DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
1. Applicant’s arguments regarding the rejection of claims 2-5 and 8-9 under 35 U.S.C. 112(b) have been fully considered but are not persuasive. Applicant argues that the claims should be interpreted in light of the specification and that the Office improperly requires definitions to be provided in the claims themselves. However, the rejection is not based on a requirement that the claims provide standalone definitions for every mathematical concept. Rather, the rejection is based on the claims reciting mathematical systems and expressions, including (
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)
)
and
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,
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)
, without adequately introducing or identifying the variables and their relationships in the claim language.
The Office agrees that claims are interpreted in light of the specification. However, MPEP 2173.02 explains that “during examination, after applying the broadest reasonable interpretation consistent with the specification to the claim, if the metes and bounds of the claimed invention are not clear, the claim is indefinite and should be rejected.” The rejection identifies mathematical variables and expressions whose meaning and relationship to one another are not reasonably clear from the claim language. While Applicant directs attention to disclosures in the specification, Applicant does not persuasively address the rejection’s finding that the claims utilize symbolic notation without adequately introducing or identifying the recited variables and expressions.
Further, MPEP 2173.02 states that the Office may properly reject claims containing language that is “ambiguous, vague, incoherent, opaque, or otherwise unclear in describing and defining the claimed invention.” The mathematical symbols and expressions identified in the rejection render the scope of the claims unclear because the variables and their relationships are not adequately established in the claim language. Accordingly, Applicant has not persuasively shown the metes and bounds of the claimed subject matter are reasonably clear, and the rejection under 35 U.S.C. 112(b) is maintained.
2. Applicant’s arguments filed on April 20, 2026 regarding the rejection under 35 U.S.C. 101 on pages 6-8 have been fully considered but are not persuasive. Applicant argues that the amended claims integrate the judicial exception into a practical application because a parameter determined from the model uncertainty and variance is used to control a technical system, and therefore provides an improved control of the technical system.
However, the cited portions of the specification primarily describe determining model uncertainty, determining and output variance, estimating function parameters, and improved parameterization of output variance. Applicant further argues that controlling a technical system using a parameter determined according to the claimed invention is better than controlling a technical system using a parameter determined in a different way. However, MPEP 2106.05(a) explains that “the claim must include the components or steps of the invention that provide the improvement described in the specification.” Although Applicant asserts that the claimed control limitations provide a technological improvement, the claims do not recite a specific manner of controlling a filter, allocating resources within a telecommunication network, or controlling movement of a semi-autonomous vehicle or robot that reflects the asserted improvement.
Further, MPEP 2106.05(a) explains that “[a]n important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome.” Here, the claims merely recite the desired result of controlling a technical system based on the determined parameter and do not recite a particular technological mechanism that improves the operation of the filter, telecommunication network, vehicle, or robot.
Accordingly, when considered as a whole, the additional limitations merely apply the result of the judicial exception in a particular technological environment and therefore do not integrate the judicial exception into a practical application.
Accordingly, for at least the reasons set forth above, Applicant’s arguments are unpersuasive and the rejection of claims 1-11 under 35 U.S.C. 101 is maintained.
3. Applicant’s argument filed on April 20, 2026 regarding the rejection under 35 U.S.C. 102 and 103 have been fully considered but are not persuasive. Applicant’s arguments regarding the prior art rejection have been fully considered but are not persuasive. Applicant argues that Kim does not disclose the limitations directed to determining a parameter based on the variance and controlling a technical system based on the determined parameter. However, Applicant’s arguments are directed to the previous rejection based on Kim alone.
The independent claims have been amended to recite additional limitations directed to determining a parameter based on the variance and controlling a technical system based on the determined parameter. In view of the amended claim language, the rejection has been modified and is now set forth as a rejection under 35 U.S.C. 103 over Kim in view of Crego. As discussed in the rejection, Crego teaches the newly added limitations and was applied to address the amended claim language.
Accordingly, Applicant’s arguments directed to the previous rejection do not persuasively rebut the rejection presently set forth. The rejection of claims 1-11 under 35 U.S.C. 103 is maintained.
Priority
Acknowledgement is made of the applicant’s claim for Foreign priority to German Patent Application No. DE10/20222030346 filed on March 28, 2022.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-5 and 8-9 are rejected under 35 U.S.C. §112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention. The claims recite mathematical symbols and expressions (
σ
z
2
,
μ
z
,
μ
y
,
D
c
,
z
,
y
,
p
z
D
c
,
a
n
d
N
(
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|
μ
z
,
σ
z
2
) that lack antecedent basis or clear definition in the claims. The meaning of these variables and functions is not apparent from the claimed language, and one of ordinary skill in the art would not understand the metes and bounds of the invention with reasonable clarity. See MPEP 2173.
Claim 7 is rejected under 35 U.S.C. §112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention. Specifically, claim 7 recites a “non-transitory architecture of a neural network including a neural process.” The meaning and scope of the term “non-transitory architecture” are unclear. While the specification describes “an architecture of a neural network,” the specification does not describe or define a “non-transitory architecture.” Accordingly, it is unclear what structure is encompassed by the recited “non-transitory architecture” and how the modifier “non-transitory” limits the claimed architecture.
Further, the claim does not identify whether recited architecture corresponds to software, hardware, circuitry, a processor implementation, a storage medium, a neural network topology, or some other arrangement. Thus, one of ordinary skill in the art would not be reasonably apprised of the metes and bounds of the claimed subject matter because the scope of the recited “non-transitory architecture of a neural network including a neural process” is unclear. Therefore, claim 7 is indefinite under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed
to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-11 are within the four statutory categories (a process, machine, manufacture or composition of matter).
Claims 1-6, and 11 are directed to a method consisting of a series of steps, meaning that it is directed to the statutory category of process.
