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 .
Status of claim
Claims 1,2,3, 13, 15,16 and 20 are amended.
Claims 1-20 are pending.
No new claim is added.
Response to arguments
Amendment claims overcome rejection under 35 U.S.C. 101. Applicant’s
amendments are entered. Applicant’s remarks are also entered into the record. A
new search was made necessitated by the applicant’s amendments and remarks.
A new reference was found. A new rejection is made herein. Applicant’s
arguments are now moot in view of the new rejection of the claims.
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 20 are rejected under 35 U.S.C. 103 as being unpatented over DE102021204961B4 to Rozo (herein after “Rozo”) in view of US 20150109202 A1 to Ataee et al. (herein after “Ataee”).
Regarding claim 1, Rozo discloses A method for an automated system on a vehicle, the method comprising: see Rozo at least para[0002] In many applications, it is desirable for robots to be able to operate autonomously in potentially dynamic and unstructured environments. ),
receiving a vector having a length from the automated system on the vehicle, (see Rozo at least para[0015] the combination being mapped to the manifold, is minimal among the set of possible vectors of weights, wherein the distance between the combination of basic movements mapped to the manifold and the demonstrated trajectory is given by summing, over the time points of the sequence of time points);
creating a plurality of distribution vectors each having a length that matches the length of the vector; (see Rozo at least para[0006] The method further comprises determining, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device by searching a vector of weights) ;
identifying a minimum output value determined by the divergence calculations; identifying a minimizing distribution vector from the plurality of distribution vectors that corresponds to the minimum output value from the divergence calculations(see Rozo at least para[0006]The method further comprises determining, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device by searching a vector of weights that minimizes a distance measure between the combination of the basic movement according to the vector of weights);; and
determining a conclusiveness of a decision based on the vector in view of the minimizing distribution vector; (see Rozo at least para [0089] In 602, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device is determined by finding a vector of weights that minimizes a distance measure between the combination of the basic movement according to the vector of weights and the demonstrated trajectory, the combination being mapped to the manifold).
controlling the vehicle with the automated system to take an action based on the conclusiveness of the decision and at least one the entries in the vector; and
(see Rozo para[0095] The approach of Fig. 6 can be used to generate a control signal to control a physical system, such as a computer.B. a computer-controlled machine, such as a robot, a vehicle, a household appliance, a power tool, a manufacturing machine, a personal assistant or an access control system, para [0006]According to various embodiments, a method for controlling a robot device is provided)
However, Rozo does not expressly disclose or otherwise teach a decision making process wherein the vector includes one of a probability vector or a confidence vector that includes a plurality of entries each having a value that represents a decision to be made by the automated system. Nevertheless, Ataee same field of endeavor teaches a decision making process (See Ataee The decision tree analysis ) performing divergence calculations with the vector and each of the plurality of distribution vectors; (see Ataee abstract combining the resulting probability vectors.)
wherein the vector includes one of a probability vector or a confidence vector that includes a plurality of entries each having a value that represents a decision to be made by the automated system;(see Ataee para[0021] an outcome of the decision tree analysis may include determining a corresponding probability vector from a set of probability vectors by the processor based at least in part on the outcome of the decision tree analysis)
providing an explanation of the action with the automated system through a human machine interface on the vehicle to an occupant of the vehicle. (see Ataee Human-Electronics Interfaces, See Ataee para[0007] A wearable electronic device may provide direct functionality for a user (such as audio playback, data display, computing functions, etc.) or it may provide electronics to interact with, receive information from, or control another electronic device).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Rozo’s method of control of vehicle/robot with Ataee’s decision making process with probability vector/confidence vector the vector includes one of a probability vector or a confidence vector that includes a plurality of entries each having a value that represents a decision to be made by the automated system to interact with, receive information from, or control another electronic device to each gesture in a gesture library (See Ataee para[0007] and abstract).
