DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 04/02/2026 is being entered. Claims 1-6 are pending. Claim 1 is amended. Claims 7-8 are cancelled. The amendment does not overcome the 35 U.S.C. 101 rejection. There are additionally claim objects and rejections under 35 U.S.C. 112(a). Therefore, responsive to this amendment, this rejection has been made final.
Claim Objections
Claim 1 is objected to because of the following informalities: in line 5 the grammar “operation” is incorrect. There are multiple operations being performed. Appropriate correction is required. Examiner suggests amending “operation” to “operations”.
Claim Interpretation
Claim 1 recites in last two lines “managing traffic flow to improve traffic safety”. According to the specification “accurate prediction of traffic events can help traffic management departments to take corresponding measures to reduce traffic congestion, improve traffic safety and provide more efficient traffic services.”. Therefore, the limitation is being interpreted to reduce traffic congestion, improve traffic safety and provide more efficient traffic services.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The claim recites, in line 2, “a computer”. There is no description of a computer in the specification. Therefore, this limitation is new matter.
The claim recites, in line 2, “at least one processor”. There is no description of at least one processor in the specification. Therefore, this limitation is new matter.
The claim recites, in line 2, “a memory”. There is no description of a memory in the specification. Therefore, this limitation is new matter.
The claim recites, in lines 2-3, “the memory stores historical spatio-temporal data and instructions”. There is no description of a memory that stores historical spatio-temporal data and instructions in the specification. Therefore, this limitation is new matter.
Claims 2-6 depend from claim 1 and are also rejected under 35 U.S.C. 112(a).
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea.
101 Analysis – Step 1
Claim 1 is directed to a method. Therefore, the claim is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong 1
Regarding Prong 1 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human behavior, and/or c) mental processes.
Regarding claim 1, the claim incudes limitations that recite an abstract idea and will be used as a representative claim for the remainder of the 101 rejections. The examiner submits that the bolded limitations constitute “abstract ideas”. Claim 1 recites:
1. A method of predicting traffic events based on a spatio-temporal Hawkes process, which is implemented by a computer comprising at least one processor and a memory, wherein the memory stores historical spatio-temporal data of all types of traffic events, a spatio-temporal Hawkes process model, and instructions that, when executed by the at least one processor, cause the at least one processor to perform operation comprising:
step 1: collecting historical spatio-temporal data of all types of traffic events, and defining historical spatio-temporal data of n types of traffic events as E=E1,E2,...,Ek,...,En, wherein a historical spatio-temporal sequence of a k-th type of traffic events is defined as Ek=e1k,e2k,...,eik,...,eEkk, wherein eik=tik,lik, and tik and lik=laik,loik are timestamp information and spatial latitude and longitude information when a traffic event eik occurs, respectively, laik is longitude information of event eik, and loik is latitude information of event eik;
step 2: establishing the spatio-temporal Hawkes process model according a following expression: λejkEtjk,ljk=flμk+∑ehk∈Ek,h<jf1ejk,ehk+∑emk'∈E-Ek,m<jf2ejk,emk',where μk is a base probability intensity for occurrence of the k-th type of traffic events ek, ∑ehk∈Ek,h<jf1ejk,ehk indicates an influence of a same type of historical traffic events ehk∈Ek on a target traffic event ejk , f1∙,∙ is an influence function of the same type of traffic events, ∑emk'∈E-Ek,m<jf2ejk,emk' is an influence of other types of historical traffic events emk'∈E-Ek on the target traffic event ejk, f2∙,∙ is an influence function of different types of traffic events, and flx=1/1+exp-x is a logistic function;
step 3: estimating parameters of the spatio-temporal Hawkes process model by training the spatio-temporal data, comprising:
defining an objective function for estimating the parameters of the spatio-temporal Hawkes process model as: O=∑Ek∈E∑ejk∈EklogpejkE,tjk,ljk,where pejkE,tjk, ljk is a probability of occurrence of the k-th type of the target traffic event ejk in time tjk and space ljk given the historical spatio-temporal data E of all n types of traffic events,
maximizing the objective function O by using a gradient descent optimization algorithm to obtain optimal values of all parameters and obtain a final spatio-temporal Hawkes process model; and
step 4: using the final spatio-temporal Hawkes process model to predict traffic events, comprising:
for given time information t and space information 1, calculating and sorting probabilities of occurrences of all n types of traffic events in a descending order of probability values,
outputting the sorted event list and probability values, and
based on the sorted event list and probability values, managing traffic flow to improve traffic safety.
