DETAILED ACTION
Claims 1-9 are presented for examination.
This office action is in response to submission of application on 23-FEBRUARY-2026.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 14-JULY-2021 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 21-SEPTEMBER-2021 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Amendment
The amendment filed 23-FEBRUARY-2026 in response to the previous office action mailed 12-MARCH-2026 has been entered. Claims 1-9 remain pending in the application.
With regards to the non-final office action’s rejections under 103, the amendments to the claims necessitated a new consideration of the art. After this consideration, the examiner respectfully disagrees with the applicant’s arguments that the art referenced in the previous office action does not teach the amendment claim limitations. A new 103 rejection over the prior art has been provided.
Regarding the amended limitation wherein the second value that characterizes the scatter of the physical variable is a reciprocal of a variance of the physical variable, the second value that characterizes the scatter of the physical variable has been previously disclosed:
Ghanadan, excerpt of abstract: The trust management system includes a detection subsystem having a plurality of sensors, and a plurality of channels. Each sensor of the plurality of sensors detects one of an occurrence and a non-occurrence of an event in the network. The trust management system further includes a fusion subsystem communicably coupled to the detection subsystem through the plurality of channels for receiving a decision of the each sensor and iteratively assigning a pre-determined weightage.
Ghanadan above teaches a fusion subsystem that receives sensor signals for fusing. The decision of each sensor is analogous to the expected value of a physical variable, as the occurrence of an event is a concrete concept that would be considered a physical variable, while the iterative assigning of a pre-determined weightage is the second value characterizing the scatter of the physical variable, with the scatter taken to mean a representation of the relevance of various data points.
Georgescu discloses [wherein the second value that characterizes the scatter of the physical variable is] a reciprocal of a variance of the physical variable:
Georgescu teaches an iterative process that eliminates outliers and generates samples with a greater precision (Paragraph 51), wherein precision is known to be a term synonymous with a reciprocal of the variance. Therefore, in combination with Ghanadan and Pascanu, the precision of Georgescu could be used as the value that categorizes the scatter for the purposes of improving precision and elimination of outliers (Paragraph 51).
Claim Interpretation
The term ‘scatter’ is used several times throughout the claims, most notably in claims 1:
a second value that characterizes a scatter of the physical variable […] an ascertained second value that characterizes a scatter of the fusion
This term is not defined in the specification, and is only used twice. In both of those instances, it is used in a similar context as in claim 1, providing no further information on its meaning. Likewise, the use of the term ‘scatter’ does not align with its known usage in the art. Scattering neural networks are a tool used in image modeling, but that field does not have a clear connection to the current application.
For the purpose of examination, the examiner has interpreted the term ‘scatter’ to refer to a consideration of the normality or importance of a particular entity, particularly with regards to ignoring outliers for improved efficiency and accuracy. This is based on the following two points:
The term ‘scatter’ is reminiscent of a scatter plot, which visualizes where the majority of data points fall while including, but isolating, outliers.
the ascertained second value of the first intermediate output being set to zero when a specifiable condition is fulfilled as claim 1 reads along with claim 1, claim 2, and claim 3’s support in the specification suggests that the ascertained second values being set to zero is for the sake of efficiency. It is therefore reasonable to assume that the values being ignored are of little importance.
With this in mind, throughout examination the term ‘scatter’ has sometimes been taken to mean a reference to weights, or entities with low enough weights as to be considered insignificant.
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, and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Ghanadan et al. (Pub. No. US 20120066169 A1, March 15th 2012, hereinafter Ghanadan) in view of Pascanu et al. (Pub. No. WO 2018071392 A1, filed October 10th 2017, hereinafter Pascanu) further in view of Georgescu et al. (Pub. No. US 20160174902 A1, June 23rd 2016, hereinafter Georgescu).
