DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-18 have been presented for examination based on the application filed on 12/19/2022, with priority date of 9/20/2022.
Claims 1-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with written description requirement.
Claims 1-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claims 1-18 are rejected under 35 U.S.C 102 as being unpatentable over NPL Andreas Sporr, “Automated HVAC Control Creation on Provisioning and Distributing Side based on Building Information Modelling and Inclusion of Renewable Energy Systems”(2021)
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Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "environmental feature analysis unit", data collection unit", and "synthetic data generation unit" in claims 7-12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Limitation 1: "Environmental feature analysis unit"
Claim Limitation: "an environmental feature analysis unit reading, from the environmental model database, a target environmental model simulating a target environment to generate synthetic data among the plurality of environmental models, and extracting an environmental feature which influences generation of data in the target environment"
Claimed Function: Reading a target environmental model, simulating a target environment, and extracting an environmental feature which influences generation of data.
Prong 1 Analysis: The limitation is expressed in purely functional language. It defines the element by the acts it performs (reading, simulating, extracting) rather than by what the element is structurally.
Prong 2 Analysis: The limitation uses the term "unit," which is recognized as a non-structural generic placeholder or "nonce word". The claim does not recite sufficiently definite structure, materials, or acts to perform the identified functions. A person of ordinary skill in the art (POSITA) would not recognize "environmental feature analysis unit" as a name for a specific structure in the absence of the functional description.
Prong 3 Analysis: The corresponding structure is disclosed as the "environmental feature analysis unit 110" [0069]. In the context of the "synthetic data generation apparatus 1000," [0069] this unit is implemented by the "control unit 100" (processor) [0069] and "storage unit 300" [0069]. For these computer-implemented functions, the specification provides an algorithm in Figure 9, specifically steps S910 ("READ ENVIRONMENTAL MODEL") and S920 ("EXTRACT ENVIRONMENTAL FEATURE"). The specification further describes the use of an "ontology" to determine correlations during the extraction process.
Corresponding Structure Citations: Specification [0014]-[0016], [0011]-[0013], [0017]-[0019]; FIG. 2 (100), FIG. 3 (110), FIG. 9 (S910, S920) [0007]-[0008].
§ 112(f) Invoked? Yes. The three-prong test is satisfied: it uses a generic placeholder, includes functional language, and lacks sufficient structure in the claim.
Limitation 2: "Synthetic data generation unit"
Claim Limitation: "a synthetic data generation unit configuring a synthetic data generation function of a synthetic data generation simulator for the target environmental model so that the synthetic data reflects the environmental feature, and generating the synthetic data by suing the synthetic data generation simulator for the target environmental model".
Claimed Function: Configuring a synthetic data generation function of a synthetic data generation simulator to reflect the environmental feature, and generating synthetic data using the simulator.
Prong 1 Analysis: The limitation is functional, identifying the element by its role in configuring a simulator and generating data.
Prong 2 Analysis: The limitation uses the nonce word "unit". No structural modifiers are present in the claim to denote a specific structural device.
Prong 3 Analysis: The corresponding structure is the "synthetic data generation unit 120" [0069]. It is implemented by the "control unit 100" [0069] and "storage unit 300" [0069]. The required algorithms for these functions are set forth in Figure 9 as steps S930 ("CONFIGURE SYNTHETIC DATA GENERATION FUNCTION") and S940 ("GENERATE SYNTHETIC DATA"). The specification clarifies that generating data may involve using "generative adversarial networks (GAN)" or "variational auto-encoders (VAE)" [0090].
Corresponding Structure Citations: Specification [16], [0022]-[0027]; FIG. 3 (120), FIG. 9 (S930, S940) [0007]-[0008], [0013], [0019].
§ 112(f) Invoked? Yes. The limitation relies on a generic placeholder modified by functional language without reciting structure.
Limitation 3: "data collection unit"
Claimed Limitation: “a data collection unit collecting actual data acquired in the target environment, wherein the synthetic data generation units further matches the data feature of the synthetic data generated by the synthetic data generation simulation for the target environmental model with a data feature to which the actual data belongs.”
Claimed Function: Collecting actual data acquired in the target environment [1].
Prong 1 Analysis: The limitation is purely functional, identifying the step of "collecting" data [1].
