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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7/29/2025 and 12/17/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
Applicant’s amendment filed 9/30/2025 has been entered. In the amendment, claims 21, 28, 34 and 37 were amended, and claim 32 was cancelled and no claims were added. Claims 1-20 were previously cancelled in the preliminary amendment filed 09/12/2022. As such, claims 21-31 and 33-40 are pending.
The objections to the drawings and specification set forth in the previous office action
are withdrawn in view of the amendments to the specification and drawings.
The objections to the claims set forth in the previous office action are withdrawn in view of the amendments to the claims.
Response to Arguments
Applicant's arguments filed 9/30/2025 with respect to the rejections of claims 21-40 under 35 U.S.C. § 101, as being directed to an abstract idea in the previous office action have been fully considered, but are not persuasive.
Applicant’s argument that “Capturing further examples in a respective surrounding area when the quality assessment ascertained for the respective surrounding area is less than a predefined quality threshold value cannot be performed in the human mind” (Applicant’s Remarks p. 12), is unpersuasive. This “capturing” is interpreted as mere data observation, analysis of values and judgement of whether a particular quality threshold criterion is met. Nothing in the claim language prevents someone from performing such capturing of examples in the human mind, or by the aid of a pen and paper.
Applicant’s argument that “capturing further examples” can correct the insufficient “basis for a safety-critical application” (Applicant’s Remarks p. 12-13), explained in paragraphs [0103-0104], is unpersuasive. The alleged improvement cited in paragraph [0104] does not include sufficient details for a person having ordinary skill in the art to recognize the improvement for providing “a sufficient basis for a safety-critical application”. There is insufficient detail regarding how the capturing of more examples technically achieves a safety-critical application, and the steps for achieving a safety-critical application is not clear.
Applicant’s argument that “Paragraph 35… explains… an adversarial attack on the areas in which too few examples have been captured… can be reduced by capturing further examples as defined in claim 21” (Applicant’s Remarks p. 13), is unpersuasive. Applicant generally asserts that identifying areas in which too few examples have been captured allows for preventatively countering adversarial attacks. This is unpersuasive because the specification provides only a conclusory hypothesis that higher data density inherently creates model robustness, and does not provide a technical explanation or algorithm for the logic required to automate the capturing of examples based on the quality assessment. Moreover, Applicant’s argument is unpersuasive because the relied upon embodiments of paragraph 35 of reducing adversarial attacks are not reflected or recited in the claims.
Applicant’s argument that “Paragraph 17… explains the division of the set of examples into multiple surrounding areas brings… the advantages that generally result from the divide-and-rule method approach known from information technology… and this enables the work entailed in assessing the overall set of examples to be considerably reduced”, is unpersuasive. The specification fails to disclose a specific technical implementation that reduces computer resource consumption. While paragraph [0017] asserts a general advantage, it does so in a conclusory manner without providing the algorithmic steps necessary for the computing unit to optimize processing.
Applicant’s remarks with respect to the rejection of claims 21-23, 25-26, and 39-40 under U.S.C 102 in the previous office action, in light of the amendments to independent claim 21, have been fully considered and are persuasive. However, as detailed below, these claims are now rejected under 35 U.S.C 103 in view of a combination of references that were cited in the previous Non-Final Office Action. In particular, as detailed below, the combination of Jeffery, Hawkins and Grichnik, teaches all of the limitations of amended independent claim 21. Claims 22-31, 35-37 and 39-40, which depend either directly or indirectly from independent claim 21, are also rejected under the combination of Jeffery, Hawkins and Grichnik. Claim 33 is rejected under the combination of Jeffery, Hawkins and Grichnik and further in view of Schupp-Omid. Amended Claim 34 is rejected using the combination of Jeffery, Hawkins and Grichnik and further in view of non-patent literature Waschulzik. Finally, Claim 38 is rejected under the combination of Jeffery, Hawkins and Grichnik and further under Leino.
Furthermore, due to the rejection of independent claim 21 under the combination of references as applied to herein, Applicant’s arguments with respect to the remaining dependent claims are rendered moot.
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 21-40 are rejected under 35 U.S.C. 101 because the claims are directed towards an abstract idea without significantly more.
Regarding Independent Claim 21:
Step 1: The claim is directed to a method, corresponding to a process, which is one of the statutory categories.
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
creating and training the example-based system based on collected examples forming a set of examples;
and ascertaining a quality assessment representing a coverage of the input space by examples in the set of examples based on a distribution of the input values in the input space;
dividing the input space hierarchically based on the quality assessment;
and capturing further examples in a respective surrounding area when the quality assessment ascertained for the respective surrounding area is less than a predefined quality threshold value,
Regarding the “creating and training the example-based system”, such creating and training of the example-based system using a set of examples, can be associated with the evaluation and management of data. Additionally, the “ascertaining a quality assessment” using the set of examples, can be associated with the evaluation/judgement/opinion based on an observed set of examples. Given a sufficiently small dataset of examples for the creation, training and ascertainment of a quality assessment, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “dividing the input space hierarchically based on the quality assessment”, such a decision can be associated with evaluating data based on a criteria and judging where to categorize such values in a hierarchical manner. Given a sufficiently small dataset of input data points for the input space, and data for the ascertainment of a quality assessment, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “capturing further examples in a respective surrounding area when the quality assessment ascertained for the respective surrounding area is greater than a predefined quality threshold value”, this capturing can be associated with the mental process of evaluation/judgement/opinion to identify and capture examples based on a quality threshold value. Given a sufficiently small example dataset for an input space, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element does not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the example data used to perform the creating, training and ascertaining of the example-based system. Therefore, the additional element does not integrate the abstract ideas into a practical application.
including an input value situated in an input space in a respective example in the set of examples;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation amounts to no more than using generic computer components (i.e., the generically-recited “example-based system”) to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 21 is not patent eligible.
Regarding Claim 22:
Step 1: The claim is directed to the method of claim 21.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 21.
The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
and ascertaining a local quality assessment for the surrounding area as a quality assessment.
Regarding the “ascertaining a local quality assessment”, the task of ascertainment can be associated with the mental process of discernment of a particular set of data. Given a sufficiently small dataset of examples for the ascertainment of a local quality assessment, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements do not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the example data used to perform the creating, training and ascertaining of the example-based system. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
which further comprises ascertaining the quality assessment by: distributing representatives in the input space;
assigning a number of examples in the set of examples to a respective representative;
placing the examples assigned to the representative in a surrounding area of the input space surrounding the representative;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 22 is not patent eligible.
Regarding Claim 23:
Step 1: The claim is directed to the method of claim 22.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 22.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements do not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the data used to ascertain a quality assessment. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
which further comprises providing the quality assessment with a statistical average ascertained based on at least one of:
the set of examples or,
the examples assigned to a respective representative
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 23 is not patent eligible.
Regarding Claim 24:
Step 1: The claim is directed to the method of claim 23.
