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 .
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 1-2, and 5-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to an information processing system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“calculate a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements”
“perform learning related to calculation of the likelihood ratio, by using a plurality of series data”
“set the degree of contribution by weighting a loss function calculated from the series data, wherein increasing a weight for a given series data increases its degree of contribution to learning, and reducing the weight for a given series data decreases its degree of contribution to learning”
As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim correspond to mathematical relationships and calculations.
The limitations:
“classify the series data into at least one class, on the basis of the likelihood ratio”
“change a degree of contribution to the learning of each of the plurality of series data in accordance with ease of classification of the series data”
“rank the plurality of series data in accordance with the ease of classification, and determine the degree of contribution on the basis of a rank”
“determine the ease of classification of the series data on the basis of at least one of a time until the likelihood ratio reaches a predetermined threshold corresponding to each of classes of classification candidates, a slope of the likelihood ratio, and variance of the slope of the likelihood ratio”
As drafted, under their broadest reasonable interpretations, cover mental processes,
i.e., concepts performed in the human mind (including an observation, evaluation,
judgement, opinion). The above limitations in the context of this claim correspond to
mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“An information processing system comprising: at least one memory that is configured to store instructions; and at least one processor that is configured to execute the instructions to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“obtain a plurality of elements included in series data”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” and “insignificant extra-solution activity”. Specifically, the obtaining limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to an information processing system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“decrease the degree of contribution of the series data that are easy to classify, and increase the degree of contribution of the series data that are hard to classify”
As drafted, under their broadest reasonable interpretations, cover mental processes,
i.e., concepts performed in the human mind (including an observation, evaluation,
judgement, opinion). The above limitations in the context of this claim correspond to
mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)).
The limitations:
“wherein the at least one processor is configured to execute the instructions to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to an information processing system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determine that the series data are easier to classify as the time until the likelihood ratio reaches a first predetermined threshold corresponding to a correct answer class is shorter, and determine that the series data are harder to classify as the time until the likelihood ratio reaches a second predetermined threshold corresponding to an incorrect answer class is shorter”
As drafted, under their broadest reasonable interpretations, cover mental processes,
i.e., concepts performed in the human mind (including an observation, evaluation,
judgement, opinion). The above limitations in the context of this claim correspond to
mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)).
The limitations:
“wherein the at least one processor is configured to execute the instructions to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to an information processing system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determine that the series data are easier to classify as the slope is larger until the likelihood ratio reaches the first predetermined threshold corresponding to the correct answer class, and determine that the series data are harder to classify as the slope is smaller until the likelihood ratio reaches the second predetermined person threshold corresponding to the incorrect answer class”
As drafted, under their broadest reasonable interpretations, cover mental processes,
i.e., concepts performed in the human mind (including an observation, evaluation,
judgement, opinion). The above limitations in the context of this claim correspond to
mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)).
The limitations:
“wherein the at least one processor is configured to execute the instructions to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to an information processing system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determine that the series data are harder to classify as the variance of the slope is larger until the likelihood ratio reaches the first predetermined threshold corresponding to the correct answer class, and determine that the series data are easier to classify as the variance of the slope is smaller until the likelihood ratio reaches the second predetermined person threshold corresponding to the incorrect answer class”
As drafted, under their broadest reasonable interpretations, cover mental processes,
i.e., concepts performed in the human mind (including an observation, evaluation,
judgement, opinion). The above limitations in the context of this claim correspond to
mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)).
The limitations:
“wherein the at least one processor is configured to execute the instructions to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to an information processing system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determine that the series data in which the likelihood ratio does not reach any of the first predetermined threshold corresponding to the correct answer class and the second predetermined threshold corresponding to the incorrect answer class within a predetermined time, are harder to classify than the series data in which the likelihood ratio reaches the first predetermined threshold, and are easier to classify than the series data in which the likelihood ratio reaches the second predetermined threshold”
As drafted, under their broadest reasonable interpretations, cover mental processes,
i.e., concepts performed in the human mind (including an observation, evaluation,
judgement, opinion). The above limitations in the context of this claim correspond to
mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)).
