DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to communications on 08/25/2022
Claims 1-6 pending
Claims 1-6 rejected
Priority
Application Data Sheet received on 08/25/2022 claims priority to PCT/IN2021/050320 filed on 2021-03-27 which claims priority to foreign application 202021013527 filing date 2020-03-27. ADS form accepted by the examiner.
Information Disclosure Statement
IDS form received on 08/25/2022 received and considered by the examiner.
Drawings
Drawings received on 08/25/2022 received and accepted by the examiner.
Specification
Abstract received on 08/25/2022 less than 150 words and contains no legal or implied phraseology. Abstract accepted by examiner.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, an abstract idea, which has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception.
Claim 1
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites A model driven sub-system (100) for design and execution of experiments which is a machine.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 1 recites
define design of an experiment, comprising:
defining a design of an experiment, under broadest reasonable interpretation, entails planning done by a scientist on what they would like to test and the steps to accomplish their goals. Ever since the creation of the scientific method, researchers have designed experiments using their brains, by providing their opinions on what to test, and from passing an evaluation of their past experiments. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.“ Therefore this claim limitation recites an abstract idea.
selecting a system process for the experiment
In light of the specifications, selecting a system process under broadest reasonable interpretation is selecting a function or workflow that relates parameters in an experiments. The specification gives an example of par 49: “The process for calculation of deflection is a function of xl,x2...xn, where xl...xn can be geometric parameters of beam and material properties of beam, results in y which is beam deflection, thus the process can be represented as: y = F(l,w,h,t) where y= beam deflection, 1= length of beam, w =width of beam, h =height of beam, t = tensile strength of beam material. Here, F() is the system process and 1, w, h, t are the system process parameters. “ Selecting a system process, can be interpreted as selecting a function that a researcher wants to experiment. This limitation relates to a selection, which is a decision made with a researcher’s opinion or judgement. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Because this limitation pertains to making a selection, which can be performed in the human mind, this limitation recites an abstract idea.
creating a functional model for the selected system process if the functional model does not already exist
In light of the specifications, creating a functional model for a selected system process, means to more specifically create a function instance that relates all the parameters together in a more concrete way. From par 50 : “The sub-system 100 creates a functional model Ffm and a functional model parameter for each system process parameter lfm wfm, hfm,tfm,.” This is a more specific instance of the system process outlined earlier. If the researchers find that they have not tested a selected system process before, they will decide to create a “functional model” of that process, which will later involve defining the range of parameters for experimentation, how they relate to each other, and so on. The “functional model” is not a concrete object, and is simply an extension of the system process outlined above. Researchers when designing experiments normally outline the variables they would like to test as well as the ranges of these variables they would like to study. This is a process usually done by hand, where researches can write down on pieces of paper what they would like to test. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Because this limitation pertains to a planning stage done by researchers, this limitation recites an abstract idea.
mapping each functional parameter in the functional model with corresponding ontology parameters;
In light of the specifications, mapping each functional parameter in the functional model with corresponding ontology parameters, means to relate the parameters in the model to parameters people are familiar with in their own ontology of a specific field. Like in par 49: “where y= beam deflection, 1= length of beam, w =width of beam, h =height of beam, t = tensile strength of beam material.” Researchers commonly do this in experiments, where they relate variables to each other (ie: y = 2x) then later define those variables in research papers to give context. This is an evaluation performed by a researcher to state which variable relates to what in the real world. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Because this limitation pertains to a planning stage done by researchers, this limitation recites an abstract idea.
initializing a meta-design space for the functional model
A meta-design space is data associated with the model. Par 46: “Further at step 312, the sub-system 100 initializes meta design space for the functional model. At this stage, the sub-system 100 initializes the parameter table and associates it with the functional model. The sub-system 100 also initializes one and only one column parameter for each functional model parameter of the functional model. The parameter table along with column parameters formulate the schema to store meta design space of respective design of the functional model.” This space is essentially a table or coordinate grid with the possible parameter values. Often when designing an experiments, researchers and scientist write tables out when planning their experiments. MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this limitation can be performed in the mind of a researcher with the aid of a pen and paper, this limitation pertains to an abstract idea.
creating the experiment from the functional model, wherein a plurality of experiment parameters of the experiment conform to the functional parameters of the functional model;
“creating the experiments” in this context is choosing the parameters that would like to be tested that meets within the bounds of the already planned model. For example in par 51: “For example, the DOE is performed when length does not exceed 35cm and the material tensile strength can not exceed 500 psi, thus the sub-system 100 can create an experiment with respective range for experiment parameters. The DOE may have to be performed on the functional model within a closed range of process parameter values, thus for each such DOE, the sub-system 100 creates an experiment, F X, for the functional model and experiment parameters for each functional model parameter, 1exwe,h,te,yex that contain the configuration of process parameters. “ This is a researcher deciding that they would like to experiments on a certain condition (l<35cm) and enforcing that on the functional model. This is normally done by researchers when trying to research some phenomena in different environments, and is a normal facet of research. The scientist simply updates or rewrites his table to exclude parameters from the functional model that does not line up with the chosen experimental parameters. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this limitation can be performed in the mind of a researcher with the aid of a pen and paper, this limitation pertains to an abstract idea.
