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 .
The 35 USC § 101 rejection regarding to claim 1-7 is withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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, 3-5, 7 are rejected under 35 U.S.C. 103 as being unpatentable over Fainekos et al. (Fainekos) US 2016/0292307 in view of Bhattacharyya et al. (Bhattacharyya) US 2020/0151291 and Takeshima US 2015/0276908
In regard to claim 1, Fainekos disclose A model verification device comprising: ([0006]-[0009] method of model verification)
a memory; ([0006]-[0009] memory) and
a processor coupled to the memory and configured to: ([0006]-[0009] processor)
extract a sample from a search space; (Fig. 1-3, [0020]-[0028] 106, 302, receive initial conditions and input signals of samples (draw a random sample, Fig.2) from the search space)
transform the extracted sample into an input on a constrained search space to which a constraint with respect to a model of a cyber-physical system is applied, according to a predetermined transform rule, ([0020]-[0028][0031]-[0032] Fig. 1-3, 5, samples are changed to a vector of execution traces 306 based on the range of the input parameters and the range of the conditions of samples from the search space, which represent a domain of the function and inputted into 308, hypercubes may be used to define the range of the initial conditions and input parameters with the goal being to find the maximum or minimum expected robustness value related to the model of cyber-physical system.)
acquire the output of the model by inputting the input into the model, (Fig. 1-3, 5, [0019]-[0028][0031]-[0032] output a vector of robustness values 310 by 302 into the model, the computed robustness scores mab be used later to decide on a next input to analyze)
determine whether the output of the model for the input satisfies a specification, (Fig. 1-3, 5, [0019]-[0028][0031]-[0032] 310 output a vector of robustness values to determine if the expected robustness value is satisfactory to a specification or too low may indicate the specification is failed. Note: please further define a specification, it is very broad.)
and determine the input as a counterexample when the output does not satisfy the specification. (Fig. 1-3, 5, [0019]-[0028][0031]-[0034] and the output failed the specification, the 108/112, the negative values may imply falsification of the specification, the model need to be modified or repaired which implied the input is a falsification case and the model need to be changed.)
modify the model based on the determined counterexample, ([0006]-[0009] [0018]-[0028] [0032]-[0034] if the minimum expected robustness value is negative, the model is modified)
But Fainekos fail to explicitly disclose “the model being configured to output a parameter related to an engine of a physical entity in the cyber-physical system as an output of the model; and control a speed of the physical entity of the cyber-physical system based on a parameter related to the engine output from the modified model.”
Bhattacharyya disclose the model being configured to output a parameter related to an engine of a physical entity in the cyber-physical system as an output of the model; ([0156]-[0160][0172] [0192]-[0199] the model outputs various output parameters related to an engine of a vehicle, for example, in the cyber-physical system as an output) and
control a speed of the physical entity of the cyber-physical system based on a parameter related to the engine output from the modified model. ([0156]-[0160][0172] [0192]-[0204] a speed of the vehicle of the cyber-physical system is controlled based on the output constraint related to the engine output from the adaptively updated model. Note: please further define “a parameter related to an engine of a physical entity”, and “based on a parameter related to the engine output”, etc. to help move forward the prosecution, what are the parameters?)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Bhattacharyya’s into Fainekos’s machine learning based prediction, planning and optimization for the cyber-physical system into invention as they are related to the same field endeavor of method of modeling based on data modification. The motivation to combine these arts, as proposed above, at least because Bhattacharyya’s control device for the cyber-physical system would help to provide control objects into Fainekos’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing control objects for the system would help to solve real-world problems in cyber-physical system.
But Fainekos and Bhattacharyya fail to explicitly disclose “wherein the predetermined transform rule includes an axial priority with respect to an axial direction of a component of the extracted sample, and the processor is configured to transform the extracted sample into the input based on the axial priority to satisfy the constraint;”
Takeshima disclose wherein the predetermined transform rule includes an axial priority with respect to an axial direction of a component of the extracted sample, and the processor is configured to transform the extracted sample into the input based on the axial priority to satisfy the constraint; ([0053]-[0065] [0089]-[0095] [0098]-[0105] the transform criterion includes a priority of the signal point to each position in the time-transformed axis direction and transform the signal into the input based on the priority and assign numbers to the respective positions in the time-transformed axis direction in the descending order of priority from the highest and eliminate the signal point at a certain position in the descending order of the number from the highest assigned number, (lowest priority) for example) and “A fixed-parameter constraint is a signal eliminating processing based on whether or not a predetermined condition is satisfied with a certain parameter being fixed,”)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Takeshima’s transformation module into Bhattacharyya and Fainekos’s machine learning based prediction, planning and optimization for the cyber-physical system into invention as they are related to the same field endeavor of method of modeling based on data modification. The motivation to combine these arts, as proposed above, at least because Takeshima’s signal transformation based on a criterion would help to provide signal transformation criterion into Bhattacharyya and Fainekos’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing signal transformation criterion would improve the quality of signal processing.
In regard to claim 3, Fainekos and Bhattacharyya, Takeshima disclose The model verification device as claimed in claim 1, the rejection is incorporated herein.
Fainekos disclose wherein the processor is further configured to, when the output satisfies the specification, control a repeating process of extracting a next sample from the search space according to a predetermined search algorithm and starting the transforming and the determining with respect to the extracted next sample. (Fig. 2 and 4, [0020]-[0028] if the determined minimum expected robustness value is satisfactory, continue draw a random candidate sample from the search space according to a hit-and-run proposal kernel and calculate a probability related to the next sample and iterating until the termination condition is met)
In regard to claim 4, Fainekos and Bhattacharyya, Takeshima disclose The model verification device as claimed in claim 3, the rejection is incorporated herein.