Regarding claim 1, which recites:
A computer-implemented method for fusing a plurality of sensor signals using a neural network, wherein each sensor signal includes at least one first value that characterizes an expected value of a physical variable, and includes a second value that characterizes a scatter of the physical variable, the method comprising the following steps: ascertaining, using the neural network, based on the plurality of sensor signals, an output that characterizes a fusion of the plurality of the sensor signals, the output being a function of a first intermediate output of the neural network, the first intermediate output being ascertained by at least one first neuron and includes an ascertained first value that characterizes an expected value of the fusion of the plurality of sensor values, and includes an ascertained second value that characterizes a scatter of the fusion, the ascertained second value of the first intermediate output being set to zero when a specifiable condition is fulfilled, wherein the ascertained second value set to zero is used in a multiplication operation of the neural network; ascertaining a control signal from the fusion of the plurality of sensor values; and controlling, on the basis of the control signal, an actuator to carry out a corresponding physical action, wherein the second value that characterizes the scatter of the physical variable is a reciprocal of a variance of the physical variable.
Ghanadan discloses a computer-implemented method for fusing a plurality of sensor signals using a neural network, wherein each sensor signal includes at least one first value that characterizes an expected value of a physical variable, and includes a second value that characterizes a scatter of the physical variable and ascertaining, using the neural network, based on the plurality of sensor signals, an output that characterizes a fusion of the plurality of the sensor signals:
Ghanadan, excerpt of abstract: The trust management system includes a detection subsystem having a plurality of sensors, and a plurality of channels. Each sensor of the plurality of sensors detects one of an occurrence and a non-occurrence of an event in the network. The trust management system further includes a fusion subsystem communicably coupled to the detection subsystem through the plurality of channels for receiving a decision of the each sensor and iteratively assigning a pre-determined weightage.
Ghanadan above teaches a fusion subsystem that receives sensor signals for fusing. The decision of each sensor is analogous to the expected value of a physical variable, as the occurrence of an event is a concrete concept that would be considered a physical variable, while the iterative assigning of a pre-determined weightage is a scatter of the physical variable, with the scatter taken to mean a representation of the relevance of various data points. Through this process, the fusion subsystem ascertains a fusion of the sensor signals through a summation of the respective sensor signals and sensor decisions (Paragraph 14).
Ghanadan does not teach the use of a neural network. This aspect of the limitation is taught further below by Pascanu.
Furthermore, Ghanadan discloses the output being a function of a first intermediate output of the neural network, the first intermediate output being ascertained by at least one first neuron and an ascertained first value that characterizes an expected value of the fusion of the plurality of sensor values:
Ghanadan, excerpt of paragraph 14: The fusion subsystem is further configured to update the assigned pre-determined weightage corresponding to the each sensor based on the comparison of the weighted summation with the corresponding decision
This discloses the output being a function of a first intermediate output of the neural network, the first intermediate output being ascertained by at least one first neuron as the fusion subsystem is an intermediate step of an overall larger system and would be analogous to the neurons taught by the neural network of Pascanu further below, and an ascertained first value that characterizes an expected value of the fusion of the plurality of sensor values since a summation of respective decisions of a plurality of sensors is analogous to a fusion.
Ghanadan does not teach the use of a neural network. This aspect of the limitation is taught further below by Pascanu.
Pascanu in the same field of endeavor of machine learning discloses a neural network:
Pascanu teaches the use of neural network which would include neurons (Paragraph 4).
Ghanadan, Pascanu, and the present application are all analogous art as they are all in the same field of endeavor of machine learning.
It would have been obvious to implement the method utilizing the teachings of Ghanadan and the teachings of Pascanu as it would have provided the improvement of faster policy learning (Pascanu, Paragraph 8).
Pascanu discloses the ascertained second value of the first intermediate output being set to zero when a specifiable condition is fulfilled.
Pascanu teaches that values of a first set of parameters are initialized to zero. (Paragraph 15) The set that the parameters are in would be a specifiable condition.
