Background:
`
`The following examples should be used in conjunction with the 2019 Revised Patent Subject
`Matter Eligibility Guidance (2019 PEG). The examples below are hypothetical and only
`intended to be illustrative of the claim analysis under the 2019 PEG. These examples should
`be interpreted based on the fact patterns set forth below as other fact patterns may have
`different eligibility outcomes. That is, it is not necessary for a claim under examination to
`mirror an example claim to be subject matter eligible under the 2019 PEG. All of the claims
`are analyzed for eligibility in accordance with their broadest reasonable interpretation.
`Note that the examples herein are numbered consecutively beginning with number 37,
`because 36 examples were previously issued.
`The examples are illustrative only of the patent-eligibility analysis under the 2019 PEG. All
`claims must be ultimately analyzed for compliance with every requirement for patentability,
`including 35 U.S.C. 102, 103, 112, and 101 (utility, inventorship and double patenting) and
`non-statutory double patenting. The analyses provided below do not address considerations
`other than subject matter eligibility under Section 101.
` Example 37 – Relocation of Icons on a Graphical User Interface
`Traditionally, computer users are limited in the ways in which they can organize icons on
`their display. Additionally, computer users may have a large number of icons on their
`display, making it difficult to find the icons most used. The typically available ways to
`organize icons are alphabetically, by file size, and by file type. If a computer user wants a
`non-typical arrangement of icons, the user would need to manually manipulate the icons on
`their display. For example, traditional software does not automatically organize icons so
`that the most used icons are located near the “start” or “home” icon, where they can be easily
`accessed. Therefore, what is needed is a method that allows for such non-traditional
`arrangements to be performed automatically.
`Accordingly, applicant’s invention addresses this issue by providing a method for
`rearranging icons on a graphical user interface (GUI), wherein the method moves the most
`used icons to a position on the GUI, specifically, closest to the “start” icon of the computer
`system, based on a determined amount of use. In a first preferred embodiment, the amount
`of use of each icon is automatically determined by a processor that tracks the number of
`times each icon is selected or how much memory has been allocated to the individual
`processes associated with each icon over a period of time (e.g., day, week, month, etc.). In
`another embodiment, the user can choose to manually enter which icons are used most often
`using any of a number of ordering and/or ranking systems known to those skilled in the art.
`
`Gree, Inc.
`Ex. 2006
`Page1
`
`

`

`Claim 1:
`
`
`
`Step
`
`Analysis
`
`A method of rearranging icons on a graphical user interface (GUI) of a computer system,
`the method comprising:
`
`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;
`
`determining, by a processor, the amount of use of each icon over a predetermined
`period of time; and
`
`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.
`1: Statutory Category?
`Yes. The claim recites a series of steps and,
`therefore, is a process.
`2A - Prong 1: Judicial Exception
`Yes. The claim recites the limitation of
`Recited?
`determining the amount of use of each icon over
`a predetermined period of time. This limitation,
`as drafted, is a process that, under its broadest
`reasonable interpretation, covers performance of
`the limitation in the mind but for the recitation of
`generic computer components. That is, other
`than reciting “by a processor,” nothing in the
`claim element precludes the step from practically
`being performed in the mind. For example, but
`for the “by a processor” language, the claim
`encompasses the user manually calculating the
`amount of use of each icon. The mere nominal
`recitation of a generic processor does not take
`the claim limitation out of the mental processes
`grouping. Thus, the claim recites a mental
`process.
`Yes. The claim recites the combination of
`additional elements of receiving, via a GUI, a user
`selection to organize each icon based on the
`amount of use of each icon, a processor for
`performing the determining step, and
`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. The claim as a whole integrates
`the mental process into a practical application.
`Specifically, the additional elements recite a
`specific manner of automatically displaying icons
`
`2A - Prong 2: Integrated into a
`Practical Application?
`
`Gree, Inc.
`Ex. 2006
`Page2
`
`

