DETAILED ACTION
This action is responsive to Applicant’s reply filed 26 December 2025. This action is made final.
Status of the Claims
Claims 1-7, 9 and 11-13 are currently amended.
Claims status is currently pending and under examination for claims 1-13 of which independent claim is 1.
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 .
Response to Amendment
Applicant’s amendments to the Claims have overcome each and every 112(b) and 101 non-statutory subject matter Step 1 rejections previously set forth in the Non-Final Office Action mailed September 30th 2025. The 112(b) rejection for dependent claim 10 is still maintained.
Applicant’s arguments regarding the art rejections are moot in view of the new grounds of rejection necessitated by applicant’s amendment.
In regards to the rejection of claims 1-13 under 35 U.S.C. 101 for being directed towards an abstract idea without significantly more, Applicant argues the claims are not directed to a judicial exception but rather to an improvement to the technical problem of using inadequate training datasets in the development and training of autonomous vehicle control systems (See Applicant’s response, page 9). On Page 8, Applicant argues that amended claim 1’s training step is impractical to perform in the human mind, or by a human using pen and paper. Claim 1’s training step is newly presented and has been addressed in the rejection below as amounting to no more than mere instructions to “apply” the judicial exception on a computer and viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers (therefore Applicant’s argument is not persuasive). On Page 9, Applicant argues that the claims are directed to an improvement of enabling a system that can reduce execution of non-optimal driving maneuvers caused by unbalanced datasets. Applicant’s arguments are not persuasive since the improvement is not reflected in the claims. On Page 9, Applicant argues that the claims provide a technical solution to the problem of using “inadequate training datasets, or datasets that bias a machine toward sub-optimal driving maneuvers” in the development and training of autonomous vehicle control systems. Applicant’s argument is not persuasive since the improvement of using a balanced dataset to ensure a trained AI agent/controller will have a uniform behavior spectrum is not reflected in the claims. Thus, the rejections of claims 1-13 as being directed towards an abstract idea without significantly more are still maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites the limitations “the raw data” and “the preprocessed data” in line 2. There is a lack of antecedent basis for these limitations. It is not clear which data “the preprocessed data” is referring to, or if ‘the raw data” is referring to the received data set of a driven machine, therefore the claim is rendered indefinite.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, independent claim 1, under the broadest reasonable interpretation, recites the following limitations that are abstract ideas:
condition said data by grouping the data based on machine-control parameters, (mental process)
wherein grouping comprises: grouping the data into groups corresponding to each of the machine-control parameters, wherein at least some of the groups comprise sub-groups and the groups and the sub-groups are arranged in a hierarchy; (mental process)
for at least some groups corresponding to statistical machine-control parameters, grouping associated data into bins representing ranges of numeric values between a minimum and maximum of corresponding machine-control parameters; (mental process)
and for at least some groups corresponding to contextual machine-control parameters, grouping associated data into clusters corresponding to sub-parameters of corresponding machine-control parameters; (mental process)
determine at least one data imbalance within the groups and the sub-groups of the conditioned data; (mental process)
balance the data for which an imbalance was determined, wherein balancing the data comprises balancing a number of data points between bins or clusters of a group or sub-group; (mental process)
The “condition” step involves identifying groups of data based on parameters which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of grouping data at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “condition” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
The “grouping the data into …. groups and the sub-groups are arranged in a hierarchy” step involves identifying groups and sub-groups of data and determining their hierarchical order which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
The “grouping associated data into bins” step involves identifying which bins data belongs to which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of grouping associated data into bins at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
The “grouping associated data into clusters corresponding to sub-parameters …” step involves determining which sub-clusters data belongs to which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of grouping associated data into clusters corresponding to sub-parameters at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
The “determine” step involves identifying data imbalances within data groups and sub-groups which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of searching conditioned data for determining data imbalances at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “search” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
The “balance” step involves identifying data to add or remove to balance data groups which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of balancing the data at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “balance” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
Therefore, the independent claim 1 recites a judicial exception.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the judicial exception recited above is not integrated into a practical application. The claims recite the following additional elements, but these additional elements are not sufficient to integrate the judicial exception into a practical application:
computer-readable instructions that, when executed in a computer system including one or more computers, cause the computer system to (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
receive data a machine driven by a human or robot driver; (MPEP § 2106.05(g) necessary data gathering and insignificant extra-solution activity to the judicial exception)
and train a second machine to execute a driving maneuver based on sensor measurements included in the balanced data (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
The “receive” step amounts to mere data gathering and is recited at a high level of generality, thus adding insignificant extra-solution activity to the judicial exception – see MPEP § 2106.05(g). Under MPEP § 2106.05(d), such additional elements have been found by the courts to not integrate a judicial exception into a practical application.
The “train” step is recited at a high-level of generality such that the limitation amounts to no more than mere instructions to “apply” the judicial exception on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f).
The remaining additional elements are recited at a high-level of generality such that they amount to no more than mere instructions to “apply” an exception using a generic component. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f).
Therefore, the above limitations do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No. The claims do not include additional elements that are sufficient for the claims to amount to significantly more than the judicial exception.
In regards to the “receive” step, this step adds insignificant extra-solution activity. An extra-solution activity is a well-understood, routine and conventional (WURC) activity per MPEP § 2106.05(d)(II), “the courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.” The “receive” step does not integrate the judicial exception into a practical application and does not amount to significantly more.
In regards to the “train” step and the remaining additional elements, the limitations are recited so generically such that they amount to no more than mere instructions to “apply” the judicial exception on a computer using generic computer components. Mere instructions to apply a judicial exception cannot provide an inventive concept. See MPEP § 2106.05(f).
Therefore, independent claim 1 is not patent eligible.
Dependent Claims 2-13
The remaining dependent claims being rejected do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than a judicial exception.
Dependent claim 2 recites the following limitations:
wherein, by comparing the number of data points of the groups or sub-groups with each other, an imbalance is determined if a difference of the number of data points included in the groups or sub-groups compared with each other is larger than a predefined threshold (mental process)
The “comparing” step involves identifying imbalanced data by determining if the differences in the number of data points of each group exceeds a threshold which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of comparing a number of data points to determine an imbalance at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “comparing” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III). This claim does not recite any non-abstract additional elements.
