DETAILED ACTION
This is a first action on the merits. Claims 1-7 are pending.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement filed on 11/18/2024 has been reviewed and considered.
Drawings
The drawings are objected to because the text of figure 1 is difficult to read because it is grainy, faint in areas, and contains graphical artifacts. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). 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.
Specification
The abstract of the disclosure is objected to because it contains the implied phrase “Disclosed is” in line 1. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The disclosure is objected to because of the following informalities:
In paragraph 46, line 3, the acronym MAPE should be defined.
In paragraphs 32-38 and 79-85, the parameter R does not appear to be defined. It is unclear if R is intended to represent a variable, or the set of real numbers 𝐑.
In paragraphs 18, 22, 26, 61, 66 and 70, it is unclear if the variable j is intended to define a path, as disclosed in paragraphs 37 and 84.
Appropriate correction is required.
Claim Objections
Claims 1-7 are objected to because they include reference characters S1-S6 which are not enclosed within parentheses.
Reference characters corresponding to elements recited in the detailed description of the drawings and used in conjunction with the recitation of the same element or group of elements in the claims should be enclosed within parentheses so as to avoid confusion with other numbers or characters which may appear in the claims. See MPEP § 608.01(m).
Claims 1-7 are objected to because of the following informalities:
In claim 1, lines 1-2 and 29, and claims 2-7, lines 1-2, “based on influence of weather factors” should read “based on weather factors”. This appears to be a typographical error.
In claim 1, lines 14-15, “correcting the predicted travel traffic volume” should read “correcting a predicted travel traffic volume”. This appears to be a typographical error.
In claim 1, lines 17-18, “correcting, after collecting the actual travel traffic volume data of the previous day during holidays, the predicted travel traffic volume” should read “after collecting the actual travel traffic volume data of the previous day during holidays, correcting the predicted travel traffic volume” to provide sufficient antecedent basis for collecting the actual travel traffic volume data of the previous day.
In claim 1, lines 22-23, “a departure volume of each starting point and an arrival volume of each ending point” should read “a departure volume of one or more starting points and an arrival volume of one or more ending points” to provide sufficient antecedent basis for the starting and arrival points.
In claims 2-7, line 1, “predicting a holiday expressway travel traffic volume” should read “predicting the holiday expressway travel traffic volume” to make it clear that the claims are directed to the same prediction as claim 1.
In claim 3, line 7, “calculating the total travel traffic volume” should read “calculating a total travel traffic volume” to provide sufficient antecedent basis for the total travel traffic volume.
In claim 3, lines 9-10, the parameters yi, wa, i, a, j, y’j, yj, and b should be defined in the claim or in parent claim 1.
In claim 5, lines 5-13, the parameters y, i, ŷ’, j, and
γ
^
'
should be defined in the claim or in parent claim 1.
In claim 6, lines 4-5, “a departure volume yo of each starting point and an arrival volume yu of each ending point” should read “the departure volume yo of the one or more starting points and the arrival volume yu of the one or more ending points” to provide sufficient antecedent basis for the starting and arrival points.
Claim 6 should be limited to a single colon because using multiple colons in a single sentence is grammatically incorrect which makes it confusing to determine the relationships between the limitations.
In claim 6, lines 14-32, the meaning of S5.2.1.-S5.2.5 are unclear. They appear to be substeps of step S5.2, however, the numbering alone does not disclose sufficient structure without explanation. Within each substep, the relationship between the layer name and the text following each colon is further unclear. It appears the each substep is intended to define the operations to generate each layer.
In claim 7, line 5, the parameters T, û, and u should be defined in the claim or in parent claim 1.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1-7 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.
Regarding claim 1, lines 4-5, the limitation “establishing a relative influence coefficient between historical weather and a travel traffic volume” renders the claim indefinite because it is unclear if the historical holiday travel traffic volume data recited in line 3 comprises the travel traffic volume, therefore, it is unclear what the relative influence coefficient represents. For the purposes of examination, it will be assumed that relative influence coefficient is established between the historical weather data and the historical holiday travel traffic volume data.