`

`N/A.
`
`Step
`
`to the user based on usage which provides a
`specific improvement over prior systems,
`resulting in an improved user interface for
`electronic devices. Thus, the claim is eligible
`because it is not directed to the recited judicial
`exception.
`2B: Claim provides an Inventive
`Concept?
` Claim 2:
`A method of rearranging icons on a graphical user interface (GUI) of a computer system,
`the method comprising:
`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;
`
`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
`
`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.
`
`1: Statutory Category?
`Yes. The claim recites a series of steps and,
`therefore, is a process.
`2A - Prong 1: Judicial Exception
`No. The claim does not recite any of the judicial
`Recited?
`exceptions enumerated in the 2019 PEG. For
`instance, the claim does not recite a mental
`process because the claim, under its broadest
`reasonable interpretation, does not cover
`performance in the mind but for the recitation of
`generic computer components. For example, the
`“determining step” now requires action by a
`processor that cannot be practically applied in
`the mind. . In particular, the claimed step of
`determining the amount of use of each icon by
`tracking how much memory has been allocated
`to each application associated with each icon
`over a predetermined period of time is not
`practically performed in the human mind, at least
`because it requires a processor accessing
`computer memory indicative of application
`usage. Further, the claim does not recite any
`
`Analysis
`
`Gree, Inc.
`Ex. 2006
`Page3
`
`

`

`method of organizing human activity, such as a
`fundamental economic concept or managing
`interactions between people. Finally, the claim
`does not recite a mathematical relationship,
`formula, or calculation. Thus, the claim is
`eligible because it does not recite a judicial
`exception.
`
`N/A.
`
`N/A.
`
`Step
`
`Analysis
`
`
`Claim 3:
`
`2A - Prong 2: Integrated into a
`Practical Application?
`2B: Claim provides an Inventive
`Concept?
`A method of ranking icons of a computer system, the method comprising:
`determining, by a processor, the amount of use of each icon over a predetermined
`
`period of time; and
`
`ranking the icons, by the processor, based on the determined amount of use.
`
`1: Statutory Category?
`Yes. The claim recites a series of steps and,
`therefore, is a process.
`2A - Prong 1: Judicial Exception
`Yes. The claim recites the limitations of
`Recited?
`determining the amount of use of each icon over
`a predetermined period of time and ranking the
`icons based on the determined amount of use.
`The determining limitation, as drafted, is a
`process that, under its broadest reasonable
`interpretation, covers performance of the
`limitation in the mind but for the recitation of
`generic computer components. That is, other
`than reciting “by a processor,” nothing in the
`claim precludes the determining step from
`practically being performed in the human mind.
`For example, but for the “by a processor”
`language, the claim encompasses the user
`manually calculating the amount of use of each
`icon. This limitation is a mental process.
` The ranking limitations, as drafted, is also a
`process that, under its broadest reasonable
`
`Gree, Inc.
`Ex. 2006
`Page4
`
`

`

`interpretation, covers performance of the
`limitation in the mind but for the recitation of
`generic computer components. That is, other
`than reciting “by a processor,” nothing in the
`claim precludes the ranking step from practically
`being performed in the human mind. For
`example, but for the “by a processor” language,
`the claim encompasses the user thinking that the
`most-used icons should be ranked higher than
`the least-used icons. Thus, this limitation is also a
`mental process.
`No. The claim recites one additional element:
`that a processor is used to perform both the
`ranking and determining steps.
`The processor in both steps is recited at a high
`level of generality, i.e., as a generic processor
`performing a generic computer function of
`processing data (the amount of use of each icon,
`or the ranking of the icons based on the
`determined amount of use). This generic
`processor limitation is no more than mere
`instructions to apply the exception using a
`generic computer component. Accordingly, this
`additional element does not integrate the
`abstract idea into a practical application because
`it does not impose any meaningful limits on
`practicing the abstract idea.
`The claim is directed to the abstract idea.
`No. As discussed with respect to Step 2A Prong
`Two, the additional element in the claim amounts
`to no more than mere instructions to apply the
`exception using a generic computer component.
`The same analysis applies here in 2B, i.e., mere
`instructions to apply an exception using a generic
`computer component cannot integrate a judicial
`exception into a practical application at Step 2A
`or provide an inventive concept in Step 2B. The
`claim is ineligible.
`
`2A - Prong 2: Integrated into a
`Practical Application?
`
`2B: Claim provides an Inventive
`Concept?
`
`
`
`Gree, Inc.
`Ex. 2006
`Page5
`
`