Dependent claim 3 recites the following limitations:
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, dependent claim 3, under the broadest reasonable interpretation, recites the following limitations that are abstract ideas:
and each group or sub-group is divided into one or more clusters of data or one or more bins of data, (mental process)
The “dividing” step involves determining which cluster or bin data belongs to which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of dividing a group or subgroup into clusters or bins at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “dividing” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
Therefore, dependent claim 3 recites a judicial exception.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the judicial exception recited above is not integrated into a practical application. The claims recite the following additional elements, but these additional elements are not sufficient to integrate the judicial exception into a practical application:
wherein the conditioned data have n hierarchically arranged groups and sub-groups, with N indicating the highest level group and N-1, N-2, .. , N-n indicating sub-groups of lower levels, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
The steps are recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f).
Therefore, the above limitations do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No. The claims do not include additional elements that are sufficient for the claims to amount to significantly more than the judicial exception.
In regards to the “wherein the conditioned data have n hierarchically arranged …” step, the limitations are recited so generically such that they amount to no more than mere instructions to “apply” the judicial exception on a computer using generic computer components. Mere instructions to apply a judicial exception cannot provide an inventive concept. See MPEP § 2106.05(f).
Therefore, dependent claim 3 is not patent eligible.
Dependent claim 4 recites the following limitations:
wherein the conditioned data is arranged to have at least one of contextual parameters and statistical parameters provided in a group or sub-group, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
wherein in the hierarchically arranged conditioned data each bin or cluster of a group of sub-group is connected to one or more bins or clusters of the next lower level sub-group (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
The steps are recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f). Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). The steps do not integrate the judicial exception into a practical application and do not amount to significantly more.
Dependent claim 5 recites the following limitations:
wherein the machine is a vehicle (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
and the received data includes data of a plurality of trajectories driven by the vehicle that is driven by a human or robot driver, (MPEP § 2106.05(g) necessary data gathering and insignificant extra-solution activity to the judicial exception)
wherein the contextual parameters include at least one of a left turn, right turn, straight road, complex turn, obstacles, free cruising, lane changing, obstacle following or overtaking, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
and the statistical parameters include at least one of speed, yaw rate and/or accelerations (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
The “and the received data includes …” limitation represents mere necessary data gathering and is recited at a high level of generality, thus adding insignificant extra-solution activity to the judicial exception - see MPEP § 2106.05(g). The extra-solution activity is a well-understood, routine and conventional (WURC) activity per MPEP § 2106.05(d)(II), “the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.” The limitation does not integrate the judicial exception into a practical application and does not amount to significantly more.
The remaining steps are recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f). Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). The steps do not integrate the judicial exception into a practical application and do not amount to significantly more.
Dependent claim 6 recites the following limitations:
wherein, if a number of clusters and bins per group or sub-group is predefined in a database, the number of clusters is equal to the number of sub-parameters of the contextual parameter associated with the clusters, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
and depending on the number of bins associated with a statistical parameter, the range of values of the statistical parameter is evenly distributed over the bins (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
The steps are recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f). Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). The steps do not integrate the judicial exception into a practical application and do not amount to significantly more.
Dependent claim 7 recites the following limitations:
wherein a hierarchical structure of groups and sub-groups as well as bins and clusters of the groups and sub-groups is predefined or is editable by a user, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
wherein in the latter case the user is at least prompted to input the number of groups and sub-groups as well as the number of bins or clusters per group or sub-group (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
The steps are recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f). Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). The steps do not integrate the judicial exception into a practical application and do not amount to significantly more.
Dependent claim 8 recites the following limitations:
wherein an imbalance is determined by comparing bins or clusters of at least one group or sub-group with each other for finding the bin or cluster with the lowest number of data points in the group or sub-group, (mental process)
and the balancing is performed by a random under-sampling of all other bins or clusters in the group or sub-group to the number of data points in the bin or cluster with the lowest number of data points (mental process)
The “comparing” step involves identifying imbalanced data by determining the bin or cluster with the lowest number of data points which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step of comparing bins and clusters at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “comparing” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III).
The “balancing” step involves choosing random data points to remove to ensure each bin and cluster have an equal number of data points which amounts to no more than observations, evaluations, and judgments that can be performed in the human mind or with the use of a physical aid (e.g., pen and paper). The claim recites the step at a high degree of generality, thus the step is not required to have any specific level of complexity that would preclude the step from being mental processes. Therefore, the “balancing” step is considered to be mental processes, see MPEP § 2106.04(a)(2)(III). This claim does not recite any non-abstract additional elements.
Dependent claim 9 recites the further limitation, “wherein the balancing of the conditioned data is performed level-by-level from a highest level group to a lowest level sub-group.” The step is recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f). Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). The step does not integrate the judicial exception into a practical application and does not amount to significantly more.
Dependent claim 10 recites the further limitation, “wherein the raw data, the preprocessed data, the conditioned data and/or the balanced data is output to a user for validation of the conditioning and/or balancing operation.” The step is recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f). Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). The step does not integrate the judicial exception into a practical application and does not amount to significantly more.
Dependent claim 11 recites the following limitations:
wherein the balanced data is output to a user such that groups and sub-groups are arranged as concentric rings with different diameters, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
and the bins or clusters are segments of the concentric rings (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
The steps are recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f). Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). The steps do not integrate the judicial exception into a practical application and do not amount to significantly more.
Dependent claim 12 recites the further limitation “the computer program product of claim 1 installed on a distributed computing resources.” The step is recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f). Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). The step does not integrate the judicial exception into a practical application and does not amount to significantly more.
Dependent claim 13 recites the following limitations:
An artificial intelligence training control device which at least has a memory unit (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
an input interface, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
an output interface (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
and a display unit, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
wherein the input interface configured to receive one or more data sets from a data source being connected to the input interface via a wired connection or a wireless connection, (MPEP § 2106.05(g) necessary data gathering and insignificant extra-solution activity to the judicial exception)
the output interface configured to output the balanced data, (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
the display unit configured to display at least one of the one or more data sets, the conditioned data or the balanced data to a user (MPEP § 2106.05(f) mere instructions to implement an abstract idea on a computer, or generally links exception to a technological environment)
The “wherein the input interface configured to receive …” limitation represents mere necessary data gathering and is recited at a high level of generality, thus adding insignificant extra-solution activity to the judicial exception - see MPEP § 2106.05(g). The extra-solution activity is a well-understood, routine and conventional (WURC) activity per MPEP § 2106.05(d)(II), “the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data.” The limitation does not integrate the judicial exception into a practical application and does not amount to significantly more.
