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 .
Claims 1-10 have been examined.
P = paragraph e.g. P[0001] = paragraph[0001]
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “forecasting module”, “determination module”, “updating module”, and “output module” in claim 6 and “target selection module” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Regarding the corresponding structure, see “processor 640” of P[0051]-P[0055], where each claimed “module” indicated above is interpreted as a software module of “processor 640”.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. See below.
Claim 1 is 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 (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for determining an alternate target for a vessel, the method comprising the steps of:
obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target;
obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target;
forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target based on the characteristics;
determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target;
updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target; and
outputting the route to a control system associated with the vessel.
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. Specifically, regarding the “forecasting…vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target based on the characteristics” limitation, a user may mentally forecast vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target based on the characteristics. Regarding the “determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target” limitation, a user may mentally determine a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target. Regarding the “updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target” limitation, a user may mentally update the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract 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 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” while the bolded portions continue to represent the “abstract idea”):
A method for determining an alternate target for a vessel, the method comprising the steps of:
obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target;
obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target;
forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target based on the characteristics;
determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target;
updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target; and
outputting the route to a control system associated with the vessel.
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.
The additional limitation “obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target” amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation “obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target” amounts to mere data gathering, which is a form of insignificant extra-solution activity. Regarding the additional limitation “using a trained machine learning model”, the “trained machine learning model” is recited at a high level of generality and amounts to nothing more than mere instructions to apply the exception using a generic computer component. Regarding the additional limitation “outputting the route to a control system associated with the vessel” amounts to merely transmission of data, where the courts have determined that transmission of data does not show an improvement in computer-functionality, see MPEP 2016.05(a), TLI Communications, 823 F.3d at 611-12, 118 USPQ2d at 1747.
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 (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 additional limitation “obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the additional limitation “obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the “trained machine learning model” is recited at a high level of generality and amounts to nothing more than mere instructions to apply the exception using a generic computer component, and the additional limitation “outputting the route to a control system associated with the vessel” amounts to merely transmission of data, where the courts have determined that transmission of data does not show an improvement in computer-functionality. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claim(s) 2-5 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims 2-5 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-5 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claim(s) 1-5 are ineligible under 35 USC §101.
See below regarding the dependent claims.
As per Claim 2, said claim is rejected as it fails to correct the deficiency of Claim 1. The limitation “obtaining a route map from storage, the route map comprising a plurality of nodes representing a real-world location, and a plurality of edges between the plurality of nodes, and wherein the route is based on the route map” amounts to mere data gathering, which is a form of insignificant extra-solution activity. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 3, said claim is rejected as it fails to correct the deficiency of Claim 1. The claim describes a target, which does not amount to significantly more than the judicial exception.
As per Claim 4, said claim is rejected as it fails to correct the deficiency of Claim 1. The claim describes characteristics, where a user may mentally perform the “forecasting” of Claim 1 using any of the characteristics of Claim 4. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 5, said claim is rejected as it fails to correct the deficiency of Claim 1. A user may mentally determine whether to select the desired alternate target based on characteristics of the desired alternate target, therefore, the claim does not amount to significantly more than the judicial exception.
Claim 6 is 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 6 is directed to a system (i.e., a machine). Therefore, claim 6 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 6 includes limitations that recite an abstract idea (emphasized below). Claim 6 recites:
A system for determining an alternate target for a vessel, the system comprising:
a control system associated with the vessel configured to receive a route comprising the alternate target for the vessel;
storage for storing at least one route, the route comprising a plurality of edges from a current position of the vessel to at least a desired target;
a remote server configured to provide characteristics for at least one alternate target, the characteristics comprising at least historical vessel density data associated with the at least one alternate target; and
a processor configured to determine the alternate target for the vessel, the processor comprising:
a forecasting module for forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics;
a determination module for determining a desired alternate target for the vessel based on the forecasted vessel density at the at least one alternate target;
an updating module for updating the route, such that the route comprises a plurality of edges from the current position of the vessel to the desired alternate target; and
an output module for outputting, to the control system, the route comprising the desired alternate target.