attaching the experiment parameters with the functional parameters
Par 28: “The experiment parameter is linked to only one functional model parameter that specifies the function parameter for which configuration is applicable.” This limitation links the experiments parameter with the already defined functional parameter. This is an observation or judgement made by a researcher, (ie: I would like to test when l<35cm, this experiments parameter is attached to the functional parameter of length). The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this limitation can be performed in the mind of a researcher with the aid of a pen and paper, this limitation pertains to an abstract idea.
selecting an input generator and a distribution generator for the design of the experiment;
Selecting an input generator and distribution generator, is deciding how the parameter values will be distributed in the experiments. For example, one may decide that the input generator will choose parameter values uniformly distributed across all allowable values with a noisy distribution. This is a selection of an algorithm. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Because this limitation pertains to making a selection, which can be performed in the human mind, this limitation recites an abstract idea.
and generate a result for the defined design of the experiment, by executing the design of the experiment.
Generating a result for the defined design of the experiments by executing design of the experiments is to come up with the final product of the experimental design. As already stated defining a design of an experiment, under broadest reasonable interpretation, entails planning done by a scientist on what they would like to test and the steps to accomplish their goals. Ever since the creation of the scientific method, researchers have designed experiments using their brains, by providing their opinions on what to test, and from passing an evaluation of their past experiments. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.“ Therefore this claim limitation recites an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 1 additionally recites
consisting of a digital workflow with one or more in-silico experiments in a model-driven system,
As stated, this workflow above can be reasonably be performed in the human mind of a researcher and has been performed by scientists who plan out experiments. The MPEP 2106.05(f)(2) states “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 fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the fact that the workflow of the experiments is occurring digitally, and the experiments themselves are in a computer environment, does not take integrate the judicial exception into a practical application or provide significantly more.
comprising: one or more hardware processors (104); one or more communication interfaces (106); and one or more memory (102) storing a plurality of instructions, wherein the plurality of instructions when executed cause the one or more hardware processors (104) to:
The MPEP 2106.05(f)(2) states “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 fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the presence of computer components to perform the abstract idea above does not take integrate the judicial exception into a practical application or provide significantly more.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The presence of computer components to perform the abstract idea above does not take integrate the judicial exception into a practical application or provide significantly more.
Based on the above facts, the office concludes that claim 1 is not eligible under 35 USC 101.
Claim 2:
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The sub-system (100) as claimed in claim 1, which is a machine.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 2 recites: wherein executing the defined design of experiment by the system comprising: initializing an experimental instance of the defined design of experiment
An experiments instance of the defined design is the details of the design of a particular experiment. See par 36-37: “Design of Experiment Instance (Sub-System Component): This entity is to capture the execution level details of design of experiment. Being the bridge entity for communication between different modules of sub-system the design space will be associated with this entity. “ As already stated under claim 1, defining a design of an experiment, under broadest reasonable interpretation, entails planning done by a scientist on what they would like to test and the steps to accomplish their goals. Ever since the creation of the scientific method, researchers have designed experiments using their brains, by providing their opinions on what to test, and from passing an evaluation of their past experiments. This limitation is an observation of this experiment design to use in this particular experimental instance. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.“ Therefore this claim limitation recites an abstract idea.
fetching and storing an experiment parameter as an experiment parameter instance;
par 37 – 38: “Experiment Parameter Instance (Sub-System Component): This entity captures the execution level details of function parameter configuration such as range bounds, standard deviation, and default/constant value of parameter. “ Again, this is the details for the experiment we already generated. Fetching and storing these details as an “experimental parameter instance” is the same as a researcher deciding which values they would like to run. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.“ Therefore this claim limitation recites an abstract idea.
; initializing a parameter table that stores the defined design of experiment, and a column parameter for every experiment in the parameter table
Par 38 – 40: “Parameter Table (Sub-System Component): This entity captures the design space information of design of experiment which includes the input and output value of each run of experiment, thus the format for data persistence is preferred to be a table. This entity contains pointer to a data storage structure. Each parameter table must have only one design of experiment instance. Further, each parameter table must have one or more than one column parameter.
Column Parameter (Sub-System Component): This entity captures the column information (input/output parameters) of a design space. Each column parameter is linked to an experiment parameter instance to specify which function parameter is referred by this column. Also, each column parameter must have only one parameter table. “
This relates to the design space of the experiment. Creating tables of parameter values with columns is commonly performed by researches when doing experiments to match up different values that are tested. MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this claim pertains to filling in a table for the values of an experiment, which can be done by researchers on a piece of paper, this limitation pertains to an abstract idea.
fetching and storing values of a plurality of algorithm parameters of the at least one input generator as an algorithm parameter instance
The algorithm parameter instance is in par 44: “entity captures the value of algorithm parameter.” The algorithm under broadest reasonable interpretation is an equation, and can be fetched and stored within the mind of a scientist and is used to generate inputs. An example can be a uniform distribution. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this claim pertains to filling in a table for the values of an experiment, which can be done and calculated by researchers on a piece of paper, this limitation pertains to an abstract idea.