Regarding claim 1, line 10, the limitation “a plurality of historical similar holidays” renders the claim indefinite because it is unclear whether the holidays are similar because they fall on the same day/date of different years, have similar characteristics (e.g., religious vs secular), or span multiple dates (e.g., Diwali). For the purposes of examination, it will be assumed that holidays are similar when they fall on the same day/date of different years.
Regarding claims 1 and 4, lines 12 and 12-13, respectively, the limitation “crossover operation” renders the claim indefinite because the specification does not clearly define the term. Where applicant acts as his or her own lexicographer to specifically define a term of a claim, the written description must clearly define the claim term so as to put one reasonably skilled in the art on notice that the applicant intended to so define that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). For the purposes of examination, it will be assumed that a crossover operation is a correlation between different types and/or sources of data.
Regarding claim 1, lines 17-18, the limitation “collecting the actual travel traffic volume data of the previous day during holidays” renders the claim indefinite because it does not use proper idiomatic English. It is unclear what day the phrase “previous day during holidays” is intended to refer to. For the purposes of examination, it will be assumed that travel traffic volume data is collected for the day before a holiday.
Regarding claim 2, line 4, the limitation “collecting historical weather data” renders the claim indefinite because it is unclear if it is the same historical weather data collected in claim 1, line 3. For the purposes of examination, it will be assumed that both claims are directed to the same historical weather data.
Regarding claim 2, lines 5-6, the limitation “collecting historical holiday travel traffic volume” renders the claim indefinite because it is unclear if is the same historical holiday travel traffic volume collected in claim 1, line 3. For the purposes of examination, it will be assumed that both claims are directed to the same historical holiday travel traffic volume.
Regarding claim 2, line 8, the limitation “establishing a relative influence coefficient” renders the claim indefinite because it is unclear if it is the relative influence coefficient established in claim 1, line 4. For the purposes of examination, it will be assumed that both claims are directed to the same relative influence coefficient.
Regarding claim 2, lines 8-9, the limitation “between historical weather and a travel traffic volume” renders the claim indefinite because it is unclear if the historical holiday travel traffic volume data recited in claim 1, line 3, comprises the travel traffic volume, therefore, it is unclear what the relative influence coefficient represents. For the purposes of examination, it will be assumed that relative influence coefficient is established between the historical weather data and the historical holiday travel traffic volume data.
Regarding claim 3, lines 7-8, the limitation “according to a weather influence coefficient” renders the claim indefinite because it is unclear if the weather influence coefficient is the historical weather relative influence coefficient established in claim 1, line 4. For the purposes of examination, it will be assumed that both claims are directed to the same influence coefficient.
Regarding claim 4, line 4, the limitation “a plurality of historical similar holidays” renders the claim indefinite because it is unclear if they are the same plurality of historical similar holidays recited in claim 1, line 10. For the purposes of examination, it will be assumed that both claims are directed to the same plurality of historical similar holidays.
Regarding claim 4, line 5, the limitation “selecting basic characteristics” renders the claim indefinite because it is unclear if they are the same basic characteristics selected in claim 1, line 10-11. For the purposes of examination, it will be assumed that both claims are directed to the same basic characteristics.
Regarding claim 4, lines 8-9 and 9-10, the limitation “a week before the same historical holidays” renders the claim indefinite because it is unclear whether the claim is directed to a plurality of weeks of different years that precede the same holiday in each year, or a single week that precedes a multiday holiday (e.g., Ramadan). For the purposes of examination, it will be assumed that the average traffic volume of a week preceding a holiday in a previous year is used to determine the characteristics.
Regarding claim 4, lines 10-11, the limitation “a normal week of the holidays” renders the claim indefinite because it is unclear how a normal holiday weeks is differentiated from an abnormal holiday week. For the purposes of examination, it will be assumed that the normal holiday week merely refers to the week that includes the date of the holiday.
Regarding claim 4, line 12, the limitation “constructing derived characteristics” renders the claim indefinite because it is unclear if they are the same derived characteristics constructed in claim 1, line 11. For the purposes of examination, it will be assumed that both claims are directed to the same derived characteristics.