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. Specifically, regarding the “determine the alternate target for the vessel” limitation, a user may mentally determine the alternate target for the vessel. Regarding the “forecasting…vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics” limitation, a user may mentally forecast vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics. Regarding the “determining a desired alternate target for the vessel based on the forecasted vessel density at the at least one alternate target” limitation, a user may mentally determine a desired alternate target for the vessel based on the forecasted vessel density at the at least one alternate target. Regarding the “updating the route, such that the route comprises a plurality of edges from the current position of the vessel to the desired alternate target” limitation, a user may mentally update the route, such that the route comprises a plurality of edges from the current position of the vessel to the desired alternate target.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract 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 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” while the bolded portions continue to represent the “abstract idea”):
A system for determining an alternate target for a vessel, the system comprising:
a control system associated with the vessel configured to receive a route comprising the alternate target for the vessel;
storage for storing at least one route, the route comprising a plurality of edges from a current position of the vessel to at least a desired target;
a remote server configured to provide characteristics for at least one alternate target, the characteristics comprising at least historical vessel density data associated with the at least one alternate target; and
a processor configured to determine the alternate target for the vessel, the processor comprising:
a forecasting module for forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics;
a determination module for determining a desired alternate target for the vessel based on the forecasted vessel density at the at least one alternate target;
an updating module for updating the route, such that the route comprises a plurality of edges from the current position of the vessel to the desired alternate target; and
an output module for outputting, to the control system, the route comprising the desired alternate target.
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 “A system for determining an alternate target for a vessel, the system comprising: a control system associated with the vessel configured to receive a route comprising the alternate target for the vessel”, the “control system” is recited at a high level of generality and amounts to nothing more than a generic computer component used to perform data gathering, which is a form of insignificant extra-solution activity. Regarding the additional limitation “storage for storing at least one route, the route comprising a plurality of edges from a current position of the vessel to at least a desired target”, the “storage” is recited at a high level of generality and amounts to nothing more than a generic computer component for data gathering, which is a form of insignificant extra-solution activity, where the Examiner notes that “for” implies an intended use. Regarding the additional limitation “a remote server configured to provide characteristics for at least one alternate target, the characteristics comprising at least historical vessel density data associated with the at least one alternate target”, the “remote server” is recited at a high level of generality and amounts to nothing more than a generic computer used to perform data gathering, which is a form of insignificant extra-solution activity. Regarding the additional limitations “a processor configured to” and “the processor comprising”, the “processor” is recited at a high level of generality and amounts to nothing more than a generic computer used to apply the exception. Regarding the additional limitation “a forecasting module for”, the “forecasting module” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception, where the Examiner notes that “for” implies an intended use. Regarding the additional limitation “using a trained machine learning model”, the “trained machine learning model” is recited at a high level of generality and amounts to nothing more than mere instructions to apply the exception using a generic computer component. Regarding the additional limitation “a determination module for”, the “determination module” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception, where the Examiner notes that “for” implies an intended use. Regarding the additional limitation “an updating module for”, the “updating module” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception, where the Examiner notes that “for” implies an intended use. Regarding the additional limitation “an output module for outputting, to the control system, the route comprising the desired alternate target”, the “output module” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception, where the “outputting” amounts to merely transmission of data, where the courts have determined that transmission of data does not show an improvement in computer-functionality, see MPEP 2016.05(a), TLI Communications, 823 F.3d at 611-12, 118 USPQ2d at 1747.
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 (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, independent claim 6 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 “control system” is recited at a high level of generality and amounts to nothing more than a generic computer component used to perform data gathering, which is a form of insignificant extra-solution activity, the “storage” is recited at a high level of generality and amounts to nothing more than a generic computer component for data gathering, which is a form of insignificant extra-solution activity, the “remote server” is recited at a high level of generality and amounts to nothing more than a generic computer used to perform data gathering, which is a form of insignificant extra-solution activity, the “processor” is recited at a high level of generality and amounts to nothing more than a generic computer used to apply the exception, the “forecasting module” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception, the “trained machine learning model” is recited at a high level of generality and amounts to nothing more than mere instructions to apply the exception using a generic computer component, the “determination module” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception, the “updating module” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception, and the “output module” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception, where the “outputting” amounts to merely transmission of data, where the courts have determined that transmission of data does not show an improvement in computer-functionality. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claim(s) 7-9 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims 7-9 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 7-9 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claim(s) 6-9 are ineligible under 35 USC §101.