Prong 2 Analysis: "Unit" is a nonce word [3]. No structural modifiers (e.g., specific sensors or hardware interfaces) are recited in Claim 10 to perform the collection [1].
Prong 3 Analysis: The specification refers to "data collection unit 130" [6, 7]. It is described as implemented by the processor 100 [8]. Paragraph [29] describes the process of "receiving" the actual data acquired in the actual environment [27]. Paragraph [30] links the acquisition to a request by the analysis unit [19].
Corresponding Structure Citations: Specification [0016], [0029]-[0030]; FIG. 3 (130) [0007]- [0008], [0019], [0027].
§ 112(f) Invoked? Yes.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-18 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The specification fails to provide an adequate written description of the broad genus of "environmental models. [0095][0101][0109]" The specification describes only physical building simulations, but the claims encompass any simulation environment, including non-physical ones. The disclosure does not reasonably convey possession of this broader scope.
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 Rejections - 35 USC § 112
Claims 1-18 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Wands Factors
Breadth of the Claims
Evidence: Claim 1 is broadly drawn to a method comprising "extracting an environmental feature" and "configuring a synthetic data generation function of a synthetic data generation simulator for the target environmental model so that the synthetic data reflects the environmental feature" for any "target environmental model simulating a target environment".
Analysis: The claims encompass a vast, potentially infinite genus of environmental models (e.g., communication, fire, HVAC, etc.) and features. There are no limiting structural definitions for what constitutes a "feature" or how a "function" is configured beyond the functional result that it "reflects" the feature.
Weight: Weighs heavily against enablement. "The more one claims, the more one must enable".
Nature of the Invention
Evidence: The invention relates to an apparatus and method for generating high-quality synthetic data for training artificial intelligence (AI) models by simulating actual environments.
Analysis: This is a complex computer-implemented modeling and simulation technology. It requires translating physical or environmental characteristics into mathematical or algorithmic parameters for a data simulator.
Weight: Weighs against enablement. Complex software/simulation inventions in emerging fields often require higher levels of detailed guidance.
Level of Predictability in the Art
Evidence: The specification acknowledges that existing synthetic data generation methods fail to "normally reflect features of the actual data".
Analysis: The conversion of a generic "environmental feature" (e.g., a "pipe" or "wall material" in a BIM model) into a "synthetic data generation function" (e.g., a specific generative adversarial network (GAN) parameter or a Fire Dynamics Simulator input) is highly unpredictable across different domains. The mapping of a physical attribute to a simulation parameter depends entirely on the specific physics or logic of the target environment.
Weight: Weighs heavily against enablement. In unpredictable arts, the disclosure of a few species may not support broad generic claims.
Amount of Direction or Guidance Presented
Evidence: The specification provides a high-level flowchart in Figure 9 and corresponding description in Paragraphs-. Paragraph states that the apparatus "configures a synthetic data generation function... by processing the feature information".
Analysis: There is a lack of specific "how-to" guidance for the critical "configuring" step (S930). The specification fails to provide an algorithm, mathematical formula, or specific transformation logic to bridge the gap between an extracted feature and a simulator's internal function for arbitrary environments.
Weight: Weighs against enablement. A specification must describe the invention such that the implementation is apparent to one of ordinary skill without undue experimentation.
Presence or Absence of Working Examples
Evidence: The application provides three "Exemplary Embodiments": Communication Data (Embodiment 1), Fire Data (Embodiment 2), and HVAC Data (Embodiment 3).
Analysis: These appear to be prophetic examples rather than working examples with actual results. They describe concepts (e.g., using a BIM model for fire simulation) but do not provide the detailed parameters, source code, or resulting data quality metrics needed to demonstrate a successful implementation.
Weight: Weighs against enablement. While working examples are not strictly required, their absence is a significant factor in unpredictable and undeveloped arts.
Quantity of Experimentation Necessary
Analysis: To practice the full scope of Claim 1—for any arbitrary environment—a POSITA would likely need to engage in extensive research and development for each new domain to determine the appropriate mapping from environmental features to simulation functions. This is not a "routine operation".
Weight: Weighs heavily against enablement.
State of the Prior Art
Evidence: The background states that synthetic data generation is a recently attracting technique but notes the difficulty in making it "well reflect actual data".