In the claims broadest reasonable interpretation (BRI), the claim recites in step 1 a mental process of defining data as variables, which can be performed in the human mind and is an abstract idea. Step 2, the claim recites a mathematical concept (Hawkes process model) which is an abstract idea and further defines additional variables similar to step 1, and therefore is a mental process and is also an abstract idea. Step 3 is an estimation by training, which is an abstract idea that can be performed with a calculator and pen and paper and further defines variables, which is a mental process of designating variables, and lastly maximizes an objective function using a gradient descent optimization to obtain values and a final model, which is an abstract idea that can be performed with a calculation and pen and paper. Step 4 is using the model to predict traffic events, which is also an abstract idea that can be performed with a calculator and pen and paper, step 4 further defines variables, which is a mental process of designating variables, a calculation which is a mathematical concept, and organizing values which is a mental process. Therefore, the claims recites abstract ideas as mental processes and a mathematical concept.
101 Analysis – Step 2A, Prong 2
Regarding Prong 2 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have determined that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In the present case, the additional limitations beyond the above mentioned abstract ideas are as follows (where the underlined portions are the “additional elements” while the bolded portions continue to represent the “abstract idea”):
1. A method of predicting traffic events based on a spatio-temporal Hawkes process, which is implemented by a computer comprising at least one processor and a memory, wherein the memory stores historical spatio-temporal data of all types of traffic events, a spatio-temporal Hawkes process model, and instructions that, when executed by the at least one processor, cause the at least one processor to perform operation comprising:
step 1: collecting historical spatio-temporal data of all types of traffic events, and defining historical spatio-temporal data of n types of traffic events as E=E1,E2,...,Ek,...,En, wherein a historical spatio-temporal sequence of a k-th type of traffic events is defined as Ek=e1k,e2k,...,eik,...,eEkk, wherein eik=tik,lik, and tik and lik=laik,loik are timestamp information and spatial latitude and longitude information when a traffic event eik occurs, respectively, laik is longitude information of event eik, and loik is latitude information of event eik;
step 2: establishing the spatio-temporal Hawkes process model according a following expression: λejkEtjk,ljk=flμk+∑ehk∈Ek,h<jf1ejk,ehk+∑emk'∈E-Ek,m<jf2ejk,emk',where μk is a base probability intensity for occurrence of the k-th type of traffic events ek, ∑ehk∈Ek,h<jf1ejk,ehk indicates an influence of a same type of historical traffic events ehk∈Ek on a target traffic event ejk , f1∙,∙ is an influence function of the same type of traffic events, ∑emk'∈E-Ek,m<jf2ejk,emk' is an influence of other types of historical traffic events emk'∈E-Ek on the target traffic event ejk, f2∙,∙ is an influence function of different types of traffic events, and flx=1/1+exp-x is a logistic function;
step 3: estimating parameters of the spatio-temporal Hawkes process model by training the spatio-temporal data, comprising:
defining an objective function for estimating the parameters of the spatio-temporal Hawkes process model as: O=∑Ek∈E∑ejk∈EklogpejkE,tjk,ljk,where pejkE,tjk,ljk is a probability of occurrence of the k-th type of the target traffic event ejk in time tjk and space ljk given the historical spatio-temporal data E of all n types of traffic events,
maximizing the objective function O by using a gradient descent optimization algorithm to obtain optimal values of all parameters and obtain a final spatio-temporal Hawkes process model; and
step 4: using the final spatio-temporal Hawkes process model to predict traffic events, comprising:
for given time information t and space information 1, calculating and sorting probabilities of occurrences of all n types of traffic events in a descending order of probability values,
outputting the sorted event list and probability values, and
based on the sorted event list and probability values, managing traffic flow to improve traffic safety.
Whether the abstract idea is integrated into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional limitation (or combination of limitations) may have integrated the judicial exception into a practical application:
• an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
• an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
• an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
• an additional element effects a transformation or reduction of a particular article to a different state or thing; and
• an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
It is clear that Applicant’s claim does not comprise any of the above additional limitations that, individually or in combination, have integrated the judicial exception into a practical application.
While the PEG further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the PEG also list examples in which a judicial exception has not been integrated into a practical application:
• an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f));
• an additional element adds insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)); and
• an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
The additional element “a computer comprising at least one processor and a memory, wherein the memory stores historical spatio-temporal data of all types of traffic events, a spatio-temporal Hawkes process model, and instructions that, when executed by the at least one processor, cause the at least one processor to perform operation” is merely using a computer to implement the abstract ideas (MPEP 2106.05(f)).