See below regarding the dependent claims.
As per Claim 7, said claim is rejected as it fails to correct the deficiency of Claim 6. The limitation “wherein the storage is further configured to store a route map comprising a plurality of nodes representing a real-world location, and a plurality of edges between the plurality of nodes, and wherein the route is based on the route map” amounts to mere data gathering, which is a form of insignificant extra-solution activity. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 8, said claim is rejected as it fails to correct the deficiency of Claim 6. The “machine learning processor” is recited at a high level of generality and amounts to nothing more than a generic computer used to apply the exception, which does not amount to significantly more than the judicial exception.
As per Claim 9, said claim is rejected as it fails to correct the deficiency of Claim 6. The “target selection module” is recited at a high level of generality and amounts to nothing more than a generic computer component used to apply the exception. Furthermore, a user may mentally determine whether to select the desired alternate. Therefore, the claim does not amount to significantly more than the judicial exception.
Claim 10 is 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 10 is directed to a computer-readable storage medium. Therefore, claim 10 is not within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 10 includes limitations that recite an abstract idea (emphasized below). Claim 10 recites:
A computer-readable storage medium, storing instructions that, when executed by a processor, cause the processor to determine an alternate target for a vessel, the instructions comprising:
obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target;
obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target;
forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics;
determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target;
updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target; and
outputting the route to a control system associated with the vessel.
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. Specifically, regarding the “forecasting…vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics” limitation, a user may mentally forecast vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics. Regarding the “determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target” limitation, a user may mentally determine a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target. Regarding the “updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target” limitation, a user may mentally update the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract 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 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” while the bolded portions continue to represent the “abstract idea”):
A computer-readable storage medium, storing instructions that, when executed by a processor, cause the processor to determine an alternate target for a vessel, the instructions comprising:
obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target;
obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target;
forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics;
determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target;
updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target; and
outputting the route to a control system associated with the vessel.
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 “A computer-readable storage medium, storing instructions that, when executed by a processor, cause the processor to determine an alternate target for a vessel, the instructions comprising”, the “computer-readable storage medium” is recited at a high level of generality and amounts to nothing more that transient, propagating signals used with mere instructions to apply the exception. The additional limitation “obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target” amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation “obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target” amounts to mere data gathering, which is a form of insignificant extra-solution activity. Regarding the additional limitation “using a trained machine learning model”, the “trained machine learning model” is recited at a high level of generality and amounts to nothing more than mere instructions to apply the exception using a generic computer component. Regarding the additional limitation “outputting the route to a control system associated with the vessel”, this limitation amounts to merely transmission of data, where the courts have determined that transmission of data does not show an improvement in computer-functionality, see MPEP 2016.05(a), TLI Communications, 823 F.3d at 611-12, 118 USPQ2d at 1747.
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 (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, independent claim 10 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 “computer-readable storage medium” is recited at a high level of generality and amounts to nothing more that transient, propagating signals used with mere instructions to apply the exception, the additional limitation “obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the additional limitation “obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the “trained machine learning model” is recited at a high level of generality and amounts to nothing more than mere instructions to apply the exception using a generic computer component, and the additional limitation “outputting the route to a control system associated with the vessel”, this limitation amounts to merely transmission of data, where the courts have determined that transmission of data does not show an improvement in computer-functionality. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Therefore, claim(s) 10 is ineligible under 35 USC §101.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because Claim 10 is nominally directed to a computer-readable storage medium and is therefore directed to non-statutory subject matter. The claims broadly cover transient, propagating signals. Since a claim to a "computer-readable storage medium" reasonably broadly covers both forms of non-transitory tangible media (e.g. memory, disk, tape) and transient, propagating signals (e.g. signals, carrier waves), it necessarily covers non-statutory subject matter. This is so because transient, propagating signals are not patentable subject matter. See In re Nuijten, 500 F.3d 1346, 1356 (Fed. Cir. 2007).
Examiner Note: Applicant can amend to narrow the claim to cover only statutory embodiments by adding the limitation “non-transitory” to the claim (i.e. A non-transitory computer-readable storage medium …”), such an amendment would not raise the issue of new matter, even when the specification is silent, unless the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4-9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Oh et al. (KR102429235B1) in view of Hirose (2005/0090974).