Regarding claim 6, Rozo and Ataee remains applied as claim 1. Rozo discloses including creating a conclusiveness vector from the minimizing distribution vector to associate the conclusiveness of the decision with at least one corresponding value in the vector ( See Rozo at least para[0006] and para[0066] the weight vectors constituting w in (12) correspond to the vector constituting the geodesic basis of MGLM) .
Regarding claim 20, Rozo discloses A vehicular system comprising:
a plurality of sensors ( see Rozo at least para[0094]);and
a controller in communication with the plurality of sensors and configured to: (see robot device controller):
receive a vector having a length from the automated system on the vehicle , (see Rozo at least para[0015] the combination being mapped to the manifold, is minimal among the set of possible vectors of weights, wherein the distance between the combination of basic movements mapped to the manifold and the demonstrated trajectory is given by summing, over the time points of the sequence of time points);
create a plurality of distribution vectors each having a length that matches the length of the vector (see Rozo at least para[0006] The method further comprises determining, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device by searching a vector of weights) ;
perform divergence calculations with the vector and each of the plurality of distribution vectors; (see Rozo at least para[0090] In 603, a probability distribution of the vectors of weights is determined by fitting a probability distribution to the vectors of weights determined for the demonstrated trajectories)
identify a minimum output value determined by the divergence calculations; identify a minimizing distribution vector from the plurality of distribution vectors that corresponds to the minimum output value from the divergence calculations(see Rozo at least para[0006]The method further comprises determining, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device by searching a vector of weights that minimizes a distance measure between the combination of the basic movement according to the vector of weights); and
determine a conclusiveness of a decision based on the vector in view of the minimizing distribution vector; (see Rozo at least para [0089] In 602, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device is determined by finding a vector of weights that minimizes a distance measure between the combination of the basic movement according to the vector of weights and the demonstrated trajectory, the combination being mapped to the manifold).
control the vehicle with the automated system to take an action based on the conclusiveness of the decision and at least one the entries in the vector(see Rozo para[0095] The approach of Fig. 6 can be used to generate a control signal to control a physical system, such as a computer.B. a computer-controlled machine, such as a robot, a vehicle, a household appliance, a power tool, a manufacturing machine, a personal assistant or an access control system, para [0006]According to various embodiments, a method for controlling a robot device is provided).
However, Rozo does not expressly disclose or otherwise teach wherein the vector includes one of a probability vector or a confidence vector that includes a plurality of entries each having a value that represents a decision to be made by the automated system. Nevertheless, Ataee same field of endeavor teaches a decision making process (See Ataee The decision tree analysis ) wherein the vector includes one of a probability vector or a confidence vector that includes a plurality of entries each having a value that represents a decision to be made by the automated system;(see Ataee para[0021] an outcome of the decision tree analysis may include determining a corresponding probability vector from a set of probability vectors by the processor based at least in part on the outcome of the decision tree analysis)
providing an explanation of the action with the automated system through a human machine interface on the vehicle to an occupant of the vehicle. (see Ataee Human-Electronics Interfaces, See Ataee para[0007] A wearable electronic device may provide direct functionality for a user (such as audio playback, data display, computing functions, etc.) or it may provide electronics to interact with, receive information from, or control another electronic device).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Rozo’s method of control of vehicle/robot with Ataee’s decision making process with probability vector/confidence vector the vector includes one of a probability vector or a confidence vector that includes a plurality of entries each having a value that represents a decision to be made by the automated system to interact with, receive information from, or control another electronic device to each gesture in a gesture library (See Ataee para[0007] and abstract).
Claims 2, 15-16 and 18 are rejected under 35 U.S.C. 103 as being unpatented over DE102021204961B4 to Rozo (herein after “Rozo”) in view of US 20150109202 A1to Ataee et al. (herein after “Ataee”) and US20230034136A1 to Chow et al. (herein after “Chow”).