fetching and storing values of a plurality of algorithm parameters of the at least one distribution generator, as the algorithm parameter instance;
The algorithm parameter instance is in par 44: “entity captures the value of algorithm parameter.” The algorithm under broadest reasonable interpretation is an equation, and can be fetched and stored within the mind of a scientist and is used to generate noise sets for a distribution. An example can be an algorithm that produces two values within 0.5 standard deviation of each input to result in a total of three values. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this claim pertains to filling in a table for the values of an experiment, which can be done and calculated by researchers on a piece of paper, this limitation pertains to an abstract idea.
invoking at least one input generator algorithm by feeding an input for the at least one input generator algorithm;
As already stated, this algorithm under broadest reasonable interpretation is a mathematic equation. For example, a uniform distribution of all allowable values for length. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this claim pertains to filling in a table for the values of an experiment, which can be done and calculated by researchers on a piece of paper, this limitation pertains to an abstract idea.
generating an input set using the at least one input generator algorithm and storing the input set in the parameter table;
As already stated, this algorithm under broadest reasonable interpretation is a mathematic equation. For example, a uniform distribution of all allowable values for length. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this claim pertains to filling in a table for the values of an experiment, which can be done and calculated by researchers on a piece of paper, this limitation pertains to an abstract idea.
invoking at least one distribution generator algorithm by feeding one or more inputs for the at least one distribution generator algorithm
As already stated, this algorithm under broadest reasonable interpretation is a mathematic equation. An example can be an algorithm that produces two values within 0.5 standard deviation of each input to result in a total of three values. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this claim pertains to filling in a table for the values of an experiment, which can be done and calculated by researchers on a piece of paper, this limitation pertains to an abstract idea.
generating input sets using the distribution generator algorithm and updating the parameter table using the generated input sets;
As already stated, this algorithm under broadest reasonable interpretation is a mathematic equation. An example can be an algorithm that produces two values within 0.5 standard deviation of each input to result in a total of three values. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this claim pertains to filling in a table for the values of an experiment, which can be done and calculated by researchers on a piece of paper, this limitation pertains to an abstract idea.
determining whether result for each input set exists in the meta-design space;
As stated, the meta-design space is essentially a table. This is an observation to see if the input already exists in the table. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.“
fetching results for each of the input sets, from the meta-design space, if the result already exists;
As stated, the meta-design space is essentially a table. This is an observation to see if the input already exists in the table and then reading the results off of the table. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.“
fetching results for each of the input sets, by invoking a system process for the input set, if the result does not exist in the meta-design space;
If the result is not in the table, then the experiment is performed. The broadest reasonable interpretation of the “system process” in this context is the performed experiment, which could be a mathematical function as outlined above. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.”
and merging the result generated for each of the input sets in the parameter table to form a complete design space of the functional model.
As stated, the meta-design space is essentially a table. This is adding the results gained from different input sets into the design space table or grid. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Because this limitation pertains to filling out a table with results, which an individual can reasonably do with a pen and paper, this limitation pertains to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 2 does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. Claim 2 does not recite additional elements that amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 2 is not eligible under 35 USC 101.
Claim 3:
Claim 3 is an effective duplicate of claim 1 with the difference being that it is “A processor implemented method (200) … the method comprising“ which is a method claim. Claim 3 also references “the one or more hardware processors (104) To perform the functions of claim 1. The MPEP 2106.05(f)(2) states “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 fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore,
Due to the reasons discussed on claim 1 and above, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 3 is not eligible under 35 USC 101.
Claim 4
Claim 4 is an effective duplicate of claim 2 with the only difference being that it depends on claim 3. Due to the reasons discussed on claim 2 and claim 3, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 4 is not eligible under 35 USC 101.
Claim 5
Claim 5 is an effective duplicate of claim 1 with the difference being that it is “A computer program product” which is a product of manufacture. Furthermore claim 5 states comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to perform” the functions of claim 1. The MPEP 2106.05(f)(2) states “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 fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 5 is not eligible under 35 USC 101.
Claim 6:
Claim 6 is an effective duplicate of claim 2 with the only difference being that it depends on claim 5. Due to the reasons discussed on claim 2 and claim 5, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 6 is not eligible under 35 USC 101.
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, 3, and 5 are rejected under 35 U.S.C. 103 as being unpatentable over (US 20210326501 A1) “Data Set Generation For Performance Evaluation” (Lekivetz_2018), “Noise generator with programmable distribution” (Mahmood_1987) and further in view of (US 20210124858 A1) “Transformation And Evaluation Of Disallowed Combinations In Designed Experiments” (Morgan_2018)
Claim 1:
Lekivetz_2018 makes obvious A model driven sub-system (100) for design and execution of experiments consisting of a digital workflow with one or more in-silico experiments in a model-driven system, comprising: (abstract : “A computing system receives a request to generate computer-generated data for an experiment. The computer-generated data comprises generated inputs defining setting(s) for a plurality of factors for a design of the experiment. “ … par 3: “Experiments may also be conducted on computer models or designed by computer models. … par 16: “FIG. 7 illustrates a flow chart showing an example process for executing a data analysis or processing project, according to at least one embodiment of the present technology.”)