Regarding claim 4, line 26, the limitation “repeating the characteristic crossover and correlation screening process” renders the claim indefinite because it is unclear which steps comprise the process. For the purposes of examination, it will be assumed that step S3.3 is repeated.
Regarding claim 4, line 30, the limitation “establishing a multivariable linear regression model” renders the claim indefinite because it is unclear if is the same multivariable linear regression model constructed in claim 1, line 13. For the purposes of examination, it will be assumed that the claims are directed to the same model.
Regarding claim 5, lines 12-13, the limitation “obtain a corrected predicted travel traffic volume of the remaining dates of holidays” renders the claim indefinite because it is unclear if it is the same corrected predicted travel traffic volume of the remaining dates of holidays obtained in claim 1, lines 20-21. For the purposes of examination, it will be assumed that the claims are directed to the same corrected prediction.
Regarding claim 6, lines 4-5, the limitation “obtaining … a departure volume yo of each starting point and an arrival volume yu of each ending point” renders the claim indefinite because it is unclear if they are the departure volume of each starting point and arrival volume of each ending point obtained in claim 1, lines 22-23. For the purposes of examination, it will be assumed that both claims are directed to the same departure and arrival volumes.
Regarding claim 6, line 6, the limitation “based on a time allocation ratio” renders the claim indefinite because it is unclear if it is the same time allocation ratio recited in claim 1, line 25. For the purposes of examination, it will be assumed that both claims are directed to the same time allocation ratio.
Regarding claim 6, lines 8, 19, 22, 28 and 31, the parameter R does not appear to be defined, therefore, it is unclear if R is intended to represent a variable, or the set of real numbers 𝐑. For the purposes of examination, it will be assumed that R is the set of real numbers.
Regarding claim 6, line 11, the limitation “constructing a travel traffic volume model of a stratified flow road network” renders the claim indefinite because it is unclear if it is the same travel traffic volume model of the stratified flow road network constructed in claim 1, line 24. For the purposes of examination, it will be assumed that both claims are directed to the same model.
Regarding claim 7, line 4, the limitation “constructing an objective function” renders the claim indefinite because it is unclear if it is the same objective function constructed in claim 1, line 26. For the purposes of examination, it will be assumed that both claims are directed to the same objective function.
Regarding claim 7, lines 6-7, the limitation “using a reverse gradient propagation method” renders the claim indefinite because it is unclear if it is the same reverse gradient propagation method recited in claim 1, lines 27-28. For the purposes of examination, it will be assumed that both claims are directed to the same reverse gradient propagation method.
Regarding claim 7, line 7, the allocation ratios W1 and W2 render the claim indefinite because they lack sufficient antecedent basis in the claim, therefore, it is unclear how the optimized travel traffic volume is obtained. For the purposes of examination, it will be assumed that claim 7 is dependent from claim 6.
Claims 2-7 are rejected as being dependent on a rejected claim and for failing to cure the deficiencies listed above.
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.