`

`Example 38 – Simulating an Analog Audio Mixer
`
`Background:
`
`Audiophiles are people interested in high-fidelity audio reproduction. For many, this means
`listening to music in its analog form, as digital audio files are considered to “lose” much of
`the sound quality in the conversion from analog to digital. Prior inventions attempted to
`create digital simulations of analog audio mixers to simulate the sounds from analog circuits.
`However, the prior art audio mixer simulations do not produce the same sound quality as
`the actual analog circuits.
`Applicant’s invention seeks to more closely replicate the sound quality of an analog audio
`mixer by accounting for the slight variances in analog circuit values that are generated
`during the circuit’s manufacturing. By simulating these variances, a more authentic sound
`can be created that is preferential for the listener. The method begins with a model of an
`analog circuit representing an audio mixing console. The model includes a location of all the
`circuit elements within the circuit, an initial value for each of the circuit elements, and a
`manufacturing tolerance range for each of the circuit elements. A randomized working value
`of each element is then determined using a normally distributed pseudo random number
`generator (PRNG) based on the initial value of the circuit element and the manufacturing
`tolerance range. The model is then simulated using a bilinear transformation to create a
`digital representation of the analog circuit. This digital representation is then presented to
`the user through a graphical user interface as an operational digital audio mixer. The user
`can use the graphical user interface to test the sound quality of the digital representation. If
`the sound quality is not acceptable to the user, the user can generate new randomized
`working values for all the circuit elements and simulate another digital representation of the
`analog audio mixer.
`A method for providing a digital computer simulation of an analog audio mixer comprising:
`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;
`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
`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.
`
`
`
`
`
`
`Claim:
`
`Gree, Inc.
`Ex. 2006
`Page6
`
`

`

`Analysis
`
`Yes. The claim recites a series of steps and, therefore, is
`a process.
`No. The claim does not recite any of the judicial
`exceptions enumerated in the 2019 PEG. The claim does
`not recite a mathematical relationship, formula, or
`calculation. While some of the limitations may be based
`on mathematical concepts, the mathematical concepts
`are not recited in the claims. With respect to mental
`processes, the claim does not recite a mental process
`because the steps are not practically performed in the
`human mind. Finally, the claim does not recite a certain
`method of organizing human activity such as a
`fundamental economic concept or commercial and legal
`interactions. The claim is eligible because it does not
`recite a judicial exception.
`
`N/A.
`
`N/A.
`
`Step
`
`1: Statutory Category?
`2A - Prong 1: Judicial
`Exception Recited?
`
`2A - Prong 2: Integrated into
`a Practical Application?
`2B: Claim provides an
`Inventive Concept?
`
`
`
`
`Gree, Inc.
`Ex. 2006
`Page7
`
`

`

`Example 39 - Method for Training a Neural Network for Facial Detection
`
`Background:
`
`Facial detection is a computer technology for identifying human faces in digital images. This
`technology has several different potential uses, ranging from tagging pictures in social
`networking sites to security access control. Some prior methods use neural networks to
`perform facial detection. A neural network is a framework of machine learning algorithms
`that work together to classify inputs based on a previous training process. In facial detection,
`a neural network classifies images as either containing a human face or not, based upon the
`model being previously trained on a set of facial and non-facial images. However, these prior
`methods suffer from the inability to robustly detect human faces in images where there are
`shifts, distortions, and variations in scale and rotation of the face pattern in the image.
`Applicant’s invention addresses this issue by using a combination of features to more
`robustly detect human faces. The first feature is the use of an expanded training set of facial
`images to train the neural network. This expanded training set is developed by applying
`mathematical transformation functions on an acquired set of facial images. These
`transformations can include affine transformations, for example, rotating, shifting, or
`mirroring or filtering transformations, for example, smoothing or contrast reduction. The
`neural networks are then trained with this expanded training set using stochastic learning
`with backpropagation which is a type of machine learning algorithm that uses the gradient
`of a mathematical loss function to adjust the weights of the network. Unfortunately, the
`introduction of an expanded training set increases false positives when classifying non-facial
`images. Accordingly, the second feature of applicant’s invention is the minimization of these
`false positives by performing an iterative training algorithm, in which the system is retrained
`with an updated training set containing the false positives produced after face detection has
`been performed on a set of non-facial images. This combination of features provides a robust
`face detection model that can detect faces in distorted images while limiting the number of
`false positives.
`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;
`
`
`Claim:
`
`Gree, Inc.
`Ex. 2006
`Page8
`
`

`

`Step
`
`Analysis
`
`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.
`
`1: Statutory Category?
`Yes. The claim recites a series of steps and, therefore,
`is a process.
`2A - Prong 1: Judicial
`No. The claim does not recite any of the judicial
`Exception Recited?
`exceptions enumerated in the 2019 PEG. For instance,
`the claim does not recite any mathematical
`relationships, formulas, or calculations. While some of
`the limitations may be based on mathematical
`concepts, the mathematical concepts are not recited in
`the claims. Further, the claim does not recite a mental
`process because the steps are not practically
`performed in the human mind. Finally, the claim does
`not recite any method of organizing human activity
`such as a fundamental economic concept or managing
`interactions between people. Thus, the claim is
`eligible because it does not recite a judicial exception.
`
`N/A.
`
`N/A.
`
`2A - Prong 2: Integrated into a
`Practical Application?
`2B: Claim provides an
`Inventive Concept?
`
`
`
`
`
`Gree, Inc.
`Ex. 2006
`Page9
`
`