The “the output interface configured …” and “the display unit configured …” steps and the remaining additional elements are recited at a high-level of generality such that the limitations amount to no more than mere instructions to “apply” the judicial exception on a computer. They can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, see MPEP § 2106.05(f). Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). The steps do not integrate the judicial exception into a practical application and do not amount to significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 9 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Suzuki et al. (US 20180336426 A1), hereinafter Suzuki, in view of Chauvin et al. (“Does the Type of Visualization Influence the Mode of Cognitive Control …”), hereinafter Chauvin, further in view of Gupta et al. (US 20150049193 A1), hereinafter Gupta, and Gaspar et al. (“Driver brake response to sudden unintended acceleration while parking”), hereinafter Gaspar, further in view of Wu et al. (“Local Decomposition for Rare Class Analysis”), hereinafter Wu, and Xu et al. (US 20200356849 A1), hereinafter Xu.
With respect to claim 1, Suzuki teaches:
a computer program product comprising computer-readable instructions that, when executed in a computer system including one or more computers, cause the computer system to (Suzuki discloses “a program that makes a computer such as an information processing device or an information processing system execute the steps in the above image processing method. The program may be distributed while being stored in a computer-readable recording medium” [0147].)
receive data a machine driven by a human or robot driver (Suzuki discloses “the camera CM that is an exemplary image pickup device is equipped in a vehicle CA. The camera CM shoots the periphery of the vehicle CA and generates an image. For example, as illustrated, the camera CM picks up an image of a forward sight of the vehicle CA. Next, the image generated by the camera CM is acquired by an image acquiring device IM” [0037]. See Figure 1 depicting a vehicle being driven with a camera and image acquiring device.
Suzuki discloses “the image processing system acquires second images on the server SR side. The second images are images previously input, images periodically sent from the vehicle CA, … Here, the second image may be partially the same as the first image. Hereinafter, an example of the second image is referred to as an “image IMG2”” [0055].);
condition said data by grouping the data based on machine-control parameters (The Examiner interprets “machine-control parameters” according to its broadest reasonable interpretation (in view of the Applicant’s specification at Page 3, lines 13-16) as encompassing parameters related to driving a vehicle, or a driving environment as disclosed by Suzuki.
Suzuki discloses “the image processing system performs grouping of the images IMG2 and analyzes a balance, on the server SR side. First, a parameter by which the analysis is performed is previously set in the server SR. The parameter is a condition when the images IMG2 are picked up, or the like. Specifically, the parameter is each time when the images IMG2 are picked up, each weather when the images IMG2 are picked up, or the like. For example, the parameter is determined after image processing of the image IMG2 … the vehicle speed may be determined based on data of a speed sensor. In addition, the parameter of weather may be determined by acquisition of meteorological data or the like from an external device” [0055-0057]. See [0057-0061] describing other parameters obtained from processing images.
Suzuki discloses “the image processing system sets images IMG2 having an identical parameter, to one group. Then, the image processing system analyzes the balance about whether the number of images is roughly equal among groups … the image processing system groups the images IMG2 into three conditions: “condition 1”, “condition 2” and “condition 3”. Specifically, suppose that the parameter is “weather”. Further, suppose that the “condition 1” is a group “fair”, the “condition 2” is a group “cloudy”, and the “condition 3” is a group “rainy”. In the figure, the abscissa axis indicates the condition, and the ordinate axis indicates the number of images” [0062-0063]. See Figure 3 depicting a graph of various groups (conditions) and their corresponding number of images.),
wherein grouping comprises: grouping the data into groups corresponding to each of the machine-control parameters (Suzuki discloses each weather condition is a parameter (‘machine-control parameter’), see [0056], and the images IMG2 are grouped by condition/parameter, see [0062-0063] above.),
determine at least one data imbalance within the groups … of the conditioned data (Suzuki discloses “there is a bias to the “condition 1”, and the image processing system outputs an analysis result of a poor balance. Specifically, on the basis of the “condition 1”, the number of the images of the “condition 2” is less than the number of the images of the “condition 1”, by a difference DF1. Furthermore, on the basis of the “condition 1”, the number of the images of the “condition 3” is less than the number of the images of the “condition 1”, by a difference DF2. On the other hand, when there is no difference or when the difference is equal to or less than a predetermined value, the image processing system outputs an analysis result of a good balance. In the case of the analysis result of a poor balance, the image processing system determines adjustment of the balance” [0064]. See Figure 3 depicting how the differences in the number of images of each condition (group) are used to determine data imbalances.);
balance the data for which an imbalance was determined (Suzuki discloses “the image processing system requests an image from the server SR side to the vehicle side. That is, in the case of the example shown in FIG. 3, in step SB05, the image processing system adjusts the balance such that the difference DF1 and the difference DF2 are reduced. Specifically, in the case of the example shown in FIG. 3, the image processing system requests images satisfying the “condition 2”, to the vehicle CA, for reducing the difference DF1. Similarly, the image processing system requests images satisfying the “condition 3”, to the vehicle CA, for reducing the difference DF2” [0067]. See Figure 13 depicting balanced groups after requesting images.),
and train a second machine to [guide] a driving maneuver based on sensor measurements included in the balanced data (Suzuki discloses images IMG2 can capture vehicle speed “by optical flow processing of the image IMG2, the image processing system can estimate vehicle speed or the like. For the determination of the parameter, sensor data or the like may be used. For example, the vehicle speed may be determined based on data of a speed sensor” [0057].
Suzuki discloses “the image processing system determines whether the balance of images to be used for the machine learning is good. That is, when the balance is adjusted in step SA04, step SA05, step SB05, step SB06 and the like shown in FIG. 2, the bias as shown in FIG. 3 is small, and the images to be used for the machine learning are often well balanced. In such a case, the image processing system determines that the balance of the images to be used for the machine learning is good … the image processing system proceeds to step SB0702.” [0080-0081].
Suzuki discloses “In step SB0702, the image processing system performs learning of signboards, or the like. Specifically, in the case of using the signboard LM for the guide as shown in FIG. 4, the image processing system performs the learning using the image containing the signboard LM. Thereby, the image processing system can perform the image recognition of the signboard LM on the image” [0082].
