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 .
DETAILED ACTION
This action is responsive to the Application filed on 07/07/2022
Claims 1-20 are pending in the case. Claims 1, 10 and 16 are independent claims
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 and similar claims in structure are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter. The limitation “approximation of a residual” renders the claim indefinite. The term “approximation” broadens the ambiguity because it does not clarify how close the estimate must be, what method is used to generate it, or what accuracy threshold qualifies as an approximation. The term “residual” has multiple certain boundaries, it is unclear terminology failing to particularly point out and distinctly claim the invention.
Claim 1 and similar claims in structure 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 limitation “causing control of a controllable system … determined at least in part based on the synthetic data cases in the synthetic data” is considered to be indefinite because it fails to provide reasonable certainty as to the scope of the claimed action. Specifically, it is unclear whether “causing control” requires: (a) initiating a process that may later result in control or (b) direct transmission of a control command to the controllable system. Furthermore, the limitation “determined at least in part based on the synthetic data cases in the synthetic data cases” is indefinite. It is unclear whether the claim requires determination based on the synthetic data cases themselves, the synthetic data generally, or synthetic data cases contained within a larger synthetic dataset.
Accordingly, one of the ordinary skill in the art would not be reasonably apprised of the means and bounds of the claim, and the claim is therefore indefinite.
Claims 1, 10 and 16 are also rejected under 35 U.S.C. 112(b) as indefinite because of semicolon, periods and commas misuse of method, system and non-transitory computer readable medium claims.
Claim 3 is rejected under 35 U.S.C 112(b) as indefinite because the phrase “the identifier” lacks antecedent basis. Claim 3 refers to terms that were not clearly introduced earlier in claim 2 (or claim 1) before being referenced with “the identifier contribution allocation” or in a definite manner.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an
abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether
the claim is directed to one of the four statutory categories of invention, i.e., process, machine,
manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory
categories, the second step in the analysis is to determine whether the claim is directed to a
judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong
(Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g.,
mathematical concepts, mental processes, certain methods of organizing human activity). If it is
determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds
to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2
that the claims do not integrate the judicial exception into a practical application, the analysis
proceeds to determining whether the claim is a patent-eligible application of the exception (Step
2B). If an abstract idea is present in the claim, any element or combination of elements in the
claim must be sufficient to ensure that the claim integrates the judicial exception into a practical
application, or else amounts to significantly more than the abstract idea itself. Applicant is
advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See
MPEP § 2106.03.
Claim(s) 1-9 are drawn to a method, claims 10-15 are drawn to a system and claims 16-
20 are drawn to a non-transitory computer readable medium, therefore each of these claim
groups falls under one of four categories of statutory subject matter (machine/products/apparatus,
process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are
directed to a judicially recognized exception of an abstract idea without significant more (Step
2A, see below). Independent claims 1, 10 and 16 are non-verbatim but similar in claim
construction, hence share the same rationale that the claimed inventions are directed to no
statutory subject matter as follows:
Regarding claim 1:
Claim 1 recites : A method comprising: receiving a request for generation of synthetic data based on a set of training data cases;
for each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual.
determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual.
causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data;
wherein the method is performed by one or more computing devices
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1)
Claim 1 is directed to an abstract idea, specifically, mental processes and mathematical concept See MPEP §2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C)..
Independent claim 1 recites in part:
for each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual
The limitation above is broadly and reasonably interpreted as a mental process and mathematical concept. For example, one can figure out value missing or not-yet-set making a prediction using estimated error amount. See MPEP § 2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C).
determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residualThe limitation above is broadly and reasonably interpreted as a mental process and mathematical concept. For example, one can figure out a value after the first value is missing or not-yet-set by making a prediction using an estimated error amount. See MPEP § 2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial
exception into a practical application? See MPEP § 2106.04(d).
Independent claim 1 recites in part:
A method comprising: receiving a request for generation of synthetic data based on a set of training data cases, as drafted, amounts to insignificant extra-solution activity (e.g., pre-solution
activity, gathering information). See MPEP §§ 2106.04(d), 2106.05(g).
causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data, as drafted amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer-based reasoning model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
wherein the method is performed by one or more computing devices, as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 1 recites in part:
A method comprising: receiving a request for generation of synthetic data based on a set of training data cases, as drafted, amounts to insignificant extra-solution activity (e.g., pre-solution
activity, gathering information). See MPEP §§ 2106.04(d), 2106.05(g).
causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data, as drafted amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer-based reasoning model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
wherein the method is performed by one or more computing devices, as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 10
Claim 10 recites: A system for performing a machine-executed operation involving instructions, wherein said instructions are instructions which, when executed by one or more computing devices, cause performance of a method comprising:
receiving a request for generation of synthetic data based on a set of training data cases;
for each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual.
determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual.
causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data;
wherein the method is performed by one or more computing devices
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1)
Claim 10 is directed to an abstract idea, specifically, mental processes and mathematical concept See MPEP §2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C)..