Analysis: The field is described as being in an early or developing stage where standard, cross-domain mapping protocols from "features" to "simulation functions" do not yet exist.
Weight: Weighs against enablement. In nascent technologies, the public's end of the bargain requires a full enabling disclosure since POSITA knowledge is limited.
Level of Ordinary Skill in the Art (POSITA)
Analysis: A POSITA would be an expert in computer simulation, modeling, and AI data preparation. While their skill level may be high, even a high level of skill cannot supplement a basic lack of enabling disclosure for the novel aspects of an invention.
Weight: Neutral.
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 1-18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term "Environmental feature" in claims 1-2, 4, 7-10, 13-14, and 16 broadly encompasses any attribute of a simulated space (e.g., material, location, air flow). The limitation "Influences generation of data" in claims 1-2, 7, 13-14 is a purely functional limitation defining the feature by its effect rather than its structure. The term "Reflects environmental feature" in claims 1, 4, 7, 13 is a vague term of degree/quality; the specification does not define a threshold for what constitutes sufficient "reflection. The term "Target environmental model" " in claims 1, 4-7, 11, 13, 17 is a generic term covering any simulation of an actual environment.
Claim limitation “ environmental feature analysis unit, synthetic data generation unit, and data collection unit" invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification fails to disclose a specific algorithm (step-by-step procedure) to perform the claimed functions. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-18 rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process and mathematical concepts without significantly more. The claim(s) recite(s) reading a model, extracting an environmental feature, and configuring a function This judicial exception is not integrated into a practical application because The additional elements beyond the abstract idea include the "target environmental model," "plurality of environmental models," and the "synthetic data generation simulator" which fail to integrate the judicial exception into a practical application because merely implementing an abstract idea on a generic computer does not integrate it into a practical application and while the disclosure asserts an effect of "generating high-quality synthetic data," the claim does not recite specific technical details that improve the functioning of the computer itself or a specific technical field, but instead; the claim uses the computer components (models and simulators) merely as tools to perform the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because The .
Claims [ 1 ]:
Step 1: the claims are drawn to a method and system respectively, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations are bolded for abstract idea/judicial exception identification.
Claim 1
Mapping Under Step 2A Prong 1
A method of synthetic data generation for training an artificial intelligence model,
the method comprising:
reading a target environmental model simulating a target environment to generate
synthetic data among a plurality of environmental models simulating a plurality of actual environments, respectively;
extracting an environmental feature which influences generation of data in the
target environment;
configuring a synthetic data generation function of a synthetic data generation simulator for the target environmental model so that the synthetic data reflects the
environmental feature;
and generating the synthetic data by using the synthetic data generation simulator for
the target environmental model.
The claim is not directed to computer implemented method using any specific computer implemented tool to generate the synthetic data, therefore given BRI can be performed using a pencil and paper.
Mental Processes: The steps of "extracting an environmental feature which influences generation of data" describe the collection and evaluation of information, which are cognitive actions that can be performed in the human mind (see MPEP §2106.04(a)(2), subsection III) (See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674)
Mental Processes: The steps of "extracting an environmental feature which influences generation of data" describe the collection and evaluation of information, which are cognitive actions that can be performed in the human mind (see MPEP §2106.04(a)(2), subsection III) (See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674)
Mental Processes: The steps of "extracting an environmental feature which influences generation of data" describe the collection and evaluation of information, which are cognitive actions that can be performed in the human mind (see MPEP §2106.04(a)(2), subsection III) (See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674) Mathematical Concept: Configuring a synthetic data generation function" relates to mathematical relationships or equations used to produce a data output (as in 2106.04(a)(2) Abstract Idea Groupings)
See mapping under Step2A Prong 2.
The claim recite the abstract idea of mathematical concepts and mental processes. Under its BRI, these cover a mental process including an observation, evaluation, judgement or opinion that could be performed in the human mind or with the aid of pencil and paper.
Step 2A, Prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). As per (1) the additional elements are identified as bolded parts of the limitations in column 1 of the table below, and as per (2) the evaluation is shown in the mapping section of the table.
In accordance with this step, the judicial exception is not integrated into a practical application.
