DETAILED ACTION
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 .
Status of Claims
Claims 1, 6, 9, 11, 15, and 18 were amended.
Claims 3-5, 7-8, 12-14, and 19-20 were cancelled.
Claims 1-2, 6-7, 9-11, and 15-18 are pending and examined herein.
Claims 1-2, 6-7, 9-11, and 15-18 are rejected under 35 U.S.C. 101.
Response to Arguments
Applicant's arguments filed 05/08/2026 regarding the 35 U.S.C. 101 rejection of claims 1-2, 6-7, 9-11, and 15-18 have been fully considered but they are not persuasive.
Applicant argues, see pages 5-12, that claim 1 represents an improvement to technology, with a technical problem and a technical solution that is present in the specification and is reflected by the claims. Specifically, Applicant argues that the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, not merely the idea of a solution or outcome. Applicant cites that the steps "generating an exploratory learning curve plot of a model loss of the machine learning algorithm versus a quantity of input data samples trained for each of the plurality of input data categories," "determining a plurality of data collection quotas," and "collecting a second plurality of input data samples based at least in part on the plurality of data collection quotas" are specific technical operations.
Examiner respectfully disagrees. The steps of "generating an exploratory learning curve plot of a model loss of the machine learning algorithm versus a quantity of input data samples trained for each of the plurality of input data categories" and "determining a plurality of data collection quotas" are the abstract idea of mental processes, and are not specific technical operations.
MPEP 2106.05(g) states "Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). In Flook, the Court reasoned that "[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance. A competent draftsman could attach some form of post-solution activity to almost any mathematical formula". 437 U.S. at 590; 198 USPQ at 197; Id. (holding that step of adjusting an alarm limit variable to a figure computed according to a mathematical formula was "post-solution activity"). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 79, 101 USPQ2d 1961, 1968 (2012) (additional element of measuring metabolites of a drug administered to a patient was insignificant extra-solution activity)."
"Collecting a second plurality of input data samples based at least in part on the plurality of data collection quotas" is the insignificant extra-solution activity of collecting data. Claim 1 is directed to the the abstract idea of analyzing data using a combination of mathematical concepts and mental processes (see 35 U.S.C. 101 rejection below), and the collection of data is not integrated into the claim as a whole, as it simply uses the result of the data analysis. Thus, this step does not meaningfully limit the claim and does not integrate the judicial exception into a practical application in Step 2A Prong Two nor recite significantly more in Step 2B.
Applicant further argues that the claim recites additional elements beyond the judicial exception which provide the improvement. Applicant cites the steps "performing at least one exploratory training session of the machine learning algorithm using an input data set," "collecting a second plurality of input data samples based at least in part on the plurality of data collection quotas," and "training the machine learning algorithm using the second plurality of input data samples and the input data set" as additional elements that implement the invention in a particular technological way.
Examiner respectfully disagrees. "Performing at least one exploratory training session of the machine learning algorithm using an input data set" is recited at a high level of generality, amounting to mere instructions to apply an exception. The training session is only performed in order to collect data to analyze, and does not meaningfully limit the claim. "Collecting a second plurality of input data samples based at least in part on the plurality of data collection quotas," as explained above, is insignificant extra-solution activity and does not meaningfully limit the claim. "Training the machine learning algorithm using the second plurality of input data samples and the input data set" is recited at a high level of generality, also amounting to mere instructions to apply an exception. Similarly to the “collecting” step, the training of the machine learning algorithm is not integrated into the claim as a whole, as the training simply uses the result of the analysis.
Applicant further argues that claim 11 represents an improvement to technology. Applicant specifically argues that "claim 11 includes multiple components or steps that provide the improvement, including a server storage device, server communication system, server controller, server-side exploratory training, category-specific learning-curve generation, quota and additional-sample determination, vehicle-directed task transmission, collection of samples from the vehicle, and retraining."