Claims 7-10, are written in the form of a “machine” claim (e.g., an architecture or device including a neural network. However, the claims do not recite any structural or hardware (e.g., processor, memory, or tangible medium) and instead describe algorithmic functionality and relationships among abstract parameters. Accordingly, although presented in machine form, claims 7-10 fail to define a statutory machine and instead directed to software per se, which is non-statutory subject matter under 35 U.S.C. 101. See MPEP 2106 (Step 1).
The Step 2A/2B analysis below is provided for all claims for completeness.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Regarding claim 1, the following claim elements are abstract ideas:
assessing uncertainties in a model using a neural network including a neural process, the model modeling a technical system and/or a system behavior of the technical system (This is an abstract idea of a “mental process.” The limitation recites observation and evaluation of uncertainty associated with a model of a system. Such assessing” constitutes an act of judgement – reviewing model behavior, recognizing its level of confidence or error, and deciding how uncertain it is. A personally could mentally examine model predictions, compare them to expected outcomes, and reason about how reliable the model is. These activities – observation, comparison, and evaluation – can be performed in the human mind or with pen and paper and therefore falls within the mental-process grouping of abstract ideas. See MPEP 2106.04(a)(2)(III).),
determining a model uncertainty
σ
z
2
(This is an abstract idea of a mathematical concept and alternatively, a mental process. It recites computing a statistical variance (
σ
2
) for a model parameter – i.e., applying known formulas/relationships among numerical values. Such calculation can be carried out mentally with basic computing tools like a paper worksheet, a hand held calculator, or a simple spreadsheet (e.g., summing squared deviations and dividing by N or N-1). Because it amounts to performing math and evaluation that can be done in the human mind or with pen-and-paper/basic tools, it falls within the abstract idea groupings of mathematical relationships/formulas and mental processes. See MPEP 2106.04(a)(a)(2)(I) and MPEP 2106.04(a)(2(III).);
determining a variance of an output of the model
σ
y
2
based on the model uncertainty (This is an abstract idea of a mathematical concept and mental process. It recites computing a statistical variance for the model’s output as a function of previously obtained uncertainty values – a mathematical relationship among numerical parameters. Such computation can be performed mentally using basic computing tools such as a calculator, spreadsheet or pen and paper, to apply the standard variance formula (e.g., summing squared deviations or propagating error terms). Because it involves mathematical analysis and evaluation that can be performed in the human mind with rudimentary tools, it falls within the mathematical concept and mental process groupings of abstract ideas.
determining a parameter based on the variance (This is an abstract idea of a mental process. It involves evaluating a variance associated with model uncertainty and determining a corresponding parameter based on that variance. A person could review the variance, evaluate the information represented by the variance, and determine a corresponding parameter through observation, evaluation, and judgement. This type of analysis and decision-making can be practically performed in the human mind or with the aid of pen and paper.); and
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
a neural network including a neural process (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
a technical system (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
on the basis of the parameter, controlling the technical system by controlling an operation of at least one of: a signal processing by a filter that filters an input signal, a resource allocation by a telecommunication network, and a movement of a semi-autonomous vehicle or robot (This limitation merely applies the abstract idea in a particular technological environment and amounts to an instruction to apply the judicial exception. The recited control a filter, telecommunication network, semi-autonomous vehicle, or robot is insignificant extra-solution activity because it merely uses the results of the abstract idea, i.e., the determined parameter, to perform a generic control operation. Accordingly, the limitation does not integrate the judicial exception into a practical application.).
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following abstract ideas:
wherein the model uncertainty is quantified by a variance of a latent spatial distribution
p
(
z
|
D
c
)
(This is abstract idea of a mathematical concept and mental process. It recites expressing model uncertainty as the variance of a probability distribution, which constitutes a mathematical relationship among statistical variables. Such calculation – determining the spread of a latent distribution
p
(
z
|
D
c
)
can be performed mentally with basic computing tools, such as a calculator, spreadsheet, or pen and paper, by applying standard variance and probability formulas. Because it involves mathematical evaluation and reasoning that can be executed conceptually or with rudimentary aid, it falls within the mathematical concept and mental process groupings of abstract ideas.).
Regarding claim 3, the rejection of claim 1 is incorporated herein. Further, claim 3 recites the following abstract ideas:
wherein the model uncertainty is calculated as a variance
σ
z
2
of a Gaussian distribution, where
σ
z
2
=
σ
z
2
(
D
c
)
, via a latent variable z from a set of contexts
D
c
of observations, wherein
p
z
D
c
=
N
(
z
|
μ
z
D
c
,
σ
z
2
D
c
)
(This is an abstract idea of a mathematical concept. The limitation recites a Gaussian probability distribution and specifies the computation of a variance and mean value
μ
z
(
D
c
)
based on contextual observations. Such formulation and parameterization of a Gaussian distribution represent the use of mathematical relationships, formulas, and statistical functions. Because it is directed to expressing data using mathematical symbols and equations that define a probability distribution, it falls within the mathematical concept grouping of abstract ideas.
Regarding claim 4, the rejection of claim 3 is incorporated herein. Further, claim 4 recites the following abstract ideas:
wherein a mean value
μ
z
of the Gaussian distribution, where
μ
z
=
μ
z
(
D
c
)
,
is calculated via the latent variable z from the set of contexts
D
C
of observations, wherein
p
z
D
c
=
N
(
z
|
μ
z
D
c
,
σ
z
2
D
c
)
(This is an abstract idea of a mathematical concept. The limitation recites calculating a mean value of a Gaussian probability distribution based on contextual observations, which constitutes the use of a mathematical formula and statistical relationships to define a distribution. Expressing or computing such mean and variance parameters involves applying mathematical equations to numerical data and therefore falls within the mathematical relationships/formula grouping of abstract ideas.).
