Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This communication is responsive to Application No. 18/751,725 and the amendments filed on 12/11/2025.
3. Claims 1-12 are presented for examination.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 1/6/2025 has been fully considered by the Examiner.
The Examiner notes that while the IDS filed 6/24/2024 has had its references thoroughly searched through, the IDS filed 6/24/2024 has not been fully considered due to being in an improper format, where the first page is subsequently cut off and there are no boxes for the Examiner to sign and date the IDS considered. Note from Section 609.02 II A 2 of the MPEP: “If resubmitting a listing of the information, applicant should submit a new listing that complies with the format requirements in 37 CFR 1.98(a)(1) and the timing requirements of 37 CFR 1.97. Applicants are strongly discouraged from submitting a list that includes copies of PTO/SB/08 or PTO-892 forms from other applications. A completed PTO/SB/08 form from another application may already have initials of an examiner and the application number of another application. This information will likely confuse the record. Furthermore, when the spaces provided on the form have initials of an examiner, there are no spaces available next to the documents listed for the examiner of the subsequent application to provide his or her initials, and the previously relevant initials may be erroneously construed as being applied for the current application.”
Response to Arguments
5. Applicant’s arguments, see page 8, filed 12/11/2025, with respect to the objection to claim 2 for minor informalities have been fully considered and are persuasive. The objection of 9/11/2025 has been withdrawn.
6. Applicant's arguments filed 12/11/2025 with respect to the rejection of claims 1-9 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Regarding independent claim 1, the Applicant argues on pages 8-12 of the Applicant’s Remarks filed 12/11/2025 that amended claim 1 is patent eligible under 35 U.S.C. 101. However, the Examiner respectfully disagrees.
On pages 10-11 of the Applicant’s remarks filed 12/11/2025, the Applicant argues that the steps of claim 1 cannot practically be performed within the human mind, failing Step 2A Prong I of the 101 analysis. As recited in the Non-Final rejection mailed 9/11/2025 and below, the Examiner submits that independent claim 1 recites limitations and concepts that may reasonably be performed within the human mind. The Applicant fails to identify which limitations of claim 1 cannot be practically performed within the human mind, and fails to provide sufficient reasoning why certain limitations cannot be performed within the human mind. Thus, under the Examiner’s broadest reasonable interpretation, the limitations of claim 1 may be performed within the human mind, without any additional elements reciting practical applications claimed.
On pages 11-12 of the Applicant’s remarks filed 12/11/2025, the Applicant argues that independent claim 1 recites practical applications to overcome the rejection, specifically with the steps of “determining … whether the second person is able to move along the road by foot or by a vehicle” and “generating recommended route information ….” However, the Examiner respectfully disagrees. For one, both of these limitations may be performed as abstract ideas within the human mind. Also, the Examiner notes that nothing is performed in light of these steps, nothing that recites a practical application using these steps. In all, the additional elements of claim 1 simply recite generic computing elements recited at a high level of generality that merely automate the abstract ideas of the claim and the insignificant extra-solution activities of data gathering, with nothing more. Therefore, the Examiner submits that independent claim 1 fails to recite any practical applications to overcome the abstract ideas of the claim.
For these reasons, the Examiner submits claim 1 is ineligible under 35 U.S.C. 101, in which will be further described later.
Regarding independent claims 8 and 9, as these claims contain similar limitations as claim 1, are still rejected under 35 U.S.C. 101, in which will be described later.
Regarding dependent claims 2-7, as all of these claims depend from claim 1, are still rejected under 35 U.S.C. 101, in which will be described later.
7. Applicant’s arguments with respect to the rejection of claim(s) 1-9 under 35 U.S.C. 102 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding independent claim 1, the Examiner agrees that US 20210095982 A1 to Kahn fails to teach all of the amendments to the claim. However, in light of the amendments and the Applicant’s remarks, an updated search was conducted, and a new ground of rejection concerning claim 1 has been determined, in which will be described later.
Regarding independent claims 8 and 9, as both of these claims contain similar limitations as claim 1, are still rejected for similar reasons that claim 1 is, in which will be described later.
Regarding dependent claims 2-7, as all of these claims depend from claim 1, are still rejected, in which will be described later.
Claim Objections
8. Claim 8 is objected to because of the following informalities:
Regarding Claim 8, the term “of a plurality of the first persons” recited in line 3 of claim 8 should read “of a plurality of first persons” to avoid a lack of antecedent basis.
Appropriate correction is required.
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.
9. Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1:
101 Analysis: Step 1
Is the claim directed to a process, machine, manufacture, or composition of matter?
Claim 1 is directed to a passage information providing device (i.e., a machine), and therefore is within at least one of the four statutory categories.
101 Analysis: Step 2A Prong I
Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea?
Regarding Claim 1, the claim is determined to fall under the category of an abstract idea,
defined as the following: a mathematical concept, certain methods of organizing human
activity, and/or mental processes.
Claim 1 recites:
A passage information providing device comprising a processor configured to:
acquire movement information of a plurality of first persons on a road as people flow information;
generate a passage availability determination model by performing a machine learning operation based on the people flow information and map data;
acquire, from a second person, information on a distance range desired to be avoided from an obstacle point;
determine, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle; and
generate recommended route information for the second person based on a result of the determination and the information on the distance range.
Under the Examiner’s broadest reasonable interpretation, the phrases bolded above in
claim 1 recite mental processes, where the limitations can be performed in the human mind.
With regards to ‘generate a passage availability determination model,’ in the context of this claim is an abstract idea, where a human generates (i.e., creates and calculates) a method of determining whether a road is passable or not. Humans have the ability to generate models and strategies on how to solve problems, such as whether a road is passable or not, within the mind. There are no limits to the simplicity or complexity of the recited model, leading the Examiner to believe that the model can be simple enough to be determined within the human mind. For example, the model can be as simple as “are people traveling along this road over a certain period of time,” and whether the answer is yes or no would determine whether the road is passable or not.
With regards to ‘determine, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle,’ in the context of this claim is an abstract idea, where a human determines (i.e., calculates, decides, etc.) whether the road is passable for human traversal based on the result of the model. Humans have the ability to infer information based on received results, all within the mind. For example, if the model outputs the road is passable, then a human can reasonably determine, within the mind, that the second person can travel along the road. The Examiner notes that the determination can be as simple as ‘yes’ or ‘no’ with regards to passage availability.
With regards to ‘generate recommended route information for the second person based on a result of the determination and the information on the distance range,’ in the context of this claim is an abstract idea, where a human generates (i.e., calculates, creates, determines, etc.) a route from one point to another based on certain factors. Humans have the ability to determine routes entirely within the mind. Humans also are able to factor in certain criteria for determining the route. The Examiner submits that the two factors of ‘the determination’ and ‘the information on the distance range’ are simple enough to be understood by humans and incorporated into the route generation. For example, if a human knows that a certain road is closed, the human may reasonably determine not to traverse on that road and pick a detour instead, entirely within the mind. Also, for example, if a human knows to avoid a certain area, the human may also reasonably determine not to travel close to that avoidance area.
101 Analysis: Step 2A Prong II
Does the claim recite any additional elements that integrate the judicial exception into a
practical application?
The additional elements of claim 1 do not recite the judicial exception into a practical
application. The additional elements of claim 1, as shown below, are underlined, while the
abstract ideas of the claim are bolded.
Claim 1 recites:
A passage information providing device comprising a processor configured to:
acquire movement information of a plurality of first persons on a road as people flow information;
generate a passage availability determination model by performing a machine learning operation based on the people flow information and map data;
acquire, from a second person, information on a distance range desired to be avoided from an obstacle point;
determine, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle; and
generate recommended route information for the second person based on a result of the determination and the information on the distance range.
The Examiner has determined that the additional elements of the claim underlined above do not integrate the abstract ideas listed above into a practical application.
Regarding the processor, the Examiner submits that this is simply generic computing elements recited at a high level of generality that merely automate the abstract ideas of the claim. As the processor is described within page 6 lines 9-14 and page 14 lines 16-21 of the specification and depicted within Figures 2 and 9 of the drawings, is a generically recited central processing unit (CPU) that executes software programs. However, the CPU has no special or unique structures/features that bring it above a generic computing element that simply automates the abstract ideas of the claim.
Regarding the limitation “acquire movement information of a plurality of first persons on a road as people flow information,” this recites the insignificant extra-solution activity of data gathering, with nothing else to bring it above this.