Examiner respectfully disagrees. Claim 11 recites substantially similar subject matter to claim 1, except that claim 11 implements the method on a server system and transmits the result of the analysis to a vehicle to collect data. The server system and the vehicle are recited at a high level of generality, and amount to mere instructions to apply an exception. The transmission step is using a computer to perform the existing process of transmitting data, which also amounts to mere instructions to apply an exception. Similar to the “collecting” and “training” steps, the transmission of data, the transmission of the data sample collection task is not integrated into the claim as a whole, as the transmission simply uses the result of the analysis.
Applicant further argues that claim 11 recites a particular machine.
Examiner respectfully disagrees. MPEP 2106.05(b) states "It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions). If applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)."
"A server system including: a server storage device; a server communication system; and a server controller in electrical communication with the server storage device and the server communication system" is a generic computer with conventional computer functions. All server systems have a server storage device, a communication system, and a server controller in electrical communication with the server storage device and the server communication system. These components are recited at a high level of generality and do not meaningfully limit the claim.
Applicant further argues that the recited machine is integral to the performance of the claim, stating "Here, claim 11 requires the server controller and server communication system to collect input data samples from the vehicle and to transmit a data sample collection task to the vehicle, and thus the recited machine is not incidental to the method, but is used to perform the very steps that produce the claimed technical result."
Examiner respectfully disagrees. MPEP 2106.05(b) states "Integral use of a machine to achieve performance of a method may integrate the recited judicial exception into a practical application or provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more. See CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) ("We are not persuaded by the appellant's argument that the claimed method is tied to a particular machine because it ‘would not be necessary or possible without the Internet.’... Regardless of whether "the Internet" can be viewed as a machine, it is clear that the Internet cannot perform the fraud detection steps of the claimed method"). For example, as described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. See, e.g., Versata Development Group v. SAP America, 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015) (explaining that in order for a machine to add significantly more, it must "play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly")."
The steps of collecting and transmitting data on a server is invoking the server merely as a tool to perform the existing processes of receiving and transmitting data. Any server could collect data from a vehicle and transmit a task to the vehicle. Thus, the server is not a particular machine.
MPEP 2106.05(b) further states "[u]se of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more."
As explained above, the collecting of data and the transmitting of the task are not integrated into the claims as a whole. Thus, the steps contribute only nominally to the execution of the data analysis method. Thus, the use of the server contributes insignificantly to the execution of the method.
For the reasons as explained above, the claim does not recite additional elements beyond the judicial exception which provide the improvement.
Applicant argues "The transmitted task is therefore not generic data transmission; it is the mechanism by which the server-side model performance analysis causes targeted vehicle-side acquisition of training data."
Examiner respectfully disagrees. Both the transmission of the task and the collection of the data is not integrated into the claim, which is directed to the “server-side model performance analysis”. There is no inventive concept in the either the transmission of the task or the collection of the data.
Applicant further argues that claim 18 represents an improvement to technology. Applicant specifically argues that "Accordingly, claim 18 includes multiple components or steps that provide the improvement, including server-to-vehicle task transmission, vehicle-side task receipt, vehicle-sensor collection, vehicle-to-server transmission of collected samples, and retraining using samples received from the vehicle communication system."
Examiner respectfully disagrees. Claim 18 recites substantially similar subject matter to claims 1 and 11. See above arguments. Claim 18, however, recites the additional elements of transmitting a data collection task to/receiving a data collection task from a vehicle communication system and “collecting a second plurality of input data samples using at least one vehicle sensor." Claim 18 recites a vehicle/vehicle communication system/vehicle sensor at a high level of generality and the existing processes of transmitting/receiving data using the generic machines. These limitations amount to mere instructions to apply an exception. Thus, claim 18 does not represent an improvement to technology.
Applicant’s arguments, see pages 28-38, filed 05/08/2026, with respect to the rejection of claims 1-2 under 35 U.S.C. 102 and the rejection of claims 6-7, 9-11, and 15-18 under 35 U.S.C. 103 have been fully considered and are persuasive. The rejection of claims 1-2 under 35 U.S.C. 102 and the rejection of claims 6-7, 9-11, and 15-18 under 35 U.S.C. 103 has been withdrawn.
Claim Rejections - 35 USC § 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.