Regarding claim 5, the rejection of claim 1 is incorporated herein. Further, claim 5 recites the following abstract ideas:
wherein a mean value
μ
y
of the output is calculated (This is an abstract idea of a mathematical concept and mental process. It recites calculating the mean (average) value for the model output, which is a mathematical operation involving summing a set of numerical values and dividing by their count. Such an operation can be performed mentally or with basic computing tools such as a calculator, spreadsheet, or pen and paper. Because it involves applying a mathematical formula and reasoning steps that can be carried out in the human mind or with rudimentary tools, it falls within the mathematical relationship/formula and mental process groupings of abstract ideas.).
Regarding claim 6, the rejection of claim 1 is incorporated herein. Further, claim 6 recites the following abstract ideas:
wherein an uncertainty of the neural network is predicted based on the model uncertainty and on the variance of the output (This is an abstract idea of a mathematical concept and mental process. It recites computing or predicting an overall uncertainty value by combing previously determined statistical quantities – namely, model uncertainty and output variance – through mathematical relationships. Such prediction can be carried out mentally or with basic computing tools such as a calculator, spreadsheet, or pen and paper by applying standard mathematical operations (e.g., addition, weighting, propagation of variance). Because it involves applying mathematical formulas and evaluative reasoning that can be performed in the human mind or with rudimentary aids, it falls within the mathematical relationship/formula and mental process groupings of abstract ideas.).
Regarding claim 7, the following claim elements are abstract ideas:
assessing uncertainties in a model using a neural network including a neural process, the model modeling a technical system and/or a system behavior of the technical system (This is an abstract idea of a “mental process.” The limitation recites observation and evaluation of uncertainty associated with a model of a system. Such assessing” constitutes an act of judgement – reviewing model behavior, recognizing its level of confidence or error, and deciding how uncertain it is. A personally could mentally examine model predictions, compare them to expected outcomes, and reason about how reliable the model is. These activities – observation, comparison, and evaluation – can be performed in the human mind or with pen and paper and therefore falls within the mental-process grouping of abstract ideas. See MPEP 2106.04(a)(2)(III).),
at least one decoder section trained to determine a variance of an output of the model based on a model uncertainty (This is an abstract idea of a mental process and mathematical concept. It recites determining a variance of a model output as a function of model uncertainty – a mathematical relationship between statistical parameters. Such calculation can be performed in the human mind with basic computing tools such a calculator, spreadsheet, or pen and paper by applying standard variance or error-propagation formulas. The recitation of a “decoder section trained” is mere instructions to apply the abstract idea using generic computer functionality and therefore represents insignificant extra-solution activity and a high-level recitation of generic computer components.).
determining a parameter based on the variance (This is an abstract idea of a mental process. It involves evaluating a variance associated with model uncertainty and determining a corresponding parameter based on that variance. A person could review the variance, evaluate the information represented by the variance, and determine a corresponding parameter through observation, evaluation, and judgement. This type of analysis and decision-making can be practically performed in the human mind or with the aid of pen and paper.); and
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
a neural network including a neural process (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
a technical system (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
the neural network being configured to:… on the basis of the parameter, control an operation of at least one of: a signal processing by a filter that filters an input signal, a resource allocation by a telecommunication network, and a movement of a semi-autonomous vehicle or robot (This limitation merely applies the abstract idea in a particular technological environment and amounts to an instruction to apply the judicial exception. The recited control a filter, telecommunication network, semi-autonomous vehicle, or robot is insignificant extra-solution activity because it merely uses the results of the abstract idea, i.e., the determined parameter, to perform a generic control operation. Accordingly, the limitation does not integrate the judicial exception into a practical application.).
Regarding claim 8, the rejection of claim 7 is incorporated herein. Further, claim 8 recites the following abstract ideas:
wherein the neural network includes at least one encoder section, the encoder section being trained to determine the model uncertainty as a variance
σ
z
2
and/or a mean value
μ
2
of a Gaussian distribution via a latent variable z from a set of contexts
D
c
of observations, where
p
z
D
c
=
N
(
z
|
μ
z
,
σ
z
2
)
(This is an abstract idea of a mathematical concept. The limitation recites the formulation of a Gaussian probability distribution and the computation of its parameters – mean and variance – based on contextual data. These operations represent the use of mathematical relationships, equations, and statistical functions to characterize data and therefore amount to the manipulation of mathematical symbols and statistical formulas.)
Regarding claim 9, the rejection of claim 7 is incorporated herein. Further, claim 9 recites the following abstract ideas:
wherein the neural network includes at least one further decoder section, the further decoder section being trained to determine a mean value
μ
y
of the output based on an input point x and on a latent sample z (This is an abstract idea of a mathematical concept. The limitation recites determining a mean value of an output as a mathematical function of an input x and a latent variable z, which represents a mathematical relationship among numerical variables. Such formulation and evaluation of a mean value constitute mathematical computation and expression of a function, which fall within the mathematical relationship/formula grouping of abstract ideas. The recitation of a “decoder section being trained” is mere instructions to apply the abstract idea using generic computer functionality and therefore represents insignificant extra solution activity and a high-level recitation of generic computer components.)