Regarding the limitation “by performing a machine learning operation based on the people flow information and map data,” is merely using a generically recited machine learning model to apply the abstract ideas to, with nothing else. As the machine learning model is claimed and recited on page 8 lines 2-5 the specification of the instant application, no improvement to the model can be found. Rather, the machine learning model as recited in claim 1 is simply a tool that has the abstract ideas applied to it (See Brown, 645 Fed. App’x 1014, 1017 (Fed. Cir. 2016) (non-precedential)), but does not do anything above this.
Regarding the limitation “acquire, from a second person, information on a distance range desired to be avoided from an obstacle point,” this recites the insignificant extra-solution activity of data gathering, with nothing else to bring it above this.
Thus, for the additional elements of claim 1 analyzed individually, there is insufficient
reasoning as to why the additional elements turn the abstract ideas into practical applications.
Furthermore, looking at the additional elements with respect to the whole claim, do not add
any more reasoning as to why the additional elements justify a practical application. Taken as a
whole, the additional elements recite generic computing elements recited at a high level of generality that merely automate the abstract ideas applied to them and the insignificant extra-solution activity of data gathering, without anything more to overcome these. Accordingly, the additional limitation(s) do/does not integrate the abstract ideas into a practical application because it does not impose any meaningful limits on practicing the abstract ideas.
101 Analysis: Step 2B
Does the claim recite any additional elements that amount to significantly more than the judicial exception?
With regards to step 2B of the 101 analysis, claim 1 does not recite any additional
elements that amount to significantly more than the judicial exception for the same reasons as
described above in step 2A prong II of the 101 analysis.
With regards to the processor and the machine learning model, these are simply generic computing elements recited at a high level of generality that merely automate the abstract ideas applied to it, with no special features or structures associated with it recited. Further, regarding the acquiring of people flow information and information on a distance range desired to be avoided, these are simply examples of insignificant extra-solution activities in the form of data gathering, with nothing else above this recited. Generally applying an exception using generic computing elements and insignificant extra-solution activities in this way cannot provide an inventive concept.
Dependent claims 2-7 and 10-12 do not recite further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claims further 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 2-7 and 10-12 are not patent eligible under the same rational as provided for in the rejection of independent claim 1.
Regarding Claim 2, “wherein the processor is further configured to generate the recommended route information based on state information corresponding to the road, information on a location of the second person, and position information on a facility designated as an evacuation spot, the recommended route information indicating a recommended route to the facility,” the dependent claim does not recite any additional elements that are significantly more than the judicial exception. The claim simply recites the abstract idea of generating (i.e., calculating and determining in this case) recommended route information based on acquired data, which may be performed by a human in the mind. The Examiner submits that the acquired data used to generate the route is simple enough to be calculated within the human mind.
Regarding Claim 3, “wherein the processor is further configured to generate the recommended route information based on state information corresponding to the road, information on a location of the second person, and position information on a destination of the second person, the recommended route information indicating a recommended route to the destination,” the dependent claim does not recite any additional elements that are significantly more than the judicial exception. The claim simply recites the abstract idea of generating (i.e., calculating and determining in this case) recommended route information based on acquired data, which may be performed by a human in the mind. The Examiner submits that the acquired data used to generate the route is simple enough to be calculated within the human mind.
Regarding Claim 4, “wherein the processor is further configured to: set, using a passage record of the road based on the people flow information, a passage index, the passage index indicating a possibility that each preset section of the road is passable, generate the recommended route information indicating the recommended route to the facility or a destination further based on the passage index, and incorporate, based on the passage index, into the recommended route a section of the road that is determined to be unpassable,” the dependent claim does not recite any additional elements that are significantly more than the judicial exception. The claim simply recites the abstract idea of setting (i.e., determining, calculating, etc.) whether the road is passable or not, and generating (i.e., calculating, creating, determining, etc.) a route based on certain information, which may all be able to be performed within the human mind. The Examiner submits that all of the factors going into the steps of ‘setting’ and ‘generating’ are simple enough to be understood by a human within the mind.
Regarding Claim 5, “wherein the processor is further configured to: acquire at least one of weather information, road congestion information, emergency notification information, and sensor information as the state information, and generate the recommended route information by using the state information in addition to the people flow information,” the dependent claim does not recite any additional elements that are significantly more than the judicial exception. The claim recites the insignificant extra-solution activity of data gathering in the form of the state information, and then performs the abstract idea of generating recommended route information, as the abstract idea is previously described within claims 2 and 3.
Regarding Claim 6, “wherein the people flow information comprises at least one of position information of mobile terminal devices of the plurality of first persons among the position information on the mobile terminal device, a captured image in which a state of the road is captured, and social networking service (SNS) information posted using an SNS,” the dependent claim does not recite any additional elements that are significantly more than the judicial exception. The claim simply defines the data used within the gathered data. However, the definitions do not recite anything above the insignificant extra-solution activities previously stated.
Regarding Claim 7, “wherein the processor is further configured to determine a type of a moving means of a person who possesses a first mobile terminal device, among the mobile terminal devices, based on a moving state of the position information on the first mobile terminal device,” the dependent claim does not recite any additional elements that are significantly more than the judicial exception. The claim recites the abstract idea of determining (i.e., calculating, estimating, assessing, etc.) a way in which a user is traveling based on gathered data, to which the Examiner submits may be performed within the human mind.
Regarding Claim 10, “wherein the processor is further configured to: determine a type of a moving means of the second person based on a moving speed of a mobile terminal device possessed by the second person, the moving speed of the mobile terminal device being calculated based on position information on the mobile terminal device; generate recommended route information using the passage availability information, the position information on the mobile terminal device of the second person, the type of the moving means of the second person and a destination of the second person, the recommended route information indicating a recommended route from a location of the mobile terminal device to the destination; and output the recommended route information to the mobile terminal device of the second person,” the dependent claim does not recite any additional elements that are significantly more than the judicial exception. The claim simply recites the abstract ideas of determining (i.e., calculating, estimating, assessing, etc.) a mode of travel of a person based on gathered data regarding the travel and generating (i.e., determining, creating, calculating, etc.) route information based on certain criteria, to which the Examiner submits both steps are simple enough to be performed within the human mind. The claim also recites the insignificant post-solution activity of outputting a result, but no practical application is performed via said result.
Regarding Claim 11, “wherein the processor is further configured to generate recommended evacuation spot information for the second person based on a result of the determination and the information on the distance range,” the dependent claim does not recite any additional elements that are significantly more than the judicial exception. The claim simply recites the abstract idea of generating recommended information (i.e., determining) based on certain criteria, to which the Examiner submits can be performed within the human mind.
Regarding Claim 12, “wherein the people flow information comprises information on speeds and directions of the mobile terminals, the speeds and the directions being obtained by tracking the mobile terminals,” the dependent claim does not recite any additional elements that are significantly more than the judicial exception. The claim simply further defines the type of information gathered and method of gathering, none of which recite a practical application that would be above the insignificant extra-solution activity of data gathering.
Independent Claim 8:
101 Analysis: Step 1
Is the claim directed to a process, machine, manufacture, or composition of matter?
Claim 8 is directed to a passage information providing method (i.e., a process), and therefore is within at least one of the four statutory categories.
101 Analysis: Step 2A Prong I
Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea?
Regarding Claim 8, the claim is determined to fall under the category of an abstract idea,
defined as the following: a mathematical concept, certain methods of organizing human
activity, and/or mental processes.
Claim 8 recites:
A passage information providing method comprising, by a computer:
acquiring movement information of a plurality of the first persons on a road as people flow information;
generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data;
acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point;
determining, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle; and
generating recommended route information for the second person based on a result of the determination and the information on the distance range.
Under the Examiner’s broadest reasonable interpretation, the phrases bolded above in
claim 8 recite mental processes, where the limitations can be performed in the human mind.
With regards to ‘generating a passage availability determination model,’ in the context of this claim is an abstract idea, where a human generates (i.e., creates and calculates) a method of determining whether a road is passable or not. Humans have the ability to generate models and strategies on how to solve problems, such as whether a road is passable or not, within the mind. There are no limits to the simplicity or complexity of the recited model, leading the Examiner to believe that the model can be simple enough to be determined within the human mind. For example, the model can be as simple as “are people traveling along this road over a certain period of time,” and whether the answer is yes or no would determine whether the road is passable or not.