`

`Example 40 – Adaptive Monitoring of Network Traffic Data
`
`Background:
`
`Network visibility tools enable close monitoring of computer network traffic, applications,
`performance, and resources. The data acquired through these network visibility tools is
`extremely useful in optimizing network performance, resolving network issues, and
`improving network security. One industry standard network visibility protocol is NetFlow.
`In a typical setup, a NetFlow exporter generates and exports network traffic statistics (in the
`form of NetFlow records) to at least one NetFlow collector that analyzes the statistics.
`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. That is, the system will only collect NetFlow
`protocol data and export a NetFlow record when abnormal network conditions are detected.
`In practice, during normal network conditions, a network appliance collects network data
`relating to network traffic passing through the network appliance. This network data, for
`example, could include network delay, packet loss, or jitter. Periodically, the network data
`is compared to a predefined quality threshold. If this network data is greater than the
`predefined quality threshold, an abnormal condition is detected. When an abnormal
`condition is present, the system begins collecting NetFlow protocol data, which can later be
`used for analyzing the abnormal condition. During this time, the network appliance
`continues to monitor the network conditions (i.e., comparing collected network data to the
`predetermined quality threshold) and when the abnormal condition no longer exists,
`NetFlow protocol data is no longer collected.
`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.
`
`
`Claim 1:
`
`
`
`
`Gree, Inc.
`Ex. 2006
`Page10
`
`

`

`Step
`
`1: Statutory Category?
`2A - Prong 1: Judicial
`Exception Recited?
`
`2A - Prong 2: Integrated into
`a Practical Application?
`
`2B: Claim provides an
`Inventive Concept?
`
`Analysis
`
`Yes. The claim recites a series of steps and, therefore, is
`a process.
`Yes. The claim recites the limitation of comparing at
`least one of the collected traffic data to a predefined
`threshold. This limitation, as drafted, is a process that,
`under its broadest reasonable interpretation, covers
`performance of the limitation in the mind but for the
`recitation of generic computer components. That is,
`other than reciting “by the network appliance,” nothing
`in the claim element precludes the step from practically
`being performed in the mind. For example, 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. The mere nominal recitation
`of a generic network appliance does not take the claim
`limitation out of the mental processes grouping. Thus,
`the claim recites a mental process.
`Yes. The claim recites the combination of additional
`elements of collecting at least one of network delay,
`packet loss, or jitter relating to the network traffic
`passing through the network appliance, and collecting
`additional Netflow protocol data relating to the network
`traffic when the collected network delay, packet loss, or
`jitter is greater than the predefined threshold. Although
`each of the collecting steps analyzed individually may be
`viewed as mere pre- or post-solution activity, the claim
`as a whole is directed to a particular improvement in
`collecting traffic data. Specifically, 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. The collected data
`can then be used to analyze the cause of the abnormal
`condition. This provides a specific improvement over
`prior systems, resulting in improved network
`monitoring. The claim as a whole integrates the mental
`process into a practical application. Thus, the claim is
`eligible because it is not directed to the recited judicial
`exception.
`
`N/A.
`
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`Ex. 2006
`Page11
`
`

`

`Step
`
`Analysis
`
` Claim 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.
`
`1: Statutory Category? Yes. The claim recites a series of steps and, therefore, is a
`process.
`2A - Prong 1: Judicial
`Yes. The claim recites the limitation of comparing at least one
`Exception Recited?
`of the collected traffic data to a predefined threshold. This
`limitation, as drafted, is a process that, under its broadest
`reasonable interpretation, covers performance of the limitation
`in the mind but for the recitation of generic computer
`components. That is, other than reciting “by the network
`appliance,” nothing in the claim element precludes the step
`from practically being performed in the mind. For example, 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. The mere nominal recitation of a generic
`network appliance does not take the claim limitation out of the
`mental processes grouping. Thus, the claim recites a mental
`process.
`
`
`network traffic passing through the network appliance, and 2A - Prong 2: Practical Application? No. The claim recites two additional elements: collecting at
`Integrated into a
`least one of network delay, packet loss, or jitter relating to the
`that a generic network appliance performs the comparing step.
`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. The network
`appliance that performs the comparison step is also recited at a
`high level of generality, and merely automates the comparison
`step. Each of the additional limitations is no more than mere
`instructions to apply the exception using a generic computer
`component (the network appliance).
`
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`Page12
`
`