Step 2A, Prong 1: The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites mathematical concepts in combination with a mental process that can be performed in the human mind or with pen and paper (including an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
which further comprises creating a histogram of the number of examples assigned to a representative as a statistical average
Regarding the “creating a histogram”, this task can be associated with the mental process of managing data combined with the mathematical process of statistical calculations used to comprise a histogram. Given a sufficiently small dataset of examples, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 25:
Step 1: The claim is directed to a the method of claim 22.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 22.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites a mental process of ascertaining (evaluation/judgement/opinion) based on a mathematical concept, which can be performed in the human mind or with pen and paper (including an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
which further comprises ascertaining a statistical measurement or at least one of an average value, a median, a minimum or quantiles of the number of examples assigned to a representative, as a statistical average
In particular the “ascertaining a statistical measurement” involves the mathematical concept of a calculation or mathematical function to determine variables or values associated with statistics. Given a sufficiently small dataset for the ascertaining, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 26:
Step 1: The claim is directed to the method of claim 22.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 22.
The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
which further comprises ascertaining adjacent surrounding areas in the input space,
Regarding the “ascertaining adjacent surrounding areas”, this ascertaining can be associated with the mental process of the discernment and observation of values in an input space. Given a sufficiently small dataset for an input space, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element does not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the example data used to perform the creating, training and ascertaining of the example-based system. Therefore, the additional element does not integrate the abstract ideas into a practical application.
and assigning a number of examples fulfilling a predefined quality criterion of the quality assessment to a respective representative of the adjacent surrounding areas
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation amounts to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 26 is not patent eligible.
Regarding Claim 27:
Step 1: The claim is directed to the method of claim 26.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 26.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
which further comprises ascertaining a relationship area inside the input space,
forming the relationship area of adjacent surrounding areas,
Regarding the “ascertaining a relationship area”, this ascertaining can be associated with the mental process of the discernment and observation of values in an input space. Given a sufficiently small dataset for an input space, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “forming the relationship area”, this forming can be associated with the mental process of discernment and data manipulation of an input space. Given a sufficiently small dataset for an input space, nothing in the claim prohibits this process from being
performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element does not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the example data used to perform the creating, training and ascertaining of the example-based system. Therefore, the additional element does not integrate the abstract ideas into a practical application.
and assigning a number of examples fulfilling a predefined quality criterion of the quality assessment to each of the representatives of the number of examples
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation amounts to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 27 is not patent eligible.
Regarding Claim 28:
Step 1: The claim is directed to the method of claim 22.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 22.
The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
removing examples from a respective surrounding area when the quality assessment ascertained for the respective surrounding area is greater than a predefined quality threshold value
Regarding the “removing examples from a respective surrounding area when the quality assessment ascertained for the respective surrounding area is greater than a predefined quality threshold value”, this removing can be associated with the mental process of evaluation/judgement/opinion to identify and capture examples based on a quality threshold value. Given a sufficiently small example dataset for an input space, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 29:
Step 1: The claim is directed to the method of claim 22.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 22.
The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
ascertaining a local complexity assessment for the respective surrounding area representing a complexity of a task of the example-based system defined by the examples in the surrounding area;
and determining the local complexity assessment by a location of the examples in the surrounding area relative to one another in the input space and the output space
Regarding the “ascertaining a local complexity area”, this ascertaining can be associated with the mental process of the discernment and observation of values in an input space. Given a sufficiently small dataset for an input space, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “determining the local complexity assessment”, this determining can be associated with the mental process of observation of data in an input space. Given a sufficiently small dataset for an input space, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element adds insignificant extra-solution activities (necessary data gathering and data storage) to the judicial exception [see MPEP 2106.05(g)].
including an output value situated in an output space in the respective example;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The following additional element is directed to storing information in memory. The courts (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) have recognized storing information in memory 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 to the judicial exception [see MPEP 2106.05(d) IV.].
including an output value situated in an output space in the respective example;
Regarding Claim 30:
Step 1: The claim is directed to the method of claim 29.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 29.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites a mental process of ascertaining (evaluation/judgement/opinion) based on a mathematical concept, which be performed in the human mind or with pen and paper (including an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
ascertaining an aggregated complexity assessment by aggregation of the local complexity assessments
Regarding the “ascertaining an aggregated complexity assessment by aggregation”, this ascertaining can be associated with the mathematical concept of calculating a sum or aggregation of values. Given a sufficiently small dataset of complexity assessment values, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 31:
Step 1: The claim is directed to the method of claim 30.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 30.
The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
identifying surrounding areas having a complexity assessment undershooting a predefined complexity threshold value,
Regarding the “identifying surrounding areas having a complexity assessment undershooting a predefined complexity threshold value”, this identifying can be associated with the mental process of observation and discernment of data in an input space. Given a sufficiently small dataset for an input space, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element does not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the data used for identifying . Therefore, the additional element does not integrate the abstract ideas into a practical application.
based on the aggregated complexity assessment;
The following additional element is 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)] and therefore fails to integrate the judicial exception into a practical application.
and implementing the task of the example-based system in the ascertained surrounding areas by an algorithmic solution
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Further, limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 31 is not patent eligible.
Regarding Claim 33:
Step 1: The claim is directed to the method of claim 29.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 29.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites a mental process of ascertaining (evaluation/judgement/opinion) based on a mathematical concept, which can be performed in the human mind or with pen and paper (including an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
which further comprises ascertaining a complexity distribution by using a histogram representation of the complexity assessment of a plurality of nearest neighbors of an example in the input space
Regarding the “ascertaining a complexity distribution”, this ascertaining can be associated with the mathematical process calculating a statistical distribution of values in an input space. Given a sufficiently small dataset of examples for an input space, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
Regarding Claim 34:
Step 1: The claim is directed to the method of claim 29.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 29.
The following limitations are directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites a mental process of ascertaining (evaluation/judgement/opinion) based on a mathematical concept, which can be performed in the human mind or with pen and paper (including an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
providing the complexity assessment as an integrated quality indicator QI2, defining the quality indicator in accordance with:
PNG
media_image1.png
37
287
media_image1.png
Greyscale
wherein
PNG
media_image2.png
41
232
media_image2.png
Greyscale
is a normalized spacing of the represented inputs, and
PNG
media_image3.png
54
223
media_image3.png
Greyscale
is a normalized spacing of the represented outputs,
Regarding the functions of an integrated quality indicator with functions of normalized spacing of inputs and normalized spacing of outputs, these functions are mathematical formulations using arithmetic operations such as summation, exponentiation, division subtraction, etc. Given a sufficiently small dataset to perform the calculations, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements do not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the mathematical formulas used to perform the creating, training and ascertaining of the example-based system. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
x is a pair (x1, x2,) formed of two examples x1 and x2. x1 and x2 are examples from the set of examples P,
P={p1,p1, ..., p|P|} is a set of elements in a multiset BAG P,
and |P| is a number of elements in the multiset BAG P,
dRE(x) is a spacing in the input space, dRA(x) is a spacing in the output space
and y is a pair of examples formed of two examples from the multiset BAG P,
dRE(y) is a spacing in an input space, and dRA(y) is a spacing in an output space
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 34 is not patent eligible.