With regards to ‘determining, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle,’ in the context of this claim is an abstract idea, where a human determines (i.e., calculates, decides, etc.) whether the road is passable for human traversal based on the result of the model. Humans have the ability to infer information based on received results, all within the mind. For example, if the model outputs the road is passable, then a human can reasonably determine, within the mind, that the second person can travel along the road. The Examiner notes that the determination can be as simple as ‘yes’ or ‘no’ with regards to passage availability.
With regards to ‘generating recommended route information for the second person based on a result of the determination and the information on the distance range,’ in the context of this claim is an abstract idea, where a human generates (i.e., calculates, creates, determines, etc.) a route from one point to another based on certain factors. Humans have the ability to determine routes entirely within the mind. Humans also are able to factor in certain criteria for determining the route. The Examiner submits that the two factors of ‘the determination’ and ‘the information on the distance range’ are simple enough to be understood by humans and incorporated into the route generation. For example, if a human knows that a certain road is closed, the human may reasonably determine not to traverse on that road and pick a detour instead, entirely within the mind. Also, for example, if a human knows to avoid a certain area, the human may also reasonably determine not to travel close to that avoidance area.
101 Analysis: Step 2A Prong II
Does the claim recite any additional elements that integrate the judicial exception into a
practical application?
The additional elements of claim 8 do not recite the judicial exception into a practical
application. The additional elements of claim 8, as shown below, are underlined, while the
abstract ideas of the claim are bolded.
Claim 8 recites:
A passage information providing method comprising, by a computer:
acquiring movement information of a plurality of the first persons on a road as people flow information;
generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data;
acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point;
determining, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle; and
generating recommended route information for the second person based on a result of the determination and the information on the distance range.
The Examiner has determined that the additional elements of the claim underlined above do not integrate the abstract ideas listed above into a practical application.
Regarding the computer, the Examiner submits that this is simply generic computing elements recited at a high level of generality that merely automate the abstract ideas of the claim. As the computer is described within page 4 lines 19-27 and page 24 lines 3-13 of the specification and depicted within Figures 1 and 10 of the drawings, is a generically recited computer that performs the abstract ideas of the claim. However, the computer has no special or unique structures/features that bring it above a generic computing element that simply automates the abstract ideas of the claim.
Regarding the limitation “acquiring movement information of a plurality of first persons on a road as people flow information,” this recites the insignificant extra-solution activity of data gathering, with nothing else to bring it above this.
Regarding the limitation “by performing a machine learning operation based on the people flow information and map data,” is merely using a generically recited machine learning model to apply the abstract ideas to, with nothing else. As the machine learning model is claimed and recited on page 8 lines 2-5 the specification of the instant application, no improvement to the model can be found. Rather, the machine learning model as recited in claim 8 is simply a tool that has the abstract ideas applied to it (See Brown, 645 Fed. App’x 1014, 1017 (Fed. Cir. 2016) (non-precedential)), but does not do anything above this.
Regarding the limitation “acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point,” this recites the insignificant extra-solution activity of data gathering, with nothing else to bring it above this.
Thus, for the additional elements of claim 8 analyzed individually, there is insufficient
reasoning as to why the additional elements turn the abstract ideas into practical applications.
Furthermore, looking at the additional elements with respect to the whole claim, do not add
any more reasoning as to why the additional elements justify a practical application. Taken as a
whole, the additional elements recite generic computing elements recited at a high level of generality that merely automate the abstract ideas applied to them and the insignificant extra-solution activity of data gathering, without anything more to overcome these. Accordingly, the additional limitation(s) do/does not integrate the abstract ideas into a practical application because it does not impose any meaningful limits on practicing the abstract ideas.
101 Analysis: Step 2B
Does the claim recite any additional elements that amount to significantly more than the judicial exception?
With regards to step 2B of the 101 analysis, claim 8 does not recite any additional
elements that amount to significantly more than the judicial exception for the same reasons as
described above in step 2A prong II of the 101 analysis.
With regards to the computer and the machine learning model, these are simply generic computing elements recited at a high level of generality that merely automate the abstract ideas applied to it, with no special features or structures associated with it recited. Further, regarding the acquiring of people flow information and information on a distance range desired to be avoided, these are simply examples of insignificant extra-solution activities in the form of data gathering, with nothing else above this recited. Generally applying an exception using generic computing elements and insignificant extra-solution activities in this way cannot provide an inventive concept.
Independent Claim 9:
101 Analysis: Step 1
Is the claim directed to a process, machine, manufacture, or composition of matter?
Claim 9 is directed to a non-transitory program storage medium storing a computer program (i.e., a machine), and therefore is within at least one of the four statutory categories.
101 Analysis: Step 2A Prong I
Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea?
Regarding Claim 9, the claim is determined to fall under the category of an abstract idea,
defined as the following: a mathematical concept, certain methods of organizing human
activity, and/or mental processes.
Claim 9 recites:
A non-transitory program storage medium storing a computer program to cause a computer to execute operations comprising:
acquiring movement information of a plurality of first persons on a road as people flow information;
generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data;
acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point;
determining, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle; and
generating recommended route information for the second person based on a result of the determination and the information on the distance range.
Under the Examiner’s broadest reasonable interpretation, the phrases bolded above in
claim 9 recite mental processes, where the limitations can be performed in the human mind.
With regards to ‘generating a passage availability determination model,’ in the context of this claim is an abstract idea, where a human generates (i.e., creates and calculates) a method of determining whether a road is passable or not. Humans have the ability to generate models and strategies on how to solve problems, such as whether a road is passable or not, within the mind. There are no limits to the simplicity or complexity of the recited model, leading the Examiner to believe that the model can be simple enough to be determined within the human mind. For example, the model can be as simple as “are people traveling along this road over a certain period of time,” and whether the answer is yes or no would determine whether the road is passable or not.
With regards to ‘determining, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle,’ in the context of this claim is an abstract idea, where a human determines (i.e., calculates, decides, etc.) whether the road is passable for human traversal based on the result of the model. Humans have the ability to infer information based on received results, all within the mind. For example, if the model outputs the road is passable, then a human can reasonably determine, within the mind, that the second person can travel along the road. The Examiner notes that the determination can be as simple as ‘yes’ or ‘no’ with regards to passage availability.
With regards to ‘generating recommended route information for the second person based on a result of the determination and the information on the distance range,’ in the context of this claim is an abstract idea, where a human generates (i.e., calculates, creates, determines, etc.) a route from one point to another based on certain factors. Humans have the ability to determine routes entirely within the mind. Humans also are able to factor in certain criteria for determining the route. The Examiner submits that the two factors of ‘the determination’ and ‘the information on the distance range’ are simple enough to be understood by humans and incorporated into the route generation. For example, if a human knows that a certain road is closed, the human may reasonably determine not to traverse on that road and pick a detour instead, entirely within the mind. Also, for example, if a human knows to avoid a certain area, the human may also reasonably determine not to travel close to that avoidance area.
101 Analysis: Step 2A Prong II
Does the claim recite any additional elements that integrate the judicial exception into a
practical application?
The additional elements of claim 9 do not recite the judicial exception into a practical
application. The additional elements of claim 9, as shown below, are underlined, while the
abstract ideas of the claim are bolded.
Claim 9 recites:
A non-transitory program storage medium storing a computer program to cause a computer to execute operations comprising:
acquiring movement information of a plurality of first persons on a road as people flow information;
generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data;
acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point;
determining, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle; and
generating recommended route information for the second person based on a result of the determination and the information on the distance range.
The Examiner has determined that the additional elements of the claim underlined above do not integrate the abstract ideas listed above into a practical application.
Regarding the computer, the Examiner submits that this is simply generic computing elements recited at a high level of generality that merely automate the abstract ideas of the claim. As the computer is described within page 4 lines 19-27 and page 24 lines 3-13 of the specification and depicted within Figures 1 and 10 of the drawings, is a generically recited computer that performs the abstract ideas of the claim. However, the computer has no special or unique structures/features that bring it above a generic computing element that simply automates the abstract ideas of the claim.
Regarding the limitation “acquiring movement information of a plurality of first persons on a road as people flow information,” this recites the insignificant extra-solution activity of data gathering, with nothing else to bring it above this.