Independent claim 10 recites in part:
for each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual
The limitation above is broadly and reasonably interpreted as a mental process and mathematical concept. For example, one can figure out value missing or not-yet-set making a prediction using estimated error amount. See MPEP § 2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C).
determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual
The limitation above is broadly and reasonably interpreted as a mental process and mathematical concept. For example, one can figure out a value after the first value is missing or not-yet-set by making a prediction using an estimated error amount. See MPEP § 2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial
exception into a practical application? See MPEP § 2106.04(d).
Independent claim 10 recites in part:
A system for performing a machine-executed operation involving instructions, wherein said instructions are instructions which, when executed by one or more computing devices, cause performance of a method comprising, as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
receiving a request for generation of synthetic data based on a set of training data cases, as drafted, amounts to insignificant extra-solution activity (e.g., pre-solution
activity, gathering information). See MPEP §§ 2106.04(d), 2106.05(g).
causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data, as drafted amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer-based reasoning model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
wherein the method is performed by one or more computing devices, as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 10 recites in part:
A system for performing a machine-executed operation involving instructions, wherein said instructions are instructions which, when executed by one or more computing devices, cause performance of a method comprising, as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
receiving a request for generation of synthetic data based on a set of training data cases, as drafted, amounts to insignificant extra-solution activity (e.g., pre-solution
activity, gathering information). See MPEP §§ 2106.04(d), 2106.05(g).
causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data, as drafted amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer-based reasoning model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
wherein the method is performed by one or more computing devices, as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 16
Claim 16 recites: A non-transitory computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a method of receiving a request for generation of synthetic data based on a set of training data cases;
for each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual.
determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual.
causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data;
wherein the method is performed by one or more computing devices
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1)
Claim 10 is directed to an abstract idea, specifically, mental processes and mathematical concept See MPEP §2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C)..
Independent claim 16 recites in part:
for each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual
The limitation above is broadly and reasonably interpreted as a mental process and mathematical concept. For example, one can figure out value missing or not-yet-set making a prediction using estimated error amount. See MPEP § 2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C).
determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual
The limitation above is broadly and reasonably interpreted as a mental process and mathematical concept. For example, one can figure out a value after the first value is missing or not-yet-set by making a prediction using an estimated error amount. See MPEP § 2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial
exception into a practical application? See MPEP § 2106.04(d).
Independent claim 16 recites in part:
A non-transitory computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a method of: as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
receiving a request for generation of synthetic data based on a set of training data cases, as drafted, amounts to insignificant extra-solution activity (e.g., pre-solution
activity, gathering information). See MPEP §§ 2106.04(d), 2106.05(g).
causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data, as drafted amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer-based reasoning model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
wherein the method is performed by one or more computing devices, as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 16 recites in part:
A non-transitory computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a method of: as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
receiving a request for generation of synthetic data based on a set of training data cases, as drafted, amounts to insignificant extra-solution activity (e.g., pre-solution
activity, gathering information). See MPEP §§ 2106.04(d), 2106.05(g).
causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data, as drafted amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer-based reasoning model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
wherein the method is performed by one or more computing devices, as drafted only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-9 which are dependent on claim 1, claims 11-15 which are dependent on claim 10 and claims 17-20 which are dependent on claim 16, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Claims 2, 8, 11 and 17 are dependent on claims 1, 10 and 16 respectively, and include mental concept- concepts performed on pen and paper (including an observation, evaluation, judgement, opinion). For example, a person could conceptually do this with a spreadsheet or by hand: Review a list of training cases, Check conditions, Decide which identifiers contribute more and Select the most relevant cases as “focal” cases.
Claims 3, 12 and 18 are dependent on claims 2, 11 and 17 respectively, and include mental concept- concepts performed on pen and paper (including an observation, evaluation, judgement, opinion). For example, a human analyst could do this manual with a table, spreadsheet, calculator, or even pen and paper for smaller datasets.
