DETAILED ACTION
Claims 1-4, 7-14, and 17-20 have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The invention taught in claims 1-4, 7-14, and 17-20 is directed to “mental steps” and “mathematical concepts” without significantly more.
The claims recite:
• a user profile (pure data/mental steps)
• a user record (pure data/mental steps)
• occupational data (mathematical data)
• efficiency data (mathematical data)
• a function (mathematical calculation)
• graphical data (mathematical data/mental steps)
• occupational data (mathematical data/mental steps)
• an ideal arrangement (mathematical data/mental steps)
• improvement data (mathematical data/mental steps)
• improvement training data (mathematical data)
• occupational data (mathematical data/mental steps)
• improvement machine learning model (mathematical calculation)
• standard calculation steps in implementing the mathematical model known as a “neural network”:
training (mathematical calculation)
training data (mathematical data)
input layer of nodes (mathematical calculation)
intermediate layers of nodes (mathematical calculation)
output layer of nodes (mathematical calculation)
adjusting one or more “connections” and one or more weights (modification of a mathematical model)
detecting additional correlations between the output layer of nodes and the input later of nodes (mathematical calculation)
iteratively updating the efficiency machine learning model as a function of he (sic.) detected additional correlations (modification of a mathematical model)
retraining the efficiency machine learning model (modification of a mathematical model)
• user feedback indicating a quality of the examples (mental steps)
• efficiency clusters (groups of mathematical data)
• ideal arrangements (groupings of mathematical data)
• correlation to improvement data (mathematical correlation)
• iterative updates as a function of previous iterations (iterative data updates - human thought)
• a comparison (mental steps)
• a notification (mental steps)
• an efficiency threshold (calculated mathematical data)
• a machine encoded text (pure data/mental steps)
• downsampling, using a compressor, the efficiency training data
by removing an nth entry in a sequence of the efficiency training data
(mental steps and mathematical calculation) Note that Applicant's
Specification, paragraph [0054] recites:
[0054] Continuing to refer to FIG. 2, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data.
***
In some embodiments, processor may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nth entry in a sequence of samples, all but every Nth entry, or the like, which is a process known as "compression," and may be performed, for instance by an N-sample compressor implemented using hardware or software. Anti-aliasing and/or anti-imaging filters, and/or low-pass filters, may be used to clean up side-effects of compression.
• upsampling the efficiency training data, using at least one of:
a set of interpolation rules in order to predict interpolated
data associated with the efficiency training data; a
sample expander method for adding expander data
associated with the training data (i.e., mental steps and mathematical
steps) (“Interpolating data” is mathematical steps and “adding data”
is mental steps.)
• a filter for filtering the efficiency training data in accordance with
a frequency (mathematical steps) Applicant's Specification,
paragraph [0054] recites:
[0054] Continuing to refer to FIG. 2, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data.
***
Alternatively or additionally, processor, computing device, and/or module may utilize sample expander methods, a low-pass filter, or both. As used in this disclosure, a "low-pass filter" is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor, and/or module may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units
Claim 1
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “1. An apparatus for the generation and improvement of efficiency data, wherein the apparatus comprises…” Therefore, it is an “apparatus”, which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES”.
Step 2A (Prong One) inquiry:
Are there limitations in Claim 1 that recite abstract ideas?
YES. The following limitations in Claim 1 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical concepts”:
• a user profile (pure data/mental steps)
• a user record (pure data/mental steps)
• occupational data (mathematical data)
• efficiency data (mathematical data)
• a function (mathematical calculation)
• graphical data (mathematical data/mental steps)
• occupational data (mathematical data/mental steps)
• an ideal arrangement (mathematical data/mental steps)
• improvement data (mathematical data/mental steps)
• improvement training data (mathematical data)
• occupational data (mathematical data/mental steps)
• improvement machine learning model (mathematical calculation)
• standard calculation steps in implementing the mathematical model known as a “neural network”:
training (mathematical calculation)
training data (mathematical data)
input layer of nodes (mathematical calculation)
intermediate layers of nodes (mathematical calculation)
output layer of nodes (mathematical calculation)
adjusting one or more “connections” and one or more weights (modification of a mathematical model)
detecting additional correlations between the output layer of nodes and the input later of nodes (mathematical calculation)
iteratively updating the efficiency machine learning model as a function of he (sic.) detected additional correlations (modification of a mathematical model)
retraining the efficiency machine learning model (modification of a mathematical model)
• user feedback indicating a quality of the examples (mental steps)
• efficiency clusters (groups of mathematical data)
• ideal arrangements (groupings of mathematical data)
• correlation to improvement data (mathematical correlation)
• iterative updates as a function of previous iterations (iterative data updates - human thought)
• a comparison (mental steps)
• a notification (mental steps)
• an efficiency threshold (calculated mathematical data)
• a machine encoded text (pure data/mental steps)
• downsampling, using a compressor, the efficiency training data
by removing an nth entry in a sequence of the efficiency training data
(mental steps and mathematical calculation) Note that Applicant's
Specification, paragraph [0054] recites:
[0054] Continuing to refer to FIG. 2, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data.
***
In some embodiments, processor may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nth entry in a sequence of samples, all but every Nth entry, or the like, which is a process known as "compression," and may be performed, for instance by an N-sample compressor implemented using hardware or software. Anti-aliasing and/or anti-imaging filters, and/or low-pass filters, may be used to clean up side-effects of compression.
