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 .
Title
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Examiner believes that the title of the invention is imprecise. A descriptive title indicative of the invention will help in proper indexing, classifying, searching, etc. See MPEP 606.01. However, the title of the invention should be limited to 500 characters. Examiner suggests including the aspect(s) of the claims which Applicant believes to be novel or nonobvious over the prior art.
Drawings
The drawings are objected to because of the following informalities.
For all drawings, the view numbers must be larger than the numbers used for reference characters. 37 C.F.R. 1.84(u)(2).
Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
The following claims are objected to because of the following informalities:
Claims 12, 18 and 22 contain a typo and should recite: wherein the plurality of monitoring points includes a gate where a person passes through.
Claim 22 contains a typo and should recite: computer-implemented method according to claim 7, wherein the plurality of monitoring points include a gate where a person passes through.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Specifically, exemplary claim 3 recites a third model; however, this element was previously introduced in claim 1 on which claim 3 depends. It is unclear if this is the same element, a different element, or related elements. For this reason, the above listed claims are rejected for containing this language or being dependent on a claim that contains this language.
Claims 19, 20, and 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Specifically, these claims recite the past monitoring data, however this term was never previously introduced and lacks proper antecedent basis in these claims and claim 7 on which they all depend.
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 1-3, 6-7, 9-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is an apparatus claim. Claim 6 is a method claim. Claim 7 is a method claim. Therefore, claims 1, 6, and 7 are directed to either a process, machine, manufacture or composition of matter.
With respect to Claim 1:
Step 2A Prong 1:
generating first corrected data from the past monitoring data, by correcting a difference between the past monitoring data and the current monitoring data, using the first model (mental process – user can manually generate first corrected data from the past monitoring data, by correcting a difference between the past monitoring data and the current monitoring data, using the first model)
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements:
a circuit (mere instructions to apply the exception using a generic computer component)
learning a first model for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and past monitoring data that is monitoring data at each of a plurality of past time points (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s Note: high level recitation of training a machine learning model with previously determined data)
learning a second model for predicting variation of the monitoring target using the past monitoring data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s Note: high level recitation of training a machine learning model with previously determined data)
learning a third model for predicting variation of the monitoring target using the current monitoring data, the first model, the second model, and the first corrected data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s Note: high level recitation of training a machine learning model with previously determined data)
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception. Additional elements:
a circuit (mere instructions to apply the exception using a generic computer component)
learning a first model for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and past monitoring data that is monitoring data at each of a plurality of past time points (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s Note: high level recitation of training a machine learning model with previously determined data)
learning a second model for predicting variation of the monitoring target using the past monitoring data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s Note: high level recitation of training a machine learning model with previously determined data)
learning a third model for predicting variation of the monitoring target using the current monitoring data, the first model, the second model, and the first corrected data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s Note: high level recitation of training a machine learning model with previously determined data)
Conclusion: The claim is not patent eligible.
Claims 6 and 7 are rejected on the same grounds as claim 1.
Regarding Claim 2: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually generating second corrected data from the estimation data, by correcting a difference between the estimation data and the current monitoring data, using the first model.
The limitation(s) includes the additional elements of wherein the learning the first model uses estimation data obtained by estimating monitoring data at each of a plurality of time points,
the circuit further configured to execute a method comprising:
generating second corrected data from the estimation data, by correcting a difference between the estimation data and the current monitoring data, using the first model; and
learning a fourth model for predicting variation of the monitoring target using the estimation data, and
learning the third model further using the second corrected data and the fourth model.
These judicial exceptions are not integrated into a practical application. The additional element(s) of the circuit further configured to execute a method are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. The additional element(s) of wherein the learning the first model uses estimation data obtained by estimating monitoring data at each of a plurality of time points, learning a fourth model for predicting variation of the monitoring target using the estimation data, and learning the third model further using the second corrected data and the fourth model recite merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, the additional element(s) of the circuit further configured to execute a method amount to no more than mere instructions to apply the exception using a generic computer component or operation. Mere instructions to apply an exception using a generic computer component or operation cannot provide an inventive concept. The additional element(s) of wherein the learning the first model uses estimation data obtained by estimating monitoring data at each of a plurality of time points, learning a fourth model for predicting variation of the monitoring target using the estimation data, and learning the third model further using the second corrected data and the fourth model recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Regarding Claim 3: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually generating second corrected data from the estimation data, by correcting a difference between the estimation data and the current monitoring data, using the first model.