The limitations:
“wherein the at least one processor is configured to execute the instructions to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to an information processing method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“calculating a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements”
“performing learning related to calculation of the likelihood ratio, by using a plurality of series data”
“setting the degree of contribution by weighting a loss function calculated from the series data, wherein increasing a weight for a given series data increases its degree of contribution to learning, and reducing the weight for a given series data decreases its degree of contribution to learning”
As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim correspond to mathematical relationships and calculations.
The limitations:
“classifying the series data into at least one class, on the basis of the likelihood ratio”
“when performing the learning, changing a degree of contribution to the learning of each of the plurality of series data in accordance with ease of classification of the series data”
“rank the plurality of series data in accordance with the ease of classification, and determine the degree of contribution on the basis of a rank”
“determine the ease of classification of the series data on the basis of at least one of a time until the likelihood ratio reaches a predetermined threshold corresponding to each of classes of classification candidates, a slope of the likelihood ratio, and variance of the slope of the likelihood ratio”
As drafted, under their broadest reasonable interpretations, cover mental processes,
i.e., concepts performed in the human mind (including an observation, evaluation,
judgement, opinion). The above limitations in the context of this claim correspond to
mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“obtaining a plurality of elements included in series data”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “insignificant extra-solution activity”. Specifically, the obtaining limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a non-transitory recording medium, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“calculating a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements”
“performing learning related to calculation of the likelihood ratio, by using a plurality of series data”
“setting the degree of contribution by weighting a loss function calculated from the series data, wherein increasing a weight for a given series data increases its degree of contribution to learning, and reducing the weight for a given series data decreases its degree of contribution to learning”
As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim correspond to mathematical relationships and calculations.
The limitations:
“classifying the series data into at least one class, on the basis of the likelihood ratio”
“when performing the learning, changing a degree of contribution to the learning of each of the plurality of series data in accordance with ease of classification of the series data”
“ranking the plurality of series data in accordance with the ease of classification”
“determining the ease of classification of the series data on the basis of at least one of a time until the likelihood ratio reaches a predetermined threshold corresponding to each of classes of classification candidates, a slope of the likelihood ratio, and variance of the slope of the likelihood ratio”
As drafted, under their broadest reasonable interpretations, cover mental processes,
i.e., concepts performed in the human mind (including an observation, evaluation,
judgement, opinion). The above limitations in the context of this claim correspond to
mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A non-transitory recording medium on which a computer program that allows a computer to execute an information processing method is recorded, the information processing method including…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“obtaining a plurality of elements included in series data”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” and “insignificant extra-solution activity”. Specifically, the obtaining limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 5-10 are rejected under 35 U.S.C. 103 as being unpatentable over Terao et al. (U.S. Patent Publication No. 2016/0267924) (“Terao”) in view of Nie et al. (Deep Hierarchical Network for Automatic Modulation Classification) (“Nie”) in further view of Lin et al. (Focal Loss for Dense Object Detection) (“Lin”) in further view of Johnson et al. (Detecting changes in real-time data: a user’s guide to optimal detection) (“Johnson”).