• upsampling the efficiency training data, using at least one of:
a set of interpolation rules in order to predict interpolated
data associated with the efficiency training data; a
sample expander method for adding expander data
associated with the training data (i.e., mental steps and mathematical
steps) (“Interpolating data” is mathematical steps and “adding data”
is mental steps.)
• a filter for filtering the efficiency training data in accordance with
a frequency (mathematical steps) Applicant's Specification,
paragraph [0054] recites:
[0054] Continuing to refer to FIG. 2, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data.
***
Alternatively or additionally, processor, computing device, and/or module may utilize sample expander methods, a low-pass filter, or both. As used in this disclosure, a "low-pass filter" is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor, and/or module may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A processor
(2) A memory
(3) A display device
(4) An optical character reader (OCR)
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0097] Processor 1104 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 1104 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 1104 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).
This “processor” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(2) A “memory” is a broad term which is described at a high level. Applicant’s Specification recites:
[0063] Still referring to FIG. 2, a machine-learning model and/or process may be deployed or instantiated by incorporation into a program, apparatus, system and/or module. For instance, and without limitation, a machine-learning model, neural network, and/or some or all parameters thereof may be stored and/or deployed in any memory or circuitry. Parameters such as coefficients, weights, and/or biases may be stored as circuit-based constants, such as arrays of wires and/or binary inputs and/or outputs set at logic "1" and "0" voltage levels in a logic circuit to represent a number according to any suitable encoding system including twos complement or the like or may be stored in any volatile and/or non-volatile memory. Similarly, mathematical operations and input and/or output of data to or from models, neural network layers, or the like may be instantiated in hardware circuitry and/or in the form of instructions in firmware, machine-code such as binary operation code instructions, assembly language, or any higher-order programming language. Any technology for hardware and/or software instantiation of memory, instructions, data structures, and/or algorithms…
This “memory” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(3) A “display device” is a broad term which is described at a high level. Applicant’s Specification recites:
[0102] Computer system 1100 may further include a video display adapter 1152 for communicating a displayable image to a display device, such as display device 1136. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 1152 and display device 1136 may be utilized in combination with processor 1104 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 1100 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 1112 via a peripheral interface 1156. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.
This “display device” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(4) An “optical character reader” is a broad term which is described at a high level. Paragraph [0019] of Applicant's Specification recites:
[0019] Still referring to FIG. 1, in some embodiments, optical character recognition or optical character reader (OCR) includes automatic conversion of images of written (e.g., typed, handwritten, or printed text) into machine-encoded text. In some cases, recognition of at least a keyword from an image component may include one or more processes, including without limitation optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine learning processes.
This “optical character reader” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A processor
(2) A memory
(3) A display device
(4) An optical character reader (OCR)
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0097] Processor 1104 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 1104 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 1104 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(2) A “memory” is a broad term which is described at a high level. Applicant’s Specification recites:
[0063] Still referring to FIG. 2, a machine-learning model and/or process may be deployed or instantiated by incorporation into a program, apparatus, system and/or module. For instance, and without limitation, a machine-learning model, neural network, and/or some or all parameters thereof may be stored and/or deployed in any memory or circuitry. Parameters such as coefficients, weights, and/or biases may be stored as circuit-based constants, such as arrays of wires and/or binary inputs and/or outputs set at logic "1" and "0" voltage levels in a logic circuit to represent a number according to any suitable encoding system including twos complement or the like or may be stored in any volatile and/or non-volatile memory. Similarly, mathematical operations and input and/or output of data to or from models, neural network layers, or the like may be instantiated in hardware circuitry and/or in the form of instructions in firmware, machine-code such as binary operation code instructions, assembly language, or any higher-order programming language. Any technology for hardware and/or software instantiation of memory, instructions, data structures, and/or algorithms…
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(3) A “display device” is a broad term which is described at a high level. Applicant’s Specification recites:
[0102] Computer system 1100 may further include a video display adapter 1152 for communicating a displayable image to a display device, such as display device 1136. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 1152 and display device 1136 may be utilized in combination with processor 1104 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 1100 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 1112 via a peripheral interface 1156. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(4) An “optical character reader” is a broad term which is described at a high level. Paragraph [0019] of Applicant's Specification recites:
[0019] Still referring to FIG. 1, in some embodiments, optical character recognition or optical character reader (OCR) includes automatic conversion of images of written (e.g., typed, handwritten, or printed text) into machine-encoded text. In some cases, recognition of at least a keyword from an image component may include one or more processes, including without limitation optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine learning processes.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 1 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 2
Claim 2 recites:
2. The apparatus of claim 1, wherein receiving the user profile from a user comprises receiving the user profile from a web crawler.
Applicant’s Claim 2 merely teaches receiving data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 2 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 3
Claim 3 recites:
3. The apparatus of claim 1, wherein receiving the user profile from a user comprises receiving the user profile from a chatbot.
Applicant’s Claim 3 merely teaches receiving data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 3 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 4
Claim 4 recites:
4. The apparatus of claim 1, wherein identifying the plurality of efficiency clusters comprises projecting each cluster of the plurality of efficiency clusters onto a continuum (i.e., a “vector space”), wherein the continuum is associated with an error rate of the user.
Applicant’s Claim 4 merely teaches the use of a “vector space”. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 4 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 5
Claim 5 recites:
5. The apparatus of claim 1, wherein generating the improvement data comprises generating the improvement data using an improvement machine learning model.