Regarding the limitation “by performing a machine learning operation based on the people flow information and map data,” is merely using a generically recited machine learning model to apply the abstract ideas to, with nothing else. As the machine learning model is claimed and recited on page 8 lines 2-5 the specification of the instant application, no improvement to the model can be found. Rather, the machine learning model as recited in claim 9 is simply a tool that has the abstract ideas applied to it (See Brown, 645 Fed. App’x 1014, 1017 (Fed. Cir. 2016) (non-precedential)), but does not do anything above this.
Regarding the limitation “acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point,” this recites the insignificant extra-solution activity of data gathering, with nothing else to bring it above this.
Thus, for the additional elements of claim 9 analyzed individually, there is insufficient
reasoning as to why the additional elements turn the abstract ideas into practical applications.
Furthermore, looking at the additional elements with respect to the whole claim, do not add
any more reasoning as to why the additional elements justify a practical application. Taken as a
whole, the additional elements recite generic computing elements recited at a high level of generality that merely automate the abstract ideas applied to them and the insignificant extra-solution activity of data gathering, without anything more to overcome these. Accordingly, the additional limitation(s) do/does not integrate the abstract ideas into a practical application because it does not impose any meaningful limits on practicing the abstract ideas.
101 Analysis: Step 2B
Does the claim recite any additional elements that amount to significantly more than the judicial exception?
With regards to step 2B of the 101 analysis, claim 9 does not recite any additional
elements that amount to significantly more than the judicial exception for the same reasons as
described above in step 2A prong II of the 101 analysis.
With regards to the computer and the machine learning model, these are simply generic computing elements recited at a high level of generality that merely automate the abstract ideas applied to it, with no special features or structures associated with it recited. Further, regarding the acquiring of people flow information and information on a distance range desired to be avoided, these are simply examples of insignificant extra-solution activities in the form of data gathering, with nothing else above this recited. Generally applying an exception using generic computing elements and insignificant extra-solution activities in this way cannot provide an inventive concept.
In conclusion, as explained above, claims 1-12 are rejected under 35 U.S.C. 101 as ineligible subject matter related to an abstract idea, with insignificant additional elements to overcome the judiciary exception.
Claim Rejections - 35 USC § 103
10. 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.
11. 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.
12. Claim(s) 1, 2, 3, 5, 8, 9, and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn et al. (US 20210095982 A1 hereinafter Kahn) in view of Roka (US 20200211376 A1 hereinafter Roka) and Fields et al. (US 10055967 B1 hereinafter Fields).
Regarding Claim 1, Kahn teaches a passage information providing device comprising a processor ([0073] via “A processor 407 includes a central processing unit, a graphics processing unit, and/or the like.”) configured to:
acquire movement information of a plurality of first persons on a road as people flow information ([0024] via “As shown in FIG. 1G, and by reference number 135, the mapping platform may receive or extract event data. The event data may identify an event and inaccessible roads, caused by the event, in the geographical region. The mapping platform may receive or extract the event data from one or more data sources, shown as server devices. For example, the mapping platform may obtain the event data from a server and/or a database that stores event data input by users (e.g., via client devices), that stores information that identifies inaccessible roads based on data input by users (e.g., via client devices), and/or the like. For example, an event such as a natural disaster, a parade, construction, a fallen tree, a road blockage, a pandemic, a military conflict, a terrorist act, civil unrest, and/or the like may result in an inaccessible road.”);
determine, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle ([0026] via “As shown in FIG. 1H, and by reference number 140, the mapping platform may update the directed graph for the geographical region based on the event data to generate an updated graph (e.g., a directed graph). For example, the mapping platform may remove, from the directed graph, one or more vertices that represent inaccessible roads. Additionally, or alternatively, the mapping platform may remove connections between an inaccessible road and other roads.”), ([0037] via “Based on user interaction with the user interface to mark a road as inaccessible, the client device may provide information that identifies the marked road to the mapping platform. … The mapping platform may provide such information to the client device from which the indication of the inaccessible road was received. Additionally, or alternatively, the mapping platform may provide such information to one or more client devices impacted by the inaccessible road.”), (Note: The Examiner interprets the user of the client device of Kahn as the second person.); and
generate recommended route information for the second person based on a result of the determination ([0037] via “Based on user interaction with the user interface to mark a road as inaccessible, the client device may provide information that identifies the marked road to the mapping platform. The mapping platform may update a directed graph based on the information (as described above), may update a recommended shelter and provide an updated recommended shelter (which may be the same shelter or a different shelter) to the client device, may determine a new route to the updated recommended shelter and provide the new route to the client device, and/or the like. … The mapping platform may identify client devices to which a route that includes the inaccessible road was indicated, and may determine an updated recommended shelter and/or a new route for those client devices, and may provide the updated recommended shelter and/or the new route to those client devices for display.”).
Kahn is silent on wherein the processor is configured to: generate a passage availability determination model by performing a machine learning operation based on the people flow information and map data; acquire, from a second person, information on a distance range desired to be avoided from an obstacle point; and generate recommended route information for the second person based on a result of the information on the distance range.
However, Roka teaches to generate a passage availability determination model by performing a machine learning operation based on the people flow information and map data ([0040] via “The Central Management System 130 is the remote Cloud system that collects data 131 from the Sensor System 110 for the purpose of machine learning 132. Depending on the efficacy of systems developed by this application, it is also likely that data collection 131
for the purpose of machine learning may reside in the Data Store 120. Data and pattern of data collected from vehicular traffic count and movement 121 and pedestrian traffic count and movement 122 are constantly processed and reviewed by the Data Processing and Machine Learning 132 module in the Central Management System 130 to identify patterns of traffic count and movement between multiple sensors and locations along a route of the traffic. The main driver of this machine learning is pattern analysis between multiple sensors and data generated by those sensors and also the ability to identify the pattern along a longer stretch of the road, rather than just analyzing the pattern in one specific location of the route.”), ([0042] via “FIG. 4 depicts the data flow supported by the Artificial Intelligence Engine 133: First, it retrieves from the Sensor System 110 vehicular and pedestrian data for a given location 134. It will then identify the density of traffic, average speed of vehicular and pedestrian traffic 135, the rate of turn of vehicles and pedestrians in all possible directions
136, and any change in nearby environmental and topographical conditions 137. The Artificial Intelligence Engine 133 may also retrieve from 3D Map 124 and Location Remote Sensing 125
data stores and coalesce that data with real time vehicle and pedestrian data.”).
Further, Fields teaches to acquire, from a second person, information on a distance range desired to be avoided from an obstacle point (Col. 18 lines 38-51, where “At block 306, a query may be made as to whether the location of the safety concern is within a certain threshold distance (e.g., one mile, one block, 100 feet) of the current location of the electronic device. The certain threshold distance may be selected by the pedestrian and/or user of the electronic device, or may be pre-set (e.g., by a device manufacturer). In some embodiments, the certain threshold distance may depend on the type of safety concern, i.e., based on a threshold distance away from the particular safety concern where a pedestrian should stay to remain safe. As one example, a pedestrian should remain a certain distance away from construction in progress to remain safe, but may need to maintain a greater distance away from a crime in progress to remain safe.”); and
generate recommended route information for the second person based on a result of the information on the distance range (Col. 9 lines 4-30, where “The various software applications may include one or more of, for example, … a proximity application 134 for analyzing the current location of the electronic device and the location of the crowd activity and/or safety concern to determine whether a received location of the crowd activity and/or safety concern is within close proximity of the current location of the electronic device 108; … a routing application 140 for generating a route for the pedestrian to avoid the crowd activity and/or safety concern; and/or a user interface application 142 for receiving inputs, selections, and/or preferences from a user, e.g., the pedestrian with which the electronic device 102 is associated.”), (Col. 16 lines 51-53, where “Additionally or alternatively, in some embodiments, a walking route for the pedestrian may be generated to assist the pedestrian in avoiding the location of the crowd.”), (Col. 18 lines 38-51, where “At block 306, a query may be made as to whether the location of the safety concern is within a certain threshold distance (e.g., one mile, one block, 100 feet) of the current location of the electronic device. The certain threshold distance may be selected by the pedestrian and/or user of the electronic device, or may be pre-set (e.g., by a device manufacturer). In some embodiments, the certain threshold distance may depend on the type of safety concern, i.e., based on a threshold distance away from the particular safety concern where a pedestrian should stay to remain safe. As one example, a pedestrian should remain a certain distance away from construction in progress to remain safe, but may need to maintain a greater distance away from a crime in progress to remain safe.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Roka wherein the processor is configured to: generate a passage availability determination model by performing a machine learning operation based on the people flow information and map data. Doing so allows the model to process additional information of traffic flow over a larger area by using machine learning than without, as stated above by Roka in paragraph [0040].