Regarding claim 10, the following claim elements are abstract ideas:
assessing uncertainties in a model using a neural network including a neural process, the model modeling a technical system and/or a system behavior of the technical system (This is an abstract idea of a “mental process.” The limitation recites observation and evaluation of uncertainty associated with a model of a system. Such assessing” constitutes an act of judgement – reviewing model behavior, recognizing its level of confidence or error, and deciding how uncertain it is. A personally could mentally examine model predictions, compare them to expected outcomes, and reason about how reliable the model is. These activities – observation, comparison, and evaluation – can be performed in the human mind or with pen and paper and therefore falls within the mental-process grouping of abstract ideas. See MPEP 2106.04(a)(2)(III).),
at least one decoder section trained to determine a variance of an output of the model based on a model uncertainty (This is an abstract idea of a mental process and mathematical concept. It recites determining a variance of a model output as a function of model uncertainty – a mathematical relationship between statistical parameters. Such calculation can be performed in the human mind with basic computing tools such a calculator, spreadsheet, or pen and paper by applying standard variance or error-propagation formulas. The recitation of a “decoder section trained” is mere instructions to apply the abstract idea using generic computer functionality and therefore represents insignificant extra-solution activity and a high-level recitation of generic computer components.).
determining a parameter based on the variance (This is an abstract idea of a mental process. It involves evaluating a variance associated with model uncertainty and determining a corresponding parameter based on that variance. A person could review the variance, evaluate the information represented by the variance, and determine a corresponding parameter through observation, evaluation, and judgement. This type of analysis and decision-making can be practically performed in the human mind or with the aid of pen and paper.); and
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
a neural network including a neural process (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
a technical system (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
A device (This a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05(f).)
the device being configured to:… on the basis of the parameter, control an operation of at least one of: a signal processing by a filter that filters an input signal, a resource allocation by a telecommunication network, and a movement of a semi-autonomous vehicle or robot (This limitation merely applies the abstract idea in a particular technological environment and amounts to an instruction to apply the judicial exception. The recited control a filter, telecommunication network, semi-autonomous vehicle, or robot is insignificant extra-solution activity because it merely uses the results of the abstract idea, i.e., the determined parameter, to perform a generic control operation. Accordingly, the limitation does not integrate the judicial exception into a practical application.).
Regarding claim 11, the rejection of claim 1 is incorporated herein. Further, claim 11 recites the following abstract ideas:
ascertaining an inadmissible deviation of the system behavior of the technical system from a standard value range based on the variance of the output of the model (This is an abstract idea of a mathematical concept and mental process. It recites evaluating where the model’s output deviates from an expected or standard range by comparing a calculated variance to predefined thresholds – an act of mathematical comparison and judgement. Such analysis can be performed in the human mind with basic computing tools such as a calculator, spreadsheet, or pen and paper by applying standard deviation and range-comparison formulas. Because it involves mathematical evaluation and decision-making that can be performed in the human mind or with rudimentary aids, it falls within the mathematical relationships/formulas and mental process groupings of abstract ideas.).
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.
Claim 7-10 are rejected under § 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 7 recites an “non-transitory architecture of a neural network including a neural process” configured for controlling a technical system by assessing uncertainties in a model. Although the claim now recites a “non-transitory architecture,” the term “non-transitory” is commonly used to distinguish a claimed article from a transitory propagating signal. However, the addition of the term “non-transitory” does not itself impart tangible structure or identify a statutory machine, manufacture, or computer-readable medium. The claim does not recite any tangible hardware elements such as a processor, memory, storage medium, or circuitry. Instead, the claim merely describes the functional behavior of a neural network and its decoder section, including determining a variance, determining a parameter based on the variance, and controlling operation of a technical system. As such, the claims amount to a description of software per se or an algorithmic design without any physical embodiment or transformation. Accordingly, claim 7 is non-statutory under 35 U.S.C. 101 for being directed to software per se.
Claims 8 and 9 depend from claim 7 and therefore also fail to define any tangible structure or statutory category. The additional limitations merely elaborate on functional relationships of the same disembodied architecture (e.g., encoder or decoder sections trained to determine statistical parameters) without introducing any physical hardware or transformation. Accordingly, claims 8 and 9 are likewise directed to software per se and are non-statutory under 35 U.S.C. §101.
Claim 10 recites a “device” that includes a neural network including a neural process, the neural network being configured for controlling a technical system by assessing uncertainties in a model. Although the claim is nominally directed to a “device,” it does not recite any tangible hardware elements such a processor, memory, storage medium, or circuitry. Instead, the claim merely describes the functional behavior of a neural network in abstract terms, including determining a variance, determining a parameter based on the variance, and controlling operation of a technical system, without specifying any structural implementation. As such, the claim encompasses only a disembodied algorithmic construct rather than a statutory machine or manufacture. Accordingly, claim 10 is non-statutory under 35 U.S.C. 101 for being directed to software per se.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9, and 11 are rejected under the 35 U.S.C. 103 as being unpatentable over Kim et. al. (NPL: “Attentive Neural Processes” (Published: 2019)) in view of Crego et al., (Pub. No.: US 20210139024 A1).
Regarding claim 1, Kim discloses:
A computer-implemented method for controlling a technical system by assessing uncertainties in a model using a neural network including a neural process, the model modeling the technical system and/or a system behavior of the technical system, the method comprising (Kim, Introduction, page 2, “In theory, increasing the dimensionality of the representation could address this issue, but we show in Section 4 that in practice, this is not sufficient. To address this issue, we draw inspiration from GPs, which also define a family of conditional distributions for regression…We implement a similar mechanism in NPs using differentiable attention that learns to attend to the contexts relevant to the given target, while preserving the permutation invariance in the contexts. We evaluate the resulting Attentive Neural Processes (ANPs) on 1D function regression and on 2D image regression. Our results show that ANPs greatly improve upon NPs in terms of reconstruction of contexts as well as speed of training, both against iterations and wall clock time. We also demonstrate that ANPs show enhanced expressiveness relative to the NP and is able to model a wider range of functions.” – describes how the Attentive Neural Process uses differentiable attention within a neural network to improve predictions and capture broader range of functions, thereby enhancing how the model represents and evaluates model uncertainty in its learned relationships between inputs and outputs.”):
determining a model uncertainty
σ
z
2
; determining a variance of an output of the model
σ
y
2
based on the model uncertainty. (Kim, section 2.1, equation 2, “The likelihood
p
y
T
x
T
,
r
C
is modelled by a Gaussian factorized across the targets…with mean and variance given by passing
x
i
and
r
C
through an MLP.” and “The latent variable version of the NP model includes a global latent z to account for uncertainty in the predictions of
y
T
for a given observed (
(
x
C
,
y
C
)
. It is incorporated into the model via a latent path that complements the deterministic path described above. Here
z
is modelled by a factorized Gaussian parametrised by
s
C
≔
s
x
C
,
y
C
,
with s being a function of the same properties as r with
q
z
s
ϕ
≔
p
z
,
the prior on
z
The likelihood is referred to as the
d
e
c
o
d
e
r
, and
q
,
r
,
s
form the
e
n
c
o
d
e
r
…The motivation for having a global latent is to model different realisations of the data generating stochastic process” – the model’s uncertainty is determined by the encoder through a latent path introducing a global latent variable z, modeled by the factorized Gaussian distribution, where the variance quantifies the model uncertainty. The output variance is then determined by the decoder, which defines a Gaussian likelihood whose variance depends on the latent variable z. The final predictive distribution integrates over this latent Gaussian, showing the output variance is computed directly based on the model uncertainty.).