Applicant’s Claim 5 merely teaches generation (i.e., calculation) of data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 5 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 6
Claim 6 recites:
6. The apparatus of claim 5, wherein generating the improvement data using the improvement machine learning model comprises:
training the improvement machine learning model using improvement training data, wherein the improvement training data contains a plurality of data entries containing a plurality of first efficiency clusters and a plurality of ideal arrangements correlated to the improvement data; and
generating improvement data as a function of the first cluster and the ideal arrangement using a trained improvement machine learning model.
Applicant’s Claim 6 merely teaches the training of a generic machine learning model on unspecified data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).) Applicant's Specification recites:
[0050] Further referring to FIG. 2, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 216. Training data classifier 216 may include a "classifier," which as used in this disclosure is a machine- learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a "classification algorithm," as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 200 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 204. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 216 may classify elements of training data (i.e., efficiency training data), for instance, occupational data to a plurality of tasks and sub-tasks as described above in this disclosure. By classifying training data, machine-leaning module 200 may build models specific to each category (i.e., sub-population) which allow for a more detailed analysis of each group's behavior, leading to a better fit of machine-learning model to classified data. Additionally, or alternatively, impact of noise and/or outliers may be reduced by classifying training data; for instance, and without limitation, each sub-population may have its own trends and/or patterns that may be better captured when they are analyzed separately by more than one machine-learning models.
Further, Applicant's Specification recites:
[0061] Still referring to FIG. 2, machine-learning module 200 may be designed and configured to create a machine-learning model 224 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure.
[0062] Continuing to refer to FIG. 2, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminant analysis. Machine-learning algorithms may include kernel ridge regression. Machine- learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naive Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine- learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized trees, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine- learning algorithms may include neural net algorithms, including convolutional neural net processes.
Claim 6 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 7
Claim 7 recites:
7. The apparatus of claim 1, wherein generating the improvement data comprises generating the improvement data as a function of a comparison between the first cluster and the ideal arrangement using a fuzzy inference set.
Applicant’s Claim 7 merely teaches the use of a fuzzy inference set (i.e., pure fuzzy numbers). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 7 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 8
Claim 8 recites:
8. The apparatus of claim 1, wherein the memory further instructs the processor to classify the occupational data into one or more efficiency categories.
Applicant’s Claim 8 merely teaches the use of a generic classifier. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).) Applicant's Specification recites:
[0050] Further referring to FIG. 2, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 216. Training data classifier 216 may include a "classifier," which as used in this disclosure is a machine- learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a "classification algorithm," as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 200 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 204. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 216 may classify elements of training data (i.e., efficiency training data), for instance, occupational data to a plurality of tasks and sub-tasks as described above in this disclosure. By classifying training data, machine-leaning module 200 may build models specific to each category (i.e., sub-population) which allow for a more detailed analysis of each group's behavior, leading to a better fit of machine-learning model to classified data. Additionally, or alternatively, impact of noise and/or outliers may be reduced by classifying training data; for instance, and without limitation, each sub-population may have its own trends and/or patterns that may be better captured when they are analyzed separately by more than one machine-learning models.
Claim 8 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 9
Claim 9 recites:
9. The apparatus of claim 8, wherein classifying the occupational data into the one or more efficiency categories comprises classifying the occupational data using the efficiency machine learning model.
Applicant’s Claim 9 merely teaches the use of a generic classifier. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).) Applicant's Specification recites:
[0050] Further referring to FIG. 2, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 216. Training data classifier 216 may include a "classifier," which as used in this disclosure is a machine- learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a "classification algorithm," as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 200 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 204. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 216 may classify elements of training data (i.e., efficiency training data), for instance, occupational data to a plurality of tasks and sub-tasks as described above in this disclosure. By classifying training data, machine-leaning module 200 may build models specific to each category (i.e., sub-population) which allow for a more detailed analysis of each group's behavior, leading to a better fit of machine-learning model to classified data. Additionally, or alternatively, impact of noise and/or outliers may be reduced by classifying training data; for instance, and without limitation, each sub-population may have its own trends and/or patterns that may be better captured when they are analyzed separately by more than one machine-learning models.
Claim 9 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 10
Claim 10 recites:
10. The apparatus of claim 1, wherein the memory further instructs the processor to:
identify one or more tasks associated with the user as a function of the occupational data, wherein the occupational data comprises a listing of a plurality of tasks associated with the user; and
generate an estimated completion time as a function of the identification of the one or more tasks associated with the user.
Applicant’s Claim 10 merely teaches the identification of a task (i.e., a mental step) and the generation of a completion time (i.e., an unspecified calculation method). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 10 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 11
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “11. A method for the generation and improvement of efficiency data, wherein the method comprises…” Therefore, it is a “method” (or “process”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES”.
Step 2A (Prong One) inquiry:
Are there limitations in Claim 11 that recite abstract ideas?