Regarding claim 2, Rozo and Ataee remains applied as claim 1. However, Rozo does not teach the divergence calculations include Jensen-Shannon divergence calculations. Nevertheless, Chow same field of endeavor teaches wherein the divergence calculations include Jensen-Shannon divergence calculations, and the vector is received from an automated system (see Chow at least para [0155] Although the Kolmogorov-Smirnov (KS) test is discussed above to compare similarity it will be appreciate that other statistical measures could be used instead. Other tests that could be used include, but are not limited to: a student t-test, Kullback-Leibler divergence or Jenson Shannon divergence.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Rozo’s method of controlling a robot/vehicle with Chow’s divergence calculation using Jensen-Shannan divergence calculation to detect a change in model confidences (see Chow para[0155] and [0165]).
Regarding claim 15, Rozo teaches A non-transitory computer-readable medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising (see Rozo[0025] Example 9 is a computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method of any of Examples 1 to 6.)
: receiving a vector having a length (see Rozo at least para[0006] The method further comprises determining, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device by searching a vector of weights));
creating a plurality of distribution vectors each having a length that matches the length of the vector(see Rozo at least para[0015] the combination being mapped to the manifold, is minimal among the set of possible vectors of weights, wherein the distance between the combination of basic movements mapped to the manifold and the demonstrated trajectory is given by summing, over the time points of the sequence of time points);
identifying a minimum output value determined by the divergence calculations; identifying a minimizing distribution vector from the plurality of distribution vectors that corresponds to the minimum output value from the divergence calculations; and
determining a conclusiveness of a decision based on the vector in view of the minimizing distribution vector(see Rozo at least para [0089] In 602, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device is determined by finding a vector of weights that minimizes a distance measure between the combination of the basic movement according to the vector of weights and the demonstrated trajectory, the combination being mapped to the manifold).
controlling the vehicle with the automated system to take an action based on the conclusiveness of the decision and at least one the entries in the vector(see Rozo para[0095] The approach of Fig. 6 can be used to generate a control signal to control a physical system, such as a computer.B. a computer-controlled machine, such as a robot, a vehicle, a household appliance, a power tool, a manufacturing machine, a personal assistant or an access control system, para [0006]According to various embodiments, a method for controlling a robot device is provided)
However, Rozo does not expressly disclose or otherwise teach wherein the vector includes one of a probability vector that includes a plurality of entries each having a value that represents a decision to be made by the automated system, providing an explanation of the action with the automated system through a human machine interface on the vehicle to an occupant of the vehicle.
Nevertheless, Ataee same field of endeavor teaches wherein the vector includes one of a probability vector that includes a plurality of entries each having a value that represents a decision to be made by the automated system; ;(see Ataee para[0021] an outcome of the decision tree analysis may include determining a corresponding probability vector from a set of probability vectors by the processor based at least in part on the outcome of the decision tree analysis)
performing divergence calculations with the vector and each of the plurality of distribution vectors; (see Ataee abstract combining the resulting probability vectors
providing an explanation of the action with the automated system through a human machine interface on the vehicle to an occupant of the vehicle. (see Ataee Human-Electronics Interfaces, para[0007] A wearable electronic device may provide direct functionality for a user (such as audio playback, data display, computing functions, etc.) or it may provide electronics to interact with, receive information from, or control another electronic device).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Rozo’s method of control of vehicle/robot with Ataee’s decision making process with probability vector/confidence vector the vector includes one of a probability vector or a confidence vector that includes a plurality of entries each having a value that represents a decision to be made by the automated system to interact with, receive information from, or control another electronic device to each gesture in a gesture library (See Ataee para[0007] and abstract).
Regarding claim 16, Rozo and Ataee remains applied as claim 15. However, Rozo does not teach the divergence calculations include Jensen-Shannon divergence calculations. Nevertheless, Chow same field of endeavor teaches wherein the divergence calculations include Jensen-Shannon divergence calculations, and the vector is received from an automated system (See Chow at least para [0155] Although the Kolmogorov-Smirnov (KS) test is discussed above to compare similarity it will be appreciate that other statistical measures could be used instead. Other tests that could be used include, but are not limited to: a student t-test, Kullback-Leibler divergence or Jenson Shannon divergence.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Rozo’s method of controlling a robot/vehicle with Chow’s divergence calculation using Jensen-Shannan divergence calculation to detect a change in model confidences (see Chow para[0155] and [0165]).