The limitation(s) includes the additional elements of the circuit further configured to execute a method comprising:
learning estimation data obtained by estimating monitoring data at each of a plurality of time points;
learning a fourth model for predicting variation of the monitoring target using the estimation data;
generating second corrected data from the estimation data, by correcting a difference between the estimation data and the current monitoring data, using the first model; and
learning a third model for predicting variation of the monitoring target using a combination of at least the current monitoring data, the first model, the fourth model, and the second corrected data.
These judicial exceptions are not integrated into a practical application. The additional element(s) of the circuit further configured to execute a method comprising are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. The additional element(s) of learning estimation data obtained by estimating monitoring data at each of a plurality of time points; learning a fourth model for predicting variation of the monitoring target using the estimation data; and learning a third model for predicting variation of the monitoring target using a combination of at least the current monitoring data, the first model, the fourth model, and the second corrected data recite merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, the additional element(s) of the circuit further configured to execute a method comprising amount to no more than mere instructions to apply the exception using a generic computer component or operation. Mere instructions to apply an exception using a generic computer component or operation cannot provide an inventive concept. The additional element(s) of learning estimation data obtained by estimating monitoring data at each of a plurality of time points; learning a fourth model for predicting variation of the monitoring target using the estimation data; and learning a third model for predicting variation of the monitoring target using a combination of at least the current monitoring data, the first model, the fourth model, and the second corrected data recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Regarding Claim 9: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the monitoring data includes a location of the monitoring target.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do 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, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 10: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) includes the additional elements of wherein the learning the third model using the difference between the past monitoring data and the current monitoring data corrects predicting the variation of the monitoring target under an irregular condition.
These judicial exceptions are not integrated into a practical application. The additional element(s) of wherein the learning the third model using the difference between the past monitoring data and the current monitoring data corrects predicting the variation of the monitoring target under an irregular condition recite merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, the additional element(s) of wherein the learning the third model using the difference between the past monitoring data and the current monitoring data corrects predicting the variation of the monitoring target under an irregular condition recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, the claims are not patent eligible.
Regarding Claim 11: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the monitoring target includes a person entering and exiting a predetermined area.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do 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, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 12: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the plurality of monitoring points include a gate where a person passes through.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do 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, no additional elements are cited. Accordingly, the claim is not patent eligible.
Claim 13 is rejected on the same grounds as claim 2.
Claim 14 is rejected on the same grounds as claim 3.
Claims 15-18 are rejected on the same grounds as claims 9-12 respectively.
Regarding Claim 19: The limitation(s), as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, other than the additional elements, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually wherein the first model extracts an attribute that quantitatively indicates a difference between the current monitoring data and the past monitoring data as a prediction result and indicates whether the current monitoring data represents a regular condition or an irregular condition.
The limitation(s) includes the additional elements of wherein the first model.
These judicial exceptions are not integrated into a practical application. The additional element(s) of wherein the first model recite merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, the additional element(s) of wherein the first model recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). . Accordingly, the claims are not patent eligible.
Claims 20-22 are rejected on the same grounds as claims 10-12 respectively.
Claim 23 is rejected on the same grounds as claim 19.
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.
Claim(s) 1-3, 6-7, 9-10, 13-16, 19-20, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kubat, Recycling Decision Trees in Numeric Domains in view of Maurya et al. (hereinafter Maurya), U.S. Patent Application Publication 2019/0221113.
Regarding Claim 1, Kubat discloses a learning apparatus comprising a circuit configured to execute a method comprising:
learning a second model for predicting variation of the monitoring target using the past monitoring data [“the first tier is implemented as a decision tree induced in the source” §2 ¶3; “agent learns from examples” §2 ¶1; Fig. 1];
generating first corrected data from the past monitoring data, by correcting a difference between the past monitoring data and the current monitoring data, using the first model [“Examples are thus transformed from the original Rn- space to the nR-dimensional space (for nR rules) where the i-th attribute gives the example's proximity example to the i-th rule.” §3 ¶4]; and
learning a third model for predicting variation of the monitoring target using the current monitoring data, the first model, the second model, and the first corrected data [“‘tailoring’ to the target context is carried out by the second tier that has the form of a linear classifier” §2 ¶3; Fig. 1].
However, Kubat fails to explicitly disclose learning a first model for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and past monitoring data that is monitoring data at each of a plurality of past time points.