YES. The following limitations in Claim 11 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical concepts”:
The claims recite:
• a user profile (pure data/mental steps)
• a user record (pure data/mental steps)
• occupational data (mathematical data)
• efficiency data (mathematical data)
• a function (mathematical calculation)
• graphical data (mathematical data/mental steps)
• occupational data (mathematical data/mental steps)
• an ideal arrangement (mathematical data/mental steps)
• improvement data (mathematical data/mental steps)
• improvement training data (mathematical data)
• occupational data (mathematical data/mental steps)
• improvement machine learning model (mathematical calculation)
• standard calculation steps in implementing the mathematical model known as a “neural network”:
training (mathematical calculation)
training data (mathematical data)
input layer of nodes (mathematical calculation)
intermediate layers of nodes (mathematical calculation)
output layer of nodes (mathematical calculation)
adjusting one or more “connections” and one or more weights (modification of a mathematical model)
detecting additional correlations between the output layer of nodes and the input later of nodes (mathematical calculation)
iteratively updating the efficiency machine learning model as a function of he (sic.) detected additional correlations (modification of a mathematical model)
retraining the efficiency machine learning model (modification of a mathematical model)
• user feedback indicating a quality of the examples (mental steps)
• efficiency clusters (groups of mathematical data)
• ideal arrangements (groupings of mathematical data)
• correlation to improvement data (mathematical correlation)
• iterative updates as a function of previous iterations (iterative data updates - human thought)
• a comparison (mental steps)
• a notification (mental steps)
• an efficiency threshold (calculated mathematical data)
• a machine encoded text (pure data/mental steps)
• downsampling, using a compressor, the efficiency training data
by removing an nth entry in a sequence of the efficiency training data
(mental steps and mathematical calculation) Note that Applicant's
Specification, paragraph [0054] recites:
[0054] Continuing to refer to FIG. 2, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data.
***
In some embodiments, processor may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nth entry in a sequence of samples, all but every Nth entry, or the like, which is a process known as "compression," and may be performed, for instance by an N-sample compressor implemented using hardware or software. Anti-aliasing and/or anti-imaging filters, and/or low-pass filters, may be used to clean up side-effects of compression.
• upsampling the efficiency training data, using at least one of:
a set of interpolation rules in order to predict interpolated
data associated with the efficiency training data; a
sample expander method for adding expander data
associated with the training data (i.e., mental steps and mathematical
steps) (“Interpolating data” is mathematical steps and “adding data”
is mental steps.)
• a filter for filtering the efficiency training data in accordance with
a frequency (mathematical steps) Applicant's Specification,
paragraph [0054] recites:
[0054] Continuing to refer to FIG. 2, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data.
***
Alternatively or additionally, processor, computing device, and/or module may utilize sample expander methods, a low-pass filter, or both. As used in this disclosure, a "low-pass filter" is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor, and/or module may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A processor
(2) A display device
(3) An optical character reader (OCR)
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0097] Processor 1104 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 1104 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 1104 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).
This “processor” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(2) A “display device” is a broad term which is described at a high level. Applicant’s Specification recites:
[0102]Computer system 1100 may further include a video display adapter 1152 for communicating a displayable image to a display device, such as display device 1136. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 1152 and display device 1136 may be utilized in combination with processor 1104 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 1100 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 1112 via a peripheral interface 1156. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.
This “display device” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(3) An “optical character reader” is a broad term which is described at a high level. Paragraph [0019] of Applicant's Specification recites:
[0019] Still referring to FIG. 1, in some embodiments, optical character recognition or optical character reader (OCR) includes automatic conversion of images of written (e.g., typed, handwritten, or printed text) into machine-encoded text. In some cases, recognition of at least a keyword from an image component may include one or more processes, including without limitation optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine learning processes.
This “optical character reader” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A processor
(2) A display device
(3) An optical character reader (OCR)
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0097] Processor 1104 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 1104 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 1104 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(2) A “display device” is a broad term which is described at a high level. Applicant’s Specification recites:
[0102] Computer system 1100 may further include a video display adapter 1152 for communicating a displayable image to a display device, such as display device 1136. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 1152 and display device 1136 may be utilized in combination with processor 1104 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 1100 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 1112 via a peripheral interface 1156. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(3) An “optical character reader” is a broad term which is described at a high level. Paragraph [0019] of Applicant's Specification recites:
[0019] Still referring to FIG. 1, in some embodiments, optical character recognition or optical character reader (OCR) includes automatic conversion of images of written (e.g., typed, handwritten, or printed text) into machine-encoded text. In some cases, recognition of at least a keyword from an image component may include one or more processes, including without limitation optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine learning processes.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 11 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 12
Claim 12 recites:
12. The method of claim 11, wherein receiving the user profile from a user comprises receiving the user profile from a web crawler.
Applicant’s Claim 12 merely teaches receiving data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 12 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 13
Claim 13 recites:
13. The method of claim 11, wherein receiving the user profile from a user comprises receiving the user profile from a chatbot.
Applicant’s Claim 13 merely teaches receiving data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 13 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 14
Claim 14 recites:
14. The method of claim 11, wherein identifying the plurality of efficiency clusters comprises projecting each cluster of the plurality of efficiency clusters onto a continuum (i.e., a “vector space”), wherein the continuum is associated with an error rate of the user.
Applicant’s Claim 14 merely teaches the use of a “vector space”. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 14 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 15
Claim 15 recites:
15. The method of claim 11, wherein the method further comprises generating, using the at least a processor, the improvement data using an improvement machine learning model.
Applicant’s Claim 15 merely teaches generation (i.e., calculation) of data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 15 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 16
Claim 16 recites:
16. The method of claim 15, wherein generating the improvement data using the improvement machine learning model comprises:
training the improvement machine learning model using improvement training data, wherein the improvement training data contains a plurality of data entries containing a plurality of first efficiency clusters and a plurality of ideal arrangements as an input correlated to the improvement data as an output; and
generating improvement data as a function of the first cluster and the ideal arrangement using a trained improvement machine learning model.