Claims 1-2, 6-7, 9-11, and 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject
matter. The analysis of claims 1-2, 6-7, 9-11, and 15-18 in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine,
manufacture, or composition of matter. Claims 1-2, 6-7, 9-10 and 18 are directed to a process and claims 11 and 15-17 are directed to a machine. All claims are directed to statutory subject matter and analysis proceeds.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Regarding claim 1, the following claim elements are abstract ideas:
dividing the input data set into a plurality of input data categories; (Dividing data into categories can be practically performed in the human mind. This is a mental process.)
determining a regression curve equation for each of the plurality of input data categories based at least in part on the at least one exploratory training session, wherein determining the regression curve equation for each of the plurality of input data categories further comprises: (Determining a regression curve equation is performing regression, which is a mathematical calculation, which is a mathematical concept.)
generating an exploratory learning curve plot of a model loss of the machine learning algorithm versus a quantity of input data samples trained for each of the plurality of input data categories in the at least one exploratory training session; and (Plotting data and generating a curve can be practically performed in the human mind, and is therefore a mental process.)
determining the regression curve equation for each of the plurality of input data categories based at least in part on the exploratory learning curve plot for each of the plurality of input data categories, wherein the regression curve equation for one of the plurality of input data categories is a power law equation having a form: (Determining a regression curve equation is performing regression, which is a mathematical calculation, which is a mathematical concept.)
ε
m
=
α
m
β
g
+
γ
wherein
ε
is the model loss of the machine learning algorithm for the one of the plurality of input data categories,
α
is a first constant factor for the one of the plurality of input data categories,
m
is a quantity of input data samples trained for the one of the plurality of input data categories,
β
g
is a steepness of a regression curve described by the regression curve equation for the one of the plurality of input data categories, and
γ
is a lower bound model loss of the machine learning algorithm for the one of the plurality of input data categories. (This is a mathematical equation, which is a mathematical concept.)
identifying a first subset of the plurality of input data categories based at least in part on a quantity of input data samples in each of the plurality of input data categories; (Identifying a subset of data can be practically performed in the human mind. This is a mental process.)
determining a plurality of data collection quotas based at least in part on the regression curve equation for each of the plurality of input data categories, wherein each of the plurality of data collection quotas corresponds to one of the first subset of the plurality of input data categories, wherein determining one of the plurality of data collection quotas corresponding to one of the first subset of the plurality of input data categories further comprises: (The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.)
identifying a second subset of the plurality of input data categories, wherein the second subset of the plurality of input data categories includes each of the plurality of input data categories not in the first subset of the plurality of input data categories; (Identifying a subset of data can be practically performed in the human mind. This is a mental process.)
determining an average steepness
β
g
-
of the regression curve equation for each of the second subset of the plurality of input data categories based at least in part on the regression curve equation of each of the second subset of the plurality of input data categories; (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
determining a constant factor
α
'
and a lower bound model loss
γ
'
of the one of the first subset of the plurality of input data categories based at least in part on the regression curve equation of the one of the first subset of the plurality of input data categories; and (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
determining the one of the plurality of data collection quotas using the predetermined equation, wherein the predetermined equation includes: (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
m
i
+
1
=
0.5
ε
m
i
-
γ
'
α
'
β
g
-
-
1
wherein
m
i
+
1
is the one of the plurality of data collection quotas,
m
i
is a quantity of input data samples in the one of the first subset of the plurality of input data categories,
α
'
is a first constant factor of the one of the first subset of the plurality of input data categories,
γ
'
is a lower bound model loss of the one of the first subset of the plurality of input data categories, and
β
g
-
is the average steepness of the regression curve equation for each of the second subset of the plurality of input data categories. (This is a mathematical equation, which is a mathematical concept.)
