Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/448,203.
This communication is the first action on the merits.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/11/2023 and 10/24/2023, 09/16/2024, and 08/27/2025 has been considered by the examiner.
Drawings
The drawings were received on 08/11/2023. These drawings are acceptable.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-14 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 “outputting at least one parameter set that is evaluated as good” in claim 1 is a relative term which renders the claim indefinite. The term “good” 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. H
Claims 2-14 are rejected under 35 U.S.C. 112(b) as being indefinite for the same reason as claim 1, since they depend from claim 1 and do not cure the indefiniteness of the base claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A method for determining an optimized parameter set having a plurality of
measurement parameters to carry out a measurement, the method comprising:
C) carrying out and storing n measurements of a measuring element, n being an integer greater than one, each measurement having one parameter set, wherein each measurement has a multiplicity of measuring points;
D) evaluating the n measurements with an evaluation function and storing the evaluation;
E) generating new parameter sets from the parameter sets used in step C);
F) carrying out steps C) to E) multiple times; and
J) outputting at least one parameter set that is evaluated as good.
The claim limitations in the abstract idea have been highlighted in bold above.
Under Step 1 of the analysis, claim 1 belongs to a statutory category, namely it is a method claim.
Under 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.
In the instant case, claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and a Mathematical Concept. This can be seen in the claim limitations of “a method for determining an optimized parameter set having a plurality of measurement parameters to carry out a measurement”, “carrying out and storing n measurements of a measuring element, n being an integer greater than one, each measurement having one parameter set, wherein each measurement has a multiplicity of measuring points”, “evaluating then measurements with an evaluation function and storing the evaluation”, “generating new parameter sets from the parameter sets used in step C)”, “F) carrying out steps C) to E) multiple times, and “outputting at least one parameter set that is evaluated as good”, which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements for determining an optimized parameter set based on measurement results and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations recite mathematical calculations, such as applying operators, algorithms, functions to generate and assess parameter sets e.g. see Spec. [0021]-[0022] which are mathematical concepts performed for the purpose of parameter optimization.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
The generic data gathering, processing, and output steps, are recited at such a high level of generality (e.g. using “storing” and “evaluating”) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, the claim does not specify any action taken with the parameter, and does not describe how these values are applied in practice. For example, the claim does not explain how the optimized parameters would be used to adjust a measurement device, alter scanning behavior, or control any external physical component.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claims 1). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-14, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for parent claim 1 Specifically:
With respect to dependent claims 2, 6, 10, 11, 12, and 13 specifically, the claims merely further expand on the limitations of claim 1 by specifying different ways of generating or initializing the parameter sets, such as applying evolutionary operators, generating parameter sets randomly, determining initial parameters and their ranges. These limitations describe mathematical operations that are a part of the abstract idea and/or merely invoke the use of general purpose computer technology to apply the abstract idea in a technological environment. As such, these claims fail to amount to a practical application or “significantly more” and are ineligible for the same reasons describe for claim 1. See MPEP 2106.05(g).
With respect to dependent claims 3 and 4 specifically, the claims further specify that the measurements are carried out in a contactless manner and using optical coherence tomography or pyrometry for measurements. However, these limitations simply recite the type of data that is collected or the environment in which the abstract idea is implemented. Such limitation constitute merely data gathering that is well understood and conventional in the field. See MPEP 2106.05(g).
With respect to dependent claim 5 specifically, the claim recites the additional element(s) of using generic AI/ML technology, i.e. “wherein the evaluation function comprises an algorithm for evaluating a recording quality of the measurement, and/or a deep convolution neural network”, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the “ wherein the evaluation function comprises an algorithm for evaluating a recording quality of the measurement, and/or a deep convolution neural network” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “wherein the evaluation function comprises an algorithm for evaluating a recording quality of the measurement, and/or a deep convolution neural network” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
With respect to dependent claims 7, specifically, the claim recites the additional element(s) of using generic AI/ML technology, i.e. “using artificial intelligence that adapts a target function by an online learning method and the evaluations of the evaluation function”, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the “using artificial intelligence that adapts a target function by an online learning method and the evaluations of the evaluation function” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “using artificial intelligence that adapts a target function by an online learning method and the evaluations of the evaluation function” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
With respect to dependent claims 8 and 9 specifically, the claims recite additional steps involving merging measurements, defining a region of interest, repeating evaluation within the ROI, or storing parameters in a database. These limitations merely further organize, process, or store the data generated in claim 1 and therefore constitute insignificant extra-solution activity under MPEP 2106.05(g).