The additional element “collecting historical spatio-temporal data of all types of traffic events” amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional element “outputting the sorted event list and probability values” amounts to mere data outputting, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional element “managing traffic flow to improve traffic safety” adds insignificant extra-solution activity (MPEP 2106.05(g)).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional element(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional element, when considered as a whole, reflects an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above -noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional element(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Under the 2019 PEG, a conclusion that an additional element is insignificant extra solution (pre-solution activity and/or post-solution activity) activity in Step 2A should be re- evaluated in Step 2B. Here, the obtain, select, and perform functions were considered to be pre- solution activity and post-solution activity in Step 2A, and thus these additional elements are re- evaluated in Step 2B to determine if the additional elements are more than what is well- understood, routine, conventional activity in the field. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Regarding the additional element “collecting historical spatio-temporal data of all types of traffic events”. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). See 2106.05(d)(II). iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 are found to be well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Regarding the additional element “outputting the sorted event list and probability values”. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). See 2106.05(d)(II). iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 are found to be well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. Therefore, the claim is ineligible.
Regarding the additional element “managing traffic flow to improve traffic safety”. Traffic management departments can take corresponding measures to reduce traffic congestion, improve traffic safety and provide more efficient traffic services has insignificant application and does not specifically state what measures are taken other than potential effects of using the process. Therefore, the additional element does not add a meaningful limitation to the method.
Even when considered in combination, the additional elements represents mere instructions to apply an exception and insignificant extra-solution activities, which cannot provide an inventive concept (Step 2B: NO). The claim is not eligible.
101 Analysis – Step 1
Claim 2 is directed to a method. Therefore, the claim is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong 1
Regarding Prong 1 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human behavior, and/or c) mental processes.
The bolded limitations represent abstract ideas:
2. The method of predicting traffic events based on the spatio-temporal Hawkes process according to claim 1, wherein the influence of the same type of traffic events on the target traffic event comprises a negative correlation and a positive correlation, the negative correlation acts as a suppression effect and the positive correlation acts as a stimulation effect.
In the claims broadest reasonable interpretation (BRI), the claim further defines what the influence indicates, which is a mental process of judgment.
101 Analysis – Step 2A, Prong 2
Regarding Prong 2 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have determined that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In the present case, there are no additional limitations beyond the above mentioned abstract ideas.
101 Analysis – Step 1
Claim 3 is directed to a method. Therefore, the claim is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong 1
Regarding Prong 1 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human behavior, and/or c) mental processes.
The bolded limitations represent abstract ideas:
3. The method of predicting traffic events based on the spatio-temporal Hawkes process according to claim 2, wherein in step 2, an influence function of the same type of traffic events ehk∈Ek on the target traffic event ejk is defined as: f1ejk,ehk=φtjk,thk∙dljk,lhk∙γk, where φtjk,thk=exp-αk∙tjk-thk indicates that the influence of the same type of historical traffic events decays exponentially with an increasing time interval, αk is a time attenuation degree coefficient for the influence of the same type (k) of events, dljk,lhk=exp-βk∙lajk-lahk2+lojk-lohk2 indicates that the influence of the same type of historical traffic events decays exponentially with an increasing space interval, lajk and lojk are spatial longitude and latitude information of the target traffic event, lahk and lohk are longitude and latitude information of the same type of historical traffic events ehk∈Ek, βk is a space attenuation degree coefficient for the influence of the same type of events, and γk is a real number parameter.
In the claims broadest reasonable interpretation (BRI), the claim further defines variables, which is a mental process of designating variables.
101 Analysis – Step 2A, Prong 2
Regarding Prong 2 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have determined that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In the present case, there are no additional limitations beyond the above mentioned abstract ideas.
101 Analysis – Step 1
Claim 4 is directed to a method. Therefore, the claim is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong 1
Regarding Prong 1 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human behavior, and/or c) mental processes.
The bolded limitations represent abstract ideas:
4. The method of predicting traffic events based on the spatio-temporal Hawkes process according to claim 3, wherein in the influence function of the same type of traffic events ehk∈Ek on the target traffic event ejk, γk indicates whether a relationship between different types of events is negatively correlated or positively correlated.
In the claims broadest reasonable interpretation (BRI), the claim recites a mental process of what the function indicates, which is a mental process of judgement.