However, Kim does not teach but Kim in view of Crego teaches the following limitations:
determining a parameter based on the variance (Crego, paragraph [0026] “To use the error models, the autonomous vehicle may determine estimated locations of an object at a future time based at least in part on the outputs from the components and the error models associated with the components. The estimated locations may correspond to a probability distribution, such as a Gaussian distribution, of locations. In some instances, the autonomous vehicle determines the estimated locations of the object by initially determining the respective probability distributions associated with each of the components and/or the parameters. The autonomous vehicle may then determine the estimated locations using the probability distributions for all of the components and/or parameters.” [0039] “In some instances, the autonomous vehicle and/or the one or more computing devices can determine the uncertainties associated with the components. For example, the autonomous vehicle and/or the one or more computing devices may input the input data into a component multiple times in order to receive multiple outputs (e.g., parameters) from the component.” [0108] “In some instances, aspects of some or all of the components discussed herein can include any models, algorithms, and/or machine learning algorithms. For example, in some instances, the components in the memory 1016 and 1022 can be implemented as a neural network.” – Crego teaches generating outputs including parameters and determining probability distributions associated with the parameters. Crego further teaches that the probability distributions may be Gaussian distributions. A Guassian distribution necessarily includes a variance term and therefore corresponds to the claimed variance under the broadest reasonable interpretation. Crego additionally teaches determining uncertainty using distributions. Further, Crego teaches the disclosed components may be implemented using machine learning algorithms and neural networks. Accordingly, Crego teaches determining a parameter based on the variance.); and
on the basis of the parameter, control an operation of at least one of: a signal processing by a filter that filters an input signal, a resource allocation by a telecommunication network, and a movement of a semi-autonomous vehicle or robot (Crego, paragraph [0037] “ the autonomous vehicle may determine a total uncertainty associated with navigating the autonomous vehicle based at least in part on the uncertainty models used to determine the estimated locations of the autonomous vehicle and the uncertainty models used to determine the estimated locations of the object(s). The autonomous vehicle may then generate different routes and perform similar processes for determining the total uncertainties associated with the different routes. Additionally, the autonomous vehicle may select the route that includes the lowest uncertainty.” [0041] “the vehicle 102 may be navigating along a trajectory 104 within the environment 100.” [0040] “Although discussed in the context of an autonomous vehicle, the methods, apparatuses, and systems described herein may be applied to a variety of systems (e.g., a sensor system or a robotic platform), and are not limited to autonomous vehicles.” – Crego teaches determining uncertainty associated with navigating an autonomous vehicle, generating multiple routes, and selecting a route having the lowest uncertainty. Under the broadest reasonable interpretation, the selected route corresponds to a parameter used to control navigation of the vehicle. Crego further teaches navigating the vehicle along a trajectory and applicability to robotic platforms. Accordingly, Crego teaches controlling a technical system by controlling movement of semi-autonomous vehicle or robot on the basis of the parameter.).
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having Kim and Crego before them, to incorporate the variance and uncertainty determinations of Kim into the collision monitoring and vehicle navigation techniques of Crego. One would have been motivated to make such a combination in order to use variance-derived uncertainty information when processing vehicle parameters and determining vehicle operation decisions. This would improve decision making under uncertain operating conditions.
Regarding claim 2, Kim in view of Crego teaches all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1. Kim in view of Crego further teaches:
The method as recited in claim 1, wherein the model uncertainty is quantified by a variance of a latent spatial distribution
p
(
z
|
D
C
)
(Kim, section 2.1, equation 2 “The likelihood
p
y
T
x
T
,
r
C
is modelled by a Gaussian factorized across the targets…with mean and variance given by passing
x
i
and
r
C
through an MLP. The unconditional distribution
p
y
T
x
T
w
h
e
n
C
=
∅
is defined by letting
r
∅
be a fixed vector. and “The latent variable version of the NP model includes a global latent z to account for uncertainty in the predictions of
y
T
for a given observed (
x
C
,
y
C
)
. It is incorporated into the model via a latent path that complements the deterministic path described above. Here
z
is modelled by a factorized Gaussian parametrised by
s
C
≔
s
x
C
,
y
C
,
with s being a function of the same properties as r with
q
z
s
ϕ
≔
p
z
,
the prior on
z
The likelihood is referred to as the decoder, and q; r; s form the encoder” – this shows that the model expresses its uncertainty through the variance of the Gaussian distribution that is condition on the context data, meaning the model uncertainty is represented as the variance of the latent distribution.).