With respect to dependent claim 14 specifically, the claim recites a device comprising a measuring device and a computing unit configured to perform the steps of the method. However, merely implementing the abstract idea on a generic computer components or a generic device is insufficient to provide significantly more. See MPEP 2106.05(f). Additionally, limiting the method to performed by such a device does not add any meaningful technological improvement and instead amounts to limiting the abstract idea to a particular technological environment.
Accordingly, for the reasons above and those discussed in relation to the independent claim 1, and the dependent claims are insufficient to integrate the recited abstract idea into a practical application or amount to significantly more.
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.
Claims 1-6, and 8-14 are rejected under 35 U.S.C. 103 as being unpatentable over US 9317626 B2, Chan et al (hereinafter Chan) in view of US 20180149471 A1, Lu et al (hereinafter Lu).
Regarding Claim 1 and 14, Chan discloses a method for determining an optimized parameter set having a plurality of measurement parameters to carry out a measurement (Chan, [Col. 4 Line 65 - Col. 5 Line 3] it is an object of the present invention to provide an evolutionary design method for combinatorial layout design automation and optimisation using GA. Another object is to provide a method and system, entitled “Intelligent Conceptual Mould Layout Design System (ICMLDS)” as a domain-specific realisation of the combinatorial layout design approach), the method comprising:
C)n being an integer greater than one (Chan, [Col. 17 Line 58-65] The algorithm will stop when the number of generated individual design solutions is equal to the specified population size “m”. Meanwhile, the generation counter (i) is set to one initially. At Step 228, the Genotype-Phenotype mapping algorithm (see FIG. 20 for more details) denoted as Step 230 is called to decode the chromosome into the phenotype for fitness evaluation), each measurement having one parameter set, wherein each measurement has a multiplicity of measuring points (Chan, [Col. 17 Line 65 - Col. 18 Line 4] according to the resulting phenotype, the fitness value of each individual in the population can be calculated using the weighted sum approach. This simple approach can integrate a number of performance goals (P.sub.f, P.sub.r, P.sub.c and P.sub.d), costs (C.sub.m, C.sub.n, C.sub.r, C.sub.s and C.sub.j) and the penalty function for handling the mould base size constraint into a single Cost Performance (CP) value);
D) evaluating the measurements with an evaluation function (Chan, [Col. 12 Line 44-48] Then the genotypes are transformed into phenotypes 120 for the fitness evaluation process at step 122. A specific scoring scheme at step 124 is used to quantify the fitness value of each individual in the population considering multiple mould design objectives and constraints) and storing the evaluation (Chan, [Col 17. Line 15-19] the system output interface module 208 further comprises a data retrieving submodule for retrieving information from the Genetic Algorithm module, a rapid visualization submodule);
E) generating new parameter sets from the parameter sets used in step C) (Chan, [Abstract Line 7-15] Genetic Algorithm module first automatically generates a population of specially designed chromosome with three interdepende.0.nt sessions. Crossover, mutation and replacement operation are applied on the population subsequently to evolve such towards a more optimal population over successive generations. In each generation step, a Genotype-Phenotype mapping module is utilized to decode the chromosome to corresponding layout design for fitness evaluation);
F) carrying out steps C) to E) multiple times (Chan, [Col. 6 Line 55-60] (k) repeating step (d) to step (j) until a pre-defined generation gap (i.e. the number of the lowest rank chromosomes being replaced at each generation) is attained and (l) repeating steps (d) to (k) over successive generations until one of predefined stopping criteria is attained); and
J) outputting at least one parameter set that is evaluated as good (Chan, [Col. 12 Line 58-61] The selection, crossover and mutation operations are applied repeatedly to the population until the termination conditions are met at step 136. Finally, the program outputs the resulting population at step 138).
Chan does not disclose C) carrying out and storing n measurements of a measuring element.