Maurya discloses learning a first model for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and past monitoring data that is monitoring data at each of a plurality of past time points [“the traffic data may comprise historic traffic data and current traffic data. Further, the historic traffic data and the current traffic data may be analysed using data analytics and a machine learning algorithm. Based on the analysis, a change in traffic pattern of each road segment, from the set of road segments, may be identified.” ¶15].
It would have been obvious to one having ordinary skill in the art, having the teachings of Kubat and Maurya before him before the effective filing date of the claimed invention, to modify the system of Kubat to incorporate the detection of a change in traffic patterns of Maurya.
Given the advantage of notification of a change in traffic patterns so that model adjustments can be made to ensure accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 2, Kubat and Maurya disclose the learning apparatus according to claim 1. Kubat further discloses wherein the learning the first model uses estimation data obtained by estimating monitoring data at each of a plurality of time points [“data files with positive and negative examples were synthesized” §4 ¶1],
the circuit further configured to execute a method comprising:
generating second corrected data from the estimation data, by correcting a difference between the estimation data and the current monitoring data, using the first model [“Examples are thus transformed from the original Rn- space to the nR-dimensional space (for nR rules) where the i-th attribute gives the example's proximity example to the i-th rule.” §3 ¶4]; and
learning a fourth model for predicting variation of the monitoring target using the estimation data [“the first tier is implemented as a decision tree induced in the source” §2 ¶3; “agent learns from examples” §2 ¶1; Fig. 1], and
learning the third model further using the second corrected data and the fourth model [“each context has its own second tier” §1 ¶7; “‘tailoring’ to the target context is carried out by the second tier that has the form of a linear classifier” §2 ¶3; Fig. 1].
Regarding Claim 3, Kubat and Maurya disclose the learning apparatus according to claim 1. Kubat further discloses the circuit further configured to execute a method comprising:
learning estimation data obtained by estimating monitoring data at each of a plurality of time points [“data files with positive and negative examples were synthesized” §4 ¶1];
learning a fourth model for predicting variation of the monitoring target using the estimation data [“the first tier is implemented as a decision tree induced in the source” §2 ¶3; “agent learns from examples” §2 ¶1; Fig. 1];
generating second corrected data from the estimation data, by correcting a difference between the estimation data and the current monitoring data, using the first model [“Examples are thus transformed from the original Rn- space to the nR-dimensional space (for nR rules) where the i-th attribute gives the example's proximity example to the i-th rule.” §3 ¶4]; and
learning a third model for predicting variation of the monitoring target using a combination of at least the current monitoring data, the first model, the fourth model, and the second corrected data [“each context has its own second tier” §1 ¶7; “‘tailoring’ to the target context is carried out by the second tier that has the form of a linear classifier” §2 ¶3; Fig. 1].
Claim 6 is rejected on the same grounds as claim 1.
Regarding Claim 7, Kubat discloses a computer-implemented method for learning, the method comprising:
learning a first model for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and estimation data obtained by estimating monitoring data at each of a plurality of time points [“data files with positive and negative examples were synthesized” §4 ¶1];
learning a fourth model for predicting variation of the monitoring target using the estimation data [“the first tier is implemented as a decision tree induced in the source” §2 ¶3; “agent learns from examples” §2 ¶1; Fig. 1];
generating second corrected data from the estimation data, by correcting a difference between the estimation data and the current monitoring data, using the first model [“Examples are thus transformed from the original Rn- space to the nR-dimensional space (for nR rules) where the i-th attribute gives the example's proximity example to the i-th rule.” §3 ¶4]; and
learning a third model for predicting variation of the monitoring target using the current monitoring data, the first model, the fourth model, and the second corrected data [“‘tailoring’ to the target context is carried out by the second tier that has the form of a linear classifier” §2 ¶3; Fig. 1].
However, Kubat fails to explicitly disclose learning a first model for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and estimation data obtained by estimating monitoring data at each of a plurality of time points.
Maurya discloses learning a first model for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and estimation data obtained by estimating monitoring data at each of a plurality of time points [“the traffic data may comprise historic traffic data and current traffic data. Further, the historic traffic data and the current traffic data may be analysed using data analytics and a machine learning algorithm. Based on the analysis, a change in traffic pattern of each road segment, from the set of road segments, may be identified.” ¶15].
It would have been obvious to one having ordinary skill in the art, having the teachings of Kubat and Maurya before him before the effective filing date of the claimed invention, to modify the system of Kubat to incorporate the detection of a change in traffic patterns of Maurya.
Given the advantage of notification of a change in traffic patterns so that model adjustments can be made to ensure accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 9, Kubat and Maurya disclose the learning apparatus according to claim 1.