one or more hardware processors (104); (par 10: “FIG. 1 illustrates a block diagram that provides an illustration of the hardware components of a computing system, according to at least one embodiment of the present technology.” … par 93: “As noted, computing environment 114 may include a communications grid 120 and a transmission network database system 118. Communications grid 120 may be a grid-based computing system for processing large amounts of data. The transmission network database system 118 may be for managing, storing, and retrieving large amounts of data that are distributed to and stored in the one or more network-attached data stores 110 or other data stores that reside at different locations within the transmission network database system 118. The compute nodes in the grid-based computing system 120 and the transmission network database system 118 may share the same processor hardware, such as processors that are located within computing environment “) one or more communication interfaces (106); (par 12: “FIG. 3 illustrates a representation of a conceptual model of a communications protocol system, according to at least one embodiment of the present technology.” … par 32: “FIGS. 23A-23B illustrate examples of graphical interfaces in at least one embodiment of the present technology.”) and one or more memory (102) storing a plurality of instructions, wherein the plurality of instructions when executed cause the one or more hardware processors (104) to: (par 7: “In another example embodiment, a computing system is provided. The computing system includes, but is not limited to, a processor and memory. The memory contains instructions that when executed by the processor control the computing system to generate the design suite that comprises the computer-generated data and evaluate the one or more resulting responses according to one or more generated responses of the computer-generated data.”)
define design of an experiment, (par 7: “In another example embodiment, a computing system is provided. The computing system includes, but is not limited to, a processor and memory. The memory contains instructions that when executed by the processor control the computing system to generate the design suite that comprises the computer-generated data and evaluate the one or more resulting responses according to one or more generated responses of the computer-generated data.”)
comprising: selecting a system process for the experiment; (par 467: “ In one or more embodiments, the computing device 5302 receives a request 5350 to generate computer-generated data for an experiment. For instance, the request 5350 may comprise a request for design data 5352 for a design suite (Examiner note: a system process) (e.g., to explore settings for an electrical, mechanical, or computer system not shown). Additionally, or alternatively, the request 5350 may be a request to validate 4060 as described herein (e.g., whether software will crash under certain conditions or whether the software is providing expected results in a timely manner). “)
creating a functional model for the selected system process par 467: The generated data in response to the request can comprise inputs defining one or more settings for a plurality of factors for a design of an experiment. For example, if the experiment is to validate that an engine system will not fail, the factors could be environmental (e.g., temperatures or humidity and the inputs could be the degrees and humidity level respectively) or the factors could be related to the engine itself (e.g., the factor could be fuel and the inputs different types of gasoline that would go into the engine). Additionally, the generated data can comprise generated responses according to the design of the experiment (e.g., how long the engine is predicted to run under these conditions). For instance, the responses can be generated based on a model developed for generating a response for the generated inputs (e.g., a simulation predicted time) (Examiner note: a functional model for the selected system process) or based on desired responses from a validator (the car needs to run for at least this long before switching from battery operation to gas).
mapping each functional parameter in the functional model with corresponding ontology parameters; (par 467: The generated data in response to the request can comprise inputs defining one or more settings for a plurality of factors for a design of an experiment. For example, if the experiment is to validate that an engine system will not fail, the factors could be environmental (e.g., temperatures or humidity and the inputs could be the degrees and humidity level respectively) or the factors could be related to the engine itself (e.g., the factor could be fuel and the inputs different types of gasoline that would go into the engine). Examiner note: Where the functional parameters in the model are mapped to ontological parameters such as a type of gasoline. )
initializing a meta-design space for the functional model; (Par 470: “ Some information or requests may be provided by the user or modified by a user (e.g., providing a disallowed combination or specifying one or more factors using input device 5304). As an example, the computing device 5302 may receive first design information 5361 comprising one or more first characteristics (e.g., a quantity of factors or a quantity of runs) for specifying generation of computer-generated data associated with a first design space of multiple design spaces. (Examiner note: initializing a first meta-design space corresponding to the functional model) The first design space can define candidate options for generating inputs according to a first set of factors of factors in an experiment. “)
creating the experiment from the functional model, wherein a plurality of experiment parameters of the experiment conform to the functional parameters of the functional model; (Par 474: “For example, in one or more embodiments, the computer-readable medium 5312 comprises a design suite application 5353 for generating a design suite that provides design cases for an experiment. The design suite can comprise the computer-generated data that represents settings constrained by different design spaces. As an example, one design suite could comprise a design with different experiment goals (e.g., whether an engine will fail and how fast can an engine drive a car) or the same goal for different sets of design cases but points of analysis (e.g., different factors that may contribute to engine failure). The generated design suite can include computer-generated data that represents, in a first set of design cases of the design suite, settings constrained by a first design space (e.g., based on first design information 5361), and represents, in a second set of design cases of the design suite, settings constrained by a second design space (e.g., based on second design information 5362). Alternatively, or additionally, the computer-readable medium 5312 comprises an experiment application 5344 for conducting an experiment or evaluating an experiment according to the design suite. “ (Examiner note: creating an experiment from the functional model, where it is understood by one ordinarily skilled in the art that the parameters of the experiment come from the same design suite and therefore conform to the functional parameters of the functional model.))