Claims 4, 13 and 19 are dependent on claims 3, 12 and 18 respectively, and include mental concept- concepts performed on pen and paper (including an observation, evaluation, judgement, opinion). For example, a human analyst could do this manual with a table, spreadsheet, calculator, or even pen and paper for smaller datasets.
Claims 5, 14 and 20 are dependent on claims 1, 10 and 16 respectively, and include mental concept- concepts performed on pen and paper (including an observation, evaluation, judgement, opinion). For example, a human analyst could do this manual with a table, spreadsheet, calculator, or even pen and paper for smaller datasets.
Claims 6 and 15 are dependent on claims 3 and 12 respectively, and include a mental concept and mathematical concept because it uses category counts and numerical functions to determine data importance.
Claim 7 is dependent on claim 3, and include mental concept- concepts performed on pen and paper (including an observation, evaluation, judgement, opinion). For example, a human analyst could do this manual with a table, spreadsheet, calculator, or even pen and paper for smaller datasets.
Claim 8 is dependent on claim 1, and include mental concept- concepts performed on pen and paper (including an observation, evaluation, judgement, opinion). For example, a human analyst could do this manual with a table, spreadsheet, calculator, or even pen and paper for smaller datasets.
Claim 9 is dependent on claim 1, and include mental concept- concepts performed on pen and paper (including an observation, evaluation, judgement, opinion). For example, a human analyst could do this manual with a table, spreadsheet, calculator, or even pen and paper for smaller datasets.
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.
Claim(s) 1, 9-10 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Iandola et al. (Pub No.: 20180275658 A1), hereinafter referred to as Iandola, in view of Mishra et al. (US Patent No.10,733,515 B1), hereinafter referred to as Mishra.
With respect to claim 1, Iandola disclose:
A method comprising: receiving a request for generation of synthetic data based on a set of training data cases (In paragraph [0050], Iandola discloses the data synthesizing module 325 may generate synthetic sensor data in response to one or more requests from modules of the model training system.)
Causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data (In paragraph [0029], Iandola discloses an autonomous control system, synthetic data generated from simulated environments to train computer models.)
Wherein the method is performed by one or more computing devices (In Fig. 1 and paragraph [0034], Iandola discloses a network environment, autonomous control system, model training systems, sensor collection system, processors, and machine-learned model implemented across system components.)
With respect to claim 1, Iandola do not explicitly disclose:
for each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual
determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual
However, it is known by Mishra to disclose:
For each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual (The specification [0018] defies "undetermined feature" as "the term "undetermined features" encompasses its plain and ordinary meaning, including, but not limited to those features for which a value has not yet been determined, and for which there is no condition or conditional requirement. Therefore, under BRI, the Examiner will interpret "undetermined feature" as any feature. In Col. 5-6, lines 58-8, Mishra discloses determining a feature value (missing feature) using a residual-based calculation. Furthermore, Iandola discloses a first set of sensing characteristics.
Determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual (In Col. 7, lines 42-46, Mishra discloses that the missing values are temporarily treated as null or zero placeholders, so the system can run calculations. Then the system calculates a residual value. )
Iandola and Mishra are analogous pieces of art because both references concern synthetic data used to train computer models. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Iandola, with generating the set of synthesized sensor data as taught by Iandola, with determining a feature value using an adjusted residual as taught by Mishra. The motivation for doing so would have been to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment (See [0007] of Iandola.)
Regarding claim 9, Iandola in view of Mishra discloses elements of claim 1. In addition, Mishra disclose:
The method of claim 1, wherein determining one or more focal training data cases from among the set of training data cases based at least in part on the value for the first undetermined feature and any previously-determined values for subsequent undetermined features comprises: determining the one or more focal training data cases from among the set of training data cases based at least in part on the value for the first undetermined feature and any previously-determined values for subsequent undetermined features and the one or more conditions (In Col. 6, lines 66–18, Mishra discloses determining missing feature values, doing so on a feature-by-feature basis using previously predicted/inputted values in later iteration, repeating until the values are filled. )
With respect to claim 10, Iandola disclose:
A system for performing a machine-executed operation involving instructions, wherein said instructions are instructions which, when executed by one or more computing devices, cause performance of a method comprising: receiving a request for generation of synthetic data based on a set of training data cases (In paragraph [0050], Iandola discloses the data synthesizing module 325 may generate synthetic sensor data in response to one or more requests from modules of the model training system.)
Causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data (In paragraph [0029], Iandola discloses an autonomous control system, synthetic data generated from simulated environments to train computer models.)