Claim 1
Mapping Under Step 2A Prong 2
A method of synthetic data generation for training an artificial intelligence model,
the method comprising:
reading a target environmental model simulating a target environment to generate synthetic data among a plurality of environmental models simulating a plurality of actual environments, respectively;
extracting an environmental feature which influences generation of data in the
target environment;
configuring a synthetic data generation function of a synthetic data generation simulator for the target environmental model so that the synthetic data reflects the
environmental feature;
and generating the synthetic data by using the synthetic data generation simulator for
the target environmental model.
Generic Computer Implementation: The specification describes these components as being implemented by a "control unit 100 (processor)" [0060] and a "storage unit 300" [0060]. The mere instruction to implement an abstract idea on a generic computer is insufficient to integrate the exception. (discussed in MPEP § 2106.05(f))
Additional Elements: The additional elements beyond the abstract idea include the "target environmental model," "plurality of environmental models," and the "synthetic data generation simulator".
Generic Computer Implementation: The specification describes these components as being implemented by a "control unit 100 (processor)" [0060] and a "storage unit 300" [0060]. The mere instruction to implement an abstract idea on a generic computer is insufficient to integrate the exception. (discussed in MPEP § 2106.05(f))
Meaningful Limits Analysis: While the disclosure asserts an effect of "generating high-quality synthetic data," this is an improvement in the outcome of the abstract idea (data generation) rather than a technical improvement to the functioning of the computer itself or a specific technical field. (See, e.g., Rapid Litigation Management v. CellzDirect, Inc., 827 F.3d 1042, 119 USPQ2d 1370 (Fed. Cir. 2016))
Meaningful Limits Analysis: While the disclosure asserts an effect of "generating high-quality synthetic data," this is an improvement in the outcome of the abstract idea (data generation) rather than a technical improvement to the functioning of the computer itself or a specific technical field. (See, e.g., Rapid Litigation Management v. CellzDirect, Inc., 827 F.3d 1042, 119 USPQ2d 1370 (Fed. Cir. 2016))
Additional Elements: The additional elements beyond the abstract idea include the "target environmental model," "plurality of environmental models," and the "synthetic data generation simulator".
Generic Computer Implementation: The specification describes these components as being implemented by a "control unit 100 (processor)" [0060] and a "storage unit 300" [0060]. The mere instruction to implement an abstract idea on a generic computer is insufficient to integrate the exception. (discussed in MPEP § 2106.05(f))
Insignificant Extra-Solution Activity: The steps of "reading" models and "generating" data using a simulator are characterized as generic computer tasks that generally link the idea to a technological environment. (discussed in MPEP § 2106.05(h)) (discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)))
Additional Elements: The additional elements beyond the abstract idea include the "target environmental model," "plurality of environmental models," and the "synthetic data generation simulator".
Generic Computer Implementation: The specification describes these components as being implemented by a "control unit 100 (processor)" [0060] and a "storage unit 300" [0060]. The mere instruction to implement an abstract idea on a generic computer is insufficient to integrate the exception. (discussed in MPEP § 2106.05(f))
Insignificant Extra-Solution Activity: The steps of "reading" models and "generating" data using a simulator are characterized as generic computer tasks that generally link the idea to a technological environment. (discussed in MPEP § 2106.05(h)) (discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)))
Meaningful Limits Analysis: While the disclosure asserts an effect of "generating high-quality synthetic data," this is an improvement in the outcome of the abstract idea (data generation) rather than a technical improvement to the functioning of the computer itself or a specific technical field. (See, e.g., Rapid Litigation Management v. CellzDirect, Inc., 827 F.3d 1042, 119 USPQ2d 1370 (Fed. Cir. 2016))
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to
significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
This step determines whether the additional elements amount to "significantly more" than the
exception by providing an unconventional technological solution. (see MPEP § 2106.05(g) and see MPEP § 2106.05(h))
The steps of "reading" data from a database (the models), "extracting" features, and "generating" output (synthetic data) are well-understood, routine, and conventional computer functions. The "simulator" and "models" are recited at a high level of generality and represent nothing more than generic computer environments for executing the abstract idea. When viewed as an ordered combination, these elements simply recite the performance of the abstract idea of data parameterization and generation on a generic computer. The claim does not add a specific limitation that is other than what is conventional in the field of data simulation. Therefore, the claim fails to provide an inventive concept.