Regarding Claim 35:
Step 1: The claim is directed to the method of claim 21.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 21.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use” [see MPEP 2106.05(h)]. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
which further comprises providing the example-based system for use in a safety-oriented function,
the safety-oriented function includes object recognition based on image recognition,
and the object is recognized by using the example-based system
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 35 is not patent eligible.
Regarding Claim 36:
Step 1: The claim is directed to the method of claim 35.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 35.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use” [see MPEP 2106.05(h)]. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
which further comprises using the object recognition in automated operation of at least one of a vehicle, a track- bound vehicle, a motor vehicle, an aircraft, a water vehicle or a space vehicle
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation amounts to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 36 is not patent eligible.
Regarding Claim 37:
Step 1: The claim is directed to the method of claim 21.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 21.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use” [see MPEP 2106.05(h)]. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
providing the example-based system for use in a safety-oriented function,
and using the safety-oriented function to represent a classification based on at least one of
sensor data of organisms, safe control of industrial plants, classification of chemical substances, signatures of vehicles or control in an area of industrial automation
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 37 is not patent eligible.
Regarding Claim 38:
Step 1: The claim is directed to the method of claim 21.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 21.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements do not meaningfully limit the judicial exception [see MPEP 2106.05(e)]. The claim simply recites additional information regarding the characteristics of the example-based system. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
providing the example-based system with: a system with supervised learning,
an artificial neural network with one or more layers of neurons not being input neurons or output neurons
and being trained with backpropagation,
a convolutional neural network, or a single-shot multibox detector network
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 38 is not patent eligible.
Regarding Claim 39:
Step 1: The claim is directed to a computer program stored on a non-transitory computer-readable medium, corresponding to an article of manufacture, which is one of the statutory categories, implementing the method of claim 21, thus the analysis for patent eligibility of claim 21 is incorporated herein.
Step 2A, Prong 1: The claim recites “A non-transitory computer-readable medium, comprising instructions stored thereon that when executed by a computer cause the computer to carry out the method according to claim 21”
Regarding “the method according to claim 21”, please see the analysis of claim 21 above. The limitation in the preamble of claim 39 only adds an additional element to the abstract ideas of claim 21.
Regarding the method steps recited in claim 21, as detailed above, these steps cover mathematical relationships, mathematical formulas or equations, or mathematical calculations combined with a mental process of “creating and training the example-based system, and ascertaining a quality assessment representing a coverage of the input space” (i.e., observation of raw data and evaluation/judgment/opinion based on the observed data). Therefore, claim 40 is directed to an abstract idea - mathematical concepts combined with a mental process (i.e., observation and evaluation/judgment/opinion).
Accordingly, claim 39 recites an abstract idea.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element is 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)] and therefore fails to integrate the judicial exception into a practical application.
A computer program stored on a non-transitory computer-readable medium, comprising instructions stored thereon that when executed by a computer cause the computer to carry out the method according to claim 21
In particular, the claim only recites these additional elements, which are recited at a high level of generality as mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation amounts to no more than using generic computer components to implement the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, claim 39 is not patent eligible.
Regarding Claim 40:
Step 1: The claim is directed to a non-transitory computer-readable medium, corresponding to an article of manufacture, which is one of the statutory categories.
Step 2A, Prong 1: The claim recites “A computer program stored on a non-transitory computer-readable medium, comprising instructions stored thereon that when executed by a computer cause the computer to carry out the method according to claim 21”
Regarding “the method according to claim 21”, please see the analysis of claim 21 above. The limitation in the preamble of claim 40 only adds an additional element to the abstract ideas of claim 21.
Regarding the method steps recited in claim 21, as detailed above, these steps cover mathematical relationships, mathematical formulas or equations, or mathematical calculations combined with a mental process of “creating and training the example-based system, and ascertaining a quality assessment representing a coverage of the input space” (i.e., observation of raw data and evaluation/judgment/opinion based on the observed data). Therefore, claim 40 is directed to an abstract idea - mathematical concepts combined with a mental process (i.e., observation and evaluation/judgment/opinion).
Accordingly, claim 40 recites an abstract idea.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are 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)] and therefore fails to integrate the judicial exception into a practical application.
A non-transitory computer-readable medium, comprising instructions stored thereon that when executed by a computer cause the computer to carry out the method according to claim 21
In particular, the claim only recites these additional elements, which are recited at a high level of generality as mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation amounts to no more than using generic computer components to implement the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, claim 40 is not patent eligible.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21-31, 35-37, and 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over Jeffery (US 10657457 B1; hereinafter Jeffery) in view in view of Hawkins (US 11651277 B2; hereinafter Hawkins) and further in view of Grichnik (US 20090300052 A1; hereinafter Grichnik).
Regarding Independent Claim 21, Jeffery teaches A quality assurance method for an example-based system, the method comprising (see, e.g., Col. 13 lines 29-37: “quality analysis performed by the quality assurance component 160 may include determining the effect of data quality fluctuations on the performance of the predictive model 130 generated from the training data 120, identifying input data samples that currently best represent examples of the modeled task, and modifying the training data 120 to enable the model to be improved incrementally by being re-trained with a currently optimal set of training data examples"):
creating and training the example-based system based on collected examples forming a set of examples (see, e.g., Col. 5 lines 30-35: “an adaptive oracle-trained learning framework 100 comprises a predictive model 130 (e.g., a classifier) that has been generated using machine learning based on a set of training data 120” and Col. 10 lines 4-8: “in response to receiving a labeled data instance from the oracle, the system stores 530 the labeled data instance in a labeled data reservoir 155, from which new training data instances may be selected for updates to training data 120”);
including an input value situated in an input space in a respective example in the set of examples (see, e.g., Cols. 6-7 lines 60-67, 1-6: “In some embodiments, the collected data instances are multi-dimensional data… the feature analysis includes clustering the collected data instances into homogeneous groups across multiple dimensions using an unsupervised learning approach that is dependent on the distribution of the input data as described... In some embodiments, the clustered data instances are sampled uniformly across the different homogeneous groups, and the sampled data instances are sent to an oracle 150 (as shown in FIG. 1) for labeling”, Col 7. lines 16-17: “the system receives an input multi-dimensional data instance having k attributes 405” and Col. 7 lines 23-27: “an input multi-dimensional data instance having k attributes is represented by a feature vector x 305 having k elements (x.sub.1, x.sub.2, . . . , x.sub.k), where each element in feature vector x represents the value of a corresponding attribute”);
and ascertaining a quality assessment representing a coverage of the input space by examples in the set of examples based on a distribution of the input values in the input space (see, e.g., Col. 9 lines 26-30: “accuracy assessment is implemented by a quality assurance component 160 to generate an aggregate/moving window estimate of accuracy. In some embodiments, the quality assurance component 160 is configured as a dynamic data quality assessment system described”, Col. 12 lines 13-15: “In some embodiments, the system performs a new distribution-based feature analysis of the training data 620 in response to the addition of newly labeled data instances” and Col. 15 lines 20-26: “Referring to the classifier example, data instances that provide maximum information about the classification task are data instances that result in classifier judgments that are closer to the decision boundary. In some embodiments, these data instances may be recognized automatically because their judgments are associated with lower confidence scores” [i.e., a classifier judgement (quality assessment) that is closer to the decision boundary represents a coverage of the input space]).