Wherein the method is performed by one or more computing devices (In Fig. 1 and paragraph [0034], Iandola discloses a network environment, autonomous control system, model training systems, sensor collection system, processors, and machine-learned model implemented across system components.)
With respect to claim 10, Iandola do not explicitly disclose:
for each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual
determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual
However, it is known by Mishra to disclose:
For each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual (The specification [0018] defies "undetermined feature" as "the term "undetermined features" encompasses its plain and ordinary meaning, including, but not limited to those features for which a value has not yet been determined, and for which there is no condition or conditional requirement. Therefore, under BRI, the Examiner will interpret "undetermined feature" as any feature. In Col. 5-6, lines 58-8, Mishra discloses determining a feature value (missing feature) using a residual-based calculation. Furthermore, Iandola discloses a first set of sensing characteristics.
Determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual (In Col. 7, lines 42-46, Mishra discloses that the missing values are temporarily treated as null or zero placeholders, so the system can run calculations. Then the system calculates a residual value. )
Iandola and Mishra are analogous pieces of art because both references concern synthetic data used to train computer models. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Iandola, with generating the set of synthesized sensor data as taught by Iandola, with determining a feature value using an adjusted residual as taught by Mishra. The motivation for doing so would have been to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment (See [0007] of Iandola.)
With respect to claim 16, Iandola disclose:
A non-transitory computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a method of receiving a request for generation of synthetic data based on a set of training data cases (In paragraph [0050], Iandola discloses the data synthesizing module 325 may generate synthetic sensor data in response to one or more requests from modules of the model training system.)
Causing control of a controllable system using a computer-based reasoning model that was determined at least in part based on the synthetic data cases in the synthetic data (In paragraph [0029], Iandola discloses an autonomous control system, synthetic data generated from simulated environments to train computer models.)
Wherein the method is performed by one or more computing devices (In Fig. 1 and paragraph [0034], Iandola discloses a network environment, autonomous control system, model training systems, sensor collection system, processors, and machine-learned model implemented across system components.)
With respect to claim 16, Iandola do not explicitly disclose:
for each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual
determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual
However, it is known by Mishra to disclose:
For each synthetic data case in the synthetic data, determining a first undetermined feature in the synthetic data case based at least in part on an approximation of a residual (The specification [0018] defies "undetermined feature" as "the term "undetermined features" encompasses its plain and ordinary meaning, including, but not limited to those features for which a value has not yet been determined, and for which there is no condition or conditional requirement. Therefore, under BRI, the Examiner will interpret "undetermined feature" as any feature. In Col. 5-6, lines 58-8, Mishra discloses determining a feature value (missing feature) using a residual-based calculation. Furthermore, Iandola discloses a first set of sensing characteristics.
Determining subsequent undetermined features in the synthetic data case based at least in part on an approximation of a residual (In Col. 7, lines 42-46, Mishra discloses that the missing values are temporarily treated as null or zero placeholders, so the system can run calculations. Then the system calculates a residual value. )
Iandola and Mishra are analogous pieces of art because both references concern synthetic data used to train computer models. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Iandola, with generating the set of synthesized sensor data as taught by Iandola, with determining a feature value using an adjusted residual as taught by Mishra. The motivation for doing so would have been to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment (See [0007] of Iandola.)
Claim(s) 2-8, 11-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Iandola, in view of Mishra and further in view of Walters et al. (Pub No.: 20200012902 A1), hereinafter referred to as Walters.
Regarding claim 2, Iandola in view of Mishra discloses elements of claim 1. Iandola in view of Mishra do not appear to explicitly disclose:
The method of claim 1, wherein determining one or more focal training data cases from among the set of training data cases based at least in part on the one or more conditions comprises: determining one or more focal training data cases from among the set of training data cases based at least in part on identifier contribution allocation
However, Walters disclose the limitation (In paragraph [0086-0087], Walters discloses that the synthetic-data system determines segment parameters for the data segments 408, the system determines distribution measures of the data segments 410. )
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Iandola’s data synthesis autonomous control system and Mishra’s generating a residual value to include Walters method for synthetic data generation. The motivation for doing so would have been to improve conventional approaches to synthetic data generation (See [0129] of Walters.)
Regarding claim 3, Iandola in view of Mishra and further in view of Walters discloses elements of claim 2. In addition, Iandola disclose:
The method of claim 2, further comprising determining the identifier contribution allocation comprises based at least in part on a function of an aggregate identifier contribution allocation for each value of an associated identifier and a number of occurrences of each value of the identifier (In Col.6, lines 14–27, Iandola discloses each record. The absolute error is the absolute value of the difference between the actual value and the predicted value. The average of the absolute error is the MAE. The residual may be adjusted for each missing feature value in the direction of zero.)