Regarding claim 18, Rozo, Ataee and Chow remain applied as claim 15. Rozo teaches including creating a conclusiveness vector from the minimizing distribution vector to associate the conclusiveness of the decision with at least one corresponding value in the vector (see Rozo at least para[0 006] The method further comprises determining, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device by searching a vector of weights that minimizes a distance measure).
Claims 3, 4 and 5 are rejected under 35 U.S.C. 103 as being unpatented over DE102021204961B4 to Rozo (herein after “Rozo”) in view of US 20150109202 A1to Ataee et al. (herein after “Ataee”) and Zhou et al. (Robust Multiple Model Estimation With Jensen-Shannon Divergence, 21st International Conference on Pattern Recognition (ICPR 2012) , November 11-15,2012, Tsukuba, Japan) (herein after “Zhou”).
Regarding claim 3, Rozo and Ataee remains applied as claim 1. However, Rozo does not teach sorting the vector to create a sorted vector, perform divergence calculation based on sorted vector. Nevertheless, Zhou same field of endeavor teaches, including sorting the vector to create a sorted vector and performing the divergence calculations based on the sorted vector and each of the plurality of distribution vectors ( see Zhou paragraph 2.2 and 2.3 in page 2137).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Zhou’s sorted matrix to perform divergence calculation to directly the normalized vector (sorted vector) to apply Jensen Shanon divergence (JSD) (see Zhou page 2137 para 2.3).
Regarding claim 4, Rozo and Ataee remains applied as claim 1. However, Rozo does not teach sorting the vector to create a sorted vector, perform divergence calculation based on sorted vector. Nevertheless, Zhou same field of endeavor teaches, wherein sorting the vector to create the sorted vector includes creating a permutation vector to determine an original order of entries in the vector (see Zhou para[2137] Then sort the rows according to the si to generate the sorted distance matrix D).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Zhou’s sorted matrix to perform divergence calculation to directly the normalized vector (sorted vector) to apply Jensen Shanon divergence (JSD) (see Zhou page 2137 para 2.3).
Regarding claim 5, Rozo, Ataee and Zhou remains applied as claim 3. Rozo teaches including creating a conclusiveness vector from the minimizing distribution vector by applying the permutation vector to the minimizing distribution vector to associate the conclusiveness of the decision with at least one corresponding value in the vector( See Rozo at least para[0006] The method further comprises determining, for each demonstrated trajectory, a representation of the trajectory as a vector of weights of predetermined basic movements of the robot device by searching a vector of weights that minimizes a distance measure).
Claims 7, 10, 11,12 and 14 are rejected under 35 U.S.C. 103 as being unpatented over DE102021204961B4 to Rozo (herein after “Rozo”) in view of US 20150109202 A1to Ataee et al. (herein after “Ataee”) and Tsai et al. (on the Jensen-Shannon Divergence and Variational distance, IEEE Transactions on information theory, vol 51, no 9, September 2005) (herein after “Tsai”).
Regarding claim 7, Rozo and Ataee remain applied as claim 1. However, Rozo does not teach determining the conclusiveness of the decision based on the minimizing distribution vector includes identifying at least one non-zero value in the minimizing distribution vector. Tsai same field of endeavor teaches wherein determining the conclusiveness of the decision based on the minimizing distribution vector includes identifying at least one non-zero value in the minimizing distribution vector (see Tsai Section E, proof, page 3336).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Tsai’s divergence calculation based on nonzero vector/matrix (part of Jensen Shannon divergence calculation) to capture some properties of mutual information and to prove lower bounds on the complexity of sampling algorithms (see Tsai 2nd column, page 3333).