The determination of whether a claim recites patent ineligible subject matter is a two-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP § 2106.03, or
STEP 2: the claim recites a judicial exception, e.g., an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP § 2106.04
STEP 2A (PRONG ONE): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP § 2106.04(II)(A)(1)
STEP 2A (PRONG TWO): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP § 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP § 2106.05
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method of predicting a holiday expressway travel traffic volume (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong One
Regarding Prong One of the Step 2A analysis, 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 activity, and/or c) mental processes. see MPEP § 2106(A)(II)(1) and MPEP § 2106.04(a)-(c) Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the analysis. Claim 1 recites:
A method of predicting a holiday expressway travel traffic volume based on influence of weather factors, comprising the following steps:
S1, collecting historical weather data and historical holiday travel traffic volume data, and establishing a relative influence coefficient between historical weather and a travel traffic volume [mental process/step];
S2, constructing, based on the relative influence coefficient between the historical weather and the travel traffic volume obtained in Step S1, a travel traffic volume transfer model based on weather data [mental process/step];
S3, analyzing, based on the historical holiday travel traffic volume data obtained in Step S1, the daily travel traffic volume of a plurality of historical similar holidays, selecting basic characteristics of the travel traffic volume, constructing derived characteristics of the travel traffic volume through crossover operation between the basic characteristics of the travel traffic volume, and then constructing a multivariable linear regression model based on historical travel traffic volume characteristics; and correcting the predicted travel traffic volume obtained by the multivariable linear regression model based on the travel traffic volume transfer model constructed in Step S2 [mental process/step];
S4, correcting, after collecting the actual travel traffic volume data of the previous day during holidays, the predicted travel traffic volume of the remaining dates of holidays by comparing the actual travel traffic volume with a travel traffic volume of a corresponding date predicted based on Step S3 to obtain the corrected predicted travel traffic volume of the remaining dates of holidays [mental process/step];
S5, obtaining, based on the methods of Step S2, Step S3 and Step S4, a departure volume of each starting point and an arrival volume of each ending point predicted for each day of holidays, and constructing a travel traffic volume model of a stratified flow road network based on a time allocation ratio of historical characteristic dates [mental process/step]; and
S6, constructing an objective function, and optimizing and solving the travel traffic volume model of the stratified flow road network obtained in Step S5 by using a reverse gradient propagation method to obtain a predicted result of the holiday expressway travel traffic volume based on influence of weather factors [mental process/step].
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind or via pen and paper (see MPEP § 2016.04(a)(1)(III)). For example, “establishing a relative influence coefficient…” in the context of this claim encompasses a person observing gathered data and assigning a value of 0 or 1 to a weighting parameter that indicates if the traffic volume is affected by rain. The limitation “selecting basic characteristics…” in the context of this claim encompasses a person determining which parameters in the gathered data are significant. The limitations “constructing … a travel traffic volume transfer model…”, “analyzing … the daily travel traffic volume…”, “constructing derived characteristics…”, “…constructing a multivariable linear regression model”, “constructing a travel traffic volume model…”, and “constructing an objective function..” in the context of this claim encompasses a person deriving a mathematical model that describes the gathered data. The limitations “…correcting the predicted travel traffic volume…”, “correcting … the predicted travel traffic volume…”, “obtaining … a departure volume … and an arrival volume…”, and “…optimizing and solving the travel traffic volume model…” in the context of this claim encompasses a person determining various intermediate and output values of the derived mathematical model. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong Two
Regarding Prong Two of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. see MPEP § 2106.04(II)(A)(2) and MPEP § 2106.04(d)(2). 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 indicated 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-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”):
A method of predicting a holiday expressway travel traffic volume based on influence of weather factors, comprising the following steps:
S1, collecting historical weather data and historical holiday travel traffic volume data [pre-solution activity (data gathering)], and establishing a relative influence coefficient between historical weather and a travel traffic volume;
S2, constructing, based on the relative influence coefficient between the historical weather and the travel traffic volume obtained in Step S1, a travel traffic volume transfer model based on weather data;
S3, analyzing, based on the historical holiday travel traffic volume data obtained in Step S1, the daily travel traffic volume of a plurality of historical similar holidays, selecting basic characteristics of the travel traffic volume, constructing derived characteristics of the travel traffic volume through crossover operation between the basic characteristics of the travel traffic volume, and then constructing a multivariable linear regression model based on historical travel traffic volume characteristics; and correcting the predicted travel traffic volume obtained by the multivariable linear regression model based on the travel traffic volume transfer model constructed in Step S2;
S4, correcting, after collecting the actual travel traffic volume data of the previous day during holidays, the predicted travel traffic volume of the remaining dates of holidays by comparing the actual travel traffic volume with a travel traffic volume of a corresponding date predicted based on Step S3 to obtain the corrected predicted travel traffic volume of the remaining dates of holidays;
S5, obtaining, based on the methods of Step S2, Step S3 and Step S4, a departure volume of each starting point and an arrival volume of each ending point predicted for each day of holidays, and constructing a travel traffic volume model of a stratified flow road network based on a time allocation ratio of historical characteristic dates; and
S6, constructing an objective function, and optimizing and solving the travel traffic volume model of the stratified flow road network obtained in Step S5 by using a reverse gradient propagation method to obtain a predicted result of the holiday expressway travel traffic volume based on influence of weather factors.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation(s) of “collecting historical weather data and historical holiday travel traffic volume data”, the examiner submits that the limitation(s) is/are insignificant extra-solution activities. The data collecting step is recited at a high level of generality (i.e., as a general means of obtaining weather and traffic information), and amounts to merely gathering data, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(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 elements, when considered as a whole, reflect 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 limitation(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
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the examiner submits that the additional limitation(s) of “collecting historical weather data and historical holiday travel traffic volume data” is/are insignificant extra-solution activities. Hence, the claim is not patent eligible.