Although Jeffery substantially teaches the claimed invention, Jeffery is not relied upon to explicitly teach dividing the input space hierarchically based on the quality assessment;
In the same field, analogous art Hawkins teaches dividing the input space hierarchically based on the quality assessment (see, e.g., Hawkins Col. 7 lines 17-23: “HTM system 200 is hierarchically structured so that the processing nodes cover a larger input space as the level ascends… Nodes 210A,… 230 are hierarchically connected in a tree-like structure such that each processing node has several children nodes” and Col. 30 lines 49-64: “FIG. 21A is a diagram illustrating an image for recognition divided into 4×4 blocks of input space A1 through D4. The lowest level of a HTM system has spatial pooler 320…The CD is mapped to learn co-occurrences such as horizontal lines, vertical lines or diagonal lines by retaining mapping to sub-sampled pixels that contribute to higher match scores but removing mapping to sub-sampled pixels that do not contribute to higher match scores”);
Jeffery and Hawkins are analogous art because they are both directed to machine learning models computing spatial relationships (see, e.g., Jeffery, Col. 8 lines 59-67, Hawkins, Col. 5, lines 50-67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery to incorporate the teachings of Hawkins to divide the input space hierarchically based on a quality assessment. Doing so would have allowed Jeffery to use Hawkins' method in order to “build models of very high dimensional input spaces using reasonable amounts of memory and processing capacity”, as suggested by Hawkins (see, e.g., Hawkins, Col. 1 lines 40-41).
Although Jeffery in view of Hawkins substantially teaches the claimed invention, Jeffery in view of Hawkins is not relied upon to explicitly teach capturing further examples in a respective surrounding area when the quality assessment ascertained for the respective surrounding area is less than a predefined quality threshold value.
In the same field, analogous art Grichnik teaches capturing further examples in a respective surrounding area when the quality assessment ascertained for the respective surrounding area is less than a predefined quality threshold value (see, e.g., Grichnik paragraphs [0031-0034]: “If the amount of data falls below a designated level, this could be considered as a data coverage condition that may trigger modification to the data coverage… a hyper-quadrant density inspection process 300 may be used to evaluate the coverage of the data records in the modeling space, as shown in FIG. 3… For example, a data coverage condition may be detected by hyper-quadrant density inspection process 300 if the statistical difference is high… the list of choices may include having the user provide additional data records for sparse regions in the modeling space…The list of choices may also include authorizing processor 102 to create additional data records for sparse regions in the modeling space… If choice No. 1 is selected by the user, processor 102 may receive additional data records for sparse regions from the user” and paragraph [0053]: “processor 102 may mark sparse hyper-quadrants with data densities below a density threshold” [i.e., the data density correlates to the coverage of the data records in the modeling space, where more dense coverage has a higher quality assessment, and sparser regions have a lower quality assessment, requiring more data records]).
Jeffery, Hawkins and Grichnik are analogous art because they are each directed to machine learning models computing spatial relationships (see, e.g., Jeffery, Col. 8 lines 59-67, Hawkins, Col. 5, lines 50-67, and Grichnik, paragraph [0040-0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery to incorporate the teachings of Grichnik to capture examples from a respective surrounding area in an input space, when quality assessments in the area are less than or greater than a predefined quality threshold value. Doing so would have allowed Jeffery to use Grichnik's method in order to “evaluate and improve the coverage of the training data, before the data are used for the modeling process”, as suggested by Grichnik (see, e.g., Grichnik, paragraph [0005]).
Regarding claim 22, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 21. Jeffery further teaches which further comprises ascertaining the quality assessment by: distributing representatives in the input space (see, e.g., Col. 6 lines 63-67: “In some embodiments, the feature analysis includes clustering the collected data instances into homogeneous groups across multiple dimensions using an unsupervised learning approach that is dependent on the distribution of the input data” [i.e., homogeneous groups serve as representatives distributed in the input space] and Col. 12 lines 23-25: “FIG. 8 is a flow diagram of an example method 800 for dynamically updating a model core group of clusters along a single dimension k” [i.e., a model core group of clusters functions as a representative set of entities of the spatial input]);
assigning a number of examples in the set of examples to a respective representative (see, e.g., Col. 12 lines 37-43: “the system classifies 815 each of the objects represented in the new data stream 125 as respectively belonging to one of the clusters within X.sub.k. In some embodiments, an object is classified by determining, based on a k-means algorithm, C.sub.k, the nearest cluster to the object in the k.sup.th dimension. In embodiments, classifying an object includes adding that object to the cluster C.sub.k”);
placing the examples assigned to the representative in a surrounding area of the input space surrounding the representative (see, e.g., Col. 12 lines 39-43: “In some embodiments, an object is classified by determining, based on a k-means algorithm, C.sub.k, the nearest cluster to the object in the k.sup.th dimension. In embodiments, classifying an object includes adding that object to the cluster C.sub.k” [i.e., an object (example) is assigned to the nearest cluster to the object in a surrounding area in the k.sup.th dimension]);
and ascertaining a local quality assessment for the surrounding area as a quality assessment (see, e.g., Col. 11 lines 15-20: “In some embodiments, a quality assurance component 660 monitors the quality of the predictive model performance as well as the quality of the input data being processed. The processed data 665 and, in some embodiments, an associated judgment are output from the framework 600 if they are determined to satisfy a quality threshold” and Col.11 lines 5 -15: “the confidence value included with a classification judgment is a score representing the distance in decision space of the judgment from the task decision boundary…Classification judgments that are more certain are associated with higher confidence scores because those judgments are at greater distances in decision space from the task decision boundary” [i.e., a confidence level is associated with the it's distance in the decision space, or surrounding area as a classification judgement (quality assessment)]).
Regarding claim 23, as discussed above the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 22. Jeffery further teaches which further comprises providing the quality assessment with a statistical average ascertained based on at least one of: the set of examples or, the examples assigned to a respective representative (see, e.g., Col. 12 lines 37-42: “In embodiments, the system classifies 815 each of the objects represented in the new data stream 125 as respectively belonging to one of the clusters within X.sub.k. In some embodiments, an object is classified by determining, based on a k-means algorithm, C.sub.k, the nearest cluster to the object in the k.sup.th dimension” [i.e., the k-means algorithm generates statistical averages contribute to the quality assessment of each object representation for classification]).
Regarding claim 24, as discussed above, the combination of Jeffery, Hawkins and Grichnik discloses the method of claim 23.
Although, Jeffery in view of Hawkins substantially teaches the claimed invention, Jeffery in view of Hawkins is not relied upon to explicitly teach which further comprises creating a histogram of the number of examples assigned to a representative as a statistical average.