However, Lu teaches C) carrying out (Lu, [0008]: calculating, by the processing device, information relating to an optimal scanning orientation of the at least one sensor) and storing (Lu, [0061]: the CAD (Computer-aided design) model coordinate system (X.sub.c, Y.sub.c, Z.sub.c) may be stored in the processing device 13) n measurements of a measuring element (Lu, [0063]: Genetic Algorithm (GA), may be performed to calculate the scanning position and the scanning angle of the sensor 12b in the CAD model coordinate system).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Chan and Lu’s teaching because Chan teaches generating, evaluating, and outputting optimized parameters using a genetic algorithm to improve quality through repeated evaluation of those results, while Lu teaches performing physical measurements of an object using at least one sensor device and evaluating measurement results to determine optimized parameters as well. One of ordinary skill in the art would have been motivated to apply Chan’s known genetic algorithm technique to Lu’s measuring system to automatically and adaptively optimize scanning parameters based on evaluated measurement results, this will result to reducing invalid measurements and improve the quality of the analysis.
Regarding Claim 2, Chan in view of Lu teaches the method according to claim 1, wherein the generation of the new parameter sets in step E) is carried out by applying evolutionary operators to the parameter sets used in step C) (Chan, [Col. 8 Line 44-47] (f) a crossover operation submodule for producing at least one offspring chromosome from the selected parents; (g) a mutation operation submodule for mutating the offspring chromosomes randomly).
Regarding Claim 3, Chan in view of Lu teaches the method according to claim 1, wherein each measurement in step C) is carried out in a contactless scan (Lu, [0040] In the embodiment, the sensors 12a and 12b may be optical sensors, such as laser-type rangefinder).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Chan and Lu’s teaching because Lu teaches performing measurements using a contactless sensors, such as optical sensors to scan a object without physical contact, while Chan teaches generating and evaluating parameters to optimize system performance. One of ordinary skill in the art would have been motivated to apply Chan’s optimization technique to Lu’s contactless scanning measurement technique in order to optimize the measurement parameters for non-contact scanning such as orientation or position, as a result will improve efficiency and repeatability while avoiding contact.
Regarding Claim 4, Chan in view of Lu teaches the method according to claim 3, wherein each measurement (Lu, [0041] And, scanning constraints of the sensors 12a and 12b may include a movable range of the sensor, a scanning range of the sensor or a scanning dead space of the sensor for a contour of the object. Specifically, the sensor 12a or 12b may be an optical rangefinder for scanning an object to measure the geometric dimension of the object) in step C) is carried out in an optical coherence tomography measurement or in a pyrometry measurement (Lu, [0040] In the embodiment, the sensors 12a and 12b may be optical sensors, such as laser-type rangefinder).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Chan and Lu’s teaching for the same reasons set forth above with respect to claim 3 because Lu further teaches that the contactless measurements may be performed using optical coherence tomography or pyrometry measurements. One of ordinary skill in the art would have recognized that optical coherence tomography or pyrometry represents known optical contactless scanning.
Regarding Claim 5, Chan in view of Lu teaches the method according to claim 1, wherein the evaluation function comprises an algorithm for evaluating a recording quality of the measurement, and/or a deep convolution neural network (Lu, [0041] Therefore, aforesaid constraints may need to be solved for obtaining more complete or valid measuring data of the object 9 from the measuring equipment 1, [0063] the optimal scanning orientation of the sensor 12b may be obtained by adopting probabilistic technique to analyzing a curve surface of the object 9, wherein the probabilistic technique may solve a combinatorial optimization problem, [0064] FIG. 7, exemplary details of performing Genetic Algorithm (GA) may include the following. [0065] Initialization [0066] Evaluation [0067] Termination Criteria).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Chan and Lu’s teaching because Chan evaluates measurements within the optimization framework, while Lu further teaches implementing such evaluation using algorithmic techniques including genetic algorithms to assess measurement quality and determine optimal parameters. One of ordinary skill in the art would have been motivated to integrate Lu’s algorithmic approach with Chan’s evaluation process to improves robustness and efficiency of evaluating measurement quality.
Regarding Claim 6, Chan in view of Lu teaches the method according to claim 1, wherein the new parameter sets are generated in step E) randomly (Chan, [Col. 7 Lines 34-40] the mutation operation performs one of the following steps: (a) changing the orientation session of the offspring chromosome randomly; (b) changing the group session of the offspring chromosome randomly and eliminating any empty cavity groups therefrom; (c) creating a new group in the group layout shape rule of the offspring chromosome randomly, assigning one of the moulding parts to the new group; and updating the group sessions of the offspring chromosome accordingly; (d) randomly modifying the group placement rule, the internal cavity layout rule or the runner layout shape rule of at least one group in the group layout shape rule session of the offspring chromosome; or (e) any combination of the above).