Claim 2 recites” The method of claim 1, wherein the extracting of the environmental feature which influences generation of data in the target environment is extracting the environmental
feature based on a correlation between the target environment and an application field of the synthetic data.” ” (Mental Processes) (see MPEP § 2106.04(a)(2), subsection III) (Step 2A Prong 1 – mathematical concept MPEP 2106.04(a)(2)(I)(C))
Claim 3 recites “The method of claim 2, wherein the correlation between the target environment and the application field of the synthetic data is determined based on ontology related to the target environment and the application field of the synthetic data.” (see MPEP § 2106.04(a)(2), subsection III) (Step 2A Prong 1 – mathematical concept MPEP2106.04(a)(2)(I)(C))
Claim 4 recites, “ The method of claim 3, wherein the configuring of the synthetic data generation function of the synthetic data generation simulator for the target environmental model so that the synthetic data reflects the environmental feature is modifying a parameter corresponding to each environmental feature among parameters of the synthetic data generation function according to the environmental feature.” (Mental Processes) (see MPEP § 2106.04(a)(2), subsection III) (Step 2A Prong 1 – mathematical concept MPEP 2106.04(a)(2)(I)(C)) (Generic Computer Implementation) (discussed in MPEP § 2106.05(f)) (Insignificant Extra-Solution Activity) (discussed in MPEP § 2106.05(h)) (discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g))) (Additional Element)
Claim 5 recites, “The method of claim 1, further comprising: matching the data feature of the synthetic data generated by the synthetic data generation simulation for the target environmental model with a data feature to which the actual data generated in the target environment belongs.” (Mental Processes) (see MPEP § 2106.04(a)(2), subsection III) (Meaningful Limits) (See, e.g., Rapid Litigation Management v. CellzDirect, Inc., 827 F.3d 1042, 119 USPQ2d 1370 (Fed. Cir. 2016)) (Generic Computer Implementation) (discussed in MPEP § 2106.05(f)) (Insignificant Extra-Solution Activity) (discussed in MPEP § 2106.05(h)) (discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)))
Claim 6 recites, “The method of claim 5, wherein the matching of the data feature of the synthetic data generated by the synthetic data generation simulation for the target environmental model with the data feature to which the actual data generated in the target environment belongs includes receiving the actual data acquired in the target environment, determining a data distribution region of the synthetic data generated by the synthetic data generation simulator for the actual data as a data distribution region to which the actual data belongs, and setting the synthetic data generation simulator to output only synthetic data included in the data distribution region to which the actual data belongs among the
synthetic data generated by the synthetic data generation simulator.” (Mental Processes) (see MPEP § 2106.04(a)(2), subsection III) (Meaningful Limits) (See, e.g., Rapid Litigation Management v. CellzDirect, Inc., 827 F.3d 1042, 119 USPQ2d 1370 (Fed. Cir. 2016)) (Generic Computer Implementation) (discussed in MPEP § 2106.05(f)) (Insignificant Extra-Solution Activity) (discussed in MPEP § 2106.05(h)) (discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g))) (mere data gathering) (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015))
Regarding Claim 7
Step 1: A Machine
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1
Regarding Claim 8
Step 1: A Machine
Step 2A Prong 1: See the analysis of Claim 2
Step 2A Prong 2: See the analysis of Claim 2
Step 2B: See the analysis of Claim 2
Regarding Claim 9
Step 1: A Machine
Step 2A Prong 1: See the analysis of Claim 3
Step 2A Prong 2: See the analysis of Claim 3
Step 2B: See the analysis of Claim 3
Regarding Claim 10
Step 1: A Machine
Step 2A Prong 1: See the analysis of Claim 4
Step 2A Prong 2: See the analysis of Claim 4
Step 2B: See the analysis of Claim 4
Regarding Claim 11
Step 1: A Machine
Step 2A Prong 1: See the analysis of Claim 5
Step 2A Prong 2: See the analysis of Claim 5
Step 2B: See the analysis of Claim 5
Regarding Claim 12
Step 1: A Machine
Step 2A Prong 1: See the analysis of Claim 6
Step 2A Prong 2: See the analysis of Claim 6
Step 2B: See the analysis of Claim 6
Regarding Claim 13
Step 1: A Manufacture
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1Regarding Claim 14
Step 1: A Manufacture
Step 2A Prong 1: See the analysis of Claim 2
Step 2A Prong 2: See the analysis of Claim 2
Step 2B: See the analysis of Claim 2Regarding Claim 15
Step 1: A Manufacture
Step 2A Prong 1: See the analysis of Claim 3
Step 2A Prong 2: See the analysis of Claim 3
Step 2B: See the analysis of Claim 3Regarding Claim 16
Step 1: A Manufacture
Step 2A Prong 1: See the analysis of Claim 4
Step 2A Prong 2: See the analysis of Claim 4
Step 2B: See the analysis of Claim 4
Regarding Claim 17
Step 1: A Manufacture
Step 2A Prong 1: See the analysis of Claim 5
Step 2A Prong 2: See the analysis of Claim 5
Step 2B: See the analysis of Claim 5
Regarding Claim 18
Step 1: A Manufacture
Step 2A Prong 1: See the analysis of Claim 6
Step 2A Prong 2: See the analysis of Claim 6
Step 2B: See the analysis of Claim 6
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Andreas Sporr, “Automated HVAC Control Creation on Provisioning and Distributing Side based on Building Information Modelling and Inclusion of Renewable Energy Systems”(2021).