Regarding claim 10, Rozo and Ataee remain applied as claim 1. However, Rozo does not teach determining the conclusiveness of the decision based on the minimizing distribution vector includes identifying at least one non-zero value in the minimizing distribution vector. Nevertheless, Tsai same field of endeavor teaches wherein each of the plurality of distribution vectors includes at least one non-zero value. (see Tsai Section E, proof, page 3336).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Tsai’s divergence calculation based on nonzero vector/matrix (part of Jensen Shannon divergence calculation) to capture some properties of mutual information and to prove lower bounds on the complexity of sampling algorithms (see Tsai 2nd column, page 3333).
Regarding claim 11, Rozo, Ataee and Tsai remain applied as claim 10. However, Rozo does not teach determining the conclusiveness of the decision based on the minimizing distribution vector includes identifying at least one non-zero value in the minimizing distribution vector. Nevertheless, Tsai same field of endeavor teaches wherein the at least one non-zero value in each of the plurality of distribution vectors is equal to the multiplicative inverse of a quantity of non-zero values in a corresponding one of the plurality of distribution vectors (see Tsai Section E, proof, page 3336).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Tsai’s divergence calculation based on nonzero vector/matrix (part of Jensen Shannon divergence calculation) to capture some properties of mutual information and to prove lower bounds on the complexity of sampling algorithms (see Tsai 2nd column, page 3333).
Regarding claim 12, Rozo, Ataee and Tsai remain applied as claim 10.
However, Rozo does not teach a quantity of vectors in the plurality of distribution vectors is less than or equal to a quantity of entries in the vector.Nevertheless, Tsai same field of endeavor teaches wherein a quantity of vectors in the plurality of distribution vectors is less than or equal to a quantity of entries in the vector (see Tsai Section E, definition 1, distributions P and Q ).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Tsai’s wherein a quantity of vectors in the plurality of distribution vectors is less than or equal to a quantity of entries in the vector (see Tsai Section E, definition 1, distributions P and Q ).
Regarding claim 14, Rozo, Ataee and Tsai remain applied as claim 10. Rozo teaches methods for controlling a robotic device. However, Rozo does not teach each of the plurality of distribution vectors are filled with at least one non-zero distribution value in a left to right order. Nevertheless, Tsai same field of endeavor teaches wherein each of the plurality of distribution vectors are filled with at least one non-zero distribution value in a left to right order (see Tsai Section E, proof, page 3336).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Tsai’s divergence calculation based on nonzero vector/matrix (part of Jensen Shannon divergence calculation) to capture some properties of mutual information and to prove lower bounds on the complexity of sampling algorithms (see Tsai 2nd column, page 3333).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatented over Rozo
in view of Ataee, Tsai and Pawar (Understanding JS Divergence for Feature Selection: A Hands-On Guide with Evidently, Medium, February 27, 2025)(herein after “Pawar”).
Regarding claim 8, Rozo, Ataee and Tsai remain applied as claim 7. Rozo teaches methods for controlling a robotic device/vehicle. However, Rozo does not teach wherein if the at least one non-zero value includes a quantity of non-zero values. Nevertheless, Tsai same field of endeavor teaches wherein if the at least one non-zero value includes a quantity of non-zero values (see Tsai Section E, proof, page 3336).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Tsai’s divergence calculation based on nonzero vector/matrix (part of Jensen Shannon divergence calculation) to capture some properties of mutual information and to prove lower bounds on the complexity of sampling algorithms (see Tsai 2nd column, page 3333).
However, Rozo does not teach non-zero values exceeding a predetermined threshold, then the conclusiveness of the decision is low. Nevertheless, Pawar same field of endeavor teaches non-zero values exceeding a predetermined threshold, then the conclusiveness of the decision is low ( see Pawar article” Understanding JS Divergence for Feature Selection: A Hands-On Guide with Evidently”.. “In practical applications, you'll set a threshold value (e.g., 0.1 or 0.2) and compare the calculated JS divergence against it.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Pawar’s threshold value based distribution to assess feature stability plurality of datasets (see first paragraph of Pawar’s article).