101 Analysis – Step 2A, Prong 2
Regarding Prong 2 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have determined that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In the present case, there are no additional limitations beyond the above mentioned abstract ideas.
101 Analysis – Step 1
Claim 5 is directed to a method. Therefore, the claim is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong 1
Regarding Prong 1 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human behavior, and/or c) mental processes.
The bolded limitations represent abstract ideas:
5. The method of predicting traffic events based on the spatio-temporal Hawkes process according to claim 2, wherein in step 2… .
In the claims broadest reasonable interpretation (BRI), the claim recites a mental process of defining data as variables, which can be performed in the human mind and is an abstract idea. The claim also recites mathematical concepts, which are also abstract ideas.
101 Analysis – Step 2A, Prong 2
Regarding Prong 2 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have determined that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In the present case, there are no additional limitations beyond the above mentioned abstract ideas.
101 Analysis – Step 1
Claim 6 is directed to a method. Therefore, the claim is within at least one of the four statutory categories.
The bolded limitations represent abstract ideas:
6. The method of predicting traffic events based on the spatio-temporal Hawkes process according to claim 5, wherein in the influence function of the other type of the historical traffic events emk'∈E-Ek on the target traffic event ejk, γkk' indicates whether the relationship between the different types of events is negatively correlated or positively correlated.
In the claims broadest reasonable interpretation (BRI), the claim further defines what the influence indicates, which is a mental process of judgment.
101 Analysis – Step 2A, Prong 2
Regarding Prong 2 of Step 2A analysis in the PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have determined that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In the present case, there are no additional limitations beyond the above mentioned abstract ideas.
Response to Arguments
Applicant's arguments filed on 04/02/2026 have been fully considered but they are not persuasive.
Applicant argues, on Page 7 of the Remarks line 10, that “the process involves computer algorithms based on a large amount of statistical data, which is a computational procedure that can’t be completed in the human mind. Based on this computation procedure, the establishment and training of the spatio-temporal Hawkes process model must be performed on a specific computer”.
Examiner disagrees, the Applicant has not claimed any sort of specific computer and has not provided evidence to why a calculator could not perform this computation. Therefore, a calculator is sufficient for performing the abstract idea as noted in the 35 U.S.C. 101 rejection above.
Applicant argues, on Page 7 of the Remarks in Paragraph 2, that “In addition, Prong 2 of Step 2A requires evaluating whether the abstract idea is integrated into a practical application. As noted in the Guidelines, the abstract idea is integrated into a practical application when an additional element uses the abstract idea with a particular machine that is integral with the claim. As recited in the amended claim 1, the steps are performed by the system. Accordingly, the abstract idea is clearly integrated into a practical application, as recited in the amended claim 1”.
Examiner disagrees, the machine in case is merely a computer that is implementing the abstract ideas (MPEP 2106.05(f)). Implementing abstract ideas on a computer according the 35 U.S.C. 101 guidance, is not an integration into a practical application.
Further, the remaining arguments on Pages 7-8 appear to be directed solely to the amended subject matter, and are not persuasive, as noted supra in the rejections of that claimed subject matter.
Allowable Subject Matter
With respect to the art claims 1-6 are allowable. See Reasons For Allowance below
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance. The claimed invention is directed at a method of predicting traffic events based on spatio-temporal Hawkes process.
The following references have been identified as the most relevant prior art to the claimed invention(s):
Zhu et al. (Titled: Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling) hereinafter Zhu is directed at modeling traffic congestion events over road networks. “For our study, we consider K = 14 traffic sensors on two major highways in metro Atlanta, I-75 and I-85, shown in Fig. 3. We denote their geo-locations (latitude and longitude) on the traffic network by rk 2 S _ R2, k = 1; : : : ;K, where S denote the location space of traffic networks, which will be discussed in Section II-C. Let fxigNx i=1 represent a sequence of traffic congestion events in a single day, where Nx is the number of the congestion events generated in oneday [0; T). The i-th congestion event xi = (ti; si) is a data tuple consisting of the occurrence time ti 2 [0; T), and the sensor location index si 2 f1; : : : ;Kg. We extract 18,618 traffic congestion events from 174 days between April 2018 and December 2018. The maximum and the minimum number of events in a single day are 168 and 19, respectively.” [see Page 3, Paragraph 2].
