you!
Maria Anderson
maria.anderson@knobbe.com
Jeremy Carney
jeremy.carney@knobbe.com
Alex Martinez
alex.martinez@knobbe.com
Bryan McWhorter
bryan.mcwhorter@knobbe.com
Mauricio A. Uribe
mauricio.uribe@knobbe.com

47. © 2020
Limitations on this Presentation
47
This presentation and our discussions constitute a high level presentation of the prosecution capabilities of Knobbe Martens and
should not be construed as representation of any individual or company.
Representation can be initiated only upon completion of our standard new client/new matter process, including completion of a
conflicts check, execution of an engagement agreement and payment of any applicable retainer.
These discussions are based solely upon nonconfidential information you provided. It is our understanding that you have not
provided us with any confidential information and will not do so until representation is initiated.