Regarding claim 1, Terao teaches an information processing system comprising: at least one memory that is configured to store instructions; and at least one processor that is configured to execute the instructions to (Terao [0056] “Each unit included in the speech detection device according to the present exemplary embodiments is implemented by use of any combination of hardware and software, in any computer, mainly including a central processing unit (CPU), a memory, a program…” Terao provides at least one memory that is configured to store instructions and at least one processor that is configured to execute the instructions.) obtain a plurality of elements included in series data (Terao [0064] “An acoustic signal is time-series data. A partial chunk in an acoustic signal is hereinafter referred to as “section.” Each section is specified/expressed by a section start point and a section end point. A section start point (start frame) and a section end point (end frame) of each section may be expressed by use of identification information (such as a serial number of a frame) of respective frames extracted (obtained) from an acoustic signal, by an elapsed time from the start point of an acoustic signal, or by another technique.” Terao provides obtaining time series data of an acoustic signal.); calculate a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements (Terao [0065] “A time-series acoustic signal may be categorized into a section including detection target voice (hereinafter referred to as “target voice”) (hereinafter referred to as “target voice section”) and a section not including target voice (hereinafter referred to as “non-target voice section”). When an acoustic signal is observed in a chronological order, a target voice section and a non-target voice section appear alternately. An object of the speech detection device 10 according to the present exemplary embodiment is to specify a target voice section in an acoustic signal.”; [0071] “The likelihood ratio calculation unit 24 calculates A being a ratio of a likelihood of a voice model 241 to a likelihood of a non-voice model 242 (may hereinafter simply referred to as “likelihood ratio” or “voice-to-non-voice likelihood ratio”), with a feature value calculated for each second frame by the spectrum shape feature calculation unit 23 as an input. The likelihood ratio A is calculated by an equation expressed by equation 1.” Terao provides calculating a likelihood ratio on chronological time series data indicating a likelihood of a section of time series data including or not including a detection of a target voice, corresponding to calculate a likelihood ratio indicating a likelihood of a class to which the series data belong, on the basis of at least two consecutive elements of the plurality of elements.); classify the series data into at least one class, on the basis of the likelihood ratio (Terao [0091] “Subsequently, the speech detection device 10 compares the likelihood ratio calculated in S35 with a predetermined threshold value, and determines a frame having a likelihood ratio greater than or equal to the threshold value as a frame including target voice and determines a frame having a likelihood ratio less than the threshold value as a frame not including target voice (S36).” Terao provides classifying time series data as including or not including a target voice based on calculated likelihood ratios, corresponding to classify the series data into at least one class, on the basis of the likelihood ratio.); perform learning related to calculation of the likelihood ratio, by using a plurality of series data (Terao [0026] “FIG. 1 is a diagram conceptually illustrating a processing configuration example of a speech detection device according to a first exemplary embodiment. The speech detection device 10 according to the first exemplary embodiment includes an acoustic signal acquisition unit 21, a sound level calculation unit 22, a spectrum shape feature calculation unit 23, a likelihood ratio calculation unit 24, a voice model 241, a non-voice model 242, a first voice determination unit 25, a second voice determination unit 26, and an integration unit 27.”; [0158] “By thus providing learning data for a classifier, the speech detection device 10 according to the first exemplary embodiment is able to learn a classifier dedicated to classifying an acoustic signal determined as voice, and therefore the rejection unit 63 is able to make yet more precise determination. By applying the speech detection device 10 according to the first exemplary embodiment to a learning acoustic signal, the classifier may be learned so as to determine whether each of a plurality of target voice sections separated from one another in an acoustic signal is a section not including target voice or not.” Terao provides performing learning of a classifier using time series data for speech detection device 10, which includes likelihood ratio calculation unit 24, corresponding to perform learning related to calculation of the likelihood ratio, by using a plurality of series data.).
Terao fails to explicitly teach change a degree of contribution to the learning of each of the plurality of series data in accordance with ease of classification of the series data wherein rank the plurality of series data in accordance with the ease of classification, and determine the degree of contribution on the basis of rank, determine the ease of classification of the series data on the basis of at least one of a time until the likelihood ratio reaches a predetermined threshold corresponding to each of classes of classification candidates, a slope of the likelihood ratio, and variance of the slope of the likelihood ratio, and set the degree of contribution by weighting a loss function calculated from the series data, wherein increasing a weight for a given series data increases its degree of contribution to learning, and reducing the weight for a given series data decreases its degree of contribution to learning.
However, Nie teaches change a degree of contribution to the learning of each of the plurality of series data in accordance with ease of classification of the series data (Nie Section III.B SNR Weight and Loss Function “Take focal loss as an example, it can be described as (Equation 10). It makes the network pay more attention to imbalanced samples by reducing the loss caused by easily classified samples, showing that the network can decide which one to get a heavier weight than the rest.” Nie provides reducing the loss caused by easily classified samples, corresponding to change a degree of contribution to the learning of each of the plurality of series data in accordance with ease of classification of the series data.) …set the degree of contribution by weighting a loss function calculated from the series data, wherein increasing a weight for a given series data increases its degree of contribution to learning, and reducing the weight for a given series data decreases its degree of contribution to learning (Nie Section III.B SNR Weight and Loss Function “Take focal loss as an example, it can be described as (Equation 10). It makes the network pay more attention to imbalanced samples by reducing the loss caused by easily classified samples, showing that the network can decide which one to get a heavier weight than the rest. However,in AMC problem, the low SNR samples make classification difficult due to its quality problem, and focal loss makes the network pay more attention to hard data. Low SNR samples comprise the majority of the loss and dominate the gradient in AMC, sothe focal loss reshape the loss function to down-weight good samples and focus on bad data.” Nie teaches weighting a loss function in accordance with Equation 10, which weights a degree of contribution to learning by increasing and decreasing the weights defined by the Equation.).