Regarding Claim 8, Chan in view of Lu teaches the method according to claim 1, further comprising, after step F) and before step
G) merging all of the measurements (Lu, [0043] a contour of the object 9 can be measured by rotating the object 9 relative to the sensor 12a or 12b, so that dimensions of the object 9 can be obtained from the measuring and some specific dynamic features) carried out in step C) (Lu, [0058] FIG. 6A, before the three steps S1 to S3 of analyzing an optimal scanning orientation of the sensor, constructing an object coordinate system (X.sub.o, Y.sub.o, Z.sub.o) of the object 9 to be tested is performed, including: obtaining an object coordinate system (X.sub.o, Y.sub.o, Z.sub.o) of the-object 9 after the object 9 is fixed in a scanning region (for example, fixed on the rotating device 10 or 30);
H) defining a smallest possible region of interest (ROI) in the merged measurements in which the evaluation function exceeds a defined threshold value (Lu, [0041] And, scanning constraints of the sensors 12a and 12b may include a movable range of the sensor, a scanning range of the sensor or a scanning dead space of the sensor for a contour of the object. Specifically, the sensor 12a or 12b may be an optical rangefinder for scanning an object to measure the geometric dimension of the object); and
I) repeating steps C) to F) multiple times (Lu, [0064] FIG. 7, exemplary details of performing Genetic Algorithm (GA) may include the following. [0065] Initialization [0066] Evaluation [0067] Termination Criteria: Judging whether those fitness values of the whole chromosomes are good or bad, and if an ending threshold is passed then calculating an average of all the chromosome and assigning the average as the optimal solutions, otherwise entering processes such as Selection, Reproduction, Crossover, Mutation, and so on for re-Evaluation), wherein the evaluation function in step D) is applied within the ROI only (Lu, [0068] And, the sensor 12b has following constraints: a movable range of the sensor 12b ; a scanning dead space relating to the contour of the object; and constraints of scanning range, such as View Angle θ of the sensor 12b, Depth of Field D of the sensor 12b, and so on. Therefore, utilizing the Optimization Algorithms to calculate an optimal scanning position and an optimal scanning angle of the sensor 12b where the sensor 12b may effectively scan the curve surface of the object 9).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Chan and Lu’s teaching because Chan teaches generating, evaluating, and repeating optimization on parameter sets, while Lu teaches acquiring multiple measurements of an object from different angles and orientations and merging the results with the respected measurement data, while also defining an region of interest with respect to an object coordinate system. A person of ordinary skill in the art would have been motivated to integrate Lu’s measurement system into Chan’s optimization technique in order to improve efficiency and reduce unnecessary measurements to focus on the evaluation of parameters.
Regarding Claim 9, Chan in view of Lu teaches the method according to claim 1, further comprising, after step J):
K) storing the at least one parameter set output in step J) in a database (Chan, [Col 17. Line 15-19] the system output interface module 208 further comprises a data retrieving submodule for retrieving information from the Genetic Algorithm module, a rapid visualisation submodule).
Regarding Claim 10, Chan in view of Lu teaches the method according to claim 1, further comprising, before step C):
B) generating the parameter sets used in step C) randomly, or by a default
initial parameterization (Chan, [Col. 8 Line 35-38] (b) an initialisation submodule for generating a population of valid chromosomes).
Regarding Claim 11, Chan in view of Lu teaches the method according to claim 9, further comprising, before step C) (Chan, [Col. 8 Line 35-38] a retrieving submodule for retrieving design specific parameters, encoded chromosome and phenotypes from storage mean of the interface module, encoding module and Genotype-Phenotype mapping module respectively):
B) generating the parameter sets used in step C): by carrying out a
measurement of the measuring element with a default initial parameterization (Chan, [Col. 8 Line 38-54] (c) a fitness evaluation submodule for computing quantitative fitness values of the phenotype; (d) a mould cost estimation submodule for calculating a plurality of cost factors of the phenotype; (e) a parent selection submodule for selecting two parents from said population of valid chromosome using tournament selection strategy; (f) a crossover operation submodule for producing at least one offspring chromosome from the selected parents; (g) a mutation operation submodule for mutating the offspring chromosomes randomly), and determining one or more nearest neighbors of the default initial parameterization (Chan, [Col. 8 Line 48-54] (h) a replacement submodule for replacing the lowest rank chromosome in the population by the offspring chromosome if the chromosome has a higher fitness value than that of the lowest rank chromosome).