Regarding claim 3, Kim in view of Crego teaches all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1. Kim in view of Crego further teaches:
The method as recited in claim 1, wherein the model uncertainty is calculated as a variance
σ
z
2
of a Gaussian distribution, where
σ
z
2
=
σ
z
2
(
D
C
)
,
via a latent variable z from a set of contexts
D
C
of observations, wherein
p
z
D
C
=
N
(
z
|
μ
z
D
C
,
σ
z
2
D
C
)
(Kim, Abstract, “Neural Processes (NPs) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions.” Section 2.1 “In practice, each context
(
x
,
y
)
pair is passed through an MLP to form a representation of each pair, and these are aggregated by taking the mean to form
r
C
… The latent variable version of the NP model includes a global latent
z
to account for uncertainty… Here
z
is modelled by a factorized Gaussian parametrised by
s
C
:
=
s
(
x
C
;
y
C
)
,
with s being a function of the same properties as
r
.
” – describe using a set of observed output-input pairs (context observations), which are encoded and aggregated to form a context representation
r
C
. This aggregated representation parameterizes a factorized Gaussian distribution for the latent variable
z
, so the model’s uncertainty is calculated as the variance of the latent Gaussian derived from the context observations.)
Regarding claim 4, Kim in view of Crego teaches all the elements of claim 3, therefore is rejected for the same reasons as those presented for claim 3. Kim in view of Crego further teaches::
The method as recited in claim 3, wherein a mean value
μ
z
of the Gaussian distribution, where
μ
z
=
μ
z
(
D
C
)
,
is calculated via the latent variable z from the set of contexts
D
C
of observations, wherein
p
(
z
|
D
C
=
N
(
z
|
μ
z
(
D
C
)
,
σ
z
2
(
D
C
)
)
(Kim, page 11, Appendix A, “The latent path outputs
μ
z
,
ω
z
∈
R
d
, which parameterises
q
z
s
C
=
N
z
μ
z
,
0.1
+
0.9
σ
ω
where
σ
is the sigmoid function
.
” and page 2, section 2.1 “Here
z
is modelled by a factorized Gaussian parametrised by
s
C
:
=
s
(
x
C
;
y
C
)
,
with s being a function of the same properties as
r
.
” – the two passages collectively describe the same latent-path encoder: it uses the aggregated context representation
s
C
(from the context observation
x
C
,
y
C
to parameterize a Gaussian distribution over z with mean
μ
z
and variance computed from
ω
z
.
)
Regarding claim 5, Kim in view of Crego teaches all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1. Kim in view of Crego further teaches::
The method as recited in claim 1, wherein a mean value
μ
y
of the output is calculated (Kim, page 2, section 2.1 “2 “The likelihood
p
y
T
x
T
,
r
C
is modelled by a Gaussian factorized across the targets
(
x
i
,
y
i
)
i
∈
T
with mean and variance given by passing
x
i
and
r
C
through an MLP.” – the “likelihood” represents the model’s predicted output distribution for each target input
x
i
.).
Regarding claim 6, Kim in view of Crego teaches all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1. Kim in view of Crego further teaches::
The method as recited in claim 1, wherein an uncertainty of the neural network is predicted based on the model uncertainty and on the variance of the output (Kim, page 2, section 2.1 “and “The latent variable version of the NP model includes a global latent z to account for uncertainty in the predictions of
y
T
for a given observed (
(
x
C
,
y
C
)
. It is incorporated into the model via a latent path that complements the deterministic path described above.” and “
p
y
T
x
T
,
x
C
,
y
C
:
=
∫
p
y
T
x
T
,
r
C
,
z
q
z
s
C
d
z
… The likelihood is referred to as the
d
e
c
o
d
e
r
, and
q
,
r
,
s
form the
e
n
c
o
d
e
r
.” – the network predicts uncertainty in its outputs by integrating over the latent variable z, which models global (task-level) uncertainty, and by using the decoder likelihood, which includes the variance of the output distribution).
Regarding claim 7, Kim discloses:
A non-transitory architecture of a neural network including a neural process, the neural network being configured for controlling a technical system by assessing uncertainties in a model, the model modeling a technical system and/or a system behavior of the technical system, the neural network comprising (Abstract, “Neural Processes (NPs) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context… Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled.” – describes a neural-network architecture – specifically, a neural process – that is inherently a technical system implemented by computational components configured to process input data and generate predictive outputs.):
at least one decoder section trained to determine a variance of an output of the model based on a model uncertainty (Kim, section 2.1 “The likelihood
p
y
T
x
T
,
r
C
is modelled by a Gaussian factorized across the targets
(
x
i
,
y
i
)
i
∈
T
with mean and variance given by passing
x
i
and
r
C
through an MLP.” And “The latent variable version of the NP model includes a global latent z to account for uncertainty…It is incorporated into the model via a latent path that complements the deterministic path described above…“
p
y
T
x
T
,
x
C
,
y
C
:
=
∫
p
y
T
x
T
,
r
C
,
z
q
z
s
C
d
z
… The likelihood is referred to as the
d
e
c
o
d
e
r
, and
q
,
r
,
s
form the
e
n
c
o
d
e
r
.” – the decoder (likelihood network) produces the mean and variance of the predicted results. Because the variance depends on the latent variable z, which represents model uncertainty, the decoder is trained to determine the variance of the output based on model uncertainty.).