Suzuki discloses a car navigation device is used for navigation, “it is desirable for the car navigation device to perform the guide of the route using a signboard LM placed near the intersection CR as a landmark, as illustrated. In such a case, in the vehicle CA, an image of a forward sight of the vehicle CA is picked up by the image pickup device, and image recognition of the signboard LM on the picked-up image is performed. For such an image recognition, for example, it is desirable to perform machine learning based on the image containing the signboard LM” [0078]. See [0075] describing a car navigation device is equipped on a vehicle (‘machine’). See [0036-0037] describing an image processing system includes an image pickup device equipped on a vehicle.).
However, Suzuki does not teach arranging sub-groups in a hierarchy, which is taught by Chauvin:
wherein at least some of the groups comprise sub-groups and the groups and the sub-groups are arranged in a hierarchy (Chauvin discloses Figure 2 (reproduced below) on P. 754 depicting a dendrogram for clustering (‘arranging’) vehicle variables into clusters (‘groups’) and sub-clusters (‘sub-groups’).
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Chauvin discloses “Figure 2 shows the dendrogram for the clustering of vehicle variables. … Examination of the composition of the clusters of variables and their factor loadings (squared correlations between variables and the first principal component of their parent group), suggests that Cluster 1 (in blue) refers to the speed of the vehicles, Cluster 2 (in red) to the smoothness of the actions upon the acceleration pedal and Cluster 3 (in green) to the smoothness of the actions upon the steering wheel” (P. 754, Sec. 3, ¶3-4).);
for at least some groups corresponding to statistical machine-control parameters, grouping associated data (Chauvin discloses vehicle variables are arranged into clusters (‘groups’), see Figure 2 above. Cluster 1 (‘group’) contains vehicle variables related to speed and Cluster 2 contains vehicle variables related to acceleration (therefore, Clusters 1 and 2 are groups corresponding to statistical machine-control parameters). Clusters 1 and 2 are further divided (grouped) into sub-clusters (sub-groups).);
Chauvin teaches sorting speed and acceleration vehicle variables into hierarchical clusters (‘groups’) and sub-clusters (‘sub-groups) is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the hierarchical clusters of Chauvin to split data into smaller groups. By splitting data into smaller groups, a more accurate and interpretable representation of data can be achieved, leading to better data management.
Furthermore, the combination of Suzuki in view of Chauvin, does not teach grouping statistical machine-control parameters into bins, which is taught by Gupta:
grouping associated data into bins representing ranges of numeric values between a minimum and maximum of corresponding machine-control parameters (Gupta discloses Figure 10 (reproduced below) depicting steering angles (‘statistical parameters’) sorted into bins based on increments of negative and positive degrees.
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Gupta discloses “FIG. 10 shows a histogram of thirty consecutive frame sets lying in the same steering angle range observed during the normal driving along urban routes by multiple drivers. The steering angle has been partitioned into bins 160 from -180 degrees to +180 degrees in varying increments of 6 degrees or more for this experiment. The way the angles are partitioned is determined by an external function in which the central angles are (such as about -6 to +6 degrees) divided into two central bins with a width of six degrees” [0091].);
Gupta teaches sorting steering angles into bins is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the bins disclosed by Gupta to bin continuous variables. By binning continuous variables into discrete categories, data is easier to understand and analyze, thereby leading to a better understanding of underlying relationships.
Furthermore, the combination of Suzuki in view of Chauvin, further in view of Gupta does not teach grouping associated data into clusters corresponding to sub-parameters of corresponding machine-control parameters, which is taught by Gaspar:
and for at least some groups corresponding to contextual machine-control parameters, grouping associated data into clusters corresponding to sub-parameters of corresponding machine-control parameters (The Examiner interprets “contextual machine-control parameters” according to its broadest reasonable interpretation in view of the Applicant’s specification. The examiner notes that the applicant provides no meaningful definition of this term in their specification. Accordingly, the examiner interprets this term as encompassing a brake response as disclosed by Gaspar. A brake response describes the amount of brake force applied by a driver while operating a vehicle, defined as Hard, Gradual, or Light (therefore break responses are qualitative descriptions of the operation of a vehicle and therefore are contextual machine-control parameters).
Gaspar discloses “The cluster analysis resulted in three distinct response clusters, seen in the cluster dendrogram in Fig. 4. By visualizing individual brake response curves by cluster affiliation, as in Fig. 5, these three clusters were defined based on the following characteristics: Hard Braking: braking with greater than 125lbs of force within the first 1.5 s following the SUA event. Brake Pumping/Gradual Braking: braking to a maximum of approximately 125lbs brake force occurring over the 5 s following the SUA event. Light Brake Press: brakeforce less than 75lbs over the span of 5 s following the SUA event. Half the participants responded with light braking, less than 75lbs brake force, whereas just three drivers responded with hard braking” (P. 4, Sec. 3.1, ¶2).
Gaspar discloses Figure 4 on P. 4 (reproduced below) depicting a cluster dendrogram of brake responses. Brake responses are arranged into Hard Brake, Gradual Brake, and Light Brake clusters (and therefore the clusters are groups corresponding to contextual machine-control parameters). The clusters are further divided into sub-clusters (and therefore these sub-clusters correspond to sub-parameters since the sub-clusters further divide the clusters corresponding to contextual machine-control parameters).
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);
Gaspar teaches arranging brake responses into hierarchical clusters and sub-clusters is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the cluster dendrogram of Gaspar to arrange qualitative data into more granular groups. By arranging qualitative data into more granular groups, a more accurate and interpretable representation of data can be achieved, leading to better data management and interpretability.
Furthermore, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar does not teach determining at least one data imbalance within the groups and the sub-groups of the conditioned data, and balancing a number of data points between bins or clusters of a group or sub-group, which is taught by Wu:
determine at least one data imbalance within the groups and the sub-groups of the conditioned data (Wu discloses balanced subclasses (‘sub-groups’) are produced from large classes (‘groups’), “Specifically, for a data set with an imbalanced class distribution, we perform clustering within each large class and produce sub-classes with relatively balanced sizes … Since the clustering is conducted independently within each class but not across the entire data set, we call it local clustering … By exploiting local clustering within large classes, we can decompose the complex concepts, e.g., nonlinear-separable concepts for linear classifiers, into relatively simple ones, e.g., linearly separable concepts. Another effect of local clustering is to produce subclasses with relatively uniform sizes. In addition, for data sets with highly skewed class distributions, we further integrate the over-sampling technique into the COG scheme and propose the COG with over-sampling technique … COG has the ability to divide imbalanced classes into relatively balanced and small sub-classes” (P. 814-815, Sec. 1).);
wherein balancing the data comprises balancing a number of data points between bins or clusters of a group or sub-group (Wu discloses above over-sampling is performed to balance sub-classes. See Figure 2 on P. 816 depicting creating balanced sub-classes using the COG (Classification using Local Clustering) procedure.);
Wu teaches creating balanced subclasses from the local clustering of large is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the balancing method of Wu to balance hierarchical data in a top-to-bottom approach. By balancing hierarchical data from top-to-bottom, it can be ensured that high-level minority classes are equally represented in a dataset since they are balanced first and their information is preserved as the balancing process continues down the hierarchy to more specific subclasses, thus leading to an accurately represented and balanced dataset.