attaching the experiment parameters with the functional parameters; (Par 474: “For example, in one or more embodiments, the computer-readable medium 5312 comprises a design suite application 5353 for generating a design suite that provides design cases for an experiment. The design suite can comprise the computer-generated data that represents settings constrained by different design spaces. As an example, one design suite could comprise a design with different experiment goals (e.g., whether an engine will fail and how fast can an engine drive a car) or the same goal for different sets of design cases but points of analysis (e.g., different factors that may contribute to engine failure). The generated design suite can include computer-generated data that represents, in a first set of design cases of the design suite, settings constrained by a first design space (e.g., based on first design information 5361), and represents, in a second set of design cases of the design suite, settings constrained by a second design space (e.g., based on second design information 5362). Alternatively, or additionally, the computer-readable medium 5312 comprises an experiment application 5344 for conducting an experiment or evaluating an experiment according to the design suite. “ (Examiner note: where it is understood by one ordinarily skilled in the art that an experiment according to the design suite is experimental parameters which are attached with the functional parameters))
and selecting an input generator
Par 494: “In the example graphical user interface 5600, the user can specify criteria related to generating data for inputs, outputs, and the number of runs in the design suite for different design spaces for the Fit Y by X platform. For instance, the user can specify that the input variable X can be one of continuous, categorical, or discrete numeric. Discrete numeric factors can sometimes be referred to as ordinal factors. The user is also able to specify the number of levels for categorical or discretized continuous inputs (e.g., 2, 3 or 10 levels). The user can specify the balance of inputs for sets of design cases in the design suite (e.g., balanced, unbalanced and random inputs for the factors). (Examiner note: this is a selection of the input generator) For instance, if a factor had two levels that could take one of L1 or L2, a balanced set of design cases would have the same or nearly the same number of L1 or L2's across all design cases of the design, whereas an unbalanced design may have more D's or more L2s, and a random set of design cases could be generated without regard to the balance. These same kinds of criteria can be specified for the output (Y) to ensure balance in output. This can be useful for when the user is checking a particular result from an experiment like certain performance criteria and wants to ensure testing of certain inputs that are likely to produce those desired criteria. In this example, the user was able to specify different numbers of runs (10, 50 and 1000) for different designs according to criteria for the input (X) and output (Y).”
and generate a result for the defined design of the experiment, by executing the design of the experiment.
Par 474: “For example, in one or more embodiments, the computer-readable medium 5312 comprises a design suite application 5353 for generating a design suite that provides design cases for an experiment. (Examiner note: This is a result for a defined design of the experiment) The design suite can comprise the computer-generated data that represents settings constrained by different design spaces. As an example, one design suite could comprise a design with different experiment goals (e.g., whether an engine will fail and how fast can an engine drive a car) or the same goal for different sets of design cases but points of analysis (e.g., different factors that may contribute to engine failure). The generated design suite can include computer-generated data that represents, in a first set of design cases of the design suite, settings constrained by a first design space (e.g., based on first design information 5361), and represents, in a second set of design cases of the design suite, settings constrained by a second design space (e.g., based on second design information 5362). Alternatively, or additionally, the computer-readable medium 5312 comprises an experiment application 5344 for conducting an experiment or evaluating an experiment according to the design suite. “
Lekivetz_2018 does not expressly recite and a distribution generator
Mahmood_1987 however makes obvious and a distribution generator (page 889: “The design and implementation of a programmable distribution noise generator is introduced. The uniform output of the PN generator is shaped by a non-linear function to produce the required PDF.”)
Lekivetz_2018 and Mahmood_1987 are analogous art to the claimed invention because they are from the same field of endeavor called experiment testing. Where Lekivetz_2018 focuses on a design of experiments workflow and Mahmood_1987 introduces a tool which generates a distribution to be used in page 885 par 1 “testing and measurement.” Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Lekivetz_2018 and Mahmood_1987. The rationale for doing so would have been to follow a teaching as proposed in the art. Mahmood_2987 page 885 par 2 states “It is well known that maximal length pseudo-noise generator (PN) can be used to produce uniform probability density function (PDF). To produce other than uniform, the output of the PN sequence is applied to a non-linear circuit that shapes the distribution to the required PDF. In this paper, design and implementation of such a type of noise generator is introduced. The main advantage of the proposed method is that the type of distribution can be easily changed without any major change in the required hardware.”
Therefore, it would have been obvious to combine the design of experiments workflow of Lekivetz_2018 with the use of a noise distribution generator of Mahmood_2987 for the benefit of allowable for an easily changeable non-uniform distribution for testing to obtain the invention as specified in the claims.