In the same field, analogous art Grichnik teaches which further comprises creating a histogram of the number of examples assigned to a representative as a statistical average (see, e.g., Grichnik paragraph [0044]: “At step 301, the modeling space of interest may be divided into a plurality of hyper-quadrants… a hyper-quadrant may be a region in the model space that corresponds to this combination set” [i.e., hyper quadrants function as representatives in the input space], paragraph [0047]: “At step 302, processor 102 may calculate data density in each hyper-quadrant. In one embodiment, processor 102 may count the data records or vectors in each hyper-quadrant. Based on the calculated data densities, a histogram may be generated (step 303). A histogram may be a graphical display of the data density distribution” and paragraph [0049]: “processor 102 may determine the statistical difference as the standard deviation of the data density distribution, and normalize it with the mean of the distribution” [i.e., the histogram displays a statistical representation of the data distribution of the hyper-quadrants (representatives) that functionally represents a statistical average of the number of data records or vectors (examples)]).
Jeffery, Hawkins and Grichnik are analogous art because they are each directed to machine learning models computing spatial relationships (see, e.g., Jeffery, Col. 8 lines 59-67, Hawkins, Col. 5, lines 50-67, and Grichnik, paragraph [0040-0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery in view of Hawkins to incorporate the teachings of Grichnik to create a histogram representing a statistical average of a number of example data points . Doing so would have allowed Jeffery in view of Hawkins to use Grichnik's method in order to “identify the probabilities of the training data elements belonging to identified classes”, as suggested by Grichnik (see, e.g., Grichnik, paragraph [0006]).
Regarding claim 25, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 22. Jeffery further teaches which further comprises ascertaining a statistical measurement or at least one of an average value, a median, a minimum or quantiles of the number of examples assigned to a representative, as a statistical average (see, e.g., Col. 12 lines 47-48: “FIG. 9 is a flow diagram of an example method 900 for dynamically updating a cluster along a single dimension k” and Col. 12 lines 61-65: “In embodiments, the system adds 910 the object to the closest cluster C.sub.k E S.sub.k for O.sup.i.sub.k, and, in response, updates 915 the properties of cluster C.sub.k. In embodiments, updating the properties includes calculating σ.sub.k, the standard deviation of the objects in cluster C.sub.k” [i.e., the calculation of a standard deviation involves the average number of examples (objects in the cluster) as a statistical average]).
Regarding claim 26, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 22. Jeffery further teaches which further comprises ascertaining adjacent surrounding areas in the input space, (see, e.g., Col. 12 lines 40-43: “an object is classified by determining, based on a k-means algorithm, C.sub.k, the nearest cluster to the object in the k.sup.th dimension” and Col. 13 lines 1-3: “updating cluster C.sub.k may include splitting cluster C.sub.k or merging cluster C.sub.k with another cluster within the core group of clusters” [i.e., merging clusters requires ascertaining the proximity of the surrounding areas of a cluster and its neighbors]).
Regarding claim 27, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 26.
Although Jeffery substantially teaches the claimed invention, Jeffery is not relied upon to explicitly teach which further comprises ascertaining a relationship area inside the input space,
forming the relationship area of adjacent surrounding areas,
and assigning a number of examples fulfilling a predefined quality criterion of the quality assessment to each of the representatives of the number of examples
Nevertheless, in the same field, analogous art Hawkins teaches which further comprises ascertaining a relationship area inside the input space, (see, e.g., Hawkins Col. 7 lines 12-19: “FIG. 2 is a diagram illustrating HTM system 200 having three levels L1, L2, L3, with level L1 being the lowest level, level L3 being the highest level, and level L2 being an intermediate level between levels L1 and L3. HTM system 200 is hierarchically structured so that the processing nodes cover a larger input space as the level ascends” [i.e., a larger input space (relationship area) is ascertained as the level ascends by the processing nodes]).
forming the relationship area of adjacent surrounding areas (see, e.g., Hawkins Col. 7 lines 19-23: “HTM system 200 is hierarchically structured so that the processing nodes cover a larger input space as the level ascends. Level L1 has nodes 210A, 210B, 210C and 210D; level L2 has nodes 220A and 220B; and level L3 has node 230. Nodes 210A, 210B, 210C, 210D, 220A, 220B, and 230 are hierarchically connected in a tree-like structure such that each processing node has several children nodes” and Col. 7 lines 39-40: “Further, a parent node may also receive partially overlapping bottom-up signals from multiple children nodes” [i.e., a larger relationship area is formed by combining the inputs from adjacent nodes with their input spaces (surrounding areas)]),
and assigning a number of examples fulfilling a predefined quality criterion of the quality assessment to each of the representatives of the number of examples (see, e.g., Hawkins Col. 2 lines 21-34: “The spatial pooler determines… generates the spatial pooler signal in sparse distributed representation to indicate which stored co-occurrence patterns closely match spatial patterns in the input signal… Each co-occurrence detector detects a spatial pattern in the input signal and produces a score representing how close the spatial pattern matches a stored co-occurrence pattern. Based on scores produced by the co-occurrence detectors, the spatial pooler selects co-occurrence detectors” [i.e., input spatial patterns (examples) are assigned to co-occurrence patterns (representatives) by selecting them based on a similarity score (predefined quality criterion)]).
Jeffery and Hawkins are analogous art because they are both directed to machine learning models computing spatial relationships (see, e.g., Jeffery, Col. 8 lines 59-67, Hawkins, Col. 5, lines 50-67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery to incorporate the teachings of Hawkins to ascertain a relationship area inside an input space, form the relationship area of the adjacent surrounding areas, and assign a number of examples that meet a predefined quality criterion to each representative of the examples. Doing so would have allowed Jeffery to use Hawkins' method in order to “identify similarity or commonality between spatial patterns by comparing the overlap in bits of vectors in sparse distributed representation”, as suggested by Hawkins (see, e.g., Hawkins, Col. 9 lines 50-52).
Regarding claim 28, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 22. Although Jeffery in view of Hawkins substantially teaches the claimed invention, Jeffery in view of Hawkins is not relied upon to explicitly teach which further comprises removing examples from a respective surrounding area when the quality assessment ascertained for the respective surrounding area is greater than a predefined quality threshold value
Nevertheless, in the same field, analogous art Grichnik teaches which further comprises removing examples from a respective surrounding area when the quality assessment ascertained for the respective surrounding area is greater than a predefined quality threshold value (see, e.g., Grichnik paragraph [0032]: “The list of choices may further include authorizing processor 102 to remove data records from dense regions in the modeling space” and paragraph [0035]: “If choice No. 2 is selected by the user, processor 102 may remove certain data records from dense regions (step 207). For example, in some cases, data records may be well over-collected or over-generated for the number of input variables available. As a result, the user may choose to have modeling system 100 automatically remove data records from the dense regions such that the overall data coverage is at a desired level” )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery to incorporate the teachings of Grichnik to remove examples from a respective surrounding area in an input space, when quality assessments in the area are less than or greater than a predefined quality threshold value. Doing so would have allowed Jeffery to use Grichnik's method in order to “evaluate and improve the coverage of the training data, before the data are used for the modeling process”, as suggested by Grichnik (see, e.g., Grichnik, paragraph [0005]).