Applicant’s Claim 16 merely teaches the training of a generic machine learning model on unspecified data. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).) Applicant's Specification recites:
[0050] Further referring to FIG. 2, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 216. Training data classifier 216 may include a "classifier," which as used in this disclosure is a machine- learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a "classification algorithm," as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 200 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 204. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 216 may classify elements of training data (i.e., efficiency training data), for instance, occupational data to a plurality of tasks and sub-tasks as described above in this disclosure. By classifying training data, machine-leaning module 200 may build models specific to each category (i.e., sub-population) which allow for a more detailed analysis of each group's behavior, leading to a better fit of machine-learning model to classified data. Additionally, or alternatively, impact of noise and/or outliers may be reduced by classifying training data; for instance, and without limitation, each sub-population may have its own trends and/or patterns that may be better captured when they are analyzed separately by more than one machine-learning models.
Further, Applicant's Specification recites:
[0061] Still referring to FIG. 2, machine-learning module 200 may be designed and configured to create a machine-learning model 224 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure.
[0062] Continuing to refer to FIG. 2, machine-learning algorithms may include, without
limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminant analysis. Machine-learning algorithms may include kernel ridge regression. Machine- learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naive Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine- learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized trees, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine- learning algorithms may include neural net algorithms, including convolutional neural net processes.
Claim 16 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 17
Claim 17 recites:
17. The method of claim 11, wherein the method further comprises generating, using the at least a processor, the improvement data as a function of a comparison between the first cluster and the ideal arrangement using a fuzzy inference set.
Applicant’s Claim 17 merely teaches the use of a fuzzy inference set (i.e., pure fuzzy numbers). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 17 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 18
Claim 18 recites:
18. The method of claim 11, wherein the method further comprises classifying, using the at least a processor, the occupational data into one or more efficiency categories.
Applicant’s Claim 18 merely teaches the use of a generic classifier. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).) Applicant's Specification recites:
[0050] Further referring to FIG. 2, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 216. Training data classifier 216 may include a "classifier," which as used in this disclosure is a machine- learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a "classification algorithm," as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 200 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 204. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 216 may classify elements of training data (i.e., efficiency training data), for instance, occupational data to a plurality of tasks and sub-tasks as described above in this disclosure. By classifying training data, machine-leaning module 200 may build models specific to each category (i.e., sub-population) which allow for a more detailed analysis of each group's behavior, leading to a better fit of machine-learning model to classified data. Additionally, or alternatively, impact of noise and/or outliers may be reduced by classifying training data; for instance, and without limitation, each sub-population may have its own trends and/or patterns that may be better captured when they are analyzed separately by more than one machine-learning models.
Claim 18 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 19
Claim 19 recites:
19. The method of claim 18, wherein classifying, using the at least a processor, the occupational data into the one or more efficiency categories comprises classifying the occupational data using the efficiency machine learning model.
Applicant’s Claim 19 merely teaches the use of a generic classifier. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).) Applicant's Specification recites:
[0050] Further referring to FIG. 2, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 216. Training data classifier 216 may include a "classifier," which as used in this disclosure is a machine- learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a "classification algorithm," as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 200 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 204. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 216 may classify elements of training data (i.e., efficiency training data), for instance, occupational data to a plurality of tasks and sub-tasks as described above in this disclosure. By classifying training data, machine-leaning module 200 may build models specific to each category (i.e., sub-population) which allow for a more detailed analysis of each group's behavior, leading to a better fit of machine-learning model to classified data. Additionally, or alternatively, impact of noise and/or outliers may be reduced by classifying training data; for instance, and without limitation, each sub-population may have its own trends and/or patterns that may be better captured when they are analyzed separately by more than one machine-learning models.
Claim 19 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 20
Claim 20 recites:
20. The method of claim 11, wherein the method further comprises:
identifying, using the at least a processor, one or more tasks associated with the user as a function of the occupational data, wherein the occupational data comprises a listing of a plurality of tasks associated with the user; and
generating, using the at least a processor, an estimated completion time as a function of the identification of the one or more tasks associated with the user.
Applicant’s Claim 20 merely teaches the identification of a task (i.e., a mental step) and the generation of a completion time (i.e., an unspecified calculation method). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 20 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Response to Arguments
Applicant's arguments filed 31 DEC 2025 have been fully considered but they are not persuasive. Specifically, Applicant argues:
Argument 1
Step 2A, Prong one
Under Step 2A, Prong One, taking claim 1 as representative, the Examiner alleges that the limitations of Applicant's claim 1 recite "mental steps" and "mathematical concepts." (Office Action p. 5). Without prejudice to any argument in the alternative that Applicant could make regarding the above allegations, Applicant respectfully submits that representative independent claim 1, at least as amended, recites limitations which are not directed to an abstract idea and provides an inventive concept amounting to significantly more than any alleged abstract idea.