Lekivetz_2018 and Mahmood_1987 do not expressly recite
Morgan_2018 however makes obvious See Fig 40 flowchart which begins with “user requests optimal design for experiment” (4002), “Are there stored instructions … for generating a design of an experiment” (4004), if No, “Is there a candidate design” (4012) if No “Use search algorithm to create design (4014). “ Where the search algorithm is as described in par 516 as “If there is not a candidate design, an operation 4014 a search algorithm is used to create the design. A search algorithm is the approach taken in other statistical software to generate a design (e.g., one or more software tools offered by SAS Institute Inc. of Cary, N.C., USA such as JMP®). Once the user provided input, a candidate design would be generated at random and then an algorithm would be used to refine the entries of this design to maximize a user-selected measure of the design's efficiency (how well the design minimizes a measure of uncertainty regarding estimates of the factor effects on the response). The user can specify how many random designs to start with. The more starts, the more likely the best design will be found, but at a cost of more computation time.” Examiner note: Where Morgan_2018 makes obvious looking for a functional model/instructions for a selected system process, and if it does not exist creating one.
Lekivetz_2018, Mahmood_1987, and Morgan_2018 are analogous art to the claimed invention because they are from the same field of endeavor called design and execution of experiments.
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Lekivetz_2018, Mahmood_1987, and Morgan_2018. The rationale for doing so would have been to follow a teaching proposed in the art. Morgan_2018 par 519 states “As shown in Table 3 compared to Table 2, the design time is significantly reduced to nearly instantaneous in some cases. (The true time is not 0; rather, it is simply so small that it has dropped below the display threshold). The time savings are particularly significant in situations such as Example 4 where the number of runs and factors is large (1059 runs and 531 factors). In all the examples, having stored instructions for generating an initial screening design improved processing time.” One ordinarily skilled in the art would recognize that having stored instructions pertaining to a design of experiments would save processing time as opposed to redoing the design of experiment every time it is to be used. Therefore, it would have been obvious to combine the design of experiments workflow and use of distributed generator of Lekivetz_2018 and Mahmood_1987 with the storing of design of experiment instructions and checking if the functional model already exists of Morgan_2018 for the benefit of saving processing time to obtain the invention as specified in the claims.
Claim 3:Claim 3 is effectively similar to claim 1 and is therefore rejected under the same rational. Additionally, Lekivetz_2018 makes obvious the additional limitations of
A processor implemented method (200) (par 7: “processor and memory. The memory contains instructions that when executed by the processor control the computing system to generate the design suite that comprises the computer-generated data and evaluate the one or more resulting responses according to one or more generated responses of the computer-generated data.”)
the method comprising: (par 8: “In another example embodiment, a method, is provided, of generating the design suite that comprises the computer-generated data and evaluating the one or more resulting responses according to one or more generated responses of the computer-generated data.”)
defining (202) design of an experiment, via one or more hardware processors (104), comprising: par 7: “processor and memory. The memory contains instructions that when executed by the processor control the computing system to generate the design suite that comprises the computer-generated data and evaluate the one or more resulting responses according to one or more generated responses of the computer-generated data.”)
via the one or more hardware processors (104). (par 93: “The compute nodes in the grid-based computing system 120 and the transmission network database system 118 may share the same processor hardware, such as processors that are located within computing environment 114.”)
Claim 5:
Claim 5 is effectively similar to claim 1 and is therefore rejected under the same rational.
Additionally, Lekivetz_2018 makes obvious the additional limitations of A computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to perform: (par 6: “In an example embodiment, a computer-program product tangibly embodied in a non-transitory machine-readable storage medium is provided. The computer-program product includes instructions operable to cause a computing system to receive a request to generate computer-generated data for an experiment.”)
Claims 2, 4, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Lekivetz_2018, Mahmood_1987, Morgan_2018, and further in consideration of “SQL EXISTS Operator” (W3Schools_2017) as motivated by “OLAP, ROLAP, MOLAP,HOLAP” (Sisense_2018)
Claim 2:
Lekivetz_2018 makes obvious The sub-system (100) as claimed in claim 1, wherein executing the defined design of experiment by the system comprising: (see claim 1)
initializing an experimental instance of the defined design of experiment; Par 474: “Alternatively, or additionally, the computer-readable medium 5312 comprises an experiment application 5344 for conducting an experiment or evaluating an experiment according to the design suite. “ (Examiner note: Where this encompasses initializing an experimental instance of the previously created design of experiment)
fetching and storing an experiment parameter as an experiment parameter instance; (Par 500: “FIGS. 57A-57B illustrate example graphical user interfaces for platform-specific data generation for an example software platform for the Fit Y by X platform. In graphical user interface 5700, the list of data set factors and platform factors can be predefined. “ (Examiner note: Where factors are the parameters used in the GUI which are stored.)