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fields wherein the processor is configured to: acquire, from a second person, information on a distance range desired to be avoided from an obstacle point; and generate recommended route information for the second person based on a result of the information on the distance range. Doing so routes the second person away from the emergency, such that the second person remains safe from the emergency, as stated above by Fields in Col. 18 lines 38-51.
Regarding Claim 2, modified reference Kahn teaches the passage information providing device according to claim 1, wherein the processor is further configured to generate the recommended route information based on state information corresponding to the road ([0026] via “As shown in FIG. 1H, and by reference number 140, the mapping platform may update the directed graph for the geographical region based on the event data to generate an updated graph (e.g., a directed graph). For example, the mapping platform may remove, from the directed graph, one or more vertices that represent inaccessible roads. … In some implementations, the mapping platform may update road weights based on the updated directed graph.”), ([0038] via “For example, as shown in FIG. 1N, and by reference number
170, the mapping platform may provide, to one or more client devices, a user interface that indicates a route from a location of the client device to a different shelter. In this case, the previously recommended shelter may be inaccessible (e.g., due to no routes form the client device location to the previously recommended shelter) or the previously recommended shelter may have been re-ranked with a lower ranking than an alternative shelter. … The client device may provide such user input to the mapping platform, and the mapping platform may mark a road as accessible or inaccessible based on the input. The mapping platform may generate new recommendations if the marking has changed, as described above.”),
information on a location of the second person ([0027] via “As shown in FIG. 1I, and by reference number 145, the mapping platform may receive location data identifying a location of a client device in the geographical region. As shown, the mapping platform may receive the location data from the client device.”), ([0035] via “As shown in FIG. 1M, and by reference number 165, the mapping platform may provide, to the client device, a user interface that indicates a route from a location of the client device to one or more shelters.”), and
position information on a facility designated as an evacuation spot, the recommended route information indicating a recommended route to the facility ([0028] via “As shown in FIG. 1J, and by reference number 150, the mapping platform may process the updated directed graph and the location data to identify one or more shelters associated with the location of the client device.”), ([0035] via “As shown in FIG. 1M, and by reference number 165, the mapping platform may provide, to the client device, a user interface that indicates a route from a location of the client device to one or more shelters.”).
Regarding Claim 3, modified reference Kahn teaches the passage information providing device according to claim 1, wherein the processor is further configured to generate the recommended route information based on state information corresponding to the road ([0026] via “As shown in FIG. 1H, and by reference number 140, the mapping platform may update the directed graph for the geographical region based on the event data to generate an updated graph (e.g., a directed graph). For example, the mapping platform may remove, from the directed graph, one or more vertices that represent inaccessible roads. … In some implementations, the mapping platform may update road weights based on the updated directed graph.”), ([0038] via “For example, as shown in FIG. 1N, and by reference number
170, the mapping platform may provide, to one or more client devices, a user interface that indicates a route from a location of the client device to a different shelter. In this case, the previously recommended shelter may be inaccessible (e.g., due to no routes form the client device location to the previously recommended shelter) or the previously recommended shelter may have been re-ranked with a lower ranking than an alternative shelter. … The client device may provide such user input to the mapping platform, and the mapping platform may mark a road as accessible or inaccessible based on the input. The mapping platform may generate new recommendations if the marking has changed, as described above.”),
information on a location of the second person ([0027] via “As shown in FIG. 1I, and by reference number 145, the mapping platform may receive location data identifying a location of a client device in the geographical region. As shown, the mapping platform may receive the location data from the client device.”), ([0035] via “As shown in FIG. 1M, and by reference number 165, the mapping platform may provide, to the client device, a user interface that indicates a route from a location of the client device to one or more shelters.”), and
position information on a destination of the second person, the recommended route information indicating a recommended route to the destination ([0028] via “As shown in FIG. 1J, and by reference number 150, the mapping platform may process the updated directed graph and the location data to identify one or more shelters associated with the location of the client device.”), ([0035] via “As shown in FIG. 1M, and by reference number 165, the mapping platform may provide, to the client device, a user interface that indicates a route from a location of the client device to one or more shelters.”), (Note: The Examiner interprets the shelter(s) of Kahn as the destination.).
Regarding Claim 5, modified reference Kahn teaches the passage information providing device according to claim 2, wherein the processor is further configured to: acquire at least one of weather information, road congestion information, emergency notification information, and sensor information as the state information ([0039] via “Additionally, or alternatively, the client device may provide information indicative of an inaccessible road to the mapping platform without direct user input. For example, if the client device traverses a route according to navigation directions, but then begins traversing a different route than one that is indicated or recommended, then the client device may provide an indication of the location where the change in travel occurred. A change in travel direction from an indicated or recommended route may be indicative of an inaccessible road. If the mapping platform receives such an indication (e.g., from a single client device, from multiple client devices, from a threshold quantity of client devices, and/or the like), then the mapping platform may mark the road as inaccessible.”), and
generate the recommended route information by using the state information in addition to the people flow information ([0038] via “The client device may provide such user input to the mapping platform, and the mapping platform may mark a road as accessible or inaccessible based on the input. The mapping platform may generate new recommendations if the marking has changed, as described above.”).
Regarding Claim 8, Kahn teaches a passage information providing method comprising, by a computer ([0072] via “Computing hardware 403 includes hardware and corresponding resources from one or more computing devices.”):
acquiring movement information of a plurality of the first persons on a road as people flow information ([0024] via “As shown in FIG. 1G, and by reference number 135, the mapping platform may receive or extract event data. The event data may identify an event and inaccessible roads, caused by the event, in the geographical region. The mapping platform may receive or extract the event data from one or more data sources, shown as server devices. For example, the mapping platform may obtain the event data from a server and/or a database that stores event data input by users (e.g., via client devices), that stores information that identifies inaccessible roads based on data input by users (e.g., via client devices), and/or the like. For example, an event such as a natural disaster, a parade, construction, a fallen tree, a road blockage, a pandemic, a military conflict, a terrorist act, civil unrest, and/or the like may result in an inaccessible road.”);
determining, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle ([0026] via “As shown in FIG. 1H, and by reference number 140, the mapping platform may update the directed graph for the geographical region based on the event data to generate an updated graph (e.g., a directed graph). For example, the mapping platform may remove, from the directed graph, one or more vertices that represent inaccessible roads. Additionally, or alternatively, the mapping platform may remove connections between an inaccessible road and other roads.”), ([0037] via “Based on user interaction with the user interface to mark a road as inaccessible, the client device may provide information that identifies the marked road to the mapping platform. … The mapping platform may provide such information to the client device from which the indication of the inaccessible road was received. Additionally, or alternatively, the mapping platform may provide such information to one or more client devices impacted by the inaccessible road.”), (Note: The Examiner interprets the user of the client device of Kahn as the second person.); and
generating recommended route information for the second person based on a result of the determination ([0037] via “Based on user interaction with the user interface to mark a road as inaccessible, the client device may provide information that identifies the marked road to the mapping platform. The mapping platform may update a directed graph based on the information (as described above), may update a recommended shelter and provide an updated recommended shelter (which may be the same shelter or a different shelter) to the client device, may determine a new route to the updated recommended shelter and provide the new route to the client device, and/or the like. … The mapping platform may identify client devices to which a route that includes the inaccessible road was indicated, and may determine an updated recommended shelter and/or a new route for those client devices, and may provide the updated recommended shelter and/or the new route to those client devices for display.”).
Kahn is silent on generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data; acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point; and generating recommended route information for the second person based on a result of the information on the distance range.