Examiner notes that the claims do have abstract ideas. For example, Claim 1 recites:
“generate user improvement data as a function of a comparison between the first efficiency cluster and the ideal arrangement”
The limitation simply requires the mental step of a comparison of data and the unspecified “generation” of “user improvement data” from the comparison. All of this is mental in nature.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 2
Applicant submits that the limitations of representative claim 1, taken at least as a whole, render any alleged judicial exception into a practical application and meaningfully limit it by providing a cohesive technique based on, among other things, meaningful interactions between specific recitations of a "an efficiency machine learning model ( including upsampling/downsampling), "a 'processor," and a "memory" operating in tandem for an automated generation and improvement of efficiency data and by specific steps involving, at least, "upsample the efficiency training data, using at least one of: a set of interpolation rules in order to predict interpolated data associated with the efficiency training data; a sample expander method for adding expander data associated with the efficiency training data; and a filter for filtering the efficiency training data in accordance with a frequency; downsample, using a compressor, the efficiency training data by removing an nth entry in a sequence of the efficiency training data," as recited in amended claim 1. Advantageously, the claimed limitations interact and impact each other in a manner such that any alleged judicial exception is integrated into a practical application by meaningfully limiting the exception.
Firstly, whether a “technique” is “cohesive” is not a consideration in 35 U.S.C. § 101 doctrine. A hypothetical “technique” may be entirely mathematical or mental regardless of how “cohesive” it may be deemed to be. Thus the “technique” would be entirely abstract and not eligible.
Secondly, the issue of whether claim limitations “interact and impact each other” is not a consideration in 35 U.S.C. § 101 doctrine. A hypothetical limitation may be entirely mental steps that, through inferences, “interact and impact each other.” The interactions and impacts would not change the fact that such a limitation would be entirely mental and ineligible.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 3
Applicant submits that representative claim 1, at least as amended, recites additional elements that integrate any alleged judicial exception into a practical application by providing an improvement in technology to the field of generating and improving efficiency data.
The technical solution here desirably provides high throughput and accurate analysis of subject data using at least a machine learning model to obtain sets of filtered (tailored) results to an end user, also utilizing upsampling and downsampling to balance the training data set. This process also mitigates, or substantially reduces, the computational expenditure incurred by a predictive analysis that provides accurate results. Thus, representative claim 1 improves the generation of efficiency data technology by providing a particular way to improve the personalized tailoring of efficiency data for a specific user by efficiently using at least a machine learning model.
Such a construction in combination with downstream activities, outputs a tailored improvement data for an end user. Advantageously, this results in substantial savings of computational power, resources and expense, thereby, and desirably, mitigating challenges and problems encountered in conventional workflows.
Thus, Applicant submits that, these limitations, individually and as an ordered combination with other claim elements, of Applicant's representative claim 1 provide a solution to a problem rooted in the technological field of AI-leveraged generation and improvement of efficiency data as machine learning is a subset of artificial intelligence. While AI is the broader concept of creating machines that can mimic human intelligence, machine learning is a specific method that allows machines to learn from data and improve at tasks without being explicitly programmed for every scenario. The technical solution provided by Applicant's claimed embodiments provides a more cost-effective approach by automating efficiency data in addition to using upsampling to improve and/or enable signal processing functions, such as improving a model's ability to handle imbalanced datasets and the use of downsampling for computational efficiency as it reduces data size, leading to faster processing, lower storage needs, and reduced computational costs
“Generating and improving data” is not a technology. The broadest reasonable interpretation of the “generating and improving data” includes pure mathematics, which is per se ineligible.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 4
Applicant's disclosure, for example, at paragraph [0054] of the specification, reproduced in part below, reflects the practical efficacy and benefits of the claimed embodiments:
[0054] Continuing to refer to FIG. 2, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data. For instance, a computing device, processor, and/or module may convert a smaller number of units, such as in a low pixel count image, into a desired number of units, for instance by upsampling and interpolating. As a non-limiting example, a low pixel count image may have 100 pixels, however a desired number of pixels may be 128. Processor may interpolate the low pixel count image to convert the 100 pixels into 128 pixels. It should also be noted that one of ordinary skill in the art, upon reading this disclosure, would know the various methods to interpolate a smaller number of data units such as samples, pixels, bits, or the like to a desired number of such units. In some instances, a set of interpolation rules may be trained by sets of highly detailed inputs and/or outputs and corresponding inputs and/or outputs downsampled to smaller numbers of units, and a neural network or other machine learning model that is trained to predict interpolated pixel values using the training data. As a non-limiting example, a sample input and/or output, such as a sample picture, with sample-expanded data units (e.g., pixels added between the original pixels) may be input to a neural network or machine-learning model and output a pseudo replica sample-picture with dummy values assigned to pixels between the original pixels based on a set of interpolation rules. As a non- limiting example, in the context of an image classifier, a machine-learning model may have a set of interpolation rules trained by sets of highly detailed images and images that have been downsampled to smaller numbers of pixels, and a neural network or other machine learning model that is trained using those examples to predict interpolated pixel values in a facial picture context. As a result, an input with sample-expanded data units (the ones added between the original data units, with dummy values) may be run through a trained neural network and/or model, which may fill in values to replace the dummy values. Alternatively or additionally, processor, computing device, and/or module may utilize sample expander methods, a low-pass filter, or both. As used in this disclosure, a "low-pass filter" is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor, and/or module may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units. In some embodiments, and with continued reference to FIG. 2, computing device, processor, and/or module may down- sample elements of a training example to a desired lower number of data elements. As a non-limiting example, a high pixel count image may have 256 pixels, however a desired number of pixels may be 128. Processor may down- sample the high pixel count image to convert the 256 pixels into 128 pixels. In some embodiments, processor may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nth entry in a sequence of samples, all but every Nth entry, or the like, which is a process known as "compression," and may be performed, for instance by an N- sample compressor implemented using hardware or software. Anti-aliasing and/or anti-imaging filters, and/or low-pass filters, may be used to clean up side- effects of compression.