Regarding claim 29, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 22. Jeffery further teaches including an output value situated in an output space in the respective example (see, e.g., Col. 8 lines 45-67: “the system receives 505 model output (i.e., a judgment) from a classifier model (e.g., model 130) that has processed an input data instance 105. Exemplary model output may be a predicted label representing a category/class to which the input data instance is likely to belong… the associated judgment confidence value may be a confidence score representing the distance in the binary decision space between the mapping of the data instance feature set and a decision boundary at the separation of the two classes in the decision space” [i.e., the binary decision space functions as an output space]);
ascertaining a local complexity assessment for the respective surrounding area representing a complexity of a task of the example-based system defined by the examples in the surrounding area (see, e.g., Col. 9 lines 4-6: “Conversely, a mapping that is located close to the decision boundary may be associated with a lower confidence score, representing a class assignment predicted at a lower confidence/certainty” and Col. 9 lines 58-63: “data instances that may provide maximum information about a classification task are data instances that result in mappings in decision space that are closer to the decision boundary. In some embodiments, these data instances may be identified automatically through active labeling analysis because their judgments are associated with lower confidence scores” [i.e., lower confidence scores closer to the decision boundaries indicate a higher complexity for classification of a surrounding area]);
and determining the local complexity assessment by a location of the examples in the surrounding area relative to one another in the input space and the output space (see, e.g., Col. 7 lines 39-46: “Using an input from a clustering/distribution algorithm 320 respectively associated with each operator estimate, a classifier 330, implementing a per operator estimator trained on the distribution, then determines a per operator estimate confidence value estimating probability P.sub.n(x|T), a probability based on the operator estimator n that the feature vector x belongs to the cluster/distribution T of multi-dimensional data instance feature vectors to which it has been assigned” [i.e., the analysis of clusters/distributions are used for spatial relationships within input feature space] and Col. 8-9 lines 60-67, 1-5 “the associated judgment confidence value may be a confidence score representing the distance in the binary decision space between the mapping of the data instance feature set and a decision boundary at the separation of the two classes in the decision space. A mapping located at a greater distance from the decision boundary may be associated with a higher confidence score, representing a class assignment predicted at a greater confidence/certainty. Conversely, a mapping that is located close to the decision boundary may be associated with a lower confidence score, representing a class assignment predicted at a lower confidence/certainty” [i.e., distance between the feature set (from the input space) to a boundary in the binary decision space (output space) represents a local complexity assessment1]).
Regarding claim 30, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 29. Jeffery further teaches ascertaining an aggregated complexity assessment by aggregation of the local complexity assessments (see, e.g., Col. 9 lines 26-28: “In some embodiments, accuracy assessment is implemented by a quality assurance component 160 to generate an aggregate/moving window estimate of accuracy”).
Regarding claim 31, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 30. Jeffery further teaches identifying surrounding areas having a complexity assessment undershooting a predefined complexity threshold value, based on the aggregated complexity assessment (see, e.g., Col. 9 lines 56-62: “Referring to the trainable classifier example, data instances that may provide maximum information about a classification task are data instances that result in mappings in decision space that are closer to the decision boundary. In some embodiments, these data instances may be identified automatically through active labeling analysis because their judgments are associated with lower confidence scores” and Col. 10 lines 27-30: “In an instance in which the desired accuracy is not satisfied 540, in embodiments, the system sends 525 the input data instance to the oracle for true labeling” [i.e., a predefined complexity threshold value (the desired accuracy) which may be an average (aggregated) accuracy (see, e.g., Col 6. lines 36-38)]);
and implementing the task of the example-based system in the ascertained surrounding areas by an algorithmic solution (see, e.g., Col. 5 lines 53-60: “the predictive model 130 is a trainable model that is derived from the training data 120 using supervised learning. An exemplary trainable model (e.g., a trainable classifier) is adapted to represent a particular task… using a set of training data 120 that consists of examples of the task being modeled” [i.e., the predictive model functions as the example-based system] and Col. 8 lines 45-67: “In embodiments, the system receives 505 model output (i.e., a judgment) from a classifier model (e.g., model 130) that has processed an input data instance 105. Exemplary model output may be a predicted label representing a category/class to which the input data instance is likely to belong… the associated judgment confidence value may be a confidence score representing the distance in the binary decision space between the mapping of the data instance feature set and a decision boundary at the separation of the two classes in the decision space”).
Regarding claim 35, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 21.
Although Jeffery substantially teaches the claimed invention, Jeffery is not relied upon to explicitly teach which further comprises providing the example-based system for use in a safety-oriented function,
the safety-oriented function includes object recognition based on image recognition,
and the object is recognized by using the example-based system
Nevertheless, in the same field, analogous art Hawkins teaches which further comprises providing the example-based system for use in a safety-oriented function (see, e.g., Hawkins Cols. 7-8 lines 44-67, 1-17: “The temporal memory system may process such inputs and produce an output representing, among others, identification of objects shown in an image… control signals for machines (e.g., automatic vehicle navigation… prediction/detection of adverse events” [i.e., automatic vehicle navigation encompasses safety-oriented functionality]),
the safety-oriented function includes object recognition based on image recognition (see, e.g., Hawkins Cols. 7-8 lines 44-67, 1-17: “In one embodiment, one or more nodes of the temporal memory system receives sensed inputs representing images, videos, audio signals…The temporal memory system may process such inputs and produce an output representing, among others…identification of objects shown in an image… control signals for machines (e.g., automatic vehicle navigation… prediction/detection of adverse events”),
and the object is recognized by using the example-based system (see, e.g., Hawkins Col. 7 lines 54-56: “The temporal memory system may process such inputs and produce an output representing, among others, identification of objects shown in an image”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery to incorporate the teachings of Hawkins to utilize the example-based system for use in a safety-oriented function including object recognition based on image recognition by the example-based system. Doing so would have allowed Jeffery to use Hawkins' method for use of the system “in applications such as recognition of objects in moving pictures”, as suggested by Hawkins (see, e.g., Hawkins, Col. 31 lines 62-63).
Regarding claim 36, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 36.
Although Jeffery substantially teaches the claimed invention, Jeffery is not relied upon to explicitly teach the limitation which further comprises using the object recognition in automated operation of at least one of a vehicle, a track- bound vehicle, a motor vehicle, an aircraft, a water vehicle or a space vehicle
Nevertheless, in the same field, analogous art Hawkins teaches which further comprises using the object recognition in automated operation of at least one of a vehicle, a track- bound vehicle, a motor vehicle, an aircraft, a water vehicle or a space vehicle (see, e.g., Hawkins Cols. 7-8 lines 44-67, 1-17: “The temporal memory system may process such inputs and produce an output representing, among others… control signals for machines (e.g., automatic vehicle navigation)” [i.e., automatic vehicle navigation inherently includes the operation of at least one vehicle]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery to incorporate the teachings of Hawkins to use the object recognition in automated operations of a vehicle. Doing so would have allowed Jeffery to use Hawkins' method for use of the system “in applications such as recognition of objects in moving pictures”, as suggested by Hawkins (see, e.g., Hawkins, Col. 31 lines 62-63).
Regarding claim 37, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 21.