However, Kubat fails to explicitly disclose wherein the monitoring data includes a location of the monitoring target.
Maurya discloses wherein the monitoring data includes a location of the monitoring target [“the data needs to be updated on regular basis to reflect exact and accurate map of a geographical location” ¶4].
It would have been obvious to one having ordinary skill in the art, having the teachings of Kubat and Maurya before him before the effective filing date of the claimed invention, to modify the system of Kubat to incorporate the detection of a change in traffic patterns of Maurya.
Given the advantage of notification of a change in traffic patterns so that model adjustments can be made to ensure accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 10, Kubat and Maurya disclose the learning apparatus according to claim 1. Kubat further discloses wherein the learning the third model using the difference between the past monitoring data and the current monitoring data corrects predicting the variation of the monitoring target under an irregular condition [“whereas the first tier is fixed, each context has its own second tier” §1 ¶7; Fig. 1].
Claims 13-14 are rejected on the same grounds as claims 2-3 respectively.
Claim 15 is rejected on the same grounds as claim 9.
Claims 16 are rejected on the same grounds as claims 10.
Regarding Claim 19, Kubat and Maurya disclose the computer-implemented method according to claim 7.
However, Kubat fails to explicitly disclose wherein the first model extracts an attribute that quantitatively indicates a difference between the current monitoring data and the past monitoring data as a prediction result and indicates whether the current monitoring data represents a regular condition or an irregular condition.
Maurya discloses wherein the first model extracts an attribute that quantitatively indicates a difference between the current monitoring data and the past monitoring data as a prediction result and indicates whether the current monitoring data represents a regular condition or an irregular condition [“the traffic data may comprise historic traffic data and current traffic data. Further, the historic traffic data and the current traffic data may be analysed using data analytics and a machine learning algorithm. Based on the analysis, a change in traffic pattern of each road segment, from the set of road segments, may be identified.” ¶15; “generating an alert based on change in traffic pattern” ¶14].
It would have been obvious to one having ordinary skill in the art, having the teachings of Kubat and Maurya before him before the effective filing date of the claimed invention, to modify the combination to incorporate the detection of a change in traffic patterns of Maurya.
Given the advantage of notification of a change in traffic patterns so that model adjustments can be made to ensure accuracy, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claims 20 are rejected on the same grounds as claims 10.
Claim 23 is rejected on the same grounds as claim 19.
Claim(s) 11-12, 17-18, 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kubat and Maurya in view of Jalalian et al. (hereinafter Jalalian), Simulating pedestrian flow dynamics for evaluating the design of urban and architectural space.
Regarding Claim 11, Kubat and Maurya disclose the learning apparatus according to claim 1.
However, Kubat fails to explicitly disclose wherein the monitoring target includes a person entering and exiting a predetermined area.
Jalalian discloses wherein the monitoring target includes a person entering and exiting a predetermined area [“Wheeler Place” Fig. 2; “As shown in Figure 2 the considered area has five entrances and exits (startix,y , endjx,y : (i,j = 1-5).” §3 ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Kubat, Maurya, and Jalalian before him before the effective filing date of the claimed invention, to modify the combination to incorporate the pedestrian flow data of Jalalian.
Given the advantage of accurately predicting pedestrian flow under a changed condition, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 12, Kubat and Maurya disclose the learning apparatus according to claim 1.
However, Kubat fails to explicitly disclose wherein the plurality of monitoring points include a gate where a person passes through.
Jalalian discloses wherein the plurality of monitoring points include a gate where a person passes through [“model idealised pedestrian flow between entry and exit points” pg. 1 ¶1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Kubat, Maurya, and Jalalian before him before the effective filing date of the claimed invention, to modify the combination to incorporate the pedestrian flow data of Jalalian.
Given the advantage of accurately predicting pedestrian flow under a changed condition, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claims 17-18 are rejected on the same grounds as claims 11-12 respectively.
Claims 21-22 are rejected on the same grounds as claims 11-12 respectively.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Conclusion
Any prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c). Additionally when amending, in their remarks Applicant should particularly cite to the supporting paragraphs in the original disclosure for the amendments.
Ma et al. (Automatic clustering method of abnormal crowd flow pattern detection) discloses an automatically clustering method for detecting abnormal flow pattern in pedestrian traffic.
Ganin et al. (Domain-Adversarial Training of Neural Networks) discloses learning approach for domain adaptation and neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary).
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/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148