Furthermore, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu does not teach training a second machine to execute a driving maneuver, which is taught by Xu:
and train a second machine to execute a driving maneuver based on sensor measurements (Xu discloses “a method of training dynamic models for autonomous driving vehicles includes receiving a first set of training data from a training data source, the first set of training data representing driving statistics for a first set of features” [0021].
Xu discloses “the training data source stores driving statistics collected from a variety of vehicles driven by human drivers, wherein the driving statistics include information indicating driving commands issued and responses of the vehicles captured by sensors of the vehicles at different points in time” [0027].
Xu discloses “Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors and high-definition maps, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers. An autonomous driving vehicle (ADV) relies on various modules to plan trajectories and control actuator commands” [0002-0003].).
Xu teaches training dynamic models for autonomous driving vehicles with training data acquired from vehicle sensors is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the training technique disclosed by Xu to train an autonomous vehicle. By training an autonomous vehicle, trained autonomous vehicles can travel with minimal human interaction, thus relieving human drivers of some driving-related responsibilities and saving drivers’ time.
With respect to claim 2, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches:
the computer program product according to claim 1, wherein, by comparing the number of data points of the groups or sub-groups with each other, an imbalance is determined if a difference of the number of data points included in the groups or sub-groups compared with each other is larger than a predefined threshold (Suzuki discloses a poor balance (‘imbalance’) is determined by comparing the number of images (‘number of data points’) in each condition (group), “there is a bias to the “condition 1”, and the image processing system outputs an analysis result of a poor balance. Specifically, on the basis of the “condition 1”, the number of the images of the “condition 2” is less than the number of the images of the “condition 1”, by a difference DF1. Furthermore, on the basis of the “condition 1”, the number of the images of the “condition 3” is less than the number of the images of the “condition 1”, by a difference DF2. On the other hand, when there is no difference or when the difference is equal to or less than a predetermined value, the image processing system outputs an analysis result of a good balance. In the case of the analysis result of a poor balance, the image processing system determines adjustment of the balance” [0064].).
With respect to claim 3, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches:
the computer program product according to claim 1, wherein the conditioned data have n hierarchically arranged groups and sub-groups, with N indicating the highest level group and N-1, N-2, .. , N-n indicating sub-groups of lower levels (Chauvin discloses Figure 2 (reproduced above) on P. 754 depicting a dendrogram for clustering (‘arranging’) vehicle variables into clusters (‘groups’) and sub-clusters (‘sub-groups’). The larger clusters on top of the dendrogram represent higher levels, while the sub-clusters represent lower levels.),
and each group or sub-group is divided into one or more clusters of data or one or more bins of data (Chauvin discloses Figure 2 depicting a dendrogram with sub-clusters (‘sub-group’). The sub-clusters are the result of splitting (‘dividing’) a larger cluster (‘group’).).
Chauvin teaches sorting data into hierarchical clusters (‘groups’) and sub-clusters (‘sub-groups) is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the hierarchical clusters of Chauvin to split data into smaller groups. By splitting data into smaller groups, a more accurate and interpretable representation of data can be achieved, leading to better data management.
With respect to claim 4, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches:
the computer program product according to claim 3, wherein the conditioned data is arranged to have at least one of contextual parameters and statistical parameters provided in a group or sub-group (Suzuki discloses grouping weather condition parameters (‘contextual parameters’), “the image processing system sets images IMG2 having an identical parameter, to one group. Then, the image processing system analyzes the balance about whether the number of images is roughly equal among groups … the image processing system groups the images IMG2 into three conditions: “condition 1”, “condition 2” and “condition 3”. Specifically, suppose that the parameter is “weather”. Further, suppose that the “condition 1” is a group “fair”, the “condition 2” is a group “cloudy”, and the “condition 3” is a group “rainy”. In the figure, the abscissa axis indicates the condition, and the ordinate axis indicates the number of images” [0062-0063]. See Figure 3 depicting a graph of various conditions and their corresponding number of images.
Suzuki discloses images IMG2 can capture a vehicle speed parameter (‘statistical parameter’), “by optical flow processing of the image IMG2, the image processing system can estimate vehicle speed or the like. For the determination of the parameter, sensor data or the like may be used. For example, the vehicle speed may be determined based on data of a speed sensor” [0057].),
wherein in the hierarchically arranged conditioned data each bin or cluster of a group of sub-group is connected to one or more bins or clusters of the next lower level sub-group (Chauvin discloses Figure 2 (reproduced above) on P. 754 depicting a dendrogram for clustering (‘sorting’) vehicle variables into clusters (‘groups’) and sub-clusters (‘sub-groups’). The larger clusters on top of the dendrogram represent higher levels, while the sub-clusters represent lower levels. Levels are represented by splitting a cluster into sub-clusters.).
With respect to claim 5, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches:
the computer program product according to claim 4, wherein the machine is a vehicle (Suzuki discloses “the camera CM that is an exemplary image pickup device is equipped in a vehicle CA. The camera CM shoots the periphery of the vehicle CA and generates an image. For example, as illustrated, the camera CM picks up an image of a forward sight of the vehicle CA. Next, the image generated by the camera CM is acquired by an image acquiring device IM” [0037]. See Figure 1 depicting a vehicle being driven with a camera and image acquiring device.)
and the received data includes data … driven by the vehicle that is driven by a human or robot driver (See Figure 1 depicting a vehicle being driven with a camera and image acquiring device.),
and the statistical parameters include at least one of speed, yaw rate or accelerations (Suzuki discloses a speed parameter, “the image processing system can estimate vehicle speed or the like. For the determination of the parameter, sensor data or the like may be used. For example, the vehicle speed may be determined based on data of a speed sensor” [0057].).