Pascanu discloses ascertaining a control signal from the fusion of the plurality of sensor values; and controlling, on the basis of the control signal, an to carry out a corresponding physical action:
Pascanu teaches a policy output that defines the possible actions that control the actions of a robot (Paragraph 71). The policy output would be analogous to the control signal as it is the product of a neural network and hence a combination of many parameters, or a fusion. One of the aspects it can control are actuators that control a robotic arm (Paragraph 71), which is a corresponding physical action.
Georgescu discloses wherein the ascertained second value set to zero is used in a multiplication operation of the neural network:
Georgescu in the same field of endeavor of machine learning teaches that in order to simplify a network, weights are tuned such that they are set to zero. When the weights are used in the multiplication operation of the neural network when an input is multiplied by the weight during the training of the neural network (which is described in Paragraph 131), the weights set to zero are therefore used, increasing efficiency without sacrificing accuracy (Paragraph 130).
Ghanadan, Pascanu, Georgescu, and the present application are all analogous art as they are all in the same field of endeavor of machine learning.
Georgescu discloses [wherein the second value that characterizes the scatter of the physical variable is] a reciprocal of a variance of the physical variable:
Georgescu teaches an iterative process that eliminates outliers and generates samples with a greater precision (Paragraph 51), wherein precision is known to be a term synonymous with a reciprocal of the variance. Therefore, in combination with Ghanadan and Pascanu, the precision of Georgescu could be used as the value that categorizes the scatter for the purposes of improving precision and elimination of outliers (Paragraph 51).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology the utilized the teachings of Ghanadan, the teachings of Pascanu, and the teachings of Georgescu. This would have granted the advantage of faster policy learning (Pascanu, Paragraph 8) as well as a more efficient network where improved computation time and memory usage (Georgescu, Paragraph 124).
Regarding claim 2, which depends on claim 1 and recites:
Claim 2 recites:
The method as recited in claim 1, wherein the ascertained second value of the first intermediate output is set to zero when the ascertained second value falls below a predefined threshold value.
Ghanadan in view of Pascanu discloses the method of claim 1 as well as the nature of the ascertained second value and the first intermediate output. Furthermore, Georgescu discloses wherein the ascertained second value of the first intermediate output is set to zero when the ascertained second value falls below a predefined threshold value.
Georgescu teaches threshold enforced sparsity which eliminates low magnitude connections by removing weights with small values, i.e., setting them to zero (Paragraph 94). This discloses the use of a threshold to determine when to set weights to zero which may be combined with the ascertained second value of the first intermediate output to set it to zero upon falling below a predefined threshold value.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology the utilized the teachings of Ghanadan, the teachings of Pascanu, and the teachings of Georgescu. This would have granted the advantage of faster policy learning (Pascanu, Paragraph 8) as well as a more efficient network where improved computation time and memory usage (Georgescu, Paragraph 124).
Regarding claim 3, which depends from claim 1 and recites:
Claim 3 recites:
The method as recited in claim 1, wherein the intermediate output is ascertained by a plurality of neurons and includes a plurality of ascertained first values and a plurality of ascertained second values, each of the ascertained second values being set to zero when the ascertained second value belongs to a predefined number of smallest values of the ascertained second values.
Ghanadan in view of Pascanu discloses the method of claim 1. Furthermore, Georgescu discloses the limitation of claim 3:
Georgescu teaches active weights which may act as a plurality of ascertained second values for the intermediate outputs as they occur within the neural network (which includes the plurality of ascertained first values) and are hence ascertained by a plurality of neurons, wherein a particular percentage of the active weights that have the smallest values are set to zero (Paragraph 96). The percentage of weights would define a predefined number of smallest values of the ascertained second values.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology the utilized the teachings of Ghanadan, the teachings of Pascanu, and the teachings of Georgescu. This would have granted the advantage of faster policy learning (Pascanu, Paragraph 8) as well as a more efficient network where improved computation time and memory usage (Georgescu, Paragraph 124).