Regarding claim 4, Iandola in view of Mishra and further in view of Walters discloses elements of claim 3. In addition, Iandola disclose:
The method of claim 3, further comprising determining the aggregate identifier contribution allocation for each value of the identifier based at least in part on setting an identical aggregate identifier contribution allocation for each value of the identifier (In Col.6, lines 14–27, Iandola discloses each record. The absolute error is the absolute value of the difference between the actual value and the predicted value. The average of the absolute error is the MAE. The residual may be adjusted for each missing feature value in the direction of zero.)
Regarding claim 5, Iandola in view of Mishra and further in view of Walters discloses elements of claim 3. In addition, Iandola disclose:
The method of claim 3, further comprising determining the aggregate identifier contribution allocation for each value of the identifier based at least in part on setting a random aggregate identifier contribution allocation for each value of the identifier (In Col.9, lines 6–12, Iandola discloses that a dataset is partitioned into Dataset A and Dataset B. In some cases, the partitioning me be based upon complete versus incomplete records. In other instances, the partitioning is done completely at random, such that missing values are randomly distributed through both Dataset A and Dataset B.)
Regarding claim 6, Iandola in view of Mishra and further in view of Walters discloses elements of claim 3. In addition, Walters disclose:
The method of claim 3, further comprising determining the aggregate identifier contribution allocation for each value of the identifier based at least in part on a function of a total number of cases for each value of the identifier and a total number of cases for the identifier (In paragraph [0087], Walters discloses determining distribution measures of data segments, consistent with the disclosed embodiment. The distribution measures may include any distribution measures as previously described (e.g., in reference to segmented 338) or any other distribution measures. For example, distribution measures may include a sequence of means corresponding to a sequence of data segments, consistent with the disclosed embodiment.)
Regarding claim 7, Iandola in view of Mishra and further in view of Walters discloses elements of claim 3. In addition, Walters disclose:
The method of claim 3, further comprising determining the aggregate identifier contribution allocation for each value of the identifier based at least in part on setting a received aggregate identifier contribution allocation for each value of the identifier (In paragraph [0087], Walters discloses determining distribution measures of data segments, consistent with the disclosed embodiment. The distribution measures may include any distribution measures as previously described (e.g., in reference to segmented 338) or any other distribution measures. For example, distribution measures may include a sequence of means corresponding to a sequence of data segments, consistent with the disclosed embodiment.)
Regarding claim 8, Iandola in view of Mishra discloses elements of claim 1. Iandola in view of Mishra do not appear to explicitly disclose:
The method of claim 1, wherein determining one or more focal training data cases from among the set of training data cases based at least in part on the one or more conditions comprises: determining one or more focal training data cases from among the set of training data cases based at least in part on two or more identifier contribution allocations
However, Walters discloses the limitation (In paragraph [0089], Walters synthetic-data system 102 may train a distribution model to generate synthetic data segments based on segment parameters and distribution measures, consistent with disclosed embodiment. For example, the distribution model may be trained based on a performance metric as previously described in reference to the segmented.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Iandola’s data synthesis autonomous control system and Mishra’s generating a residual value to include Walters method for synthetic data generation. The motivation for doing so would have been to improve conventional approaches to synthetic data generation (See [0129] of Walters.)
Regarding claim 11, Iandola in view of Mishra discloses elements of claim 10. Iandola in view of Mishra do not appear to explicitly disclose:
The system of claim 10, wherein determining one or more focal training data cases from among the set of training data cases based at least in part on the one or more conditions comprises: determining one or more focal training data cases from among the set of training data cases based at least in part on identifier contribution allocation
However, Walters disclose the limitation (In paragraph [0086-0087], Walters discloses that the synthetic-data system determines segment parameters for the data segments 408, the system determines distribution measures of the data segments 410. )
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Iandola’s data synthesis autonomous control system and Mishra’s generating a residual value to include Walters method for synthetic data generation. The motivation for doing so would have been to improve conventional approaches to synthetic data generation (See [0129] of Walters.)
Regarding claim 12, Iandola in view of Mishra and further in view of Walters discloses elements of claim 11. In addition, Iandola disclose:
The method of claim 2, further comprising determining the identifier contribution allocation comprises based at least in part on a function of an aggregate identifier contribution allocation for each value of an associated identifier and a number of occurrences of each value of the identifier (In Col.6, lines 14–27, Iandola discloses each record. The absolute error is the absolute value of the difference between the actual value and the predicted value. The average of the absolute error is the MAE. The residual may be adjusted for each missing feature value in the direction of zero.)