However, Suzuki does not teach receiving a plurality of trajectories, which is taught by Gupta:
and the received data includes data of a plurality of trajectories driven by the vehicle that is driven by a human or robot driver (Gupta discloses “the vehicle is shown moving in a straight line so as to enable the central vanishing point to be determined. However, when the vehicle turns as a result of a change in its steering angle, the motion of the vehicle can be approximated over relatively short distances (approximately 0.5 to 2 seconds of travel time, depending of vehicle speed) as a straight motion at an angle with respect to the ground Y-axis. Repeating the foregoing process of extracting and tracking the trajectories of feature points for various steering angle ranges as the vehicle moves will enable other vanishing points to be determined, hence enabling the determination of the vanishing line” [0075].).
Gupta teaches extracting and tracking trajectories by moving vehicles is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the trajectory data disclosed by Gupta because trajectories reveal insights about driver behavior and traffic congestion. By analyzing trajectory data, more information can be learned about driver behavior and traffic congestion, thereby providing data that can be useful to make accurate predictions in autonomous driving.
Furthermore, the combination of Suzuki in view of Gupta does not teach contextual parameters including turns and related sub-parameters, which is taught by Xu:
wherein the contextual parameters include at least one of a left turn, right turn, straight road, complex turn, obstacles, free cruising, lane changing, obstacle following or overtaking (Xu discloses “examples of the driving parameters can include braking, accelerating, idle speeding, reverse driving, driving straight, left turn or right turn, U-turn, lane changing, and parking driving” [0062]. Xu further discloses “once the value ranges for each driving parameter are determined, the training controller 309 can extract data from each range for that driving parameter to create a number of feature scenarios, each of which represents a value for a driving parameter, or a combination of values for multiple driving parameters” [0046].).
Xu teaches collecting data for turn and lane changing driving parameters is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the driving parameters disclosed by Xu to predict complex driving behavior. By analyzing multiple turn and lane changing driving parameters, underlying relationships between driving parameters can be learned which can be used to accurately predict safe driving behavior in autonomous driving systems.
With respect to claim 9, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches:
the computer program product according to claim 1, wherein the balancing of the conditioned data is performed level-by-level from a highest level group to a lowest level sub-group (Wu discloses balanced subclasses (‘sub-groups’) are produced from large classes (‘highest level group’), “Specifically, for a data set with an imbalanced class distribution, we perform clustering within each large class and produce sub-classes with relatively balanced sizes … Since the clustering is conducted independently within each class but not across the entire data set, we call it local clustering … By exploiting local clustering within large classes, we can decompose the complex concepts, e.g., nonlinear-separable concepts for linear classifiers, into relatively simple ones, e.g., linearly separable concepts. Another effect of local clustering is to produce subclasses with relatively uniform sizes. In addition, for data sets with highly skewed class distributions, we further integrate the over-sampling technique into the COG scheme and propose the COG with over-sampling technique … COG has the ability to divide imbalanced classes into relatively balanced and small sub-classes” (P. 814-815, Sec. 1). See Figure 2 on P. 816 depicting creating balanced subgroups using the COG (Classification using Local Clustering) procedure.).
Wu teaches creating balanced subclasses (‘lowest level sub-groups’) from the local clustering of large classes (‘high level groups’) is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the balancing method of Wu to balance hierarchical data in a top-to-bottom approach. By balancing hierarchical data from top-to-bottom, it can be ensured that high-level minority classes are equally represented in a dataset since they are balanced first and their information is preserved as the balancing process continues down the hierarchy to more specific subclasses, thus leading to an accurately represented and balanced dataset.
With respect to claim 12, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches:
the computer program product of claim 1 installed on a distributed computing resource (Suzuki discloses “the image acquiring device IM includes communication parts such as an antenna and a processing integrated circuit (processing IC), and sends the image to an external device such as the server SR, through a network NW” [0038]. Suzuki further discloses “the image processing system acquires second images on the server SR side. The second images are images previously input, images periodically sent from the vehicle CA, … the image processing system performs grouping of the images IMG2 and analyzes a balance, on the server SR side” [0054-055]. See Figure 1 depicting an image processing system configured to send images to a server (‘distributed computing resource’) for processing.).
With respect to claim 13, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches:
an artificial intelligence training control device which at least has a memory unit with the computer program product according to claim 1 stored therein (Suzuki discloses “car navigation device is equipped in the vehicle CA, the car navigation device guides a driver who operates the vehicle CA, through a voice, an image, a combination thereof or the like, such that the vehicle CA turns right at the intersection CR … it is desirable for the car navigation device to perform the guide of the route using a signboard LM placed near the intersection CR as a landmark, as illustrated. In such a case, in the vehicle CA, an image of a forward sight of the vehicle CA is picked up by the image pickup device, and image recognition of the signboard LM on the picked-up image is performed. For such an image recognition, for example, it is desirable to perform machine learning based on the image containing the signboard LM” [0075].
Suzuki discloses “the image processing system determines whether the balance of images to be used for the machine learning is good. … when the image processing system determines that the balance of the images to be used for the machine learning is good (YES in step SB0701), the image processing system proceeds to step SB0702 … In step SB0702, the image processing system performs learning of signboards, or the like. Specifically, in the case of using the signboard LM for the guide as shown in FIG. 4, the image processing system performs the learning using the image containing the signboard LM. Thereby, the image processing system can perform the image recognition of the signboard LM on the image” [0080-0082]. See Figure 1 depicting a image processing system that includes a storage device (memory).),
an input interface (Suzuki discloses an input interface as an image acquiring device, “the image acquiring device IM includes an arithmetic device such as an electronic circuit, an electronic control unit (ECU) or a central processing unit (CPU), and a control device. The image acquiring device IM further includes an auxiliary storage device such as a hard disk, and stores the image acquired from the camera CM” [0038].),
an output interface (Suzuki discloses an output interface as an analyzing unit “the analyzing unit ISF6 performs an analyzing step of analyzing the balance among the plurality of groups that is the balance of the number of the second images that belong to the plurality of groups, and outputting an analysis result ANS. For example, the analyzing unit ISF6 is realized by the CPU SH1” [0122]. See Figure 11 depicting an analyzing unit outputting an analysis result in an image processing system.)