Terao and Nie are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to time-series classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terao with the above teachings of Nie. Doing so would allow the network to have better learning ability such as optimized loss functions (Nie Section III.B SNR Weight and Loss Function “In the past few years, many works have been devoted to optimize the loss function, and some different loss functions have been proposed such as center loss [24] and focal loss [25]. In some cases, they can help the network to have better learning ability.”).
Further, Lin teaches rank the plurality of series data in accordance with the ease of classification, and determine the degree of contribution on the basis of rank (Lin Section 3.2 Focal Loss Definition “While α balances the importance of positive/negative examples, it does not differentiate between easy/hard examples. Instead, we propose to reshape the loss function to down-weight easy examples and thus focus training on hard negatives… The focusing parameter γ smoothly adjusts the rate at which easy examples are down weighted.”; Section 5 Experiments “We apply this model to a large number of random im ages and sample the predicted probability for ∼107 negative windows and ∼105 positive windows. Next, separately for positives and negatives, we compute FL for these samples, and normalize the loss such that it sums to one. Given the normalized loss, we can sort the loss from lowest to highest and plot its cumulative distribution function (CDF) for both positive and negative samples and for different settings for γ (even though model was trained with γ = 2).” Lin teaches sorting the data samples from lowest to highest normalized loss and determining different γ parameter settings of the focal loss function therefrom, which is the degree of contribution of the data samples to the loss function, as shown in Section 3.2, Equation 4.)
Terao, Nie, and Lin are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to time-series classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terao in view of Nie with the above teachings of Lin. Doing so would allow for focusing training on hard examples (Lin Section 3.2 Focal Loss Definition “While α balances the importance of positive/negative examples, it does not differentiate between easy/hard examples. Instead, we propose to reshape the loss function to down-weight easy examples and thus focus training on hard negatives.”).
Further, Johnson teaches …determine the ease of classification of the series data on the basis of at least one of a time until the likelihood ratio reaches a predetermined threshold corresponding to each of classes of classification candidates (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides a graph of likelihood ratios over time in accordance with Equation 3.1, as shown in Figure 4, wherein the “A” and “B” boundaries of Figure 4.), a slope of the likelihood ratio (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides the slope of the likelihood ratio, in accordance with Equation 3.1 as depicted in the graph of Figure 4), and variance of the slope of the likelihood ratio (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides the slope of the likelihood ratio, including its variance, in accordance with Equation 3.1 and depicted in the graph of Figure 4.).
Terao, Nie, Lin and Johnson are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to time-series classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terao in view of Nie in further view of Lin with the above teachings of Johnson. Doing so would allow for optimal stopping times for different measures of the delay and false alarm constraints (Johnson Section 3 Change-point detection “In this section, we will consider which procedures provide optimal stopping times for different measures of the delay and false alarm constraints.”).
Regarding claim 2, Terao in view of Nie in further view of Lin and Johnson teaches wherein the at least one processor is configured to execute the instructions to decrease the degree of contribution of the series data that are easy to classify (Nie Section III.B SNR Weight and Loss Function “Take focal loss as an example, it can be described as (Equation 10). It makes the network pay more attention to imbalanced samples by reducing the loss caused by easily classified samples, showing that the network can decide which one to get a heavier weight than the rest.” Nie provides reducing the loss caused by easily classified samples, corresponding to decrease the degree of contribution of the series data that are easy to classify.), and increase the degree of contribution of the series data that are hard to classify (Nie Section III.B SNR Weight and Loss Function "It makes the network pay more attention to imbalanced samples by reducing the loss caused by easily classified samples, showing that the network can decide which one to get a heavier weight than the rest. However, in AMC problem, the low SNR samples make classification difficult due to its quality problem, and focal loss makes the network pay more attention to hard data.” Nie provides the focal loss makes the network pay more attention to the hard data, corresponding to increase the degree of contribution of the series data that are hard to classify.).