Regarding Claim 1, Oh et al. teaches the claimed method for determining an alternate target for a vessel, the method comprising the steps of:
obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target (“…a traffic network generation unit extracting track data of a target sea area to be predicted for traffic congestion from a track database and generating a traffic network consisting of nodes and edges using the track data”, see P[0016] and “The traffic network generated is a graph representing the navigation path and pattern of a ship, as shown in FIG. 2, and consists of nodes and edges”, see P[0032]);
obtaining…characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target (“The traffic volume prediction unit (400) classifies track data sorted by area by the area-specific track data classification unit (300) into set time units (e.g., 10 minutes) to extract time series characteristics, which are traffic volume characteristics over time, and predicts, for example, the traffic volume for the next 24 hours based on the time series characteristics of the track data”, see P[0037] and “When predicting traffic volume using machine learning methods, traffic volume prediction data can be extracted by inputting time series characteristic data of the extracted trajectory data into an artificial neural network machine-learned from a dataset of time series data of trajectory data and traffic volume data”, see P[0039] and “The color of the edge connected to the surrounding node is different according to the time-dependent congestion of each area predicted by the area-specific congestion prediction unit (500) and is displayed on the electronic chart in the form of a graph”, see P[0054]);
forecasting, using a trained machine learning model (“When predicting traffic volume using machine learning methods, traffic volume prediction data can be extracted by inputting time series characteristic data of the extracted trajectory data into an artificial neural network machine-learned from a dataset of time series data of trajectory data and traffic volume data”, see P[0039]), vessel density at the at least one alternate target…(“The color of the edge connected to the surrounding node is different according to the time-dependent congestion of each area predicted by the area-specific congestion prediction unit (500) and is displayed on the electronic chart in the form of a graph”, see P[0054]).
Oh et al. does not expressly recite the bolded portions of the claimed
obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target
and
forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target based on the characteristics;
determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target;
updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target; and
outputting the route to a control system associated with the vessel.
However, Hirose (2005/0090974) teaches obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target (Hirose; “…the server 500 controls the interface 510 on the basis of the terminal-specific information received in step S406 and appropriately transmits the travel route information, the traffic-congestion prediction information”, see P[0207]), forecasting vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target based on the characteristics, determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target, updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target, and outputting the route to a control system associated with the vessel (Hirose; “Then, the traffic-congestion recognizer 187 predicts the condition of the traffic-congestion at the desired location on each of the candidate travel routes at the predicted arrival time on the basis of the time-series data 12i acquired in step S208 and generates traffic-congestion prediction information about the predicted condition of the traffic-congestion”, see P[0121] and “…reroute processing…candidate travel route information is generated to the destination requiring the shortest period of time or the shortest traveling distance by using the current traffic-congestion information and the traffic-congestion prediction information for instance, and the navigation is performed again based on the travel route with the desired setting”, see P[0132] and “…the travel routes may be selected base only on the traffic-congestion prediction information. Then, the route processor 188 estimates the time required to arrive at the destination for each of the selected travel routes to generate required time information, and the display controller 184 makes the terminal display 140 display the calculated candidate travel routes and an indication for prompting the user to select a travel route. The user selects and inputs the travel route information about any one of the route, and thus the travel route is set. If only one route is set, that route is set as the travel route without displaying the instruction to demand the selection”, see P[0122]-P[0123], and see FIGS. 2-3).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Oh et al. with the teachings of Hirose, and obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target, and forecasting vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target based on the characteristics, determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target, updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target, and outputting the route to a control system associated with the vessel, as rendered obvious by Hirose, in order to provide “appropriately smooth navigation” (Hirose; see P[0136]).
Regarding Claim 2, Oh et al. teaches the claimed method for determining an alternate target for a vessel according to claim 1, further comprising obtaining a route map from storage, the route map comprising a plurality of nodes representing a real-world location, and a plurality of edges between the plurality of nodes, and wherein the route is based on the route map (“The traffic network generated is a graph representing the navigation path and pattern of a ship, as shown in FIG. 2, and consists of nodes and edges”, see P[0032]).