A method for training a machine learning algorithm, the method comprising: (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
performing at least one exploratory training session of the machine learning algorithm using an input data set, wherein the input data set includes a first plurality of input data samples; (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
collecting a second plurality of input data samples based at least in part on the regression curve equation for each of the plurality of input data categories, wherein collecting the second plurality of input data samples further comprises: (This is the insignificant extra-solution activity of “Mere Data Gathering”, See MPEP § 2106.05(g), ‘Mere Data Gathering’, ex. i-vi.)
collecting a second plurality of input data samples based at least in part on the plurality of data collection quotas; and (This is the insignificant extra-solution activity of “Mere Data Gathering”, See MPEP § 2106.05(g), ‘Mere Data Gathering’, ex. i-vi.)
training the machine learning algorithm using the second plurality of input data samples and the input data set. (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, the following are abstract ideas:
wherein dividing the input data set into the plurality of input data categories further comprises: (Dividing data into categories can be practically performed in the human mind. This is a mental process.)
identifying at least one data set parameter by which to categorize the input data set; and (Identifying a parameter to categorize the data set can be practically performed in the human mind. This is a mental process.)
dividing the input data set into the plurality of input data categories based on the at least one data set parameter. (Dividing data into categories based on a parameter can be practically performed in the human mind. This is a mental process.)
Regarding claim 6, the rejection of claim 1 is incorporated herein. Further, the following are abstract ideas:
wherein identifying the first subset of the plurality of input data categories further comprises: (Identifying a subset of data can be practically performed in the human mind. This is a mental process.)
comparing a quantity of input data samples in each of the plurality of input data categories to a previous data collection quota, wherein the previous data collection quota is one of the plurality of data collection quotas determined during a previous execution of the method; and (Comparing data can be practically performed in the human mind. This is a mental process.)
determining each of the plurality of input data categories having a quantity of input data samples less than the previous data collection quota to be one of the first subset of the plurality of input data categories. (Determining a category that has not met a quota can be practically performed in the human mind. This is a mental process.)
Regarding claim 9, the rejection of claim 1 is incorporated herein. The following are abstract ideas.
determining a quantity of additional input data samples to collect for each of the first subset of the plurality of input data categories using an equation: (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
s
=
m
i
+
1
-
m
i
wherein
s
is the quantity of additional input data samples to collect for the one of the first subset of the plurality of input data categories,
m
i
+
1
is the one of the plurality of data collection quotas for the one of the first subset of the plurality of input data categories, and
m
i
is the quantity of input data samples in the one of the first subset of the plurality of input data categories; and (This is a mathematical equation, which is a mathematical concept.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein collecting the second plurality of input data samples further comprises: (This is the insignificant extra-solution activity of “Mere Data Gathering”, See MPEP § 2106.05(g), ‘Mere Data Gathering’, ex. i-vi.)
transmitting a data sample collection task to a vehicle, wherein the data sample collection task includes at least the quantity of additional input data samples to collect for each of the first subset of the plurality of input data categories. (Transmitting data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
Regarding claim 10, the rejection of claim 9 is incorporated herein. Further, the following are abstract ideas:
generating an updated input data set, wherein the updated input data set includes the second plurality of input data samples and the input data set; and (Generating a combined dataset can be practically performed in the human mind. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein training the machine learning algorithm using the second plurality of input data samples and the input data set further comprises: (This recites generic machine learning components/processes; this amounts to mere instructions to apply an exception.)
receiving the second plurality of input data samples from the vehicle; (Receiving data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
performing the method using the updated input data set. (This is the insignificant extra-solution activity of ‘Selecting a particular data source or type of data to be manipulated’. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i-iv.)
Regarding claim 11, the following are abstract ideas:
divide the input data set into a plurality of input data categories; (Dividing data into categories can be practically performed in the human mind. This is a mental process.)
generate an exploratory learning curve plot of a model loss of the machine learning algorithm versus a quantity of input data samples trained for each of the plurality of input data categories in the at least one exploratory training session; (Plotting data and generating a curve can be practically performed in the human mind, and is therefore a mental process.)
determine a regression curve equation for each of the plurality of input data categories based at least in part on the exploratory learning curve plot for each of the plurality of input data categories, wherein the regression curve equation for one of the plurality of input data categories is a power law equation having a form: (Determining a regression curve equation is performing regression, which is a mathematical calculation, which is a mathematical concept.)