See also par 511 which further makes obvious that these parameters are stored: “FIG. 60 presents a visual summary with simply a reference to 12 different design spaces. The visual summary could have been presented differently such as with explicit references to the characteristics that go into data generation associated with those design spaces. For example, FIGS. 61A-61C illustrate an example graphical user interface of a visual summary of a design based on a strength 2 covering array for specified factors of design spaces in the visual summary 6050 of FIG. 60. As in FIG. 60 a computing system can be used to generate a data set for each specified row (e.g., Custom Design provided by SAS Institute Inc. or a random distribution tool). In this display format, the user can easily see that each of the rows of the graphical user interface describes the factor type for each of different factors, specifies disallowed combinations, specifies options for generating the responses, and specifies options for testing the prediction profiler. For instance, in FIG. 61A, portion 6100 of the graphical user interface shows summarized computer generated criteria for inputs for factors (e.g., X1-X10) (e.g., according to user criteria in FIG. 59 specified for X1-X4 and remaining factors)”
initializing a parameter table that stores the defined design of experiment, and a column parameter for every experiment in the parameter table;
par 510: “Once a table is built as shown in FIG. 60. A user can select to save the table (e.g., by selecting a save table option 6030 to save a table to a disk or memory). The computing system can then save instructions to create the data tables without needing to store the actual generated inputs.” (Examiner note: an initialized parameter table.) … Par 517: “FIGS. 62A-E illustrate example graphical user interfaces for design cases corresponding to a single design space of multiple different design spaces of a design suite corresponding to the specifications in FIGS. 61A-61C. “Examiner note: See Figures 61A-C and figures 62B-E. Which represent parameter tables that store the defined design of the experiment as well as column parameters for every experiment in the parameter table
fetching and storing values of a plurality of algorithm parameters of the at least one input generator, as an algorithm parameter instance; (Par 516: “The user can have the flexibility to specify other factors than what is shown here. For example, the user could add a factor that determines the seed used to generate random output or even a factor that determines how the data table is generated (i.e., full factorial vs. fractional factorial designs). Accordingly, embodiments herein provide flexible interactive graphical user interfaces for a user to specify criteria for generating data even with complex platforms and to view evaluations performed according to that generated data. Should an evaluation indicate a problem with one or more design case in a design set, embodiments enable graphical user interfaces for detecting problematic design cases.” Examiner note: Where the “seed” used are the values of the algorithm parameter instance corresponding to an input generator, see also Figures 61A-C and figures 62B-E which show stored parameter values.
fetching and storing values of a plurality of algorithm parameters of the
(Par 516: “The user can have the flexibility to specify other factors than what is shown here. For example, the user could add a factor that determines the seed used to generate random output or even a factor that determines how the data table is generated (i.e., full factorial vs. fractional factorial designs). Accordingly, embodiments herein provide flexible interactive graphical user interfaces for a user to specify criteria for generating data even with complex platforms and to view evaluations performed according to that generated data. Should an evaluation indicate a problem with one or more design case in a design set, embodiments enable graphical user interfaces for detecting problematic design cases.” Examiner note: see also Figures 61A-C and figures 62B-E which show stored parameter values. Where Lekivetz_2018 makes obvious the storing of values from an algorithm parameter, but not explicitly of a distribution generator.
invoking at least one input generator algorithm by feeding an input for the at least one input generator algorithm; generating an input set using the at least one input generator algorithm and storing the input set in the parameter table; (Par 517: “In FIGS. 62B and 62C, the computing system has synthesized inputs for 12 factors corresponding to design space (Examiner note: An invocation of an input generator algorithm). “5” in design space column 6190 in FIGS. 61A-C. For instance, there is input data generated for 64 design cases and 3 response values simulated. FIGS. 62B-62C show portions of a graphical user interface 6240 containing the specified input values for the 12 factors and the 64 runs corresponding to this one set of design cases of the design suite.” Examiner note: Where one ordinarily skilled in the art understands that these inputs are “fed” into the algorithm resulting in the different values generated)
invoking at least one Par 517: “In FIGS. 62B and 62C, the computing system has synthesized inputs for 12 factors corresponding to design space (Examiner note: An invocation of an input generator algorithm). “5” in design space column 6190 in FIGS. 61A-C. For instance, there is input data generated for 64 design cases and 3 response values simulated. FIGS. 62B-62C show portions of a graphical user interface 6240 containing the specified input values for the 12 factors and the 64 runs corresponding to this one set of design cases of the design suite.” Examiner note: Where one ordinarily skilled in the art understands that these inputs are “fed” into the algorithm resulting in the different values generated. Where the prior art Lekivetz_2018 makes obvious making a distribution of values, but not explicitly a distribution generator.)
Par 486: “In some embodiments, a generated design suite can be applied to conduct an experiment. For instance, in this example, an optional operation 5405 of the method 5400 comprises receiving one or more responses corresponding to conducting the first set of design cases in the experiment. For instance, the generated inputs of the design suite may comprise synthetic inputs created by the computing system for designing the experiment without real-world or simulated data. Generated responses can be generated according to the generated inputs. The synthetic inputs can then be used for an experiment to generate real-world or simulated responses which can then be evaluated. Additionally, or alternatively, the generated inputs may comprise generated inputs based on or informed by one or more previous experiments of one or more of synthetic inputs, real-world data, and simulated data. For example, the generated design suite may comprise some pre-existing data (Examiner note: Results that already exist) and some supplemental synthetic data. Received responses could be computer-generated according to a simulation of a test system or observed responses of a test system.”) Examiner note: Where Lekivetz_2018 makes obvious a design suite that contains both pre-existing and generated data/results, but does not necessarily expressly recite fetching a result that already exists.