Regarding Claim 4, Oh et al. does not expressly recite the claimed method for determining an alternate target for a vessel according claim 1,
wherein the characteristics of the at least one alternate target comprise at least one of:
weather data associated with the at least one alternate target at the estimated vessel arrival time;
environmental characteristics associated with the at least one alternate target at the estimated vessel arrival time;
historical wait times associated with the at least one alternate target; and
delay-inducing factors associated with the at least one alternate target as the estimated vessel arrival time.
However, Hirose (2005/0090974) teaches wherein the characteristics of the at least one alternate target comprise at least one of: weather data associated with the at least one alternate target at the estimated vessel arrival time; environmental characteristics associated with the at least one alternate target at the estimated vessel arrival time; historical wait times associated with the at least one alternate target; and delay-inducing factors associated with the at least one alternate target as the estimated vessel arrival time (Hirose; “Then, the traffic-congestion recognizer 187 predicts the condition of the traffic-congestion at the desired location on each of the candidate travel routes at the predicted arrival time on the basis of the time-series data 12i acquired in step S208 and generates traffic-congestion prediction information about the predicted condition of the traffic-congestion”, see P[0121] and “…reroute processing…candidate travel route information is generated to the destination requiring the shortest period of time or the shortest traveling distance by using the current traffic-congestion information and the traffic-congestion prediction information for instance, and the navigation is performed again based on the travel route with the desired setting”, see P[0132] and “…the travel routes may be selected base only on the traffic-congestion prediction information. Then, the route processor 188 estimates the time required to arrive at the destination for each of the selected travel routes to generate required time information, and the display controller 184 makes the terminal display 140 display the calculated candidate travel routes and an indication for prompting the user to select a travel route. The user selects and inputs the travel route information about any one of the route, and thus the travel route is set. If only one route is set, that route is set as the travel route without displaying the instruction to demand the selection”, see P[0122]-P[0123], and see FIGS. 2-3), where traffic-congestion is equivalent to “delay-inducing factors”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Oh et al. with the teachings of Hirose, and wherein the characteristics of the at least one alternate target comprise at least one of: weather data associated with the at least one alternate target at the estimated vessel arrival time; environmental characteristics associated with the at least one alternate target at the estimated vessel arrival time; historical wait times associated with the at least one alternate target; and delay-inducing factors associated with the at least one alternate target as the estimated vessel arrival time, as rendered obvious by Hirose, in order to provide “appropriately smooth navigation” (Hirose; see P[0136]).
Regarding Claim 5, Oh et al. does not expressly recite the claimed method for determining an alternate target for a vessel according to claim 1,
further comprising a step of determining whether to select the desired alternate target based on characteristics of the desired alternate target.
However, Hirose (2005/0090974) teaches determining whether to select the desired alternate target based on characteristics of the desired alternate target (Hirose; “Then, the traffic-congestion recognizer 187 predicts the condition of the traffic-congestion at the desired location on each of the candidate travel routes at the predicted arrival time on the basis of the time-series data 12i acquired in step S208 and generates traffic-congestion prediction information about the predicted condition of the traffic-congestion”, see P[0121] and “…reroute processing…candidate travel route information is generated to the destination requiring the shortest period of time or the shortest traveling distance by using the current traffic-congestion information and the traffic-congestion prediction information for instance, and the navigation is performed again based on the travel route with the desired setting”, see P[0132] and “…the travel routes may be selected base only on the traffic-congestion prediction information. Then, the route processor 188 estimates the time required to arrive at the destination for each of the selected travel routes to generate required time information, and the display controller 184 makes the terminal display 140 display the calculated candidate travel routes and an indication for prompting the user to select a travel route. The user selects and inputs the travel route information about any one of the route, and thus the travel route is set. If only one route is set, that route is set as the travel route without displaying the instruction to demand the selection”, see P[0122]-P[0123], and see FIGS. 2-3).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Oh et al. with the teachings of Hirose, and the method for determining an alternate target for a vessel according to claim 1, further comprising a step of determining whether to select the desired alternate target based on characteristics of the desired alternate target, as rendered obvious by Hirose, in order to provide “appropriately smooth navigation” (Hirose; see P[0136]).
Examiner’s Note:
Regarding Claim 6, each instance of “for” and the limitations associated with each instance of “for” in the claimed “storage for storing”, “a forecasting module for forecasting”, “a determination module for determining”, “an updating module for updating” and “an output module for outputting” is directed to an intended use that does not further limit the claim.