Applicant submits that the limitations as recited in claim 1, at least as amended, do not recite any abstract idea much like Example 47, claim 3, listed in the July 2024 Subject Matter Eligibility Examples (see also, 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence). For example, Example 47 presents allowable claims (Subject Matter Eligibility Examples: Abstract Ideas, pgs. 10-13) directed to "to identify[ing] or detect[ing] anomalies" akin to the technical solution provided by Applicant's representative claim 1, at least as amended, wherein anomalous data that interferes with downstream processing is identified and effectively removed using a practical multi-stage methodology rooted in Al-assisted technology.
“Generating and improving data” is not a technology. The broadest reasonable interpretation of the “generating and improving data” includes pure mathematics, which is per se ineligible.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 5
Additionally, Applicant submits that the limitations as recited in claim 1, at least as amended, do not recite any abstract idea, mental steps and mathematical concepts, much like Example 48, claim 2, listed in the July 2024 Subject Matter Eligibility Examples (see also, 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence). For example, Example 48 presents allowable claims (Subject Matter Eligibility Examples: Abstract Ideas, pgs. 21-24) directed to "separating desired speech from extraneous or background speech" similar to the technical solution provided by Applicant's representative claim 1, at least as amended, anomalous data that interferes with downstream processing is effectively separated from preferred data using a practical multi-stage methodology rooted in Al-assisted technology.
Applicant submits that Applicant's representative claim 1 and claims presented in Examples 47 and 48 are analogous, at least, because both provide a solution to a data management problem by automatedly processing and transforming data (e.g., by identification and removal of anomalous or irrelevant data) for downstream analysis by utilizing a specific multi-stage technical scheme for real-time implementation activities.
“Generating and improving data” is not a technology. The broadest reasonable interpretation of the “generating and improving data” includes pure mathematics, which is per se ineligible.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 6
Furthermore, the disclosed apparatus is associated with improving accuracy, efficiency and performance in complex and heterogeneous systems where data is problematic or unreliable e.g., not of required quality, format, incomplete or incoherent by preconditioning training data. At least the amended features of claim 1 provide technological improvements by technically compensating for data that is lacking by "for instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data" (e.g., see paragraph 0054). Further, "in some embodiments, and with continued reference to FIG. 2, computing device, processor, and/or module may down-sample elements of a training example to a desired lower number of data elements."
The receipt, manipulation, and output of “bits, samples, or other units of data”, without more, is not a technology. The broadest reasonable interpretation of such limitations includes pure mathematics, which is per se ineligible.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 7
Further, such technical features of the disclosed apparatus provide technological improvements by configuring a computer's machine learning process. By providing specific training data and incorporating it into the machine learning model, these additions result in optimal accuracy and efficiency. This structured approach ensures the machine learning model is trained with consistently relevant data, improving its performance and predictive capabilities. These enhancements are practical, offering substantial improvements in machine learning accuracy and efficiency, and contribute to the advancement of Al technology by setting a precedent for more effective training methods.
Accordingly, representative claim 1, at least as a whole, is directed to an improvement to existing technology of AI-assisted modification of visual content, and the claim integrates any alleged abstract idea into a practical application, such that the claim is not directed to any abstract idea or judicial exception.
Applicant merely argues that the use of machine learning is per se eligible. M.P.E.P. § 2106.05 (f)(2) recites in part:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 8
Additionally, to the extent the Examiner relies on the "apply it" consideration when evaluating the claims under Step 2A, Prong 2, Applicant respectfully directs the Examiner's attention to pages 4 and 5 of the August 2025 Memo reproduced, in part below (emphasis added):
Examiners are cautioned not to oversimplify claim limitations and expand the application of the "apply it" consideration.
For example, the examiner should consider whether the claim as a whole provides an improvement to technology or a technical field. Claims that are determined to improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception itself.
Applicant respectfully submits, as discussed herein and above, that the recitations of representative claim 1 clearly "improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception itself."
Considering this, Applicant submits that representative claim 1, as amended, is not directed to the "certain methods of organizing human activity" activities that § 101 is meant to exclude, if any, and further is not directed to a judicial exception. Therefore, Applicant respectfully submits that the activities of claim 1, and its dependent claims, do not recite any abstract idea of mental processes as exemplified by the teachings of the MPEP and USPTO guidelines.
For at least these reasons, Applicant respectfully requests withdrawal of the Section 101 rejections.
Firstly, the claims were not rejected on the basis of organizing human activity.
Secondly, Examiner points out that the claims do have abstract ideas. For example, Claim 1 recites:
“generate user improvement data as a function of a comparison between the first efficiency cluster and the ideal arrangement”
The limitation simply requires the mental step of a comparison of data and the unspecified “generation” of “user improvement data” from the comparison. All of these are mental steps.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 9
Step 2B
In view of the above arguments presented with respect to Step 2A, the Step 2B analysis of the Examiner stands moot. Applicant submits that representative claim 1, as amended, amounts to significantly more than the judicial exception under step 2B and the Office Action has not shown otherwise.