Dependent claim(s) 2-7 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception. Dependent claims 2-7 further include mathematical formulas or equations that fall within the mathematical concepts group of abstract ideas. Therefore, dependent claims 2-7 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1-7 is/are ineligible under 35 U.S.C 101.
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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (CN 110223510) in view of Zhou et al. (CN 116704754), hereinafter Zhao and Zhou, respectively.
Regarding claim 1, as best understood, Zhao discloses a method of predicting a holiday expressway travel traffic volume based on influence of weather factors, comprising the following steps: S1, collecting historical weather data (Zhao; para. 52: Query weather records) and historical holiday travel traffic volume data (Zhao; para. 49: Traffic flow data is obtained from the database of highway toll stations), and establishing a relative influence coefficient between historical weather and a travel traffic volume (Zhao; paras. 58: calculate the influence coefficients Ci … Considering the weather conditions, the analysis shows that the worse the weather (e.g., light rain turning into moderate rain), the smaller the influence coefficient Ci); S2, constructing, based on the relative influence coefficient between the historical weather and the travel traffic volume obtained in Step S1, a travel traffic volume transfer model based on weather data (Zhao; para. 54: Vi represents traffic flow under different weather conditions); S3, analyzing, based on the historical holiday travel traffic volume data obtained in Step S1, the daily travel traffic volume of a plurality of historical similar holidays (Zhao; para. 67: Obtain the holiday impact coefficient according to formula II … Wherein, Ej represents the traffic flow under different holidays); correcting the travel traffic volume (Zhao; para. 76: in step 3, the MinMaxScaler method is used for data cleaning and data reconstruction); S5, obtaining, based on the methods of Step S2 and Step S3, a departure volume of each starting point and an arrival volume of each ending point predicted for each day of holidays (Zhao; para. 49: The traffic flow data includes the toll booth opening time, entrance network number, entrance shift, entrance vehicle type, and vehicle entry and exit date and time.), and constructing a travel traffic volume model of a stratified flow road network based on a time allocation ratio of historical characteristic dates (Zhao; para. 47: Based on the weather and holiday conditions of the date to be predicted, select one of the normalized first, second, third, and fourth datasets obtained in Step 3. Train the LSTM neural network model using the selected dataset and adjust the LSTM parameters.); and S6, constructing an objective function (Zhao; para. 79: This invention also uses multiple model evaluation metrics to evaluate the quality of the prediction results, including percentage squared error (MAPE), absolute squared error (MAE), mean squared error (MSE), and R-squared.), and optimizing and solving the travel traffic volume model of the stratified flow road network obtained in Step S5 by using a reverse gradient propagation method to obtain a predicted result of the holiday expressway travel traffic volume based on influence of weather factors (Zhao; para. 77-78: The traffic flow for the predicted date is obtained based on the established LSTM neural network model as follows: The gradient value is propagated backward using the gradient descent method. After multiple iterations, the optimal LSTM parameters are obtained, thus obtaining the optimal LSTM neural network model. The traffic flow on the predicted date is obtained through the optimal LSTM neural network model.).