However, Roka teaches generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data ([0040] via “The Central Management System 130 is the remote Cloud system that collects data 131 from the Sensor System 110 for the purpose of machine learning 132. Depending on the efficacy of systems developed by this application, it is also likely that data collection 131
for the purpose of machine learning may reside in the Data Store 120. Data and pattern of data collected from vehicular traffic count and movement 121 and pedestrian traffic count and movement 122 are constantly processed and reviewed by the Data Processing and Machine Learning 132 module in the Central Management System 130 to identify patterns of traffic count and movement between multiple sensors and locations along a route of the traffic. The main driver of this machine learning is pattern analysis between multiple sensors and data generated by those sensors and also the ability to identify the pattern along a longer stretch of the road, rather than just analyzing the pattern in one specific location of the route.”), ([0042] via “FIG. 4 depicts the data flow supported by the Artificial Intelligence Engine 133: First, it retrieves from the Sensor System 110 vehicular and pedestrian data for a given location 134. It will then identify the density of traffic, average speed of vehicular and pedestrian traffic 135, the rate of turn of vehicles and pedestrians in all possible directions
136, and any change in nearby environmental and topographical conditions 137. The Artificial Intelligence Engine 133 may also retrieve from 3D Map 124 and Location Remote Sensing 125
data stores and coalesce that data with real time vehicle and pedestrian data.”).
Further, Fields teaches acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point (Col. 18 lines 38-51, where “At block 306, a query may be made as to whether the location of the safety concern is within a certain threshold distance (e.g., one mile, one block, 100 feet) of the current location of the electronic device. The certain threshold distance may be selected by the pedestrian and/or user of the electronic device, or may be pre-set (e.g., by a device manufacturer). In some embodiments, the certain threshold distance may depend on the type of safety concern, i.e., based on a threshold distance away from the particular safety concern where a pedestrian should stay to remain safe. As one example, a pedestrian should remain a certain distance away from construction in progress to remain safe, but may need to maintain a greater distance away from a crime in progress to remain safe.”); and
generating recommended route information for the second person based on a result of the information on the distance range (Col. 9 lines 4-30, where “The various software applications may include one or more of, for example, … a proximity application 134 for analyzing the current location of the electronic device and the location of the crowd activity and/or safety concern to determine whether a received location of the crowd activity and/or safety concern is within close proximity of the current location of the electronic device 108; … a routing application 140 for generating a route for the pedestrian to avoid the crowd activity and/or safety concern; and/or a user interface application 142 for receiving inputs, selections, and/or preferences from a user, e.g., the pedestrian with which the electronic device 102 is associated.”), (Col. 16 lines 51-53, where “Additionally or alternatively, in some embodiments, a walking route for the pedestrian may be generated to assist the pedestrian in avoiding the location of the crowd.”), (Col. 18 lines 38-51, where “At block 306, a query may be made as to whether the location of the safety concern is within a certain threshold distance (e.g., one mile, one block, 100 feet) of the current location of the electronic device. The certain threshold distance may be selected by the pedestrian and/or user of the electronic device, or may be pre-set (e.g., by a device manufacturer). In some embodiments, the certain threshold distance may depend on the type of safety concern, i.e., based on a threshold distance away from the particular safety concern where a pedestrian should stay to remain safe. As one example, a pedestrian should remain a certain distance away from construction in progress to remain safe, but may need to maintain a greater distance away from a crime in progress to remain safe.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Roka wherein passage information providing method comprises generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data. Doing so allows the model to process additional information of traffic flow over a larger area by using machine learning than without, as stated above by Roka in paragraph [0040].
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fields wherein passage information providing method comprises acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point; and generating recommended route information for the second person based on a result of the information on the distance range. Doing so routes the second person away from the emergency, such that the second person remains safe from the emergency, as stated above by Fields in Col. 18 lines 38-51.
Regarding Claim 9, Kahn teaches a non-transitory program storage medium storing a computer program ([0073] via “In some implementations, memory 408 and/or storage component 409 is/are implemented as a non-transitory computer readable medium. A networking component 410 includes a network interface and corresponding hardware that enables the mapping platform 401 to communicate with other devices of environment 400
via a wired connection and/or a wireless connection, such as via network 420.”) to cause a computer to execute operations comprising ([0072] via “Computing hardware 403 includes hardware and corresponding resources from one or more computing devices. … As shown, computing hardware 403 may include one or more processors 407, one or more memories 408, one or more storage components 409, and/or one or more networking components 410.”):
acquiring movement information of a plurality of first persons on a road as people flow information ([0024] via “As shown in FIG. 1G, and by reference number 135, the mapping platform may receive or extract event data. The event data may identify an event and inaccessible roads, caused by the event, in the geographical region. The mapping platform may receive or extract the event data from one or more data sources, shown as server devices. For example, the mapping platform may obtain the event data from a server and/or a database that stores event data input by users (e.g., via client devices), that stores information that identifies inaccessible roads based on data input by users (e.g., via client devices), and/or the like. For example, an event such as a natural disaster, a parade, construction, a fallen tree, a road blockage, a pandemic, a military conflict, a terrorist act, civil unrest, and/or the like may result in an inaccessible road.”);
determining, based on the passage availability determination model, whether the second person is able to move along the road by foot or by a vehicle ([0026] via “As shown in FIG. 1H, and by reference number 140, the mapping platform may update the directed graph for the geographical region based on the event data to generate an updated graph (e.g., a directed graph). For example, the mapping platform may remove, from the directed graph, one or more vertices that represent inaccessible roads. Additionally, or alternatively, the mapping platform may remove connections between an inaccessible road and other roads.”), ([0037] via “Based on user interaction with the user interface to mark a road as inaccessible, the client device may provide information that identifies the marked road to the mapping platform. … The mapping platform may provide such information to the client device from which the indication of the inaccessible road was received. Additionally, or alternatively, the mapping platform may provide such information to one or more client devices impacted by the inaccessible road.”), (Note: The Examiner interprets the user of the client device of Kahn as the second person.); and
generating recommended route information for the second person based on a result of the determination ([0037] via “Based on user interaction with the user interface to mark a road as inaccessible, the client device may provide information that identifies the marked road to the mapping platform. The mapping platform may update a directed graph based on the information (as described above), may update a recommended shelter and provide an updated recommended shelter (which may be the same shelter or a different shelter) to the client device, may determine a new route to the updated recommended shelter and provide the new route to the client device, and/or the like. … The mapping platform may identify client devices to which a route that includes the inaccessible road was indicated, and may determine an updated recommended shelter and/or a new route for those client devices, and may provide the updated recommended shelter and/or the new route to those client devices for display.”).
Kahn is silent on generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data; acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point; and generating recommended route information for the second person based on a result of the information on the distance range.
However, Roka teaches generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data ([0040] via “The Central Management System 130 is the remote Cloud system that collects data 131 from the Sensor System 110 for the purpose of machine learning 132. Depending on the efficacy of systems developed by this application, it is also likely that data collection 131
for the purpose of machine learning may reside in the Data Store 120. Data and pattern of data collected from vehicular traffic count and movement 121 and pedestrian traffic count and movement 122 are constantly processed and reviewed by the Data Processing and Machine Learning 132 module in the Central Management System 130 to identify patterns of traffic count and movement between multiple sensors and locations along a route of the traffic. The main driver of this machine learning is pattern analysis between multiple sensors and data generated by those sensors and also the ability to identify the pattern along a longer stretch of the road, rather than just analyzing the pattern in one specific location of the route.”), ([0042] via “FIG. 4 depicts the data flow supported by the Artificial Intelligence Engine 133: First, it retrieves from the Sensor System 110 vehicular and pedestrian data for a given location 134. It will then identify the density of traffic, average speed of vehicular and pedestrian traffic 135, the rate of turn of vehicles and pedestrians in all possible directions
136, and any change in nearby environmental and topographical conditions 137. The Artificial Intelligence Engine 133 may also retrieve from 3D Map 124 and Location Remote Sensing 125
data stores and coalesce that data with real time vehicle and pedestrian data.”).
Further, Fields teaches acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point (Col. 18 lines 38-51, where “At block 306, a query may be made as to whether the location of the safety concern is within a certain threshold distance (e.g., one mile, one block, 100 feet) of the current location of the electronic device. The certain threshold distance may be selected by the pedestrian and/or user of the electronic device, or may be pre-set (e.g., by a device manufacturer). In some embodiments, the certain threshold distance may depend on the type of safety concern, i.e., based on a threshold distance away from the particular safety concern where a pedestrian should stay to remain safe. As one example, a pedestrian should remain a certain distance away from construction in progress to remain safe, but may need to maintain a greater distance away from a crime in progress to remain safe.”); and
generating recommended route information for the second person based on a result of the information on the distance range (Col. 9 lines 4-30, where “The various software applications may include one or more of, for example, … a proximity application 134 for analyzing the current location of the electronic device and the location of the crowd activity and/or safety concern to determine whether a received location of the crowd activity and/or safety concern is within close proximity of the current location of the electronic device 108; … a routing application 140 for generating a route for the pedestrian to avoid the crowd activity and/or safety concern; and/or a user interface application 142 for receiving inputs, selections, and/or preferences from a user, e.g., the pedestrian with which the electronic device 102 is associated.”), (Col. 16 lines 51-53, where “Additionally or alternatively, in some embodiments, a walking route for the pedestrian may be generated to assist the pedestrian in avoiding the location of the crowd.”), (Col. 18 lines 38-51, where “At block 306, a query may be made as to whether the location of the safety concern is within a certain threshold distance (e.g., one mile, one block, 100 feet) of the current location of the electronic device. The certain threshold distance may be selected by the pedestrian and/or user of the electronic device, or may be pre-set (e.g., by a device manufacturer). In some embodiments, the certain threshold distance may depend on the type of safety concern, i.e., based on a threshold distance away from the particular safety concern where a pedestrian should stay to remain safe. As one example, a pedestrian should remain a certain distance away from construction in progress to remain safe, but may need to maintain a greater distance away from a crime in progress to remain safe.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Roka wherein the computer executes operations comprising generating a passage availability determination model by performing a machine learning operation based on the people flow information and map data. Doing so allows the model to process additional information of traffic flow over a larger area by using machine learning than without, as stated above by Roka in paragraph [0040].
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fields wherein the computer executes operations comprising acquiring, from a second person, information on a distance range desired to be avoided from an obstacle point; and generating recommended route information for the second person based on a result of the information on the distance range. Doing so routes the second person away from the emergency, such that the second person remains safe from the emergency, as stated above by Fields in Col. 18 lines 38-51.
Regarding Claim 11, modified reference Kahn teaches the passage information providing device according to claim 1, wherein the processor is further configured to generate recommended evacuation spot information for the second person based on a result of the determination ([0037] via “Based on user interaction with the user interface to mark a road as inaccessible, the client device may provide information that identifies the marked road to the mapping platform. The mapping platform may update a directed graph based on the information (as described above), may update a recommended shelter and provide an updated recommended shelter (which may be the same shelter or a different shelter) to the client device, may determine a new route to the updated recommended shelter and provide the new route to the client device, and/or the like. … The mapping platform may identify client devices to which a route that includes the inaccessible road was indicated, and may determine an updated recommended shelter and/or a new route for those client devices, and may provide the updated recommended shelter and/or the new route to those client devices for display.”).
Kahn is silent on wherein the processor is further configured to generate recommended evacuation spot information for the second person based on a result of the information on the distance range.
However, Fields teaches wherein the processor is further configured to generate recommended evacuation spot information for the second person based on a result of the information on the distance range (Col. 9 lines 4-30, where “The various software applications may include one or more of, for example, … a proximity application 134 for analyzing the current location of the electronic device and the location of the crowd activity and/or safety concern to determine whether a received location of the crowd activity and/or safety concern is within close proximity of the current location of the electronic device 108; … a routing application 140 for generating a route for the pedestrian to avoid the crowd activity and/or safety concern; and/or a user interface application 142 for receiving inputs, selections, and/or preferences from a user, e.g., the pedestrian with which the electronic device 102 is associated.”), (Col. 16 lines 51-53, where “Additionally or alternatively, in some embodiments, a walking route for the pedestrian may be generated to assist the pedestrian in avoiding the location of the crowd.”), (Col. 18 lines 38-51, where “At block 306, a query may be made as to whether the location of the safety concern is within a certain threshold distance (e.g., one mile, one block, 100 feet) of the current location of the electronic device. The certain threshold distance may be selected by the pedestrian and/or user of the electronic device, or may be pre-set (e.g., by a device manufacturer). In some embodiments, the certain threshold distance may depend on the type of safety concern, i.e., based on a threshold distance away from the particular safety concern where a pedestrian should stay to remain safe. As one example, a pedestrian should remain a certain distance away from construction in progress to remain safe, but may need to maintain a greater distance away from a crime in progress to remain safe.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fields wherein the processor is further configured to generate recommended evacuation spot information for the second person based on a result of the information on the distance range. Doing so routes the second person away from the emergency, such that the second person remains safe from the emergency, as stated above by Fields in Col. 18 lines 38-51.
13. Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn et al. (US 20210095982 A1 hereinafter Kahn) in view of Roka (US 20200211376 A1 hereinafter Roka) and Fields et al. (US 10055967 B1 hereinafter Fields), and further in view of Nakamura (US 20190259272 A1 hereinafter Nakamura).
Regarding Claim 4, modified reference Kahn teaches the passage information providing device according to claim 2, but is silent on wherein the processor is further configured to: set, using a passage record of the road based on the people flow information, a passage index, the passage index indicating a possibility that each preset section of the road is passable, generate the recommended route information indicating the recommended route to the facility or a destination further based on the passage index, and incorporate, based on the passage index, into the recommended route a section of the road that is determined to be unpassable.
However, Nakamura teaches to set, using a passage record of the road based on the people flow information, a passage index, the passage index indicating a possibility that each preset section of the road is passable ([0043] via “If it is determined that the vehicle has arrived at the destination G (step S125: Y), by the function of the inaccessible road section estimation module 21c, the control part 20 estimates a target section as an inaccessible road section (step S130). By this, the road section with no vehicles passing through Z1 is estimated as an inaccessible road section.”), ([0044] via “Then, by the function of the inaccessible road section estimation module 21c, the control part 20 revises upward a search cost for the inaccessible road section (step S135). By this, the road section with no vehicles passing through Z1 can be made less likely to be adopted as a road section forming a guidance route. Namely, the possibility that the user is to be guided to a road section having become inaccessible due to a disaster can be reduced.”),
generate the recommended route information indicating the recommended route to the facility or a destination further based on the passage index ([0033] via “When the inaccessible road section is estimated, by the function of the inaccessible road section estimation module 21c, the control part 20 revises upward a search cost for the inaccessible road section to a very larger value than that obtained before estimating it as the inaccessible road section. As a result, the inaccessible road section is less likely to be adopted as a road section forming a guidance route.”), ([0044] via “Then, by the function of the inaccessible road section estimation module 21c, the control part 20 revises upward a search cost for the inaccessible road section (step S135). By this, the road section with no vehicles passing through Z1 can be made less likely to be adopted as a road section forming a guidance route. Namely, the possibility that the user is to be guided to a road section having become inaccessible due to a disaster can be reduced.”), and
incorporate, based on the passage index, into the recommended route a section of the road that is determined to be unpassable ([0033] via “When the inaccessible road section is estimated, by the function of the inaccessible road section estimation module 21c, the control part 20 revises upward a search cost for the inaccessible road section to a very larger value than that obtained before estimating it as the inaccessible road section. As a result, the inaccessible road section is less likely to be adopted as a road section forming a guidance route.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Nakamura wherein the processor is further configured to: set, using a passage record of the road based on the people flow information, a passage index, the passage index indicating a possibility that each preset section of the road is passable, generate the recommended route information indicating the recommended route to the facility or a destination further based on the passage index, and incorporate, based on the passage index, into the recommended route a section of the road that is determined to be unpassable. By determining which sections of the roads are inaccessible or likely inaccessible, doing so reduces the amount of route searching, resulting in a more efficient search process, as stated by Nakamura ([0036] via “In addition, by narrowing down first a road section corresponding to an inaccessible road section, a process of extracting an inaccessible road section can be efficiently implemented. Particularly, by extracting the road sections with no vehicles passing through Z1 and Z2 where even a single piece of probe information has not been collected, before extracting a road section around which the vehicle has detoured, the number of road sections serving as processing targets for determining whether the vehicle has detoured can be significantly narrowed down, enabling to efficiently perform the process. Furthermore, by allowing a disaster to serve as a factor causing a road section to become inaccessible, a road section having become inaccessible due to a disaster can be estimated.”).
14. Claim(s) 6 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn et al. (US 20210095982 A1 hereinafter Kahn) in view of Roka (US 20200211376 A1 hereinafter Roka) and Fields et al. (US 10055967 B1 hereinafter Fields), and further in view of Rowitch (US 20190301891 A1 hereinafter Rowitch).