Regarding Claim 6, Oh et al. teaches the claimed system for determining an alternate target for a vessel, the system comprising:
a control system associated with the vessel configured to receive a route comprising the alternate target for the vessel (see P[0026] and “…a traffic network generation unit extracting track data of a target sea area to be predicted for traffic congestion from a track database and generating a traffic network consisting of nodes and edges using the track data”, see P[0016] and “The traffic network generated is a graph representing the navigation path and pattern of a ship, as shown in FIG. 2, and consists of nodes and edges”, see P[0032]);
storage for storing at least one route, the route comprising a plurality of edges from a current position of the vessel to at least a desired target (“…a traffic network generation unit extracting track data of a target sea area to be predicted for traffic congestion from a track database and generating a traffic network consisting of nodes and edges using the track data”, see P[0016] and “…the traffic network generation unit (100) extracts track data of the target sea area to be predictedfor traffic congestion from the track database (D)…”, see P[0062]);
…provide characteristics for at least one alternate target, the characteristics comprising at least historical vessel density data associated with the at least one alternate target (“The traffic volume prediction unit (400) classifies track data sorted by area by the area-specific track data classification unit (300) into set time units (e.g., 10 minutes) to extract time series characteristics, which are traffic volume characteristics over time, and predicts, for example, the traffic volume for the next 24 hours based on the time series characteristics of the track data”, see P[0037] and “When predicting traffic volume using machine learning methods, traffic volume prediction data can be extracted by inputting time series characteristic data of the extracted trajectory data into an artificial neural network machine-learned from a dataset of time series data of trajectory data and traffic volume data”, see P[0039] and “The color of the edge connected to the surrounding node is different according to the time-dependent congestion of each area predicted by the area-specific congestion prediction unit (500) and is displayed on the electronic chart in the form of a graph”, see P[0054]);
and a processor configured to determine the alternate target for the vessel, the processor comprising:
a forecasting module for forecasting, using a trained machine learning model (“When predicting traffic volume using machine learning methods, traffic volume prediction data can be extracted by inputting time series characteristic data of the extracted trajectory data into an artificial neural network machine-learned from a dataset of time series data of trajectory data and traffic volume data”, see P[0039]), vessel density at the at least one alternate target…(“The color of the edge connected to the surrounding node is different according to the time-dependent congestion of each area predicted by the area-specific congestion prediction unit (500) and is displayed on the electronic chart in the form of a graph”, see P[0054])…based on the characteristics.
Oh et al. does not expressly recite the bolded portions of the claimed
a remote server configured to provide characteristics for at least one alternate target, the characteristics comprising at least historical vessel density data associated with the at least one alternate target
and
and a processor configured to determine the alternate target for the vessel, the processor comprising:
a forecasting module for forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics;
a determination module for determining a desired alternate target for the vessel based on the forecasted vessel density at the at least one alternate target;
an updating module for updating the route, such that the route comprises a plurality of edges from the current position of the vessel to the desired alternate target; and
an output module for outputting, to the control system, the route comprising the desired alternate target.
However, Hirose (2005/0090974) teaches a remote server configured to provide characteristics for at least one alternate target, the characteristics comprising at least historical vessel density data associated with the at least one alternate target (Hirose; “…the server 500 controls the interface 510 on the basis of the terminal-specific information received in step S406 and appropriately transmits the travel route information, the traffic-congestion prediction information”, see P[0207]), determine the alternate target for the vessel, forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics; a determination module for determining a desired alternate target for the vessel based on the forecasted vessel density at the at least one alternate target; an updating module for updating the route, such that the route comprises a plurality of edges from the current position of the vessel to the desired alternate target; and an output module for outputting, to the control system, the route comprising the desired alternate target (Hirose; “Then, the traffic-congestion recognizer 187 predicts the condition of the traffic-congestion at the desired location on each of the candidate travel routes at the predicted arrival time on the basis of the time-series data 12i acquired in step S208 and generates traffic-congestion prediction information about the predicted condition of the traffic-congestion”, see P[0121] and “…reroute processing…candidate travel route information is generated to the destination requiring the shortest period of time or the shortest traveling distance by using the current traffic-congestion information and the traffic-congestion prediction information for instance, and the navigation is performed again based on the travel route with the desired setting”, see P[0132] and “…the travel routes may be selected base only on the traffic-congestion prediction information. Then, the route processor 188 estimates the time required to arrive at the destination for each of the selected travel routes to generate required time information, and the display controller 184 makes the terminal display 140 display the calculated candidate travel routes and an indication for prompting the user to select a travel route. The user selects and inputs the travel route information about any one of the route, and thus the travel route is set. If only one route is set, that route is set as the travel route without displaying the instruction to demand the selection”, see P[0122]-P[0123], and see FIGS. 2-3).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Oh et al. with the teachings of Hirose, and to provide a remote server configured to provide characteristics for at least one alternate target, the characteristics comprising at least historical vessel density data associated with the at least one alternate target, and a processor configured to determine the alternate target for the vessel, the processor comprising a forecasting module for forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics, a determination module for determining a desired alternate target for the vessel based on the forecasted vessel density at the at least one alternate target, an updating module for updating the route, such that the route comprises a plurality of edges from the current position of the vessel to the desired alternate target, and an output module for outputting, to the control system, the route comprising the desired alternate target, as rendered obvious by Hirose, in order to provide “appropriately smooth navigation” (Hirose; see P[0136]).