Terao Nie, Lin and Johnson are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to time-series classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terao in view of Nie in further view of Lin and Johnson with the above teachings of Nie. Doing so would allow the network to have better learning ability such as optimized loss functions (Nie Section III.B SNR Weight and Loss Function “In the past few years, many works have been devoted to optimize the loss function, and some different loss functions have been proposed such as center loss [24] and focal loss [25]. In some cases, they can help the network to have better learning ability.”).
Regarding claim 5, Terao in view of Nie in further view of Lin and Johnson teaches wherein the at least one processor is configured to execute the instructions to determine that the series data are easier to classify as the time until the likelihood ratio reaches a first predetermined threshold corresponding to a correct answer class is shorter (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides a plurality of likelihood ratios plotted over time, wherein a likelihood ratio reaches a first predetermined threshold in a shorter time than others, corresponding to determine that the series data are easier to classify as the time until the likelihood ratio reaches a first predetermined threshold corresponding to a correct answer class is shorter.), and determine that the series data are harder to classify as the time until the likelihood ratio reaches a second predetermined threshold corresponding to an incorrect answer class is shorter (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides a likelihood ratio reaching a second predetermined threshold at one or more times corresponding to determine that the series data are harder to classify as the time until the likelihood ratio reaches a second predetermined threshold corresponding to an incorrect answer class is shorter.).
Terao, Nie, Lin and Johnson are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to time-series classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terao in view of Nie in further view of Lin with the above teachings of Johnson. Doing so would allow for optimal stopping times for different measures of the delay and false alarm constraints (Johnson Section 3 Change-point detection “In this section, we will consider which procedures provide optimal stopping times for different measures of the delay and false alarm constraints.”).
Regarding claim 6, Terao in view of Nie in further view of Lin and Johnson teaches wherein the at least one processor is configured to execute the instructions to determine that the series data are easier to classify as the slope is larger until the likelihood ratio reaches the first predetermined threshold corresponding to the correct answer class (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides varying slopes of likelihood ratios reaching a first predetermined threshold for classification, corresponding to determine that the series data are easier to classify as the slope is larger until the likelihood ratio reaches the first predetermined threshold corresponding to the correct answer class.), and determine that the series data are harder to classify as the slope is smaller until the likelihood ratio reaches the second predetermined person threshold corresponding to the incorrect answer class (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides varying slopes of likelihood ratios reaching a second predetermined threshold, as shown in Figure 4, including a false alarm answer class, corresponding to determine that the series data are harder to classify as the slope is smaller until the likelihood ratio reaches the second predetermined person threshold corresponding to the incorrect answer class.).
Terao, Nie, Lin and Johnson are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to time-series classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terao in view of Nie in further view of Lin with the above teachings of Johnson. Doing so would allow for optimal stopping times for different measures of the delay and false alarm constraints (Johnson Section 3 Change-point detection “In this section, we will consider which procedures provide optimal stopping times for different measures of the delay and false alarm constraints.”).
Regarding claim 7, Terao in view of Nie in further view of Lin and Johnson teaches wherein the at least one processor is configured to execute the instructions to determine that the series data are harder to classify as the variance of the slope is larger until the likelihood ratio reaches the first predetermined threshold corresponding to the correct answer class (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides varying slopes of likelihood ratios reaching a first predetermined threshold for classification, including their variance, as shown in Figure 4, corresponding to determine that the series data are harder to classify as the variance of the slope is larger until the likelihood ratio reaches the first predetermined threshold corresponding to the correct answer class.), determine that the series data are easier to classify as the variance of the slope is smaller until the likelihood ratio reaches the second predetermined person threshold corresponding to the incorrect answer class (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides varying slopes of likelihood ratios reaching a second predetermined threshold for classification, including their variance, as shown in Figure 4, corresponding to determine that the series data are easier to classify as the variance of the slope is smaller until the likelihood ratio reaches the second predetermined person threshold corresponding to the incorrect answer class.).