Regarding claim 9, Rozo, Ataee and Tsai remain applied as claim 7. Rozo teaches methods for controlling a robotic device/vehicle. However, Rozo does not teach wherein if the at least one non-zero value includes a quantity of non-zero values below a predetermined threshold, then the conclusiveness of the decision is high. Nevertheless, Tsai same field of endeavor teaches wherein if the at least one non-zero value includes a quantity of non-zero values (see Tsai Section E, proof, page 3336).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Tsai’s divergence calculation based on nonzero vector/matrix (part of Jensen Shannon divergence calculation) to capture some properties of mutual information and to prove lower bounds on the complexity of sampling algorithms (see Tsai 2nd column, page 3333).
However, Rozo does not teach non-zero values exceeding a predetermined threshold, then the conclusiveness of the decision is low. Nevertheless, Pawar same field of endeavor teaches non-zero values exceeding a predetermined threshold, then the conclusiveness of the decision is low ( see Pawar article” Understanding JS Divergence for Feature Selection: A Hands-On Guide with Evidently”.. “In practical applications, you'll set a threshold value (e.g., 0.1 or 0.2) and compare the calculated JS divergence against it.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Pawar’s threshold value based distribution to assess feature stability plurality of datasets (see first paragraph of Pawar’s article).
Claim 13 and 19 are rejected under 35 U.S.C. 103 as being unpatented over Rozo, Ataee, Tsai and Chow.
Regarding claim 13, Rozo, Ataee and Tsai remain applied as claim 12. Rozo teaches methods for controlling a robotic device. However, Rozo does not teach the divergence calculations include Jensen-Shannon divergence calculations. Nevertheless, Chow same field of endeavor teaches wherein the divergence calculations include Jensen-Shannon divergence calculations, and the vector is received from an automated system (See Chow at least para [0155] Although the Kolmogorov-Smirnov (KS) test is discussed above to compare similarity it will be appreciate that other statistical measures could be used instead. Other tests that could be used include, but are not limited to: a student t-test, Kullback-Leibler divergence or Jenson Shannon divergence.).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Chow’s divergence calculation using Jensen-Shannan divergence calculation to detect a change in model confidences (see Chow para [0155] and [0165]).
Regarding claim 19, Rozo, Ataee and Chow remain applied as claim 15. Rozo teaches methods for controlling a robotic device. However, Rozo does not teach determining the conclusiveness of the decision based on the minimizing distribution vector includes identifying at least one non-zero value in the minimizing distribution vector. Tsai same field of endeavor teaches wherein determining the conclusiveness of the decision based on the minimizing distribution vector includes identifying at least one non-zero value in the minimizing distribution vector (see Tsai Section E, proof, page 3336).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Tsai’s divergence calculation based on nonzero vector/matrix (part of Jensen Shannon divergence calculation) to capture some properties of mutual information and to prove lower bounds on the complexity of sampling algorithms (see Tsai 2nd column, page 3333).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatented over Rozo, Ataee, Chow and Zhou Robust Multiple Model Estimation With Jensen-Shannon Divergence, 21st International Conference on Pattern Recognition (ICPR 2012) , November 11-15,2012, Tsukuba, Japan).
Regarding claim 17, Rozo, Ataee and Chow remain applied as claim 15. Rozo teaches methods for controlling a robotic device. However, Rozo does not teach sorting the vector to create a sorted vector and performing the divergence calculations. Nevertheless, Zhou same field of endeavor teaches, including sorting the vector to create a sorted vector and performing the divergence calculations based on the sorted vector and each of the plurality of distribution vectors ( see Zhou para[2137] Then sort the rows according to the si to generate the sorted distance matrix D).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Rozo’s method of controlling a robot with Zhou’s sorted matrix to perform divergence calculation to directly the normalized vector (sorted vector) to apply Jensen Shanon divergence (JSD) (see Zhou page 2137 para 2.3).
Conclusion
THIS ACTION IS MADE FINAL. 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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/NAZIA AFRIN/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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