Regarding Claim 7, Oh et al. teaches the claimed system for determining an alternate target for a vessel according to claim 6, wherein the storage is further configured to store a route map comprising a plurality of nodes representing a real-world location, and a plurality of edges between the plurality of nodes, and wherein the route is based on the route map (“The traffic network generated is a graph representing the navigation path and pattern of a ship, as shown in FIG. 2, and consists of nodes and edges”, see P[0032]).
Regarding Claim 8, Oh et al. teaches the claimed system for determining an alternate target for a vessel according to claim 6 or claim 7, wherein the processor is a machine learning processor configured to execute the trained machine learning model (“When predicting traffic volume using machine learning methods, traffic volume prediction data can be extracted by inputting time series characteristic data of the extracted trajectory data into an artificial neural network machine-learned from a dataset of time series data of trajectory data and traffic volume data”, see P[0039]).
Examiner’s Note:
Regarding Claim 9, the instance of “for” and the limitations associated with the instance of “for” in the claimed “a target selection module for determining” is directed to an intended use that does not further limit the claim.
Regarding Claim 9, Oh et al. does not expressly recite the claimed system for determining an alternate target for a vessel according to claim 6, further comprising a target selection module for determining whether to select the desired alternate target based on characteristics of the desired alternate target.
However, Hirose (2005/0090974) teaches determining whether to select the desired alternate target based on characteristics of the desired alternate target (Hirose; “Then, the traffic-congestion recognizer 187 predicts the condition of the traffic-congestion at the desired location on each of the candidate travel routes at the predicted arrival time on the basis of the time-series data 12i acquired in step S208 and generates traffic-congestion prediction information about the predicted condition of the traffic-congestion”, see P[0121] and “…reroute processing…candidate travel route information is generated to the destination requiring the shortest period of time or the shortest traveling distance by using the current traffic-congestion information and the traffic-congestion prediction information for instance, and the navigation is performed again based on the travel route with the desired setting”, see P[0132] and “…the travel routes may be selected base only on the traffic-congestion prediction information. Then, the route processor 188 estimates the time required to arrive at the destination for each of the selected travel routes to generate required time information, and the display controller 184 makes the terminal display 140 display the calculated candidate travel routes and an indication for prompting the user to select a travel route. The user selects and inputs the travel route information about any one of the route, and thus the travel route is set. If only one route is set, that route is set as the travel route without displaying the instruction to demand the selection”, see P[0122]-P[0123], and see FIGS. 2-3).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Oh et al. with the teachings of Hirose, and the system for determining an alternate target for a vessel according to claim 6, further comprising a target selection module for determining whether to select the desired alternate target based on characteristics of the desired alternate target, as rendered obvious by Hirose, in order to provide “appropriately smooth navigation” (Hirose; see P[0136]).