`

`The combination of these additional elements is no more than
`mere instructions to apply the exception using a generic
`computer component (the network appliance). Accordingly,
`even in combination, these additional elements do not integrate
`the abstract idea into a practical application because they do
`not impose any meaningful limits on practicing the abstract
`idea.
`The claim is directed to the abstract idea.
`No. As discussed with respect to Step 2A Prong Two, the
`additional elements in the claim amount to no more than mere
`instructions to apply the exception using a generic computer
`component. The same analysis applies here in 2B, i.e., mere
`instructions to apply an exception on a generic computer
`cannot integrate a judicial exception into a practical application
`at Step 2A or provide an inventive concept in Step 2B.
`Under the 2019 PEG, a conclusion that an additional element is
`insignificant extra-solution activity in Step 2A should be re-
`evaluated in Step 2B. Here, the collecting step was considered
`to be extra-solution activity in Step 2A, and thus it is re-
`evaluated in Step 2B to determine if it is more than what is
`well-understood, routine, conventional activity in the field. The
`background of the example does not provide any indication
`that the network appliance is anything other than a generic, off-
`the-shelf computer component, and the Symantec, TLI, and OIP
`Techs. court decisions cited in MPEP 2106.05(d)(II) indicate
`that mere collection or receipt of data over a network is a well-
`understood, routine, and conventional function when it is
`claimed in a merely generic manner (as it is here). Accordingly,
`a conclusion that the collecting step is well-understood,
`routine, conventional activity is supported under Berkheimer
`Option 2.
`For these reasons, there is no inventive concept in the claim,
`and thus it is ineligible.
`
`
`2B: Claim provides an
`Inventive Concept?
`
`
`
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`Ex. 2006
`Page13
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`

`

`Example 41 – Cryptographic Communications
`
`Background:
`Security of information is of increasing importance in computer technology. It is critical that
`data being sent from a sender to a recipient is unable to be intercepted and understood by
`an intermediate source. In addition, authentication of the source of the message must be
`ensured along with the verification of and security of the message content. 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. There is a need to perform these same security and authentication functions
`efficiently over a public key system so that information can be shared easily between users
`who do not know each other and have not shared the key used to encrypt and decrypt the
`information.
`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
`includes at least one encoding device and at least one decoding device, which are computer
`terminals, and a communication channel, where the encoding and decoding devices are
`coupled to the communication channel. The encoding device is responsive to a precoded
`message-to-be-transmitted M and an encoding key E to provide a ciphertext word C for
`transmission to a particular decoding device. The message-to-be-transmitted is precoded
`by converting it to a numerical representation which is broken into one or more blocks MA
`of equal length. This precoding may be done by any conventional means. The resulting
`message MA is a number representative of a message-to-be-transmitted, where 0 ≤ MA ≤ n-1,
`where n is a composite number of the form n=p*q, where p and q are prime numbers. The
`encoding key E is a pair of positive integers e and n, which are related to the particular
`decoding device. The encoding device distinctly encodes each of the n possible messages.
`The transformation provided by the encoding device is described by the relation CA=MAe
`(mod n) where e is a number relatively prime to (p-1)*(q-1). The encoding device transmits
`the ciphertext word signal CA to the decoding device over the communications channel. The
`decoding device is responsive to the received ciphertext word CA and a decoding key to
`transform the ciphertext to a received message word MA’.
`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. The variables p and q are only known by the owner of the decryption
`key d and are used to generate the decryption key (private key d is not claimed below). Thus,
`the security of the cipher relies on the difficulty of factoring large integers by computers, and
`there is no known efficient algorithm to recover the plaintext given the ciphertext and the
`public information (n, e) (assuming that p and q are sufficiently large).
`Claim:
`A method for establishing cryptographic communications between a first computer
`terminal and a second computer terminal comprising:
`
`
`
`Gree, Inc.
`Ex. 2006
`Page14
`
`

`

`Step
`
`
`
`
`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.
`
`
`1: Statutory Category? Yes. The claim recites a series of steps and, therefore, is a
`process.
`2A - Prong 1: Judicial
`Yes. The claim recites a mathematical formula or calculation
`Exception Recited?
`that is used to encode each of the message block word signals
`MA to produce a ciphertext word signal CA, whereby CA=MAe
`(mod n). Thus, the claim recites a mathematical concept. Note
`that, in this example, the “encoding” step is determined to
`recite a mathematical concept because the claim explicitly
`recit