Regarding claim 13, Iandola in view of Mishra and further in view of Walters discloses elements of claim 12. In addition, Iandola disclose:
The system of claim 12, wherein the method further comprises determining the aggregate identifier contribution allocation for each value of the identifier based at least in part on setting an identical aggregate identifier contribution allocation for each value of the identifier (In Col.6, lines 14–27, Iandola discloses each record. The absolute error is the absolute value of the difference between the actual value and the predicted value. The average of the absolute error is the MAE. The residual may be adjusted for each missing feature value in the direction of zero.)
Regarding claim 14, Iandola in view of Mishra and further in view of Walters discloses elements of claim 12. In addition, Iandola disclose:
The system of claim 12, wherein the method further comprises determining the aggregate identifier contribution allocation for each value of the identifier based at least in part on setting a random aggregate identifier contribution allocation for each value of the identifier (In Col.9, lines 6–12, Iandola discloses that a dataset is partitioned into Dataset A and Dataset B. In some cases, the partitioning me be based upon complete versus incomplete records. In other instances, the partitioning is done completely at random, such that missing values are randomly distributed through both Dataset A and Dataset B.)
Regarding claim 15, Iandola in view of Mishra and further in view of Walters discloses elements of claim 12. In addition, Walters disclose:
The system of claim 12, wherein the method further comprises determining the aggregate identifier contribution allocation for each value of the identifier based at least in part on a function of a total number of cases for each value of the identifier and a total number of cases for the identifier (In paragraph [0087], Walters discloses determining distribution measures of data segments, consistent with the disclosed embodiment. The distribution measures may include any distribution measures as previously described (e.g., in reference to segmented 338) or any other distribution measures. For example, distribution measures may include a sequence of means corresponding to a sequence of data segments, consistent with the disclosed embodiment.)
Regarding claim 17, Iandola in view of Mishra discloses elements of claim 16. Iandola in view of Mishra do not appear to explicitly disclose:
The non-transitory computer readable medium of claim 16, wherein determining one or more focal training data cases from among the set of training data cases based at least in part on the one or more conditions comprises: determining one or more focal training data cases from among the set of training data cases based at least in part on identifier contribution allocation
However, Walters disclose the limitation (In paragraph [0086-0087], Walters discloses that the synthetic-data system determines segment parameters for the data segments 408, the system determines distribution measures of the data segments 410. )
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Iandola’s data synthesis autonomous control system and Mishra’s generating a residual value to include Walters method for synthetic data generation. The motivation for doing so would have been to improve conventional approaches to synthetic data generation (See [0129] of Walters.)
Regarding claim 18, Iandola in view of Mishra and further in view of Walters discloses elements of claim 17. In addition, Iandola disclose:
The non-transitory computer readable medium of claim 17, wherein the method further comprises determining the identifier contribution allocation comprises based at least in part on a function of an aggregate identifier contribution allocation for each value of an associated identifier and a number of occurrences of each value of the identifier (In Col.6, lines 14–27, Iandola discloses each record. The absolute error is the absolute value of the difference between the actual value and the predicted value. The average of the absolute error is the MAE. The residual may be adjusted for each missing feature value in the direction of zero.)
Regarding claim 19, Iandola in view of Mishra and further in view of Walters discloses elements of claim 18. In addition, Iandola disclose:
The non-transitory computer readable medium of claim 18, wherein the method further comprises determining the aggregate identifier contribution allocation for each value of the identifier based at least in part on setting an identical aggregate identifier contribution allocation for each value of the identifier (In Col.6, lines 14–27, Iandola discloses each record. The absolute error is the absolute value of the difference between the actual value and the predicted value. The average of the absolute error is the MAE. The residual may be adjusted for each missing feature value in the direction of zero.)
Regarding claim 20, Iandola in view of Mishra and further in view of Walters discloses elements of claim 18. In addition, Iandola disclose:
The non-transitory computer readable medium of claim 18, wherein the method further comprises determining the aggregate identifier contribution allocation for each value of the identifier based at least in part on setting a random aggregate identifier contribution allocation for each value of the identifier (In Col.9, lines 6–12, Iandola discloses that a dataset is partitioned into Dataset A and Dataset B. In some cases, the partitioning me be based upon complete versus incomplete records. In other instances, the partitioning is done completely at random, such that missing values are randomly distributed through both Dataset A and Dataset B.)
Conclusion
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EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights