DETAILED ACTION
Claims 1-10 are presented for examination.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in the instant application, filed on 05 June 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05 June 2023 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings received on 05 June 2023 are accepted.
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 1-10 are 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 “large” in claims 1, 3 and 4 is a relative term which renders the claim indefinite. The term “large” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Claims 2-10 are rejected by virtue of their dependency on claim 1.
Claims 2 and 5 are rejected as failing to define the invention in the manner required by 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
The claim(s) are narrative in form and replete with indefinite language. The structure which goes to make up the device must be clearly and positively specified. The structure must be organized and correlated in such a manner as to present a complete operative device. The claim(s) must be in one sentence form only. Claims 2 and 5 recite the term “assuming”, which renders the claims indefinite because it sets forth an unquantifiable mental or speculative action rather than a definite physical state or operational parameter of the device.
Claim 6 recites the limitation "the data set" in the 3rd paragraph of the claim. There is insufficient antecedent basis for this limitation in the claim. Claim 8 is rejected by virtue of its dependency on claim 6. Applicant may amend the claim such that “the data set” is replaced with “a data set”, or define “a data set” earlier in the claim or in claim 1. Appropriate correction is required.
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.
Regarding claims 1-10, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-8 are directed to a method, which is a process, which is a statutory category of invention. Claim 9 is directed to a device, which is a machine, which is a statutory category of invention. Claim 10 is directed to a storage medium containing computer-executable instructions, which is a manufacture, which is a statutory category of invention. Therefore, claims 1-10 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 9 and 10 recite the abstract idea of measuring the quality of a built environment, constituting an abstract idea based on Mathematical Concepts including mathematical formulas or equations as well as calculations or alternatively Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper. As per claim 1, and similarly recited in claims 9 and 10, the limitation of “identifying key influencing factors determining an environmental quality … wherein the key influencing factors comprise non-observation elements and observation elements” covers mental processes including observing factors that influence the quality of an environment. Additionally, the limitation of “establishing an index system of environmental quality influencing factors” covers mental processes including obtaining the factors and observation elements and organizing them in a way that can be drawn out with the use of a pencil and paper. Additionally, the limitation of “analyzing a relationship between the environmental quality and the key influencing factors to form a theoretical model” covers mental processes including observing the environmental quality and determining the relationships between each of the key influencing factors. Additionally, this limitation covers mental processes including using algorithms and equations to form the theoretical model, which can be performed with the use of a pencil and paper. Alternatively, this limitation covers mathematical calculations including the use of algorithms and equations to form the theoretical model. Additionally, the limitation of “calculating path coefficients of each of the key influencing factors of the environmental quality according to a distribution of a sample data in the large sample database, and converting the path coefficients into weights” covers mathematical calculations including using equations and matrices to obtain the path coefficients of each of the key influencing factors, and using equations and matrices to convert the coefficients into weights. Alternatively, this limitation covers mental processes including using equations and matrices to obtain the path coefficients of each of the key influencing factors, and using equations and matrices to convert the coefficients into weights, which can be performed with the use of a pencil and paper. Additionally, the limitation of “dividing distribution intervals of all of the observation elements dynamically according to the distribution of the sample data” covers mathematical concepts including the use of mathematical equations and statistical partitioning. Alternatively, this limitation covers mental processes including using mathematical equations and statistical partitioning, which can be performed with the use of a pencil and paper. Additionally, the limitation of “defining quality assignments of all of the observation elements” covers mental processes including analyzing the intervals and assigning a quality label to each distribution. Additionally, the limitation of “performing a comprehensive quality measurement of samples in combination with the weights and the quality assignments” covers mathematical concepts including using a series of mathematical equations including the weights and quality assignment values in order to receive the end result. Alternatively, this limitation covers mental processes including using a series of mathematical equations including the weights and quality assignment values in order to receive the end result, which can be performed with the use of a pencil and paper. That is, other than reciting “by the computing system”, “memory configured to one program”, and “storage medium containing computer-executable instructions”, nothing in the claim element precludes the step from practically being performed in the mind.
Dependent claims 2-8 further narrow the abstract ideas, identified in the independent claims.
Step 2A, Prong 2: The judicial exception is not integrated into a practical application. The additional element of “one or more processors” and “memory configured to one program” in claim 9, and “storage medium containing computer-executable instructions” in claim 10, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation of “acquiring the observation elements to form a large sample database” is mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Alternatively, this limitation can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information regarding the quality of an environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-8 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above.
Step 2B: Claims 1, 9 and 10 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of “one or more processors” and “memory configured to one program” in claim 9, and “storage medium containing computer-executable instructions” in claim 10, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitation of “acquiring the observation elements to form a large sample database” is mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Alternatively, this limitation can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information regarding the quality of an environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claim 2 is directed to further defining the regression equations, latent variables and observation variables, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Dependent claim 3 is directed to further defining an establishment process, which consists of choosing samples based on an analysis, collecting locational data, and preprocessing a data set, which further narrows the abstract idea identified in the independent claim, which is directed to “Insignificant Extra-Solution Activity” and “Mental Processes” or alternatively “Mathematical Concepts.”
Dependent claim 4 is directed to further defining the selection of samples, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes”.
Dependent claim 5 is directed to further evaluating how well the model fits the actual sample data through the use of multiple tests and equations, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Dependent claim 6 is directed to further defining the calculation of the weights, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Dependent claim 7 is directed to further defining the number of free parameters to be estimated in the model, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Dependent claim 8 is directed to further defining the sum of the weights of all observation variables, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Accordingly, claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
As per claim 10, it is rejected because the applicant has provided evidence that the applicant intends the term "storage medium containing computer-executable instructions" to include non-statutory matter. The applicant describes a computer-readable storage medium as including open ended language and thus it is reasonable to interpret it to include all possible mediums, including non-statutory mediums (see paragraph [0086].) The words "storage" and/or "recording" are insufficient to convey only statutory embodiments to one of ordinary skill in the art absent an explicit and deliberate limiting definition or clear differentiation between storage media and transitory media in the disclosure. As such, the claim(s) is/are drawn to a form of energy. Energy is not one of the four categories of invention and therefore this/these claim(s) is/are not statutory. Energy is not a series of steps or and thus is not a process. Energy is not a physical article or object and as such is not a machine or manufacture. Energy is not a combination of substances and therefore not a composition of matter. The Examiner suggests amending the claim(s) to read as a "non-transitory storage medium containing computer-executable instructions".
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Xue Fang, Xinyu Shi, Weijun Gao, Measuring urban sustainability from the quality of the built environment and pressure on the natural environment in China: A case study of the Shandong Peninsula region, Journal of Cleaner Production, Volume 289, 2021, 125145, ISSN 0959-6526, hereafter F in view of Xiaolei Ma, Jiyu Zhang, Chuan Ding, Yunpeng Wang, A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership, Computers, Environment and Urban Systems, Volume 70, 2018, Pages 113-124, ISSN 0198-9715, hereafter M, further in view of Westland, J. C. (2015). Structural equation models. Stud. Syst. Decis. Control, 22(5), 152, hereafter W.
Regarding Claim 1: F discloses a method for measuring the quality of a built environment comprising the following steps:
identifying key influencing factors determining an environmental quality, and establishing an index system of environmental quality influencing factors, wherein the key influencing factors comprise non-observation elements and observation elements;
F [Page 3: Section 2.1] “Then, to make the indicator system is comprehensive, accessible, and widely reliable and used, we specified that all data of selected indicators are derived from the public China Statistical Yearbook, which is considered as the most comprehensive and authoritative dataset provided by the National Bureau of Statistics of China … After multiple filtering out, we had 14 indicators categorized into four categories: urbanization economies, infrastructure development, resource consumption, and environmental pollution.” Examiner notes that the index system is the indicator system and that infrastructure development, resource consumption, and environmental pollution are observational elements, and urbanization economies are non-observational elements.
analyzing a relationship between the environmental quality and the key influencing factors to form a theoretical model;
F [Page 3: Section 2] “Therefore, we named the two dimensions of the ratio model as “Quality of Built Environment” (QU)/"Environmental Pressure” (PU), which is defined as “Urban Sustainability” (SU) shown in Fig. 1.”
acquiring the observation elements to form a large sample database;
F [Page 5: Section 3.1.2] “The main source of our research is national statistical data
(National Bureau of Statistics of China, 2009-2018), originally collected from the China Statistical Yearbook (2009-2018), China Statistical Yearbook on Shandong province (2009-2018) and relevant cities that are publicly available on government websites.”
F [Page 4: Section 2.2] “A factor analysis is calculated to worked out the component score coefficient matrix (λ values) of all representative indicators in each category using SPSS (version 26). Then, the contribution rate of the component score in the corresponding category is determined as the weight coefficient of the indicator.”
and performing a comprehensive quality measurement of samples in combination with the weights
F [Page 8: Section 4.2] “Fig. 8 and Fig. 9 show the analysis results of the urban sustainability measurement. We created the radar charts to present the weighted scores of five indicator categories in QU and PU, in order to analyze the specific development characteristics of different types of cities.”
F does not disclose dividing distribution intervals of all of the observation elements dynamically according to the distribution of the sample data, and defining quality assignments of all of the observation elements.
However, M discloses dividing distribution intervals of all of the observation elements dynamically according to the distribution of the sample data, and defining quality assignments of all of the observation elements.
M [Page 119: Section 4.2] “One important characteristic of GWR-based models is that local parameter estimates, which denote spatial relationships, are mappable for visual analysis. We can group the coefficients into several intervals and use different colors to visualize the spatial variations of the effects of the built environment variables on transit ridership.” Examiner notes that the different colors represent the different assignments for each interval, which the spatial distribution is shown in M [Pages 120-121: Figures 5-7].
F and M are analogous to the claimed invention because both center around the analysis of characteristics of a built environment.
It would have been obvious to one with ordinary skill in the art before the effective filing date to combine the teachings of F with the teachings of M because understanding the influence of the built environment on transit ridership can increase the attractiveness of public transportation. In turn, the increase of public transportation correlates to the increase of the quality of a built environment as well as influencing infrastructure development, urbanization economies and resource consumption. (See M [Abstract])
F and M do not disclose calculating path coefficients.
However, W discloses calculating path coefficients.
W [Page 27: Chapter 3.3] “Compute a coefficient between each pair of latent variables in the model using, for example, either an OLS or a PLSR regression on the first PCA components of the treatment and response (i.e., tail and head of the link arrow) latent variables. In the OLS case Wold called PCA-OLS setup a PCR. Unless the correlations between any two variables are greater than 0.95, both methods produce nearly the same coefficient. … The clusters of indicators for each latent variable (with links being the factor weights on the first principal component) are sometimes called the “outer” model.” Examiner notes that path coefficients are the coefficients between each pair of latent variables in the model, and Wold's PLSR algorithm uses iterative alternating regressions to calculate weights for each latent variable.
F, M and W are analogous to the claimed invention because they all center around the analysis of characteristics of a built environment, which Structural Equation Models are commonly used to analyze built environments.
It would have been obvious to one with ordinary skill in the art before the effective filing date to combine the teachings of W with F and M because a Structural Equation Model (SEM) is particularly useful in the social sciences where many if not most key concepts are not directly observable, and models that inherently estimate latent variables are desirable. SEM provides one pathway to quantify concepts and theories that previously had only existed in the realm of ideological disputations. (See W [Page 2: Introduction])
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Xue Fang, Xinyu Shi, Weijun Gao, Measuring urban sustainability from the quality of the built environment and pressure on the natural environment in China: A case study of the Shandong Peninsula region, Journal of Cleaner Production, Volume 289, 2021, 125145, ISSN 0959-6526, hereafter F in view of Xiaolei Ma, Jiyu Zhang, Chuan Ding, Yunpeng Wang, A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership, Computers, Environment and Urban Systems, Volume 70, 2018, Pages 113-124, ISSN 0198-9715, hereafter M, further in view of Westland, J. C. (2015). Structural equation models. Stud. Syst. Decis. Control, 22(5), 152, hereafter W, further in view of Zhang, X., Huang, B., & Zhu, S. (2020). Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City. ISPRS International Journal of Geo-Information, 9(8), 475, hereafter Z.
Regarding Claim 9: F in view of M, further in view of W disclose the method for measuring the quality of the built environment according to claim 1.
However, F and W do not disclose an electronic device.
However, M discloses an electronic device.
M [Page 115: Section 2] “The GTWR, which holds a similar idea to that of GWR in managing spatial variation, constructs a space–time weight matrix using space–time distance to measure the relationship between ridership and different TAZs and times … From Eq. (3), the first step of parameter estimation is to construct
the space–time weight matrix W (ui, νi, ti), which depends on space–time
distance. TAZs near the observation area exert considerable influence
on calculating the βk(ui, νi, ti) parameters.
F, M, and W are analogous to the claimed invention because both center around the analysis of characteristics of a built environment.
It would have been obvious to one with ordinary skill in the art before the effective filing date to combine the teachings of F and W with the teachings of M because understanding the influence of the built environment on transit ridership can increase the attractiveness of public transportation. In turn, the increase of public transportation correlates to the increase of the quality of a built environment as well as influencing infrastructure development, urbanization economies and resource consumption. (See M [Abstract])
F, M and W do not explicitly disclose comprising one or more processors, a memory configured to store one or more programs, and one or more programs being executed by the one or more processors.
However, Z discloses a GTWR model comprising one or more processors, a memory configured to store one or more programs, and one or more programs being executed by the one or more processors.
Z [Page 6: Section 3.2] “The computation of the GTWR model is intensive because each sample uses an adaptive type of bandwidth, which leads to (t*(n - 1)n) combinations of possible values that must be computed for the optimal bandwidth [15]. The computing time will exponentially increase as the number of samples and timestamps increases by, for example, using grid-based data as the spatial unit or constructing the daily GTWR model based on several years of data. An optimized modeling approach is needed to reduce computation consumption. In particular, we employed parallel computing to break down the computational loops of optimal parameter selection into independent parts with different values of q and τ. These parts can be executed simultaneously by multiple processors communicating via shared memory, the results of which are combined upon completion as part of the overall algorithm.
F, M, W and Z are analogous to the claimed invention because they all center around the analysis of characteristics of a built environment.
It would have been obvious to one with ordinary skill in the art before the effective filing date to combine the teachings of Z with F, M and W because the GTWR model of Z was improved by parallel computing technology such that the model can achieve better fitting results than the ordinary least squares model, and it is more efficient for big datasets. (See Z [Abstract])
Regarding Claim 10: F in view of M, further in view of W discloses the method for measuring the quality of the built environment according to claim 1.
F and W do not disclose a storage medium containing computer-executable instructions, wherein the computer-executable instructions are used to execute the method when executed by a computer processor.
However, M discloses a model comprising a storage medium containing computer-executable instructions, wherein the computer-executable instructions are used to execute the method when executed by a computer processor.
M [Page 115: Section 2] “The GTWR, which holds a similar idea to that of GWR in managing spatial variation, constructs a space–time weight matrix using space–time distance to measure the relationship between ridership and different TAZs and times … From Eq. (3), the first step of parameter estimation is to construct
the space–time weight matrix W (ui, νi, ti), which depends on space–time
distance. TAZs near the observation area exert considerable influence
on calculating the βk(ui, νi, ti) parameters.
F, M and W are analogous to the claimed invention because both center around the analysis of characteristics of a built environment.
It would have been obvious to one with ordinary skill in the art before the effective filing date to combine the teachings of F and W with the teachings of M because understanding the influence of the built environment on transit ridership can increase the attractiveness of public transportation. In turn, the increase of public transportation correlates to the increase of the quality of a built environment as well as influencing infrastructure development, urbanization economies and resource consumption. (See M [Abstract])
F, M and W do not explicitly disclose a storage medium containing computer-executable instructions, wherein the computer-executable instructions are used to execute the method when executed by a computer processor
However, Z discloses a GTWR model comprising a storage medium containing computer-executable instructions, wherein the computer-executable instructions are used to execute the method when executed by a computer processor
Z [Page 6: Section 3.2] “The computation of the GTWR model is intensive because each sample uses an adaptive type of bandwidth, which leads to (t*(n - 1)n) combinations of possible values that must be computed for the optimal bandwidth [15]. The computing time will exponentially increase as the number of samples and timestamps increases by, for example, using grid-based data as the spatial unit or constructing the daily GTWR model based on several years of data. An optimized modeling approach is needed to reduce computation consumption. In particular, we employed parallel computing to break down the computational loops of optimal parameter selection into independent parts with different values of q and τ. These parts can be executed simultaneously by multiple processors communicating via shared memory, the results of which are combined upon completion as part of the overall algorithm.
F, M, W and Z are analogous to the claimed invention because they all center around the analysis of characteristics of a built environment.
It would have been obvious to one with ordinary skill in the art before the effective filing date to combine the teachings of Z with F and M because the GTWR model of Z was improved by parallel computing technology such that the model can achieve better fitting results than the ordinary least squares model, and it is more efficient for big datasets. (See Z [Abstract])
Allowable Subject Matter
Claims 2-8 would be allowable pending resolving all intervening issues such as the 101 and 112 rejections above.
Claim 2 recites:
assuming that there are m types of the non-observation elements and i types of the observation elements among the key influencing factors of environmental quality, wherein the observation elements are the elements that can be measured directly in the built environment, and the corresponding data set is the observation variables; the non- observation elements are the elements that cannot be measured directly in the built environment, which need to be reflected indirectly by actual index values, that is, the latent variables, acquired through an observation, therefore, a matrix equation between the latent variables and the observation variables is:
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675
616
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62
598
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The limitation of claim 2 recited above is allowable. The prior art fails to teach this specific mathematical arrangement for the matrix equations.
Claim 3 recites:
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285
620
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The limitation of claim 3 recited above is allowable. The prior art fails to teach these specific equations to obtain a reverse assignment of negative correlated elements.
Claim 5 recites:
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742
584
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731
559
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727
589
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634
546
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The limitations above of claim 5 are allowable. The prior art fails to teach all of the equations above in conjunction with one another to evaluate how well the model fits the actual sample data.
Claim 6 recites:
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369
543
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The limitation above in claim 6 is allowable. The prior art fails to teach calculating the threshold intervals according to the distribution intervals and defining the dataset into five interval levels.
The closest prior art of record includes:
Gul, M. S., & NezamiFar, E. (2020). Investigating the Interrelationships among Occupant Attitude, Knowledge and Behaviour in LEED-Certified Buildings Using Structural Equation Modelling. Energies, 13(12), 3158.
This reference discloses a study that investigates complex interrelationships between many observed and unobserved variables using data from four LEED-certified multi-residential buildings in the United Arab Emirates using Structural Equation Modelling.
Yingqi Guo, Yuqi Liu, Shiyu Lu, On Fung Chan, Cheryl Hiu Kwan Chui, Terry Yat Sang Lum, Objective and perceived built environment, sense of community, and mental wellbeing in older adults in Hong Kong: A multilevel structural equation study, Landscape and Urban Planning, Volume 209, 2021, 104058, ISSN 0169-2046
This reference discloses an analysis of the distal mediation pathway from objective built environment to both mental health and subjective wellbeing of older adults in Hong Kong through perceived built environment and sense of community, using multilevel structural equation modeling.
Beran TN, Violato C. Structural equation modeling in medical research: a primer. BMC Res Notes. 2010 Oct 22;3:267. doi: 10.1186/1756-0500-3-267. PMID: 20969789; PMCID: PMC2987867.
This reference discloses the use of structural equation modeling (SEM), which is a set of statistical techniques used to measure and analyze the relationships of observed and latent variables, and the application of SEM to research problems in medical and health sciences research.
However, the closest prior art of record does not explicitly teach or render obvious the limitations above, particularly in combination with the other limitations within the claims. The claims dependent on these claims are allowable for at least the same reasons as their respective dependent claims.
Conclusion
All Claims are rejected.
The prior art made record of and not relied upon is considered pertinent to the applicant’s disclosure.
Gul, M. S., & NezamiFar, E. (2020). Investigating the Interrelationships among Occupant Attitude, Knowledge and Behaviour in LEED-Certified Buildings Using Structural Equation Modelling. Energies, 13(12), 3158.
Yingqi Guo, Yuqi Liu, Shiyu Lu, On Fung Chan, Cheryl Hiu Kwan Chui, Terry Yat Sang Lum, Objective and perceived built environment, sense of community, and mental wellbeing in older adults in Hong Kong: A multilevel structural equation study, Landscape and Urban Planning, Volume 209, 2021, 104058, ISSN 0169-2046
Beran TN, Violato C. Structural equation modeling in medical research: a primer. BMC Res Notes. 2010 Oct 22;3:267. doi: 10.1186/1756-0500-3-267. PMID: 20969789; PMCID: PMC2987867.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott T. Tran whose telephone number is (571) 272-8533. The examiner can normally be reached on M-Thurs, 8:00-4:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiner’s fax phone number (571) 272-8533.
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STT
/SCOTT THANH BINH TRAN/Examiner, Art Unit 2186
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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