Ilhan et al. (Modeling of Spatio-Temporal Hawkes Processes With Randomized Kernels) hereinafter Ilhan is directed at spatio-temporal event analysis using spatio-temporal Hawkes process. “observe an event sequence E = {ei}Ni=1, and model it with spatio-temporal Hawkes processes. Here, N is the total number of events and ei = {ui, ti, si}N i=1 is the ith event in the sequence with type ui ∈ N, time ti ∈ T and location si = [xi, yi]T ∈ S.2” [see Page 4948, II. PROBLEM DESCRIPTION].
Ding (TRAFFIC CONGESTION MODELING WITH DEEP ATTENTION HAWKES PROCESS) hereinafter Ding is directed at a spatio-temporal point process (STPP) is a random collection of points, where each point represents the time and location of an event.[29] In our case, traffic congestion fx1; : : : ; xNx(T)g is treated as a set of events, where Nx(T) represents the total number of events happen in time horizon [0; T) and in the location space K. Let Ht = Xt denote the collection of events taking place before time t, where Xt = fxigxi<t. This congestion STPP model is characterized via the conditional intensity function (t; kjHt), which is the conditional probability of observing a traffic congestion (t; k) 2 [0; T) K given the history Ht. (t; kjHt) = E[Nk(t; t + dt)jHt]=dt (2.1) where Nk(t; t + dt) is the counting measure of events on sensor k in [t; t + dt] [see Page 6, 2.1 Spatio-temporal Point Pro].
Dubey et al. (Bayesian neural hawkes process for event uncertainty prediction) hereinafter Dubey is directed at a spatio-temporal Hawkes process for traffic flow prediction. [see Page 575 and 580].
However, the combination of Zhu, Ilhan, Ding, and Dubey fails to disclose:
1. A method of predicting traffic events based on a spatio-temporal Hawkes process, which is implemented by a computer comprising at least one processor and a memory, wherein the memory stores historical spatio-temporal data of all types of traffic events, a spatio-temporal Hawkes process model, and instructions that, when executed by the at least one processor, cause the at least one processor to perform operation comprising:
step 1: collecting historical spatio-temporal data of all types of traffic events, and defining historical spatio-temporal data of n types of traffic events as E=E1,E2,...,Ek,...,En, wherein a historical spatio-temporal sequence of a k-th type of traffic events is defined as Ek=e1k,e2k,...,eik,...,eEkk, wherein eik=tik,lik, and tik and lik=laik,loik are timestamp information and spatial latitude and longitude information when a traffic event eik occurs, respectively, laik is longitude information of event eik, and loik is latitude information of event eik;
step 2: establishing the spatio-temporal Hawkes process model according a following expression: λejkEtjk,ljk=flμk+∑ehk∈Ek,h<jf1ejk,ehk+∑emk'∈E-Ek,m<jf2ejk,emk',where μk is a base probability intensity for occurrence of the k-th type of traffic events ek, ∑ehk∈Ek,h<jf1ejk,ehk indicates an influence of a same type of historical traffic events ehk∈Ek on a target traffic event ejk , f1∙,∙ is an influence function of the same type of traffic events, ∑emk'∈E-Ek,m<jf2ejk,emk' is an influence of other types of historical traffic events emk'∈E-Ek on the target traffic event ejk, f2∙,∙ is an influence function of different types of traffic events, and flx=1/1+exp-x is a logistic function;
step 3: estimating parameters of the spatio-temporal Hawkes process model by training the spatio-temporal data, comprising:
defining an objective function for estimating the parameters of the spatio-temporal Hawkes process model as: O=∑Ek∈E∑ejk∈EklogpejkE,tjk,ljk,where pejkE,tjk,ljk is a probability of occurrence of the k-th type of the target traffic event ejk in time tjk and space ljk given the historical spatio-temporal data E of all n types of traffic events,
maximizing the objective function O by using a gradient descent optimization algorithm to obtain optimal values of all parameters and obtain a final spatio-temporal Hawkes process model; and
step 4: using the final spatio-temporal Hawkes process model to predict traffic events, comprising:
for given time information t and space information 1, calculating and sorting probabilities of occurrences of all n types of traffic events in a descending order of probability values,
outputting the sorted event list and probability values, and
based on the sorted event list and probability values, managing traffic flow to improve traffic safety.
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. The combination of features as claimed would not have been obvious to one of ordinary skill in the art as combining various references from the totality of the evidence to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias. It is thereby asserted by the Examiner that, in light of the above and in further deliberation over all of the evidence at hand, that the claims are allowable as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
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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).
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/SHAYNE M. GILBERTSON/Examiner, Art Unit 3665
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