Although Jeffery substantially teaches the claimed invention, Jeffery is not relied upon to explicitly teach the limitation providing the example-based system for use in a safety-oriented function,
and using the safety-oriented function to represent a classification based on at least one of sensor data of organisms,
and using the safety-oriented function to represent a classification based on at least one of sensor data of organisms, safe control of industrial plants, classification of chemical substances, signatures of vehicles or control in an area of industrial automation
Nevertheless, in the same field, analogous art Hawkins teaches providing the example-based system for use in a safety-oriented function (see, e.g., Cols. 7-8 lines 44-67, 1-17: “The temporal memory system may process such inputs and produce an output representing, among others… control signals for machines (e.g., automatic vehicle navigation)”),
and using the safety-oriented function to represent a classification based on at least one of sensor data of organisms, safe control of industrial plants, classification of chemical substances, signatures of vehicles or control in an area of industrial automation (see, e.g., Hawkins Cols. 7-8 lines 44-67, 1-17: “In one embodiment, one or more nodes of the temporal memory system receives sensed inputs representing…sensor signals…biometric information… …parameters for manufacturing process…The temporal memory system may process such inputs and produce an output representing,…gene expression and protein interactions… prediction of failures in a large-scale power system… indication of illness that a person is likely to experience…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery to incorporate the teachings of Hawkins to apply the examples-based system for safety-oriented functions in areas such as organism sensor data analysis, industrial plant control, chemical substance classification, vehicle identification, or industrial automation control. Doing so would have allowed Jeffery to use Hawkins' method in order to “generate an output indicative of the underlying cause or event associated with the novel input”, as suggested by Hawkins (see, e.g., Hawkins, Col. 1 lines 41-42).
Examiner’s Note: claim 39 and 40, as drafted, depend from claim 21. If applicant intended for claim 39 and 40 to be an independent claims, the examiner suggests that one way to do so is to amend the last portion of claims 39 and 40 to explicitly recite the limitations of claim 21 instead of the current recitation of “to carry out the method according to claim 21”.
Regarding claim 39, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method. Jeffery further teaches A computer program stored on a non-transitory computer-readable medium (see, e.g., Col. 16 lines 20-22: “a computer program product comprising computer-readable program instructions stored on a non-transitory computer-readable medium”),
comprising instructions stored thereon that when executed by a computer cause the computer to carry out the method2 according to claim 21 (see, e.g., Col. 18 lines 9-23: “Adaptive learning module 1410 may also or instead be included and configured to perform the functionality discussed herein related to the adaptive learning oracle-based framework discussed above… For example, non-transitory computer readable media can be configured to store firmware, one or more application programs, and/or other software, which include instructions and other computer-readable program code portions that can be executed to control each processor (e.g., processor 1402 and/or adaptive learning module 1410) of the components of system 400 to implement various operations, including the examples shown above”).
Regarding claim 40, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 21. Jeffery further teaches A non-transitory computer-readable medium (see, e.g., Col. 1 lines 47-50: “In general, embodiments of the present invention provide herein systems, methods and computer readable media for building and maintaining machine learning models that process dynamic data”),
comprising instructions stored thereon that when executed by a computer cause the computer to carry out the method3 according to claim 21 (see, e.g., Col. 18 lines 9-23: “Adaptive learning module 1410 may also or instead be included and configured to perform the functionality discussed herein related to the adaptive learning oracle-based framework discussed above… For example, non-transitory computer readable media can be configured to store firmware, one or more application programs, and/or other software, which include instructions and other computer-readable program code portions that can be executed to control each processor (e.g., processor 1402 and/or adaptive learning module 1410) of the components of system 400 to implement various operations, including the examples shown above”).
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Jeffery, Hawkins and Grichnik in view of Schupp-Omid (US 20170046615 A1; hereinafter Schupp-Omid).
Regarding claim 33, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 29.
Although the combination of Jeffery, Hawkins and Grichnik substantially teaches the claimed invention, said combination fails to explicitly teach which further comprises ascertaining a complexity distribution by using a histogram representation of the complexity assessment of a plurality of nearest neighbors of an example in the input space.
Nevertheless, in the same field, analogous art Schupp-Omid teaches which further comprises ascertaining a complexity distribution by using a histogram representation of the complexity assessment of a plurality of nearest neighbors of an example in the input space (see, e.g., Schupp-Omid paragraphs [0056-0057]: “The classifier may include any number of different types of classifiers such as, for example… a k-NN classifier, and/or the like. In embodiments, the classifier 228 may be configured to define at least one decision hyperplane that separates a first classification region of a virtual feature space from a second classification region of the virtual feature space… That is, for example, after an SVM is trained on a test set, distance features may be computed for each sample point between the sample point and the separating hyperplane. The result may be binned into a histogram, as shown, for example, in FIG. 7A. From the example, it will be readily appreciated that a sort of confidence interval can be obtained, for example, by applying Bayesian decision theory” [i.e., distance features binned into a histogram shows a complexity distribution of the features in a K-NN classification (complexity assessment of nearest neighbors of an input space example)]).
Jeffery, Hawkins, Grichnik and Schupp-Omid are analogous art because they are each directed to machine learning models computing spatial relationships (see, e.g., Jeffery, Col. 8 lines 59-67, Hawkins, Col. 5, lines 50-67, Grichnik, paragraph [0040-0041], and Schupp-Omid, paragraph [0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery in view of Hawkins and Grichnik to incorporate the teachings of Schupp-Omid to determine a complexity distribution from a histogram of nearest neighbors’ complexity assessments of an input space. Doing so would have allowed Jeffery in view of Hawkins and Grichnik to use Schupp-Omid's method in order to “to model the distribution to generate a projected score for a bin... ” and “use computed probabilities directly… in the case of data that has, for example, a statistically insignificant density”, as suggested by Schupp-Omid (see, e.g., Schupp-Omid, paragraph [0061]).
Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Jeffery, Hawkins and Grichnik in view of non-patent literature Waschulzik et. al. (“Quality Assured Efficient Engineering of Feedforward Neural Networks with Supervised Learning (QUEEN) Evaluated with the ‘Pima Indians Diabetes Database’” International Joint Conference on Neural Networks. IJCNN, 2000; hereinafter Waschulzik).