Zhao does not explicitly disclose selecting basic characteristics of the travel traffic volume, constructing derived characteristics of the travel traffic volume through crossover operation between the basic characteristics of the travel traffic volume, and then constructing a multivariable linear regression model based on historical travel traffic volume characteristics; and correcting the predicted travel traffic volume obtained by the multivariable linear regression model based on the travel traffic volume transfer model constructed in Step S2; and correcting, after collecting the actual travel traffic volume data of the previous day during holidays, the predicted travel traffic volume of the remaining dates of holidays by comparing the actual travel traffic volume with a travel traffic volume of a corresponding date predicted based on Step S3 to obtain the corrected predicted travel traffic volume of the remaining dates of holidays.
Zhou, in the same field of endeavor (traffic congestion prediction), discloses selecting basic characteristics of a travel traffic volume (Zhou; para. 32: the average vehicle speed, average vehicle travel time, average vehicle delay time, whether it is morning rush hour, whether it is evening rush hour, and whether it is a weekday or weekend are defined as independent variables), constructing derived characteristics of the travel traffic volume through crossover operation between the basic characteristics of the travel traffic volume (Zhou; para. 36: The traffic congestion index (y) is a quantitative method used to measure the degree of road traffic congestion. It is based on the real-time speed of each segment within a certain area as the basic indicator, and then the traffic conditions of the area are digitally represented. It is then weighted and synthesized by various factors of road facilities at all levels and traffic capacity, and then standardized to obtain the average speed and perception of traffic congestion within a certain area.), and then constructing a multivariable linear regression model based on historical travel traffic volume characteristics (Zhou; para. 32: A multivariate linear regression prediction model is established).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the prediction method of Zhao to include a multivariable linear regression model, as disclosed by Zhou, to yield the predictable result of accurately modelling the traffic volume based on multiple independent variables that affect traffic flow.
Zhao, as modified, does not explicitly disclose correcting the predicted travel traffic volume obtained by the multivariable linear regression model based on the travel traffic volume transfer model constructed in Step S2.
Zhou further discloses correcting a predicted travel traffic volume obtained by a multivariable linear regression model (Zhou; para. 6: Regression diagnosis is then performed on the traffic congestion prediction model using least squares regression. Using the t-distribution, the p-values of regression coefficients … are calculated. It is then determined whether the p-value of each regression coefficient is greater than 0.50. If the p-value of a coefficient is greater than 0.50, the corresponding term for that coefficient in the traffic congestion prediction model is deleted, resulting in an optimized traffic congestion regression model.).
Zhao, as modified, discloses cleaning data that is used in subsequent calculations (Zhao; para. 76: in step 3, the MinMaxScaler method is used for data cleaning and data reconstruction), therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have also cleaned the data generated by the traffic congestion regression model of Zhao, as modified, to remove traffic congestion predictions that do no strongly correlate with the processed traffic data, as disclosed by Zhou, to yield the predictable result of removing erroneous predictions.
Zhao, as modified, does not explicitly disclose correcting, after collecting the actual travel traffic volume data of the previous day during holidays, the predicted travel traffic volume of the remaining dates of holidays by comparing the actual travel traffic volume with a travel traffic volume of a corresponding date predicted based on Step S3 to obtain the corrected predicted travel traffic volume of the remaining dates of holidays.
Zhou further discloses correcting, after collecting actual travel traffic volume data of a previous day, a predicted travel traffic volume of subsequent dates by comparing the actual travel traffic volume with a travel traffic volume of a corresponding date prediction to obtain a corrected predicted travel traffic volume of the subsequent dates (Zhou; para. 6: In step S3, a model experiment is conducted on the traffic congestion regression model to obtain detection conclusion data. This data is compared with the original traffic data to determine the overfitting effect of all traffic data.).
Zhao, as modified, discloses reconstructing data that is used in subsequent calculations (Zhao; para. 76: in step 3, the MinMaxScaler method is used for data cleaning and data reconstruction), therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have also corrected the data generated by the traffic congestion regression model of Zhao, as modified, by comparing the original traffic data with the output of the traffic congestion regression model, as disclosed by Zhou, to yield the predictable result of removing erroneous predictions.
Conclusion
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/JOSEPH THOMPSON/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665