ε
m
=
α
m
β
g
+
γ
wherein
ε
is the model loss of the machine learning algorithm for the one of the plurality of input data categories,
α
is a first constant factor for the one of the plurality of input data categories,
m
is a quantity of input data samples trained for the one of the plurality of input data categories,
β
g
is a steepness of a regression curve described by the regression curve equation for the one of the plurality of input data categories, and
γ
is a lower bound model loss of the machine learning algorithm for the one of the plurality of input data categories; (This is a mathematical equation, which is a mathematical concept.)
identify a first subset of the plurality of input data categories based at least in part on a quantity of input data samples in each of the plurality of input data categories; (Identifying a subset of data can be practically performed in the human mind. This is a mental process.)
determine a plurality of data collection quotas based at least in part on the regression curve equation for each of the plurality of input data categories, wherein each of the plurality of data collection quotas corresponds to one of the first subset of the plurality of input data categories; wherein to determine one of the plurality of data collection quotas corresponding to one of the first subset of the plurality of input data categories, (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
identify a second subset of the plurality of input data categories, wherein the second subset of the plurality of input data categories includes each of the plurality of input data categories not in the first subset of the plurality of input data categories; (Identifying a subset of data can be practically performed in the human mind. This is a mental process.)
determine an average steepness
β
g
-
of the regression curve equation for each of the second subset of the plurality of input data categories based at least in part on the regression curve equation of each of the second subset of the plurality of input data categories; (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
determine a constant factor
α
'
and a lower bound model loss
γ
'
of the one of the first subset of the plurality of input data categories based at least in part on the regression curve equation of the one of the first subset of the plurality of input data categories; and (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
determine the one of the plurality of data collection quotas using the predetermined equation, wherein the predetermined equation includes: (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
m
i
+
1
=
0.5
ε
m
i
-
γ
'
α
'
β
g
-
-
1
wherein
m
i
+
1
is the one of the plurality of data collection quotas,
m
i
is a quantity of input data samples in the one of the first subset of the plurality of input data categories,
α
'
is a first constant factor of the one of the first subset of the plurality of input data categories,
γ
'
is a lower bound model loss of the one of the first subset of the plurality of input data categories, and
β
g
-
is the average steepness of the regression curve equation for each of the second subset of the plurality of input data categories; (This is a mathematical equation, which is a mathematical concept.)
determine a quantity of additional input data samples to collect for each of the first subset of the plurality of input data categories using an equation: (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
s
=
m
i
+
1
-
m
i
wherein
s
is the quantity of additional input data samples to collect for the one of the first subset of the plurality of input data categories,
m
i
+
1
is the one of the plurality of data collection quotas for the one of the first subset of the plurality of input data categories, and
m
i
is the quantity of input data samples in the one of the first subset of the plurality of input data categories; and (This is a mathematical equation, which is a mathematical concept.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A system for training a machine learning algorithm for a vehicle, the system comprising: (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
a server system including: (This recites a generic computer. This amounts to mere instructions to apply an exception.)
a server storage device; (This recites a generic computer component. This amounts to mere instructions to apply an exception.)
a server communication system; and (This recites a generic computer component. This amounts to mere instructions to apply an exception.)
a server controller in electrical communication with the server storage device and the server communication system, wherein the server controller is programmed to: (This recites a generic computer component and processes. This amounts to mere instructions to apply an exception.)
perform at least one exploratory training session of the machine learning algorithm using an input data set, wherein the input data set includes a first plurality of input data samples, and wherein the input data set is stored on the server storage device; (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
collect a second plurality of input data samples from the vehicle using the server communication system, wherein the second plurality of input data samples is based at least in part on the regression curve equation for each of the plurality of input data categories, wherein to collect the second plurality of input data samples from the vehicle using the server communication system, the server controller is further programmed to: (This is the insignificant extra-solution activity of “Mere Data Gathering”, See MPEP § 2106.05(g), ‘Mere Data Gathering’, ex. i-vi.)