Regarding Claim 1 and 7
Sporr teaches A method of synthetic data generation for training an artificial intelligence model, the method comprising: (Sporr [P.2523 §III¶2] : “For the generation of new control strategies, the cognitive system uses high-level. Semantic knowledge, to decrease the problem space, as well as low-level information from past episodes, to evaluate previous solutions and adapt the control strategy accordingly.” The examiner interprets where synthetic data generation is shown as generation of new control strategies, where artificial intelligence model is shown as a cognitive system, and where training is shown as evaluating previous solutions and adapt the control strategy accordingly.) reading a target environmental model1 simulating a target environment to generates synthetic data among a plurality of environmental models simulating a plurality of actual environments, respectively; (Sporr [P.61 §7.1¶1] : “In this thesis a process was developed by extracting relevant information from Open. BIM models (stored as IFC files) and augmenting it with additional data sources for weather data, occupancy information and any data that is currently. Still missing in a BIM model due to lack of standardization or modeling guidelines.” The examiner interpreted where environmental model is shown as central information storage model.) extracting an environmental feature2 which influences generation of data in the target environment; (Sporr P.74750 [Figure 2]:
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configuring a synthetic data generation function3 so that the synthetic data reflects the environmental feature; and (Sporr P.74753 [Fig. 8]:
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Sporr P.7 [§4¶1]: “As a first step, a library of control blocks for the energy components has to be defined. These control blocks are then interconnected and parameterized based on the interconnection of the energy provisioning system.” Sporr P.74753 [§V¶7]: “Therefore, the parameters of the CO2 room model are set using the information about room size, the number and size of windows and doors, and an occupancy profile with the above-mentioned number of people.”) generating the synthetic by using the synthetic data generation simulator for the target environmental model. (Sporr [P.1 §Abstract] : “This paper examines the use of BIM data for automated generation of control strategies for energy systems, thus simplifying and accelerating the commissioning phase. We present a methodology to create control strategies of a building heating system with several variation of renewable energy systems and include both heat provisioning and a distribution system. The control goals Include a favoring the use of non-fossil energy, which is provided by a combination of photovoltaic system (PV), heat pump (HP) and industrial excess-heat source. Thermal energy storages are integrated for load shifting purposes and the control of the heat distribution system is designed. Towards the requirements of the building physics, occupancy and outside climate conditions. A validation of the approach is presented in a combined SIMULINK and TRNSYS simulation environment.” Sporr P.26 [Table 1]:
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Sporr P.39 [§4.2.2¶2]: “The setpoints for the storage tank and the heat pump are given in the format specified in Table 1.” Sporr P.48 [§6¶2]: “In the following chapters, methods are described how setpoints can be modified based on simplified models to allow more energy-efficient operation. The models were developed sufficiently. complex, so that a certain dynamic for the predictions can be obtained, though the number of parametrization values is reduced to a minimum. The parametrization values are based on Table 2 and design points for the heat pump. These changes include forecast data that includes weather and zone occupancy. Based on this data and the forecast data from the component models, setpoints are modified to specify, for example, the storage tank setpoint or the flow temperature of the panel heating system.” Sporr [P.74756 §VIII¶2]: “The simulation with two different programs is performed in order to clearly separate the automatically created controller and the virtual real world.” The examiner interprets where the limitation is shown as the system "automatically" creating "control strategies" and operational set-points (synthetic data) using a "simulation environment" (Simulink and TRNSYS) to model the "virtual real world".