However, Kim does not teach but Kim in view of Crego teaches the following limitations:
the neural network being configured to: determining a parameter based on the variance (Crego, paragraph [0026] “To use the error models, the autonomous vehicle may determine estimated locations of an object at a future time based at least in part on the outputs from the components and the error models associated with the components. The estimated locations may correspond to a probability distribution, such as a Gaussian distribution, of locations. In some instances, the autonomous vehicle determines the estimated locations of the object by initially determining the respective probability distributions associated with each of the components and/or the parameters. The autonomous vehicle may then determine the estimated locations using the probability distributions for all of the components and/or parameters.” [0039] “In some instances, the autonomous vehicle and/or the one or more computing devices can determine the uncertainties associated with the components. For example, the autonomous vehicle and/or the one or more computing devices may input the input data into a component multiple times in order to receive multiple outputs (e.g., parameters) from the component.” [0108] “In some instances, aspects of some or all of the components discussed herein can include any models, algorithms, and/or machine learning algorithms. For example, in some instances, the components in the memory 1016 and 1022 can be implemented as a neural network.” – Crego teaches generating outputs including parameters and determining probability distributions associated with the parameters. Crego further teaches that the probability distributions may be Gaussian distributions. A Guassian distribution necessarily includes a variance term and therefore corresponds to the claimed variance under the broadest reasonable interpretation. Crego additionally teaches determining uncertainty using distributions. Further, Crego teaches the disclosed components may be implemented using machine learning algorithms and neural networks. Accordingly, Crego teaches determining a parameter based on the variance.); and
on the basis of the parameter, control an operation of at least one of: a signal processing by a filter that filters an input signal, a resource allocation by a telecommunication network, and a movement of a semi-autonomous vehicle or robot (Crego, paragraph [0037] “ the autonomous vehicle may determine a total uncertainty associated with navigating the autonomous vehicle based at least in part on the uncertainty models used to determine the estimated locations of the autonomous vehicle and the uncertainty models used to determine the estimated locations of the object(s). The autonomous vehicle may then generate different routes and perform similar processes for determining the total uncertainties associated with the different routes. Additionally, the autonomous vehicle may select the route that includes the lowest uncertainty.” [0041] “the vehicle 102 may be navigating along a trajectory 104 within the environment 100.” [0040] “Although discussed in the context of an autonomous vehicle, the methods, apparatuses, and systems described herein may be applied to a variety of systems (e.g., a sensor system or a robotic platform), and are not limited to autonomous vehicles.” – Crego teaches determining uncertainty associated with navigating an autonomous vehicle, generating multiple routes, and selecting a route having the lowest uncertainty. Under the broadest reasonable interpretation, the selected route corresponds to a parameter used to control navigation of the vehicle. Crego further teaches navigating the vehicle along a trajectory and applicability to robotic platforms. Accordingly, Crego teaches controlling a technical system by controlling movement of semi-autonomous vehicle or robot on the basis of the parameter.).
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having Kim and Crego before them, to incorporate the variance and uncertainty determinations of Kim into the collision monitoring and vehicle navigation techniques of Crego. One would have been motivated to make such a combination in order to use variance-derived uncertainty information when processing vehicle parameters and determining vehicle operation decisions. This would improve decision making under uncertain operating conditions.
Regarding claim 8, Kim discloses:
wherein the neural network includes at least one encoder section, the encoder section being trained to determine the model uncertainty as a variance
σ
z
2
and/or a mean value
μ
z
of a Gaussian distribution via a latent variable z from a set of contexts
D
C
of observations, where
p
z
D
C
=
N
(
z
|
μ
z
,
σ
z
2
)
(Kim, section 2.1, “The latent variable version of the NP model includes a global latent z to account for uncertainty in the predictions of
y
T
for a given observed (
x
C
,
y
C
)
. It is incorporated into the model via a latent path that complements the deterministic path described above. Here z is modelled by a factorized Gaussian parametrised by
s
C
:
=
s
(
x
C
,
y
C
)
,
with s being a function of the same properties as
r
” Page 11, appendix A “The latent path outputs
μ
z
,
ω
z
∈
R
d
, which parameterises
q
z
s
C
=
N
z
μ
z
,
0.1
+
0.9
σ
ω
where
σ
is the sigmoid function
.
” – describe an encoder section (latent path) that processes the set of context observations
(
x
C
,
y
C
)
to produce a factorized Gaussian representing the latent variable z. The encoder outputs a mean
μ
z
and another parameter
ω
z
that determines the variance of that Gaussian.).
Regarding claim 9, Kim discloses:
wherein the neural network includes at least one further decoder section, the further decoder section being trained to determine a mean value
μ
y
of the output based on an input point x and on a latent sample z (Kim, section 2.1 “The likelihood
p
y
T
x
T
,
r
C
is modelled by a Gaussian factorized across the targets
(
x
i
,
y
i
)
i
∈
T
with mean and variance given by passing
x
i
and
r
C
through an MLP.” And “The likelihood is referred to as the
d
e
c
o
d
e
r
, and
q
,
r
,
s
form the
e
n
c
o
d
e
r
.” And “The latent variable version of the NP model includes a global latent z to account for uncertainty in the predictions of
y
T
for a given observed (
x
C
,
y
C
)
. It is incorporated into the model via a latent path that complements the deterministic path described above.” – describes a decoder network (the “likelihood”) that outputs the mean and variance of the model’s predictions. The latent-variable version, the decoder receives both the input x and a latent sample z drawn from the encoder’s latent distribution, and uses these as inputs to the MLP that produces the predicted mean value
μ
y
of the output.).
Regarding claim 11, Kim discloses:
The method as recited in claim 1, further comprising ascertaining an inadmissible deviation of the system behavior of the technical system from a standard value range based on the variance of the output of the model (Kim, page 5 “We first explore the (A)NPs trained on data that is generated from a Gaussian Process with a squared-exponential kernel and small likelihood noise… Thus we use the same encoder/decoder architecture for NP and ANP, except for the cross-attention. See Appendix B for experimental details… In Figure 3 (right) we visualise the learned conditional distribution for a qualitative comparison of the attention mechanisms. The context is drawn from the GP with the hyperparameter values that give the most fluctuation. Note that the predictive mean of the NP underfits the context, and tries to explain the data by learning a large likelihood noise. Laplace shows similar behaviour, whereas dot product attention gives predictive means that accurately predict almost all context points.” – the model computes a predictive variance as part of its output distribution, and this variance reveals when the model’s predictions underfit the context or produce excessive noise compared to the Gaussian Process baseline. In other words, by examining the model’s output variance and the corresponding predicted mean, the process determines when the neural network’s behavior departs from the normal or acceptable range of expected outputs.).