the server controller is further programmed to: (This recites a generic computer component at a high level of generality. This amounts to mere instructions to apply an exception.)
transmit a data sample collection task to the vehicle using the server communication system, wherein the data sample collection task includes at least the quantity of additional input data samples to collect for each of the first subset of the plurality of input data categories; and (Transmitting data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
train the machine learning algorithm using the second plurality of input data samples and the input data set. (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
Regarding claim 15, the rejection of claim 11 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein to transmit the data sample collection task to the vehicle using the server communication system, the server controller is further programmed to: (Transmitting data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
transmit a data sample collection task to the vehicle using the server communication system, wherein the data sample collection task includes a validation algorithm describing one of the plurality of input data categories and at least one of: a task priority, a projected decrease in model loss, and the quantity of additional input data samples to collect. (Transmitting data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
Regarding claim 16, the rejection of claim 15 is incorporated herein. The following are abstract ideas:
determine a priority of the data sample collection task based at least in part on at least one of the task priority, the projected decrease in model loss, and the quantity of additional input data samples to collect; and (Determining a priority can be practically performed in the human mind. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
a vehicle system including: (This recites a generic system. This amounts to mere instructions to apply an exception.)
at least one vehicle sensor; (This recites a generic sensor. This amounts to mere instructions to apply an exception.)
a vehicle communication system; and (This recites a generic system. This amounts to mere instructions to apply an exception.)
a vehicle controller in electrical communication with the at least one vehicle sensor and the vehicle communication system, wherein the vehicle controller is programmed to: (This recites a generic computer component. This amounts to mere instructions to apply an exception.)
receive the data sample collection task from the server system using the vehicle communication system; (Receiving data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
perform the data sample collection task using the at least one vehicle sensor. (This is the insignificant extra-solution activity of “Mere Data Gathering”, See MPEP § 2106.05(g), ‘Mere Data Gathering’, ex. i-vi.)
Regarding claim 17, the rejection of claim 16 is incorporated herein. Further, the following are abstract ideas:
determine a second plurality of input data samples based at least in part on the validation algorithm, wherein the second plurality of input data samples is a subset of the plurality of unvalidated input data samples; and (Determining a variable based on an algorithm is a mathematical calculation, which is a mathematical calculation.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein to perform the data sample collection task, the vehicle controller is further programmed to: (This is the insignificant extra-solution activity of “Mere Data Gathering”, See MPEP § 2106.05(g), ‘Mere Data Gathering’, ex. i-vi.)
record a plurality of unvalidated input data samples using the at least one vehicle sensor; : (This is the insignificant extra-solution activity of “Mere Data Gathering”, See MPEP § 2106.05(g), ‘Mere Data Gathering’, ex. i-vi.)
transmit the second plurality of input data samples to the server communication system using the vehicle communication system. (Transmitting data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
Regarding claim 18, the following are abstract ideas:
dividing the input data set into a plurality of input data categories; (Dividing data into categories can be practically performed in the human mind. This is a mental process.)
generating an exploratory learning curve plot of a model loss of the machine learning algorithm versus a quantity of input data samples trained for each of the plurality of input data categories in the at least one exploratory training session; and (Plotting data and generating a curve can be practically performed in the human mind, and is therefore a mental process.)
determining a regression curve equation for each of the plurality of input data categories based at least in part on the exploratory learning curve plot for each of the plurality of input data categories, wherein the regression curve equation for one of the plurality of input data categories is a power law equation having a form: (Determining a regression curve equation is performing regression, which is a mathematical calculation, which is a mathematical concept.)