Examiner addresses the Step 2B issues again in the rejection above. The claimed “additional elements” are well-understood, routine and conventional.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 10
The Office asserts that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Applicant respectfully disagrees and submits that at least the limitations of claim 1 as amended recite additional elements that amount to significantly more than the judicial exception (i.e., inventive concept).
Firstly, at least the limitations of claim 1 as amended recite meaningful limits on practicing the abstract idea. Further, this can be evidenced at least by the "practical application" analysis presented above in connection with Prong 2 of Step 2A.
Applicant's Step 2A, Prong 2 analysis, above, was unpersuasive.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 11
Applicant further respectfully asserts that at least the limitations of claim 1 as amended amount to an inventive concept, and thus "significantly more" than any alleged abstract idea recited therein. "The second step of the Alice test is satisfied when the claim limitations "involve more than performance of 'well-understood, routine, [and] conventional activities previously known to the industry." Berkheimer V. HP, Inc., 881 F.3d 1360, 1367 (Fed. Cir. 2018) (citation omitted). Here, at least the limitations of claim 1 as amended recite the use of technical features associated with an apparatus for data efficiency analysis.
The above recited limitations, including at least the limitations claim 1 as amended, are not generic and instead recite a novel approach "upsample the efficiency training data, using at least one of: a set of interpolation rules in order to predict interpolated data associated with the efficiency training data; a sample expander method for adding expander data associated with the efficiency training data; and a filter for filtering the efficiency training data in accordance with a frequency; downsample, using a compressor, the efficiency training data by removing an nth entry in a sequence of the efficiency training data."
Firstly, downsampling is mental steps and mathematical steps. Applicant's Specification, paragraph [0054] recites:
[0054] Continuing to refer to FIG. 2, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data.
***
In some embodiments, processor may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nth entry in a sequence of samples, all but every Nth entry, or the like, which is a process known as "compression," and may be performed, for instance by an N-sample compressor implemented using hardware or software. Anti-aliasing and/or anti-imaging filters, and/or low-pass filters, may be used to clean up side-effects of compression.
Upsampling is known in the art as the reverse operation which simply adds zeros to the data.
Secondly, the claimed filtering is mathematical steps. Applicant's Specification, paragraph [0054] recites:
[0054] Continuing to refer to FIG. 2, computing device, processor, and/or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and/or process has one or more inputs and/or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples' elements to be used as or compared to inputs and/or outputs may be modified to have such a number of units of data.
***
Alternatively or additionally, processor, computing device, and/or module may utilize sample expander methods, a low-pass filter, or both. As used in this disclosure, a "low-pass filter" is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor, and/or module may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units
Thirdly, the claimed “learning” is mathematical steps. Applicant's Specification, paragraph [0056] recites:
[0056] Alternatively or additionally, and with continued reference to FIG. 2, machine-learning processes as described in this disclosure may be used to generate machine-learning models 224. A "machine-learning model," as used in this disclosure, is a data structure representing and/or instantiating a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above, and stored in memory; an input is submitted to a machine-learning model 224 once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum.
Applicant's argument is unpersuasive.
The rejections stand.
Argument 12
Further, claim 1 has features that amount to significantly more than the abstract idea, because such features provide a technical contribution to the field of data analysis and personalization of data, which differs from conventional systems that do not perform a proper analysis of an associated large body of data.
Applicant respectfully submits that at least the limitations of claim 1 as amended are not "well-understood, routine, [and] conventional," and thus amount to an inventive concept.
Firstly, “analysis” of data is not a technology. The broadest reasonable interpretation of the “analysis” includes pure mathematics, which is per se ineligible.
Secondly, Applicant does have well-understood, routine, and conventional additional elements in claim 1. For instance, the claimed processor is well-understood, routine, and conventional. Note that M.P.E.P. § 2106.05 (I)(A)(i-ii) recites:
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Further, M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The processor is well understood, routine, and conventional. Applicant's Specification, paragraph [0097] recites:
[0097] Processor 1104 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 1104 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 1104 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Applicant's argument is unpersuasive.
The rejections stand.
Argument 13
Further, amended independent claim 11 recites limitations similar to amended independent claim 1 and overcomes this rejection for at least the same reasons as discussed above with reference to claim 1.
Similar arguments for similar claims are similarly unpersuasive.
The rejections stand.
Argument 14
Claims 2-4, 7-10, 12-14 and 17-20 depend, directly or indirectly, on claim 1 and claim 11 and thus recite all the same elements as claim 1 and claim 11. Applicant therefore submits claims 2-4, 7-10, 12-14 and 17-20 overcome these rejections for at least the same reasons as discussed above with reference to amended claims 1 and 11.
The independent clams do not have eligible matter. Therefore, there is no such matter that may be incorporated by reference to the dependent claims.
Applicant's argument is unpersuasive.
The rejections stand.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiries concerning this communication or earlier communications from the examiner should be directed to Wilbert L. Starks, Jr., who may be reached Monday through Friday, between 8:00 a.m. and 5:00 p.m. EST. or via telephone at (571) 272-3691 or email: Wilbert.Starks@uspto.gov.
If you need to send an Official facsimile transmission, please send it to (571) 273-8300.
If attempts to reach the examiner are unsuccessful the Examiner’s Supervisor (SPE), Kakali Chaki, may be reached at (571) 272-3719.
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/WILBERT L STARKS/
Primary Examiner, Art Unit 2122
WLS
24 MAR 2026
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