Regarding claim 34, as discussed above, Jeffery discloses the method of claim 29. However, Jeffery fails to explicitly teach providing the complexity assessment as an integrated quality indicator Q1^2, defining the quality indicator in accordance with:
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37
287
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wherein:
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41
232
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is a normalized spacing of the represented inputs,
and
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54
223
media_image3.png
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is a normalized spacing of the represented outputs,
x is a pair (x1, x2,) formed of two examples x1 and x2. x1 and x2 are examples from the set of examples P,
P={p1,p1, ... ,p|P|} is a set of elements in a multiset BAG P,
and |P| is a number of elements in the multiset BAG P
Nevertheless, in the same field, analogous art Waschulzik teaches providing the complexity assessment as an integrated quality indicator Q1^2, defining the quality indicator in accordance with:
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37
287
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4 (see, e.g., Page 98 Section 2.2: “The results of these evaluations motivate the setting-up of a quality indicator for the representations and encodings that is independent of the chosen neural network or other continuous approximator. The result of the modelling is the QUEEN integrated quality indicator (QJQJ or QJ2) given in (1):
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34
46
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”),
wherein:
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41
232
media_image2.png
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is a normalized spacing of the represented inputs (see, e.g., Page 99 Section 2.2: “
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67
253
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17
477
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”, [i.e., dnci is normalized input spacing]),
and
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54
223
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Greyscale
is a normalized spacing of the represented outputs (see, e.g., Page 99 Section 2.2: “
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67
253
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Greyscale
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21
496
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” [i.e., dnco is normalized output spacing]),
x is a pair (x1, x2,) formed of two examples x1 and x2. x1 and x2 are examples from the set of examples P (see, e.g., Page 99, section 2.2: “
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20
398
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” and “
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24
466
media_image9.png
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” [i.e., each x represents a pair (i, k) where k is 1 or 2]),
P={p1,p1, ... ,p|P|} is a set of elements in a multiset BAG P (see, e.g., Page 99 section 2.2: “
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17
202
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” [i.e., E is interchangeable with P of the set of examples (elements)]).
and |P| is a number of elements in the multiset BAG P (see. e.g., Page 98 Section 2.2: “
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34
46
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”5).
dRE(x) is a spacing in the input space, dRA(x) is a spacing in the output space (see, e.g., Page 99 Section 2.2: “the data set is transformed in that way, that each distance dCI(xi) in the input space… and each distance dCO(xi) in the output space is transformed” [i.e., dCI(xi) maps to dRE(x) and dCO(xi) maps to dRA(x), in the context of the equation – input/output distance (spacing) in space]),
and y is a pair of examples formed of two examples from the multiset BAG P (see, e.g., see, e.g., Page 99 Section 2.2: “
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67
253
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…
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146
497
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… E21: set of all possible pairs xi of the examples from E1” [i.e., the term y ∊ E21 shows that y is a pair of examples from the multiset BAG E21 (multiset BAG P)]),
dRE(y) is a spacing in an input space, and dRA(y) is a spacing in an output space (see, e.g., Page 99 Section 2.2: ““
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67
253
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…
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146
497
media_image11.png
Greyscale
… the data set is transformed in that way, that each distance dCI(xi) in the input space… and each distance dCO(xi) in the output space is transformed” [i.e., since y represents a pair of examples formed from multiset BAG E21 (multiset BAG P), and dCO/dCI are abbreviations for distance between encodings, the terms dCI(y) and dCO(y) represent the spacing in an input space and an output space respectively, mapping directly to the terms dRE(y) and dRA(y)]).
Jeffery, Hawkins, Grichnik and Waschulzik are analogous art because they are each directed to machine learning models computing spatial relationships (see, e.g., Jeffery, Col. 8 lines 59-67, Hawkins, Col. 5, lines 50-67, Grichnik, paragraph [0040-0041], and Waschulzik, Page 98 Section 2.2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Jeffery in view of Hawkins and Grichnik to incorporate the teachings of Waschulzik to provide a complexity assessment as an integrated quality indicator as defined in the claimed formula. Doing so would have allowed Jeffery in view of Hawkins and Grichnik to use Waschulzik's method in order to “rate candidates for representations, encodings and feature combinations for a given task”, as suggested by Waschulzik (see, e.g., Waschulzik, Page 99 Section 2.2).
Claim 38 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Jeffery, Hawkins and Grichnik in view of Leino (US 20210357729 A1; hereinafter Leino).
Regarding claim 38, as discussed above, the combination of Jeffery, Hawkins and Grichnik teaches the method of claim 21.
Although the combination of Jeffery, Hawkins and Grichnik substantially teaches the claimed invention, said combination is not relied upon to explicitly teach providing the example-based system with: a system with supervised learning,
an artificial neural network with one or more layers of neurons not being input neurons or output neurons
and being trained with backpropagation,
a convolutional neural network, or a single-shot multibox detector network
Nevertheless, in the same field, analogous art Leino teaches providing the example-based system with: a system with supervised learning (see, e.g., Leino paragraph [0038]: “Models may be run against a training dataset for several epochs (e.g., iterations), in which the training dataset is repeatedly fed into the model to refine its results. For example, in a supervised learning phase, a model is developed to predict the output for a given set of inputs”),
an artificial neural network with one or more layers of neurons not being input neurons or output neurons (see, e.g., Leino paragraph [0024]: “The artificial neurons may be arranged into layers. A first layer processes the input, a last layer provides the output. Intermediate layers, called ‘hidden layers’ provide intermediate processing for computing the output from the input. An ANN that includes at least one hidden layer is called a deep neural network (DNN)” [i.e., hidden layers are neither input or output neurons]),
and being trained with backpropagation (see, e.g., Leino paragraph [0049]: “In training of a DNN architecture… backpropagation is used, where backpropagation is a common method of training artificial neural networks that are used with an optimization method such as a stochastic gradient descent (SGD) method”),
a convolutional neural network, or a single-shot multibox detector network (see, e.g., Leino paragraph [0044]: “In some example embodiments, the neural network 204 (e.g., deep learning, deep convolutional, or recurrent neural network) comprises a series of neurons 208”).
Jeffery, Hawkins, Grichnik and Leino are analogous art because they are each directed machine learning models computing spatial relationships (see, e.g., Jeffery, Col. 8 lines 59-67, Hawkins, Col. 5, lines 50-67, Grichnik, paragraph [0040-0041], and Leino, paragraphs [0049-0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Jeffery, Hawkins and Grichnik to incorporate the teachings of Leino to equip the example-based system with a supervised learning framework, including an artificial neural network having one or more layers of neurons that are neither input nor output neurons, trained by either backpropagation, a CNN or a single-shot multibox detector network. Doing so would have allowed the combination of Jeffery, Hawkins and Grichnik to use Leino's method to “be widely used in a number of application settings, including but not limited to diagnosis of radiology images, identification of oil and natural gas prospects, and self-driving cars” and to , as suggested by Leino (see, e.g., Leino, paragraph [0095]).
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 Kamran Afshar whose telephone number is (571)272-7796. The examiner can normally be reached Mon-Fri 7:30-5:00 EST.
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/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
1 Under the broadest reasonable interpretation (BRI), a lower confidence/certainty score (closer to the decision boundary) and a higher confidence/certainty score (further from the decision boundary) correlates to a local complexity assessment for a given classification task in the decision space. The distance of features from the decision boundary implies a level of difficulty or uncertainty of correct classification in a particular local region of the decision space.
2 As discussed above in the 101 rejection of this claim, the computer-readable medium is recited at a high level of generality as mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
3 As discussed above in the 101 section, claim is recited at a high level of generality as mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
4 As noted in the objection to this claim above, the recited terms are not explicitly defined or spelled out in the claim. For examination purposes “dRE(x)” and “dRA(x)” recited in this claim are being interpreted as “dRE(x)” and “dRA(x)” as defined in Page 14 lines 6-8 of applicant’s specification.
5 Under the broadest reasonable interpretation (BRI), in view of the specification, the notation |E12| denotes the cardinality of the set E to one of ordinary skill in the art, and P can be substituted for E in this equation as the set of examples (elements).
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