and a display unit (Suzuki discloses “The output device SH4 is a display or the like, and outputs a processing result or the like to the user” [0044].),
wherein the input interface configured to receive one or more data sets from a data source being connected to the input interface via a wired connection or a wireless connection (Suzuki discloses an image acquiring device (‘input interface’) receives images (‘data sets’) from a camera (‘data source’), “image acquiring device IM further includes an auxiliary storage device such as a hard disk, and stores the image acquired from the camera CM. Furthermore, the image acquiring device IM includes communication parts such as an antenna and a processing integrated circuit (processing IC), and sends the image to an external device such as the server SR, through a network NW” [0038]. See Figure 1 depicting a connected camera and image acquiring device.),
the output interface configured to output the balanced data (“the analyzing unit ISF6 performs an analyzing step of analyzing the balance among the plurality of groups that is the balance of the number of the second images that belong to the plurality of groups, and outputting an analysis result ANS” [0122].),
the display unit configured to display at least one of the one or more data sets, the conditioned data or the balanced data to a user (Suzuki discloses “The output device SH4 is a display or the like, and outputs a processing result or the like to the user” [0044]. Suzuki further discloses “FIG. 13 is a diagram showing an exemplary processing result in an image processing system according to an embodiment of the disclosure. The left side of FIG. 13 is a diagram showing an analysis result ANS that is the same balance as that in FIG. 3. When images IMG3 are sent, images satisfying the “condition 2” and images satisfying the “condition 3” are added … the image processing system IS roughly equalizes the respective image numbers for the conditions, as shown in the right side of FIG. 13, for example, and thereby, can improve the balance” [0139-0140]. See Fig. 13 depicting unbalanced condition groups and balanced condition groups.).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu, further in view of Ma (US 9244790 B1).
With respect to claim 6, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches the computer program product according to claim 5. However, the combination does not teach “wherein, if a number of clusters and bins per group or sub-group is predefined in a database, the number of clusters is equal to the number of sub-parameters of the contextual parameter associated with the clusters and depending on the number of bins associated with a statistical parameter, the range of values of the statistical parameter is evenly distributed over the bins”, which is taught by Ma:
wherein, if a number of clusters and bins per group or sub-group is predefined in a database, the number of clusters is equal to the number of sub-parameters of the contextual parameter associated with the clusters (Ma discloses “a quantile distribution graph for a set of known working disks 1102 for a particular diagnostic parameter (e.g., RAS) have been created … the Y axis represents the values of the corresponding diagnostic parameter and the X axis represents the distribution of the values of the diagnostic parameter. The values of the diagnostic parameter may be sorted and evenly distributed in a predetermined number of intervals, which may be in percentiles or deciles” (Col. 15, line 63 to Col. 15, line 5).),
and depending on the number of bins associated with a statistical parameter, the range of values of the statistical parameter is evenly distributed over the bins (Ma discloses deciles (‘bins’) are evenly distributed, “In this example, the values are evenly distributed in 10 deciles. A decile represents any of the nine values that divide the sorted data into ten equal parts, so that each part represents 1/10 of the sample or population” (Col. 15, lines 5-8).).
Ma teaches evenly distributing values based on a predetermined number of intervals (‘bins’) is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the technique disclosed by Ma to create consistent bins. By using a predetermined number of intervals, data from different datasets can be distributed consistently therefore ensuring that different datasets are processed in a consistent manner which leads to fair comparisons and reliable results.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu, further in view of Hu et al. (“A Novel Decision-Tree Method for Structured Continuous-Label Classification”), hereinafter Hu.
With respect to claim 7, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches the computer program product according to claim 1, however the combination does not teach a predefined hierarchical structure of groups and sub-groups, which is taught by Hu:
wherein a hierarchical structure of groups and sub-groups as well as bins and clusters of the groups and sub-groups is predefined (Hu discloses Figure 1 depicting a predefined hierarchy tree (‘hierarchical structure’) consisting of predefined ranges (‘bins’) and multiple levels (‘sub-groups’).
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Hu discloses “the goal is to classify data into classes that are a set of predefined ranges and can be organized in a hierarchy. In the hierarchy, the ranges at the lower levels are more specific and inherently more difficult to predict, whereas the ranges at the upper levels are less specific and inherently easier to predict” (P. 1734, Abstract).
Hu further discloses “a predefined hierarchy tree comprises a set of predefined ranges of expenditures that can be organized in a hierarchy. Each node in a hierarchy tree can be treated as a class label. Because each label is continuous in the data and may belong to multiple class labels, we can further define a label as a hierarchical continuous label. In this example, constructing DTs with hierarchical continuous labels involves building a DT from the data in Table I and simultaneously selecting the most appropriate class label according to the label distribution in the predefined hierarchy tree in Fig. 1” (P. 1735, Sec. 1, First Paragraph).) or is editable by a user, wherein in the latter case the user is at least prompted to input the number of groups and sub-groups as well as the number of bins or clusters per group or sub-group.
Hu teaches a predefined hierarchy tree consisting of predefined ranges (‘bins’) is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the predefined hierarchy tree of Hu to group continuous data into ranges. By grouping continuous data into predefined ranges, data can be simplified into few, discrete categories which allows for a clearer understanding of patterns and trends.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu, further in view of Rayhan et al. (“CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification”), hereinafter Rayhan.
With respect to claim 8, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches:
the computer program product according to claim 1 wherein an imbalance is determined by comparing bins or clusters of at least one group or sub-group with each other for finding the bin or cluster with the lowest number of data points in the group or sub-group (Suzuki discloses Figure 14 (reproduced below) depicting the calculated differences (DF1 and DF2) between the number of images belonging to each condition group (‘cluster’).
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Suzuki discloses condition groups are formed from the weather parameter group, “the image processing system sets images IMG2 having an identical parameter, to one group. Then, the image processing system analyzes the balance about whether the number of images is roughly equal among groups … the image processing system groups the images IMG2 into three conditions: “condition 1”, “condition 2” and “condition 3”. Specifically, suppose that the parameter is “weather” [0062-0063].),
and the balancing is performed by … under-sampling of all other bins or clusters in the group or sub-group to the number of data points in the bin or cluster with the lowest number of data points (Suzuki discloses images (‘data points’) are deleted from each condition group (‘clusters’) to match the number of images of the smallest condition group, “for adjusting the balance, a method of deleting data is possible. Specifically, in the case of the same balance as the balance on the left side of FIG. 13, images of the “condition 1” and images of the “condition 2” are deleted such that the number of the images of the “condition 1” and the number of the images of the “condition 2” become equal to the number of the images of the “condition 3”. When the balance is adjusted in such a method, the deleted images are wasted” [0145]. See Figure 14 depicting balanced condition groups after deleting images.).