Claims 10 are rejected under the 35 U.S.C. 103 as being unpatentable over Kim et al., (NPL: “Attentive Neural Processes” (Published: 2019)) in view of Crego et al., (Pub. No.: US 20210374545 A1 (Filed: 2021)) in view of Kim, Hyun Woo et al., (Pub. No: US 20210374545 A1 (Filed: 2021)).
Regarding claim 10, Kim in view of Kim, Hyun Woo teaches the following limitation:
A device that includes a neural network including a neural process, the neural network being configured for controlling a technical system by assessing uncertainties in a model, the model modeling the technical system and/or a system behavior of the technical system, the neural network including at least one decoder section trained to determine a variance of an output of the model based on a model uncertainty (Kim, Hyun Woo, paragraph [0053] “the AI agent 200 programmed and executed by the computing device 100 or the processor 121 may be configured to include two artificial neural networks which include a policy network 210 and a value network 220.” Kim, section 2.1 “The likelihood
p
y
T
x
T
,
r
C
is modelled by a Gaussian factorized across the targets
(
x
i
,
y
i
)
i
∈
T
with mean and variance given by passing
x
i
and
r
C
through an MLP.” And “The latent variable version of the NP model includes a global latent z to account for uncertainty in the predictions of
y
T
for a given observed (
x
C
,
y
C
)
. It is incorporated into the model via a latent path that complements the deterministic path described above…
p
y
T
x
T
,
x
C
,
y
C
:
=
∫
p
y
T
x
T
,
r
C
,
z
q
z
s
C
d
z
… The likelihood is referred to as the
d
e
c
o
d
e
r
, and
q
,
r
,
s
form the
e
n
c
o
d
e
r
.” -Kim teaches a decoder (likelihood) that outputs variance and uses a latent variable z to represent model uncertainty.).
However, Kim does not teach but Kim in view of Crego teaches the following limitations:
the device being configured to: determining a parameter based on the variance (Crego, paragraph [0026] “To use the error models, the autonomous vehicle may determine estimated locations of an object at a future time based at least in part on the outputs from the components and the error models associated with the components. The estimated locations may correspond to a probability distribution, such as a Gaussian distribution, of locations. In some instances, the autonomous vehicle determines the estimated locations of the object by initially determining the respective probability distributions associated with each of the components and/or the parameters. The autonomous vehicle may then determine the estimated locations using the probability distributions for all of the components and/or parameters.” [0039] “In some instances, the autonomous vehicle and/or the one or more computing devices can determine the uncertainties associated with the components. For example, the autonomous vehicle and/or the one or more computing devices may input the input data into a component multiple times in order to receive multiple outputs (e.g., parameters) from the component.” [0108] “In some instances, aspects of some or all of the components discussed herein can include any models, algorithms, and/or machine learning algorithms. For example, in some instances, the components in the memory 1016 and 1022 can be implemented as a neural network.” – Crego teaches generating outputs including parameters and determining probability distributions associated with the parameters. Crego further teaches that the probability distributions may be Gaussian distributions. A Guassian distribution necessarily includes a variance term and therefore corresponds to the claimed variance under the broadest reasonable interpretation. Crego additionally teaches determining uncertainty using distributions. Further, Crego teaches the disclosed components may be implemented using machine learning algorithms and neural networks. Accordingly, Crego teaches determining a parameter based on the variance.); and
on the basis of the parameter, control an operation of at least one of: a signal processing by a filter that filters an input signal, a resource allocation by a telecommunication network, and a movement of a semi-autonomous vehicle or robot (Crego, paragraph [0037] “ the autonomous vehicle may determine a total uncertainty associated with navigating the autonomous vehicle based at least in part on the uncertainty models used to determine the estimated locations of the autonomous vehicle and the uncertainty models used to determine the estimated locations of the object(s). The autonomous vehicle may then generate different routes and perform similar processes for determining the total uncertainties associated with the different routes. Additionally, the autonomous vehicle may select the route that includes the lowest uncertainty.” [0041] “the vehicle 102 may be navigating along a trajectory 104 within the environment 100.” [0040] “Although discussed in the context of an autonomous vehicle, the methods, apparatuses, and systems described herein may be applied to a variety of systems (e.g., a sensor system or a robotic platform), and are not limited to autonomous vehicles.” – Crego teaches determining uncertainty associated with navigating an autonomous vehicle, generating multiple routes, and selecting a route having the lowest uncertainty. Under the broadest reasonable interpretation, the selected route corresponds to a parameter used to control navigation of the vehicle. Crego further teaches navigating the vehicle along a trajectory and applicability to robotic platforms. Accordingly, Crego teaches controlling a technical system by controlling movement of semi-autonomous vehicle or robot on the basis of the parameter.).
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having Kim, Kim Hyun Woo, and Crego before them, to incorporate the model uncertainty methodology of Kim into the hardware architecture system of Kim Hyun Woo and utilizing the resulting uncertainty information in the vehicle operation techniques of Crego. One would have been motivated to make such a combination in order to implement neural-network uncertainty processing on a computer device using known processor and memory components and apply the resulting uncertainty information when processing parameters associated with operation of a technical system.
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 Daravanh Phakousonh whose telephone number is (571)272-6324. The examiner can normally be reached Mon - Thurs 7 AM - 5 PM, Every other Friday 7 AM - 4PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at 571-272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Daravanh Phakousonh/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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