Terao, Nie, Lin and Johnson are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to time-series classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terao in view of Nie in further view of Lin with the above teachings of Johnson. Doing so would allow for optimal stopping times for different measures of the delay and false alarm constraints (Johnson Section 3 Change-point detection “In this section, we will consider which procedures provide optimal stopping times for different measures of the delay and false alarm constraints.”).
Regarding claim 8, Terao in view of Nie in further view of Lin and Johnson teaches wherein the at least one processor is configured to execute the instructions to determine that the series data in which the likelihood ratio does not reach any of the first predetermined threshold corresponding to the correct answer class and the second predetermined threshold corresponding to the incorrect answer class within a predetermined time, are harder to classify than the series data in which the likelihood ratio reaches the first predetermined threshold and are easier to classify than the series data in which the likelihood ratio reaches the second predetermined threshold (Johnson Figure 4; Section 3 Change-point detection “Detection algorithms then convert all the required information from the observed process into a test statistic. When this test statistic hits a corresponding boundary B, then it is declared that a change has occurred. If this hitting time τ occurs before a change has occurred, then this is called a false alarm while if this happens after the change-point then the distance between these times is called the detection delay (see figure 4)… As such it is possible to see that f0 =f∞ and f1 =f0 and the likelihood ratio process from the sequential testing problem may be written Equation (3.1).” Johnson provides a likelihood ratio, as shown in Figure 4 that does not reach either the first or second threshold, and another likelihood ratio reaching the second threshold.).
Terao, Nie, Lin and Johnson are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to time-series classification. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terao in view of Nie in further view of Lin with the above teachings of Johnson. Doing so would allow for optimal stopping times for different measures of the delay and false alarm constraints (Johnson Section 3 Change-point detection “In this section, we will consider which procedures provide optimal stopping times for different measures of the delay and false alarm constraints.”).
Regarding claim 9, it is the information processing method embodiment of claim 1 with similar limitations to the claim 1 and is rejected using the same reasoning found above in the rejection of claim 1.
Regarding claim 10, it is the non-transitory recording medium embodiment of claim 1 with similar limitations to the claim 1 and is rejected using the same reasoning found above in the rejection of claim 1. Further, Terao teaches a non-transitory recording medium on which a computer program that allows a computer to execute an information processing method is recorded (Terao [0057] “As illustrated, the speech detection device according to the present exemplary embodiments includes, for example, a CPU 1A, a random access memory (RAM) 2A, a read only memory (ROM) 3A, a display control unit 4A, a display 5A, an operation acceptance unit 6A, and an operation unit 7A, interconnected by a bus 8A.” Terao provides a non-transitory recording medium on which a computer program that allows a computer to execute an information processing method is recorded.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Terao in view of Nie in further view of Lin and Johnson for the same reasons disclosed above in the rejection of claim 1.
Response to Arguments
Regarding the rejection applied under 35 U.S.C. 101, Applicant asserts that the claims recite a specific improvement in training time series classifiers (“Remarks”, Page 10). Applicant further asserts that the claims of the present invention are similar to the claims of Desjardins, and therefore, the present claims should be eligible under 35 U.S.C. 101 (“Remarks”, Pages 11-13).
However, even assuming the claims did recite a specific improvement in training time series classifiers, it would be an improvement in the abstract idea of “setting the degree of contribution by weighting a loss function…”, which as discussed above in the 35 U.S.C. 101 rejection of claim 1 above, corresponds to an abstract idea (i.e., mathematical relationships and calculations). The MPEP notes that it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a)(II). Therefore, even if the claims did recite an improvement, it would be an improvement in the abstract idea of weighting a loss function.
Further, Desjardins includes an improvement related to catastrophic forgetting while the current claims only use machine learning models at a high level to perform abstract ideas. Therefore, the claims remain rejected under 35 U.S.C. 101.
Regarding the rejection applied under 35 U.S.C. 103, Applicant’s Applicant asserts that Nie does not teach weighting a loss function based on a rank (“Remarks”, Page 16). However, the present claims do not recite “weighting a loss function based on rank…”. Rather, the claim recites “weighting a loss function calculated from the series data…”, which as discussed in the 35 U.S.C. 103 rejection of claim 1 above is taught by Nie.
Applicant’s further arguments with respect to claims 1-2 and 5-10 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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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/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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