However, Suzuki does not teach balancing by performing random under-sampling, which is taught by Rayhan:
and the balancing is performed by a random under-sampling of all other bins or clusters in the group or sub-group to the number of data points in the bin or cluster with the lowest number of data points (Rayhan discloses “our proposed CUSBoost uses cluster-based sampling from the majority class. CUSBoost separates the majority and minority class instances from the original dataset and clusters the majority class instances into k clusters using k-means clustering algorithm. Here, the parameter k is determined by hyperparameter optimisation. After that, random under-sampling is conducted on each of the created clusters by randomly selecting 50% of the instances (but it can be tuned according to the domain problem or dataset) and removing the rest. As clustering is used before sampling, theoretically this algorithm will perform best when the dataset is highly cluster-able. These representative samples are joined with the unchanged minority class instances to achieve balanced datasets. Our algorithm’s strength lies in the fact that it considers examples from all subspaces of the majority class since k-means clustering puts each instance in some cluster” (P. 72, Sec. IV, First Paragraph).).
Rayhan teaches clustering a majority class (‘group’) and performing random under-sampling on each cluster is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the random under-sampling technique of Rayhan to randomly remove samples from clusters. Randomly removing samples ensures that the data removal process is unbiased and that the remaining dataset remains representative of the original population, thus creating a reliable balanced dataset.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu, further in view of Zhang et al. (US 20210208545 A1), hereinafter Zhang.
With respect to claim 10, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches: the computer program product according to claim 1,
wherein the raw data, the preprocessed data, the conditioned data and/or the balanced data is output to a user … (Suzuki discloses “The output device SH4 is a display or the like, and outputs a processing result or the like to the user” [0044]. Suzuki further discloses “FIG. 13 is a diagram showing an exemplary processing result in an image processing system according to an embodiment of the disclosure. The left side of FIG. 13 is a diagram showing an analysis result ANS that is the same balance as that in FIG. 3. When images IMG3 are sent, images satisfying the “condition 2” and images satisfying the “condition 3” are added … the image processing system IS roughly equalizes the respective image numbers for the conditions, as shown in the right side of FIG. 13, for example, and thereby, can improve the balance” [0139-0140]. See Fig. 13 depicting unbalanced condition groups and balanced condition groups.).
However, Suzuki does not teach outputting data to a user for validation, which is taught by Zhang:
wherein the raw data, the preprocessed data, the conditioned data and/or the balanced data is output to a user for validation of the conditioning and/or balancing operation (Zhang discloses “present the data processing results for user validation via a tag evaluation GUI, through which a user can review, modify, accept, and/or reject the data processing results, and (4) store the user validated data processing results” [0020].
Zhang further discloses “the tag evaluation service 440 may also be linked to a tag evaluation graphical user interface 450 configured to allow user to validate data processing by the tag evaluation service 440. For example, the tag evaluation graphical user interface 450 may be configured to present the valid windows of industrial process data selections, exclusions and/or modifications for user to review, modify, and/or validate. The tag evaluation graphical user interface 450 may be a readable and writeable screen or a webpage assessable by user. User may be allowed to validate the data processing by accepting, rejecting and/or modifying the data processing. Since it is possible that the tag evaluation service 440 may erroneously exclude valid data, it is best for the customer to be the final decider” [0086].).
Zhang teaches displaying data in a graphical user interface for a user to validate is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the user validation method of Zhang to allow users to confirm data processing results. By allowing users to validate data processing results, users can make adjustments to the data and ensure the results received are suitable for their needs.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu, further in view of Herman et al. (US 20160275144 A1), hereinafter Herman.
With respect to claim 11, the combination of Suzuki in view of Chauvin, further in view of Gupta and Gaspar, further in view of Wu and Xu teaches:
the computer program product according to claim 1, wherein the balanced data is output to a user (Suzuki discloses “The output device SH4 is a display or the like, and outputs a processing result or the like to the user” [0044]. Suzuki further discloses “FIG. 13 is a diagram showing an exemplary processing result in an image processing system according to an embodiment of the disclosure. The left side of FIG. 13 is a diagram showing an analysis result ANS that is the same balance as that in FIG. 3. When images IMG3 are sent, images satisfying the “condition 2” and images satisfying the “condition 3” are added … the image processing system IS roughly equalizes the respective image numbers for the conditions, as shown in the right side of FIG. 13, for example, and thereby, can improve the balance” [0139-0140]. See Fig. 13 depicting unbalanced condition groups and balanced condition groups.).
However, the combination does not teach outputting data arranged as concentric rings, which is taught by Herman:
wherein the … data is output to a user such that groups and sub-groups are arranged as concentric rings with different diameters (Herman discloses Figure 3 (reproduced below) depicting a sunburst chart with concentric circles (‘concentric rings’) with differently sized diameters in a graphical user interface.
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Herman discloses “sections 310 are graphically displayed in sunburst chart 303. Sections 310 represent the employee distribution for sections of organization 106. As depicted, the size of each section in sections 310 is based on the number of employees in the section … concentric circles 312 are graphically displayed in sunburst chart 303. Concentric circles 312 represent levels of hierarchy for sections 310. … Concentric circles 312 located toward the center of sunburst chart 303 represent higher levels of hierarchy for sections 310 than concentric circles 312 located toward the outside of sunburst chart 303” [0093-0094].),
and the bins or clusters are segments of the concentric rings (Herman discloses Figure 3 depicting a sunburst chart with concentric circles with differently sized sections (‘bins’).).
Herman teaches displaying a sunburst chart with hierarchical data and concentric rings is a known method in the art. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the method of Suzuki with the sunburst chart of Herman to display hierarchical data. By using a sunburst chart to display hierarchical data, the relative size of each category and subcategory can be quickly compared by a user, thus improving user interpretability of the data and helping users make informed data-driven decisions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 inquiry concerning this communication or earlier communications from the examiner should be directed to PEDRO J MORALES whose telephone number is (571)272-6106. The examiner can normally be reached 8:30 AM - 6:00 PM.
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/PEDRO J MORALES/Examiner, Art Unit 2124
/VINCENT GONZALES/Primary Examiner, Art Unit 2124