Fainekos disclose wherein the processor repeats the repeating process until the counterexample is detected or a predetermined terminal condition is satisfied. (Fig. 2 and 4, [0020]-[0028] continue draw a random candidate sample from the search space and calculate a probability related to the next sample and iterating until the termination condition is met)
In regard to claim 5, Fainekos and Bhattacharyya, Takeshima disclose The model verification device as claimed in claim 1, the rejection is incorporated herein.
Fainekos disclose wherein the processor uses a robustness function that derives a degree of satisfaction with respect to the specification based on the output and the specification to determine the degree of satisfaction of the output with respect to the specification. (Fig. 2 and 4, [0020]-[0028] estimate the probability that the model behavior with the worst expected robustness satisfies a specification and with what confidence level the model satisfies the specification)
In regard to claim 7, claim 7 is a method claim corresponding to the device claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
Claims 2 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Fainekos et al. (Fainekos) US 2016/0292307 and Bhattacharyya et al. (Bhattacharyya) US 2020/0151291, and Takeshima US 2015/0276908 as applied to claim 1, further in view of Bae et al. (Bae) US 2021/0360005
In regard to claim 2, Fainekos and Bhattacharyya, Takeshima disclose The model verification device as claimed in claim 1, the rejection is incorporated herein.
Fainekos disclose wherein the search space is formed as a hypercube or a hyperrectangle, ([0020-[0025] Fig.2, the search space formed as hypercube)
But Fainekos and Bhattacharyya fail to explicitly disclose “and wherein the axial priority is defined for each sample extracted from the search space.”
Takeshima disclose and wherein the axial priority is defined for each sample extracted from the search space. ([0053]-[0065] [0089]-[0095] [0098]-[0105] the priority of the signal point to each position is set in the time-transformed axis direction)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Takeshima’s transformation module into Bhattacharyya and Fainekos’s machine learning based prediction, planning and optimization for the cyber-physical system into invention as they are related to the same field endeavor of method of modeling based on data modification. The motivation to combine these arts, as proposed above, at least because Takeshima’s signal transformation based on a criterion would help to provide signal transformation criterion into Bhattacharyya and Fainekos’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing signal transformation criterion would improve the quality of signal processing.
But Fainekos and Bhattacharyya, Takeshima fail to explicitly disclose “and wherein the predetermined transform rule is a proportional transformation.”
Bae disclose and wherein the predetermined transform rule is a proportional transformation. ([0040]-[0052] the modification is incremental modification which is a proportional transformation)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Bae’s method of data modification into Takeshima, Bhattacharyya and Fainekos’s invention as they are related to the same field endeavor of method of modeling based on data modification. The motivation to combine these arts, as proposed above, at least because Bae’s method of data modification’s with a proportional transformation would help to provide more data modification method into Takeshima, Krivoshein and Fainekos’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing a proportional data transformation method would help to provide more data manipulation based on the problem solved and therefore improve user experience using the system.
In regard to claim 6, Fainekos and Bhattacharyya, Takeshima disclose The model verification device as claimed in claim 3, the rejection is incorporated herein.
Fainekos disclose and wherein the processor extracts a next sample based on a history of a robustness value for the extracted sample. (Fig. 2 and 4, [0019]-[0028] iteratively draw a random candidate sample from the search space based on a robustness value for the sample, Fig. 2 (the probability the model behavior with the worst expected robustness of model satisfies the specification)
But Fainekos and Bhattacharyya fail to explicitly disclose “identify the next sample based on the axial priority used for the extracted sample.”
Takeshima disclose identify the next sample based on the axial priority used for the extracted sample ([0053]-[0065] [0089]-[0095] [0098]-[0105] identify the signal point based on the axial priority to each position in the time-transformed axis direction)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Takeshima’s transformation module into Bhattacharyya and Fainekos’s machine learning based prediction, planning and optimization for the cyber-physical system into invention as they are related to the same field endeavor of method of modeling based on data modification. The motivation to combine these arts, as proposed above, at least because Takeshima’s signal transformation based on a criterion would help to provide signal transformation criterion into Bhattacharyya and Fainekos’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing signal transformation criterion would improve the quality of signal processing.
But Fainekos and Bhattacharyya, Takeshima fail to explicitly disclose “wherein the predetermined search algorithm is hill climbing,”
Bae disclose wherein the predetermined search algorithm is hill climbing, ([0016] [0030] hill climbing algorithm is used)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Bae’s method of data modification into Takeshima, Bhattacharyya and Fainekos’s invention as they are related to the same field endeavor of method of modeling based on data modification. The motivation to combine these arts, as proposed above, at least because Bae’s method of data searching algorithm would help to provide more data searching method into Takeshima, Krivoshein and Fainekos’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing a more data searching method would help to provide more data identification based on the problem solved and therefore improve user experience using the system.
Response to Arguments
Applicant’s arguments with respect to claims 1-7 filed on 3/27/2026 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20140093030 A1 2014-04-03 Mukumoto et al.
X-RAY CT SYSTEM, IMAGE DISPLAY DEVICE, AND IMAGE DISPLAY METHOD
Mukumoto et al. disclose Techniques are provided that enable displaying of medical images that depict cyclic motions in the subject. An X-ray CT system scans, with X-rays, the subject whose targeted region is experiencing a cyclic motion and acquires detection data. This X-ray CT system comprises a reconstruction processor, a moving image creator, and a display controller. The reconstruction processor generates a plurality of sets of volumetric data based on a plurality of sets of detection data that have been acquired during one cycle of the cyclic motion. The moving-image creator creates a moving image that shows the cyclic motion, on the basis of at least a part of the plural sets of volumetric data. The display controller superposes the moving image over an image based on the volumetric data and displays these images on the display unit… see abstract.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
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