Regarding claim 5, which recites:
A computer-implemented method for training a neural network, wherein the neural network is configured to ascertain, based on a plurality of sensor signals, an output that characterizes a fusion of the plurality of the sensor signals, each sensor signal including at least one first value that characterizes an expected value of a physical variable, and includes a second value that characterizes a scatter of the physical variable, the output being a function of a first intermediate output of the neural network, the first intermediate output being ascertained by at least one first neuron and includes an ascertained first value that characterizes an expected value of the fusion of the plurality of sensor values, and includes an ascertained second value that characterizes a scatter of the fusion, the ascertained second value of the first intermediate output being set to zero when a specifiable condition is fulfilled, the method comprising: training the neural network based on a loss function; ascertaining a control signal from the fusion of the plurality of sensor values; and controlling, on the basis of the control signal, an actuator to carry out a corresponding physical action, wherein the ascertained second value set to zero is used in a multiplication operation of the neural network, wherein the second value that characterizes the scatter of the physical variable is a reciprocal of a variance of the physical variable.
Ghanadan teaches:
Ghanadan, excerpt of abstract: The trust management system includes a detection subsystem having a plurality of sensors, and a plurality of channels. Each sensor of the plurality of sensors detects one of an occurrence and a non-occurrence of an event in the network. The trust management system further includes a fusion subsystem communicably coupled to the detection subsystem through the plurality of channels for receiving a decision of the each sensor and iteratively assigning a pre-determined weightage.
This discloses a computer-implemented method for training a neural network, wherein the neural network is configured to ascertain, based on a plurality of sensor signals […] each sensor signal including at least one first value that characterizes an expected value of a physical variable, and includes a second value that characterizes a scatter of the physical variable. The decision of each sensor is analogous to the expected value of a physical variable, while the iterative assigning of a pre-determined weightage is a scatter of the physical variable, with the scatter taken to mean a representation of the relevance of various data points.
Furthermore, this also discloses an output that characterizes a fusion of the plurality of the sensor signals.
Furthermore, Ghanadan teaches:
Ghanadan, excerpt of paragraph 14: The fusion subsystem is further configured to update the assigned pre-determined weightage corresponding to the each sensor based on the comparison of the weighted summation with the corresponding decision
This discloses the output being a function of a first intermediate output of the neural network, the first intermediate output being ascertained by at least one first neuron the fusion subsystem is an intermediate step of an overall larger system, and an ascertained first value that characterizes an expected value of the fusion of the plurality of sensor values since a summation of respective decisions of a plurality of sensors is analogous to a fusion.
Pascanu discloses the ascertained second value of the first intermediate output being set to zero when a specifiable condition is fulfilled.
However, Pascanu teaches in the related field of endeavor of reinforcement learning that values of a first set of parameters are initialized to zero. (Paragraph 15) The set that the parameters are in would be a specifiable condition.
Ghanadan and Pascanu are analogous art because they in the same field of endeavor.
Pascanu discloses training the neural network based on a loss function:
However, Pascanu teaches the use of entropy costs, which are a type of cost function or loss function for training (Paragraph 60).
Pascanu discloses ascertaining a control signal from the fusion of the plurality of sensor values; and controlling, on the basis of the control signal, an to carry out a corresponding physical action:
Pascanu teaches a policy output that defines the possible actions that control the actions of a robot (Paragraph 71). The policy output would be analogous to the control signal as it is the product of a neural network and hence a combination of many parameters, or a fusion. One of the aspects it can control are actuators that control a robotic arm (Paragraph 71), which is a corresponding physical action.
Georgescu discloses wherein the ascertained second value set to zero is used in a multiplication operation of the neural network:
Georgescu teaches that in order to simplify a network, weights are tuned such that they are set to zero. When the weights are used in the multiplication operation of the neural network when an input is multiplied by the weight, the weights set to zero are therefore used, increasing efficiency without sacrificing accuracy (Paragraph 130).