ε
m
=
α
m
β
g
+
γ
wherein
ε
is the model loss of the machine learning algorithm for the one of the plurality of input data categories,
α
is a first constant factor for the one of the plurality of input data categories,
m
is a quantity of input data samples trained for the one of the plurality of input data categories,
β
g
is a steepness of a regression curve described by the regression curve equation for the one of the plurality of input data categories, and
γ
is a lower bound model loss of the machine learning algorithm for the one of the plurality of input data categories; (This is a mathematical equation, which is a mathematical concept.)
identifying a first subset of the plurality of input data categories based at least in part on a quantity of input data samples in each of the plurality of input data categories; (Identifying a subset of data can be practically performed in the human mind. This is a mental process.)
determining a plurality of data collection quotas based at least in part on the regression curve equation for each of the plurality of input data categories, wherein each of the plurality of data collection quotas corresponds to one of the first subset of the plurality of input data categories, wherein determining one of the plurality of data collection quotas corresponding to one of the first subset of the plurality of input data categories further comprises: (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
identifying a second subset of the plurality of input data categories, wherein the second subset of the plurality of input data categories includes each of the plurality of input data categories not in the first subset of the plurality of input data categories; (Identifying a subset of data can be practically performed in the human mind. This is a mental process.)
determining an average steepness
β
g
-
of the regression curve equation for each of the second subset of the plurality of input data categories based at least in part on the regression curve equation of each of the second subset of the plurality of input data categories; (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
determining a constant factor
α
'
and a lower bound model loss
γ
'
of the one of the first subset of the plurality of input data categories based at least in part on the regression curve equation of the one of the first subset of the plurality of input data categories; and (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
determining the one of the plurality of data collection quotas using the predetermined equation, wherein the predetermined equation includes: (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
m
i
+
1
=
0.5
ε
m
i
-
γ
'
α
'
β
g
-
-
1
wherein
m
i
+
1
is the one of the plurality of data collection quotas,
m
i
is a quantity of input data samples in the one of the first subset of the plurality of input data categories,
α
'
is a first constant factor of the one of the first subset of the plurality of input data categories,
γ
'
is a lower bound model loss of the one of the first subset of the plurality of input data categories, and
β
g
-
is the average steepness of the regression curve equation for each of the second subset of the plurality of input data categories; (This is a mathematical equation, which is a mathematical concept.)
determining a quantity of additional input data samples to collect for each of the first subset of the plurality of input data categories using an equation: (Determining a number based on an equation is a mathematical calculation, which is a mathematical concept.)
s
=
m
i
+
1
-
m
i
wherein
s
is the quantity of additional input data samples to collect for the one of the first subset of the plurality of input data categories,
m
i
+
1
is the one of the plurality of data collection quotas for the one of the first subset of the plurality of input data categories, and
m
i
is the quantity of input data samples in the one of the first subset of the plurality of input data categories; and (This is a mathematical equation, which is a mathematical concept.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A method for training a machine learning algorithm for a vehicle, the method comprising: (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
performing at least one exploratory training session of the machine learning algorithm using an input data set, wherein the input data set includes a first plurality of input data samples, and wherein the input data set is stored on a server storage device; (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
transmitting a data sample collection task to a vehicle communication system of the vehicle using a server communication system; (Transmitting data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
collecting a second plurality of input data samples using at least one vehicle sensor, wherein the second plurality of input data samples is based at least in part on the regression curve equation for each of the plurality of input data categories, wherein collecting the second plurality of input data samples from the vehicle using the server communication system further comprises: (This is the insignificant extra-solution activity of “Mere Data Gathering”, See MPEP § 2106.05(g), ‘Mere Data Gathering’, ex. i-vi.)
receiving the data sample collection task using a vehicle communication system; collecting a second plurality of input data samples using at least one vehicle sensor, wherein the second plurality of input data samples is based at least in part on the regression curve equation for each of the plurality of input data categories; (Receiving data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
transmitting the data sample collection task to the vehicle communication system using the server communication system, wherein the data sample collection task includes at least the quantity of additional input data samples to collect for each of the first subset of the plurality of input data categories; (Transmitting data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
transmitting the second plurality of input data samples from the vehicle communication system to the server communication system; and (Transmitting data is an existing process on a computer. This amounts to mere instructions to apply an exception.)
training the machine learning algorithm using the second plurality of input data samples received from the vehicle communication system and the input data set. (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
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.
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 inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA THUY PHAM whose telephone number is (571)272-2605. The examiner can normally be reached Monday - Friday, 9 A.M. - 5:00 P.M..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.T.P./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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