Regarding Claim 12 and 13, Chan in view of Lu teaches the method according to claim 10, further comprising, before step B):
A) defining value ranges for the measurement parameters of the parameter sets (Lu, [0040-0047] And, scanning constraints of the sensors 12a and 12b may include a movable range of the sensor, a scanning range of the sensor or a scanning dead space of the sensor for a contour of the object. Specifically, the sensor 12a or 12b may be an optical rangefinder for scanning an object to measure the geometric dimension of the object, but a scanning dead space (for example, as the dash line shown in FIG. 2G) of the sensor may be aroused due to a contour of the object 9′; and the sensor 12a or 12b may have intrinsic constraints in specifications, such as the range of a View Angle θ (for example, the limitation of an angle between an incident line of light-beam and a normal line of the scanned surface shown in FIG. 2F), the valid Depth of Field D (for example, the limitation of scanning depth), and so on), wherein the parameters of the parameter sets are generated in steps B) and E) within the value ranges (Lu, [0065-0069] And, the sensor 12b has following constraints: a movable range of the sensor 12b ; a scanning dead space relating to the contour of the object; and constraints of scanning range, such as View Angle θ of the sensor 12b, Depth of Field D of the sensor 12b, and so on.).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Chan and Lu’s teaching because Chan teaches generating parameters within a predefined value range as part of the optimization process, while Lu teaches that measurement parameters are constrained by physical and operational limitations of the sensor as well, including the moveable range, scanning range, dead space, view angle, and depth of the field. A person of ordinary skill in the art would have been motivated to define the value ranges of the parameters prior to the parameter generation as taught by Lu, in order to ensure the generated parameters generated in Chan remain feasible and reduce inaccurate measurements and improve efficiency of Chan’s method.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over US 9317626 B2, Chan et al (hereinafter Chan) in view of US 20180149471 A1, Lu et al (hereinafter Lu), in further view of US 7835786 B2, Palmer et al (hereinafter Palmer).
Regarding Claim 7, Chan in view of Lu in further view of Palmer teaches the method according to claim 1, wherein the new parameter sets are generated in step E) using artificial intelligence that adapts a target function by an online learning method and the evaluations of the evaluation function (Palmer, [Col. 7 Line 5-20] three simulated data sets were generated using Monte Carlo simulations: training, validation, and testing data sets. The training data set was used to train the neural network algorithm to extract optical properties from diffuse reflectance measurements with a particular probe geometry over a wide range of optical properties. The validation and testing data sets were used in two different stages of the optimization process. The validation data set was used in each iteration of the optimization loop shown in FIG. 2, to evaluate the fitness of a given probe geometry with an independent set of optical properties (which were not used in training the algorithm). The RMSE calculated from the results of the validation data set was used as the measure of probe fitness. The optimal probe design selected by the genetic algorithm at the end of the iterative process was applied to the testing data set).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Palmer with Chan in view of Lu’s teaching because Palmer teaches training and updating a neural network based on evaluation results, which corresponds to an online learning method. One of ordinary skill in the art would be motivated to combine since Chad in view of Lu already discloses iteratively generating and evaluating parameters and combing these teachings would improve accuracy and efficiency of the system.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to
applicant disclose:
-US 20130211391 A1, describing systems and methods for optical imaging and treatment using optical coherence tomography (OCT) and related optical measurement systems. The reference discloses optical measurement devices, imaging probes, and scanning systems configured to collect optical data for analysis.
-US 20090237656 A1, describing systems and methods for tomographic imaging using hyperspectral absorption spectroscopy. The reference discloses performing optical measurements at multiple wavelengths, collecting absorption data, and generating tomographic images based on the measured optical data. The reference further describes tomographic techniques, image processing, and the use of captured measurement data to determine the properties of the region.
-US 20220057367 A1, describing computer implemented methods for predicting pipe failure using measurement data, evaluations, and machine learning techniques. The reference discloses collecting measurement inputs related to pipe conditions, classifying pipe segments into groups, evaluating measurements using models, and generating assessments of future pipe degradation or failure probability.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM NAGI SHOHATEE whose telephone number is (571)272-6612. The examiner can normally be reached 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IBRAHIM NAGI SHOHATEE/Examiner, Art Unit 2857 /SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
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