Georgescu discloses [wherein the second value that characterizes the scatter of the physical variable is] a reciprocal of a variance of the physical variable:
Georgescu teaches an iterative process that eliminates outliers and generates samples with a greater precision (Paragraph 51), wherein precision is known to be a term synonymous with a reciprocal of the variance. Therefore, in combination with Ghanadan and Pascanu, the precision of Georgescu could be used as the value that categorizes the scatter for the purposes of improving precision and elimination of outliers (Paragraph 51).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology the utilized the teachings of Ghanadan, the teachings of Pascanu, and the teachings of Georgescu. This would have granted the advantage of faster policy learning (Pascanu, Paragraph 8) as well as a more efficient network where improved computation time and memory usage (Georgescu, Paragraph 124).
Regarding claim 6, which is dependent on claim 5 and recites:
The method as recited in claim 5, wherein the loss function includes a norm of at least a portion of a plurality of weights of a stochastic neuron.
Ghanadan in view of Pascanu in view of Georgescu discloses the method of claim 5. However, Ghanadan in view of Pascanu does not teach the limitation of claim 6.
However, Georgescu teaches:
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Georgescu, excerpt of paragraph 96: At 1512, in the final step of each iteration (training round) the supervised training of the deep neural network is continued on the remaining active connections, guiding the recovery of the neurons from the missing information by minimizing the original network loss function:
where w.sup.(t) and b.sup.(t) (computed from the values in round t−1) are used as initialization for the deep neural network parameters in the optimization.
Georgescu, excerpt of paragraph 99: the weights w.sup.(l)
This discloses wherein the loss function includes a norm of at least a portion of a plurality of weights of a stochastic neuron. The excerpt of paragraph 99 is provided to show that w.sup are a reference to the weights in paragraph 96.
It would have been obvious to one of ordinary skill in the art before the effective filing date of It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology the utilized the teachings of Ghanadan, the teachings of Pascanu, and the teachings of Georgescu. This would have granted the advantage of faster policy learning (Pascanu, Paragraph 8) as well as a more efficient network where improved computation time and memory usage (Georgescu, Paragraph 124).
Claim 8 and 13 recite a device that parallels the method of claims 5 and 11. Therefore, the analysis discussed above with respect to claims 5 and 11 also applies to claims 8 and 13. Accordingly, claims 8 and 13 are rejected based on substantially the same rationale as set forth above with respect to claims 5 and 11.
Claim 9 recites a non-transitory machine-readable storage medium that parallels the method of claim 1. Therefore, the analysis discussed above with respect to claim 1 also applies to claim 9. Accordingly, claim 9 is rejected based on substantially the same rationale as set forth above with respect to claim 1.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Ghanadan in view of Pascanu further in view of Georgescu, further in view of Strauss et al. (Pub. No. US 20150067273 A1, March 5th 2015, hereinafter Strauss).
Regarding claim 4, which depends on claim 1 and recites:
The method as recited in claim 1, wherein the ascertaining of the first intermediate output is carried out by a computing unit for operations on sparsely occupied matrices, or sparse matrix operations, the computing unit being configured to carry out the operations using a hardware acceleration
Ghanadan in view of Pascanu further in view of Georgescu discloses the method of claim 1. However, Ghanadan in view of Pascanu further in view of Georgescu does not teach wherein the ascertaining of the first intermediate output is carried out by a computing unit for operations on sparsely occupied matrices, or sparse matrix operations, the computing unit being configured to carry out the operations using a hardware acceleration.
Strauss from the same field of endeavor of increasing efficiency of a neural network teaches:
Strauss, excerpt of paragraph 13: Moreover, the increase in efficiency may allow for the computation device to be employed in high performing uses of sparse matrix multiplication or other sparse matrix operations, for example
This discloses wherein the ascertaining of the first intermediate output is carried out by a computing unit for operations on sparsely occupied matrices, or sparse matrix operations.
Furthermore, Strauss teaches:
Strauss, excerpt of paragraph 1: Some computing systems include hardware dedicated to performing specific computations in a very fast manner in order to increase overall processing speed and efficiency of the computing system. For example, a computation device may be employed in a computing system to accelerate training and evaluation of deep neural network models (e.g., machine learning)
This discloses the computing unit being configured to carry out the operations using a hardware acceleration, since it discusses a computation device used in accelerate training and evaluation of deep neural networks.