Regarding Claim 2, 8, and 14
Sporr teaches The method of claim 1, (See claim 1) wherein the extracting of the environmental feature which influences generation of data in the target environment is extracting the environmental feature based on a correlation4 between the target environment and an application field of the synthetic data. (Sporr P.28 [§3.2¶1]: “An ontology containing all necessary data was developed for a structure that can be analyzed easily and quickly. This data is obtained from the IFC models described in the previous chapter and the additional files. Subsequently, an ontology is used to easily identify similarities of rooms with respect to the information relevant for the control, such as orientation of the rooms, size, insulation. This helps to incorporate changes in the BIM models faster and to develop control strategies for similar rooms more quickly.” The examiner interprets where the limitation is shown as Sporr using an ontology to define the correlation (mapping) between structural features (environment) and "functional blocks" for designated operations (application field).
Regarding Claim 3, 9, and 15
Sporr teaches the method of claim 2, (see claim 2) wherein the correlation between the target environment and the application field of the synthetic data is determined based on ontology (Spoor P.2523 [§III¶A]: “.To this aim, an ontology has been defined, which originates in the ThinkHome ontology [27] (which in turn incorporates concepts from the DogOnt [28] and the BonSAI [29] projects) and the SeWoA ontology [30].” related to the target environment and the application field of the synthetic data.
Regarding Claim 4, 10, and 16
Sporr teaches the method of claim 3, (see claim 3) wherein the configuring of the synthetic data generation function of the synthetic data generation simulator for the target environmental model so that the synthetic data reflects the environmental feature is modifying a parameter (Sporr P.24 [Fig. 8: Overview]:
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) corresponding to each environmental feature among parameters of the synthetic data generation function according to the environmental feature. Sporr P.74753 §V¶9]: “Thereby, every room 's air quality controller is either parameterized for the maximum number of people which are present in this specific room, or depending on given information, which was fetched from an IFC or csv file. Therefore the parameters of the CO2 room model are set using the information about room size, the number and size of windows and doors, and an occupancy profile with the above-mentioned number of people.”
Regarding Claim 5, 11, and 17
Sporr teaches the method of claim 1, (see claim 1) further comprising: matching the data feature of the synthetic data generated by the synthetic data generation simulation for the target environmental model (Sporr P.28 [§3.2¶1]: “Subsequently, an ontology is used to easily identify similarities of rooms with respect to the information relevant for the control, such as orientation of the rooms, size, insulation. This helps to incorporate changes in the BIM models faster and to develop control strategies for similar rooms more quickly.” Sporr P.2523 [§III¶A]: “The control blocks available to the cognitive system for generating a control structure are also part of the ontology. They can be seen as functional blocks for designated, basic operations, and with interfaces specified in a semantically meaningful way.”) with a data feature to which the actual data generated in the target environment belongs. (Sporr P.25265 [Fig.3]:
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The examiner interprets where the limitation is shown in the cognitive architecture that "identifies similarities of rooms" and uses an ontology to ensure that the generated "functional blocks" are correctly mapped to the physical "building structure" defined by the BIM. This process matches the generated logic behavior with the "actual data" requirements (sensor/actuator feedback loops) of the specific building.
Regarding Claim 6, 12, and 18
Sporr teaches the method of claim 5, (see claim 5) wherein the matching of the data feature of the synthetic data generated by the synthetic data generation simulation for the target environmental model with the data feature to which the actual generated in the target environment belongs includes receiving the actual data (Sporr P.23 §2.6¶1]: “The calculations were compared with real measured data and validated. Based on the validated calculations, a control strategy for the fan pressure in a ventilation system was developed.” Sporr P.21 §2.65¶3]: “However, the majority of methods are based either on exact, manually created models based on planning data or on historical measurement data that can be implemented in existing buildings.” Sporr P.74747 [§Abstract¶1] “It also shows a way, how building, which is already operating, can be optimized using the operation data from energy systems to modify the existing controllers.”