Regarding Claim 10, Oh et al. teaches the claimed computer-readable storage medium, storing instructions that, when executed by a processor, cause the processor to determine an alternate target for a vessel (see P[0026]-P[0027]), the instructions comprising:
obtaining, from the vessel, at least a route, the route comprising a plurality of edges from a current position of the vessel, to at least a desired target (“…a traffic network generation unit extracting track data of a target sea area to be predicted for traffic congestion from a track database and generating a traffic network consisting of nodes and edges using the track data”, see P[0016] and “The traffic network generated is a graph representing the navigation path and pattern of a ship, as shown in FIG. 2, and consists of nodes and edges”, see P[0032]);
obtaining…characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target (“The traffic volume prediction unit (400) classifies track data sorted by area by the area-specific track data classification unit (300) into set time units (e.g., 10 minutes) to extract time series characteristics, which are traffic volume characteristics over time, and predicts, for example, the traffic volume for the next 24 hours based on the time series characteristics of the track data”, see P[0037] and “When predicting traffic volume using machine learning methods, traffic volume prediction data can be extracted by inputting time series characteristic data of the extracted trajectory data into an artificial neural network machine-learned from a dataset of time series data of trajectory data and traffic volume data”, see P[0039] and “The color of the edge connected to the surrounding node is different according to the time-dependent congestion of each area predicted by the area-specific congestion prediction unit (500) and is displayed on the electronic chart in the form of a graph”, see P[0054]).
Oh et al. does not expressly recite the bolded portions of the claimed
obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target
and
forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics;
determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target;
updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target; and
outputting the route to a control system associated with the vessel.
However, Hirose (2005/0090974) teaches obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target (Hirose; “…the server 500 controls the interface 510 on the basis of the terminal-specific information received in step S406 and appropriately transmits the travel route information, the traffic-congestion prediction information”, see P[0207]), and forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics; determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target; updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target; and outputting the route to a control system associated with the vessel (Hirose; “Then, the traffic-congestion recognizer 187 predicts the condition of the traffic-congestion at the desired location on each of the candidate travel routes at the predicted arrival time on the basis of the time-series data 12i acquired in step S208 and generates traffic-congestion prediction information about the predicted condition of the traffic-congestion”, see P[0121] and “…reroute processing…candidate travel route information is generated to the destination requiring the shortest period of time or the shortest traveling distance by using the current traffic-congestion information and the traffic-congestion prediction information for instance, and the navigation is performed again based on the travel route with the desired setting”, see P[0132] and “…the travel routes may be selected base only on the traffic-congestion prediction information. Then, the route processor 188 estimates the time required to arrive at the destination for each of the selected travel routes to generate required time information, and the display controller 184 makes the terminal display 140 display the calculated candidate travel routes and an indication for prompting the user to select a travel route. The user selects and inputs the travel route information about any one of the route, and thus the travel route is set. If only one route is set, that route is set as the travel route without displaying the instruction to demand the selection”, see P[0122]-P[0123], and see FIGS. 2-3).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Oh et al. with the teachings of Hirose, and obtaining, from a remote server, characteristics of at least one alternate target, the characteristics comprising at least historical vessel density data associated with at least one alternate target, and forecasting, using a trained machine learning model, vessel density at the at least one alternate target, at an estimated vessel arrival time for the vessel at the at least one alternate target, based on the characteristics; determining a desired alternate target for the vessel based on at least the forecasted vessel density at the at least one alternate target; updating the route, such that the route comprises the plurality of edges from the current position of the vessel to the desired alternate target; and outputting the route to a control system associated with the vessel, as rendered obvious by Hirose, in order to provide “appropriately smooth navigation” (Hirose; see P[0136]).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Oh et al. (KR102429235B1) in view of Hirose (2005/0090974) further in view of Fleming (2019/0325747).
Regarding Claim 3, Oh et al. does not expressly recite the claimed method for determining an alternate target for a vessel according to claim 2, wherein the at least one alternate target is one of the plurality of nodes of the route map.
However, Fleming (2019/0325747) teaches a destination node (Fleming; see P[0117] and P[0165]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Oh et al. with the teachings of Fleming, and wherein the at least one alternate target is one of the plurality of nodes of the route map, as rendered obvious by Fleming, in order to select “paths from one or more path networks to satisfy the respective route requests, wherein the paths are selected based, at least in part, on or responsive to one or more path criteria for the respective paths”, and generate “route instructions for the respective route requests, wherein the route instructions comprise the selected paths” (Fleming; see Abstract).
Conclusion
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/ISAAC G SMITH/ Primary Examiner, Art Unit 3662
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