Ghanadan in view of Pascanu further in view of Georgescu and Strauss are analogous art because they are from the same field of endeavor of reinforcement learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology that utilized the method of Ghanadan in view of Pascanu further in view of Georgescu that disclosed the method of claim 1 and the method of Strauss that disclosed:
wherein the ascertaining of the first intermediate output is carried out by a computing unit for operations on sparsely occupied matrices, or sparse matrix operations, the computing unit being configured to carry out the operations using a hardware acceleration.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology the utilized the teachings of Ghanadan, the teachings of Pascanu, and the teachings of Georgescu, as well as the teachings of Strauss. This would have granted the advantage of faster policy learning (Pascanu, Paragraph 8) as well as a more efficient network where improved computation time and memory usage (Georgescu, Paragraph 124) as well as performing multiple computations in parallel for greater efficiency (Strauss, Paragraph 1).
Claim 7 recites a computer that parallels the method of claim 1 with the additional limitation of claim 4. Therefore, the analysis discussed above with respect to claim 1 and 4 also applies to claim 7. Accordingly, claim 7 is rejected based on substantially the same rationale as set forth above with respect to claim 1 and 4.
.
Response to Arguments
Applicant’s arguments filed 23-FEBRUARY-2026 have been considered, but not all are fully persuasive.
With regards to the applicant’s remarks regarding the 103 rejections, the examiner respectfully requests the applicant to consider the following:
The applicant argues that regarding the amended claim limitation of claim 1 previously included in claims 10-14 which reads wherein the second value that characterizes the scatter of the physical variable is a reciprocal of a variance of the physical variable, the “rejection is inconsistent with the earlier mapping in the Office Action of Ghanadan that characterizes a scatter of the physical variable”, because if “Ghanadan fails to disclose claim 10, that logically means that the ‘iterative assigning of pre-determined weightage’ of Ghanadan earlier mapped to the ‘second value that characterizes the scatter of the physical variable’ is not ‘a reciprocal of a variance of the physical variable’”. Therefore, the applicant reasons, the “reliance […] on Georgescu to meet claims 10-14 represents a shift in the mapping of ‘the second value that characterizes the scatter of the physical variable’ from one thing in Ghanadan to another thing in Georgescu”. The examiner respectfully disagrees with this argument. The use of Georgescu in previous claims 10-14 does not negate the original teaching of Ghanadan. Rather, Georgescu is used to supplement the additional limitation a reciprocal of a variance of the physical variable, as was stated in the previous office action. The previous rejections of claims 10-14 acknowledges that Ghanadan had previously taught the claimed scatter. The teachings of Georgescu should be view in combination with the previous art, enabling Ghanadan to use an additional form of the second value that characterizes the scatter of the physical variable, wherein that additional form is a reciprocal of a variance of the physical variable.
Furthermore, the applicant argues that the examiner “overreads the statement in [0051]” and “assumes, without establishing, that the use of ‘precision’ in this blurb is intended to refer to ‘precision’ in its strict, statistical sense, instead of in the loose sense of simply the quality of being accurate according to some criterion”. However, Georgescu describes the improved precision as resulting from “eliminating samples far from the solution” which would directly improve statistical precision (Paragraph 51). An improvement to the precision reasonably indicates a form of comparison to previous precision results, which would require a known value of the precision. For reasons of the context and the implicit comparison, the examiner believes it is reasonable to assume that Georgescu refers to a statistical metric rather than in “the loose sense of simply the quality of being accurate” as claimed by the applicant. This is further supported by the fact that Georgescu uses the term ‘accuracy’ as well (see Paragraph 62) which is used in a different context. Within the context of Georgescu, the two terms are clearly different and should not be taken to be synonymous with each other.
Conclusion
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/A.J.M./Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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