acquired in the target environment, determining a data distribution region (Sporr P.74754 [Table 3 & Figure. 12]:
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of the synthetic data generated by the synthetic data generation simulator for the actual data as a data distribution region to which the actual data belongs, and (Sporr P.74754. [§VI¶5]: “Thereby, the correlation between dissatisfied persons (DP) and the difference between the inside and outside CO2
level (CO2,diff) is calculated by:
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”
The examiner interprets where the limitation is shown in the system calculating. a "correlation between dissatisfied persons (DP) and the difference between the inside and outside CO2 level" to weight the distribution.) setting the synthetic data generation simulator to output only synthetic data included in the data distribution region to which the actual data belongs among the synthetic data generated by the synthetic data generation simulator. (Sporr P.74754 [§VI¶6]; “Thus, the algorithm focuses on
Improving the indoor comfort level and sets different CO2 set-points for reaching an energy-efficient and convenient operation. Furthermore, during the improving process, values within these fuzzified limits are compared between all rooms and it is checked whether a comfort level reduction in one specific room is in an appropriate relation to the comfort level improvement in another one.” The examiner interprets where the limitation is shown in the setting of these set-points to stay within the determined "acceptable limits" (the distribution region), the simulator is restricted to outputting only data valid for that region.
Regarding Claim 13
An electronic device for synthetic data generation for training an artificial intelligence model, the electronic device comprising: (Article of manufacturing version of claim 1, similar rejection to claim 1) communication circuit; : (Sporr P.74748 [§II¶5]: “Chapter IV gives an insight into fetching necessary data from Industry Foundation Classes (IFC) files and extending missing information with additional data as well as allocating geometry to given room zones..” Sporr P.2523 [§III¶A]: “To retrieve and to modify information stores in the KB, a semantic query interface in form of an SPARQL endpoint is available. Through this query interface, thee cognitive system gains information on the optimization problem and retrieve metadata on the involved components to support the evaluation mechanism.” The recitation of a "communication circuit" is inherent in the disclosure. The examiner interprets where the communication circuit is shown as the networked data retrieval and file access necessarily requiring communication circuitry. In any computer system configured to execute the Building Information Modeling tasks described by Sporr, communication circuitry is necessarily present and not merely probable.) a memory; (Sporr [P.2523 §A¶1] : “It constitutes the model behind the KB, which acts as a central information storage for the cognitive system: it specifies the building structure, the zones of the system and their usage, and the building services, i.e., the sensors and actuators that are present in a zone.” and a processor operatively connected to the memory, (Sporr [P.2522 §II¶A]: “In this context, a cognitive architecture is a software program inspired by biological systems, which processes sensor data and uses a KB to execute actions that enable a system to reach its goals.”) wherein when the memory is executed, the processor reads a target environmental model simulating a target environment to generate synthetic data among a plurality of environmental models simulating a plurality of actual environments, respectively, (Article of manufacturing version of claim 1, similar rejection to claim 1) extracts an environmental feature which influences generation of data in the target environment, (Article of manufacturing version of claim 1, similar rejection to claim 1) configures a synthetic data generation function of a synthetic data generation simulator for the target environmental model so that the synthetic data reflects the environmental feature, and (Article of manufacturing version of claim 1, similar rejection to claim 1) stores instructions to generate the synthetic data by using the synthetic data 01 generation simulator for the target environmental model. (Article of manufacturing version of claim 1, similar rejection to claim 1)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AARIC RAYJEE MARKS whose telephone number is (571)467-6372. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AARIC R MARKS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
1 Spec [0052 “ Here, an environmental model may be digital twin, a building model, or a building information modeling (BIM) model for a targeted actual environment.”
2 Spec [0055] “In the present disclosure, a factor which influences a similar feature of actual data may be referred to as a feature (environmental feature) of an actual environment, and the feature of the actual environment is extracted based on the environmental model, which may be utilized for finding (approximating or estimating) a distribution of data more similar to the actual data.”
3 Spec [0057] “ In this case, in the method, a synthetic data generation function or synthetic data generation logic of the synthetic data generation simulator is configured by using the extracted environmental feature, and then targeted data is generated in the synthetic data generation simulator.”
4 Spec [0082]: “The derivation process of the correlation information may be automatically performed through a mathematical model or an artificial intelligence model.”
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