fetching results for each of the input sets, by invoking a system process for the input set,( par 517: “For instance, there is input data generated for 64 design cases and 3 response values simulated. FIGS. 62B-62C show portions of a graphical user interface 6240 containing the specified input values for the 12 factors and the 64 runs corresponding to this one set of design cases of the design suite. FIGS. 62D and 62E show a graphical user interface 6260 which displays example generated response values in columns 6262 for 3 responses for each of the 64 runs according to the design. The generated response values can be used for evaluating responses according to the generated inputs in FIGS. 62B and 62C.” Examiner note: Whereas understood, a generated response value is a value gained from the system process) if the result does not exist in the meta-design space; (par 486: “For instance, the generated inputs of the design suite may comprise synthetic inputs created by the computing system for designing the experiment without real-world or simulated data.”) Examiner note: Where this is done when there isn’t real world data making obvious a result not existing in the meta-design space.
and merging the result generated for each of the input sets in the parameter table to form a complete design space of the functional model. (Par 517: “FIGS. 62D and 62E show a graphical user interface 6260 which displays example generated response values in columns 6262 for 3 responses for each of the 64 runs according to the design. The generated response values can be used for evaluating responses according to the generated inputs in FIGS. 62B and 62C.” Examiner note: Where the generated Responses are stored in the parameter table. )
Lekivetz_2018 does not expressly recite
fetching results for each of the input sets
Mahmood_1987 however makes obvious page 889: “The design and implementation of a programmable distribution noise generator is introduced. The uniform output of the PN generator is shaped by a non-linear function to produce the required PDF.”)
page 889: “The design and implementation of a programmable distribution noise generator is introduced. The uniform output of the PN generator is shaped by a non-linear function to produce the required PDF.”)
As already stated, Lekivetz_2018 and Mahmood_1987 are analogous art to the claimed invention because they are from the same field of endeavor called experiment testing. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Lekivetz_2018 and Mahmood_1987. As already stated, it would have been obvious to combine the design of experiments workflow of Lekivetz_2018 with the use of a noise distribution generator of Mahmood_2987 for the benefit of allowable for an easily changeable non-uniform distribution for testing to obtain the invention as specified in the claims.
Lekivetz_2018 and Mahmood_1987 do not expressly recite determining whether result for each input set exists in the meta-design space;
fetching results for each of the input sets,
W3Schools_2017 makes obvious determining whether result for each input set exists in the meta-design space; (page 1 par 1: “The SQL EXISTS Operator. The EXISTS operator is used to test for the existence of any record in a subquery. The EXISTS operator returns true if the subquery returns one or more records.) Examiner note: Where the meta-design space is interpreted as including an SQL table.
fetching results for each of the input sets, page 3 par 1: “The following SQL statement returns TRUE and lists the suppliers with a product price equal to 22:”) Examiner note: Using the exists operator to fetch a result if it exists in the meta-design space.
Lekivetz_2018, Mahmood_1987, Morgan_2018 and w3Schools_2017 are analogous arts to the claimed invention, as they are from the same field of endeavor of data processing and experimentation.
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Lekivetz_2018, Mahmood_1987, Morgan_2018 and w3Schools_2017. The rationale for doing so would have been applying a combination of prior art elements according to known methods to yield a predictable result. The prior art of Lekivetz_2018 teaches the use of database systems, see par 86: “For example, data may be stored in a hierarchical data structure, such as a ROLAP OR MOLAP database, or may be stored in another tabular form, such as in a flat-hierarchy form.” Sisense_2018 states page 2 par 5: “ROLAP stands for Relational Online Analytical Processing. ROLAP stores data in columns and rows (also known as relational tables) and retrieves the information on demand through user submitted queries. A ROLAP database can be accessed through complex SQL queries to calculate information.” When using a ROLAP database to access data information using SQL queries, a person ordinarily skilled in the art would recognize the usage of an SQL query, such as “exists” to be able to identify if a result already exists and fetch the result. A person ordinarily skilled in the art would recognize the benefit of retrieving results of an experiment, and would understand that it would be predictable to get a result from a table if it already exists.
Therefore, it would have been obvious to combine the design of experiment workflow of Lekivetz_2018, Mahmood_1987, Morgan_2018 with the ability to retrieve existing data of W3Schools_2017 and as motivated by Sisense_2018 for the benefit of retrieving data without needing to repeat the result system process obtain the invention as specified in the claims.
Claim 4:Claim 4 is effectively similar to claim 2 except that it is dependent on claim 3 and is therefore rejected under the same rational as claims 2 and 3.
Claim 6:Claim 4 is effectively similar to claim 2 except that it is dependent on claim 5 and is therefore rejected under the same rational as claims 2 and 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20140107925 A1 “SYSTEMS AND METHODS FOR TRACKING A SET OF EXPERIMENTS” (Chang_2014) teaches a workflow that involves creating and managing a database of different experiments and tracking their differences.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMAD HUSSAM SHALABY whose telephone number is (571)272-7414. The examiner can normally be reached Mon-Fri 7:30am - 5pm.
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/A.H.S./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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