Regarding Claim 6, modified reference Kahn teaches the passage information providing device according to claim 1, but is silent on wherein the people flow information comprises at least one of position information of mobile terminal devices of the plurality of first persons among the position information on the mobile terminal device, a captured image in which a state of the road is captured, and social networking service (SNS) information posted using an SNS.
However, Rowitch teaches wherein the people flow information comprises at least one of position information of mobile terminal devices of the plurality of first persons among the position information on the mobile terminal device, a captured image in which a state of the road is captured, and social networking service (SNS) information posted using an SNS ([0056] via “In step 510, the crowd source server 140 and/or map server 150 receives location information from a plurality of mobile devices 100, relating to a plurality of road segments. In an embodiment, mobile devices 100 send information regarding their location and, in some embodiments, velocity or speed and heading, to a crowd source server 140 which is utilized to collect data from mobile devices 100 and to analyze the data, or, in other embodiments, to a map server 150.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Rowitch wherein the people flow information comprises at least one of position information of mobile terminal devices of the plurality of first persons among the position information on the mobile terminal device, a captured image in which a state of the road is captured, and social networking service (SNS) information posted using an SNS. Crowdsourcing the position information of mobile terminal devices quickly determines road closures without having to be present on each road, by detecting a lack of crowdsourced data, as stated by Rowitch ([0047] via “In an embodiment, the road closure status of a road segment that has historically high traffic levels may be determined more quickly (e.g., by using a shorter threshold time applied to the period during which to crowd source data is received from mobile devices 100 to determine that a road closure exists) by crowd sourcing the location information (e.g., location, velocity or speed and/or heading information) of mobile devices on that road segment and determining the lack of crowd sourced information on that road segment for a relatively smaller time threshold. For example, if a segment of highway historically has one car per second throughput, a road closure event such as a scheduled closure or road closing accident or other road closing event could be confidently determined quickly, perhaps within minutes or less, based on the difference between historic traffic levels and the current lack of traffic on a particular road segment.”).
Regarding Claim 12, modified reference Kahn teaches the passage information providing device according to claim 6, but is silent on wherein the people flow information comprises information on speeds and directions of the mobile terminals, the speeds and the directions being obtained by tracking the mobile terminals.
However, Rowitch teaches wherein the people flow information comprises information on speeds and directions of the mobile terminals, the speeds and the directions being obtained by tracking the mobile terminals ([0039] via “In an embodiment, the mobile device may send a heading and a speed or velocity of the mobile device. This information may be provided to a map server 150 as part of the map request or as part of a map update request, or location, velocity or speed and heading may be provided to a crowd source server 140 so that the crowd server may utilize the information to determine traffic conditions on various road segments where crowd sourcing mobile devices 100 are present and are collecting and providing location, velocity or speed and/or heading data.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Rowitch wherein the people flow information comprises information on speeds and directions of the mobile terminals, the speeds and the directions being obtained by tracking the mobile terminals. Doing so directly updates the map information to determine the road status information, as stated by Rowitch ([0039] via “If velocity or speed and/or heading are sent to the map server, for example, as part of a map request or a map update request, in some embodiments, the map server 150
may determine road status directly or the map server 150 may provide/forward the location, velocity or speed and/or heading information to a crowd source server 140 to determine road status.”).
15. Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn et al. (US 20210095982 A1 hereinafter Kahn) in view of Roka (US 20200211376 A1 hereinafter Roka) and Fields et al. (US 10055967 B1 hereinafter Fields), further in view of Rowitch (US 20190301891 A1 hereinafter Rowitch), and further in view of Lee et al. (US 20140118168 A1 hereinafter Lee).
Regarding Claim 7, modified reference Kahn teaches the passage information providing device according to claim 6, but is silent on wherein the processor is further configured to determine a type of a moving means of a person who possesses a first mobile terminal device, among the mobile terminal devices, based on a moving state of the position information on the first mobile terminal device.
However, Lee teaches wherein the processor is further configured to determine a type of a moving means of a person who possesses a first mobile terminal device, among the mobile terminal devices, based on a moving state of the position information on the first mobile terminal device ([0067] via “For example, mobile device may use information about the location of the user to determine whether the user/mobile device 230 is located outside or within a building and may automatically execute the application when the user is located outside. Mobile device 230 may use information about the speed of the user to determine whether the user is walking or traveling in a vehicle and may automatically execute the application when the user is traveling in a vehicle.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lee wherein the processor is further configured to determine a type of a moving means of a person who possesses a first mobile terminal device, among the mobile terminal devices, based on a moving state of the position information on the first mobile terminal device. Doing so allows the system to be able to identify where the user is and how the user is travelling based on the moving state of the user, as stated above by Lee.
16. Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn et al. (US 20210095982 A1 hereinafter Kahn) in view of Roka (US 20200211376 A1 hereinafter Roka) and Fields et al. (US 10055967 B1 hereinafter Fields), and further in view of Lee et al. (US 20140118168 A1 hereinafter Lee) and Weir (US 20150100231 A1 hereinafter Weir).
Regarding Claim 10, modified reference Kahn teaches the passage information providing device according to claim 1, wherein the processor is further configured to: generate recommended route information using the passage availability information ([0037] via “Based on user interaction with the user interface to mark a road as inaccessible, the client device may provide information that identifies the marked road to the mapping platform. The mapping platform may update a directed graph based on the information (as described above), may update a recommended shelter and provide an updated recommended shelter (which may be the same shelter or a different shelter) to the client device, may determine a new route to the updated recommended shelter and provide the new route to the client device, and/or the like. … The mapping platform may identify client devices to which a route that includes the inaccessible road was indicated, and may determine an updated recommended shelter and/or a new route for those client devices, and may provide the updated recommended shelter and/or the new route to those client devices for display.”),
the position information on the mobile terminal device of the second person ([0035] via “As shown in FIG. 1M, and by reference number 165, the mapping platform may provide, to the client device, a user interface that indicates a route from a location of the client device to one or more shelters.”), and
a destination of the second person ([0035] via “As shown in FIG. 1M, and by reference number 165, the mapping platform may provide, to the client device, a user interface that indicates a route from a location of the client device to one or more shelters.”),
the recommended route information indicating a recommended route from a location of the mobile terminal device to the destination ([0035] via “As shown in FIG. 1M, and by reference number 165, the mapping platform may provide, to the client device, a user interface that indicates a route from a location of the client device to one or more shelters.”); and
output the recommended route information to the mobile terminal device of the second person ([0035] via “As shown in FIG. 1M, and by reference number 165, the mapping platform may provide, to the client device, a user interface that indicates a route from a location of the client device to one or more shelters.”).
Kahn is silent on wherein the processor is further configured to: determine a type of a moving means of the second person based on a moving speed of a mobile terminal device possessed by the second person, the moving speed of the mobile terminal device being calculated based on position information on the mobile terminal device; and generate recommended route information using the type of the moving means of the second person.
However, Lee teaches to determine a type of a moving means of the second person based on a moving speed of a mobile terminal device possessed by the second person, the moving speed of the mobile terminal device being calculated based on position information on the mobile terminal device ([0067] via “For example, mobile device may use information about the location of the user to determine whether the user/mobile device 230 is located outside or within a building and may automatically execute the application when the user is located outside. Mobile device 230 may use information about the speed of the user to determine whether the user is walking or traveling in a vehicle and may automatically execute the application when the user is traveling in a vehicle.”), ([0068] via “Process 700 may include determining travel information (block 715). For example, mobile device 230 may determine information regarding a current location, a current speed, a direction of travel, a destination, and/or route information of mobile device 230.”).
Further, Weir teaches to generate recommended route information using the type of the moving means of the second person ([0052] via “In one embodiment, the route engine 204 may determine the route based on a user preference or default settings. … The user can modify the default setting after receiving a user interface from the user interface module 214. For example, the user can modify the setting to a preference for the least use of highways, the most use of highways, the shortest route, the longest route, etc. In another embodiment, the route engine 204 can also determine the route based on the user specifying whether the user will be walking, driving, taking public transit or bicycling.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lee wherein the processor is further configured to: determine a type of a moving means of the second person based on a moving speed of a mobile terminal device possessed by the second person, the moving speed of the mobile terminal device being calculated based on position information on the mobile terminal device. Doing so allows the system to be able to identify where the user is and how the user is travelling based on the moving state of the user, as stated above by Lee in paragraph [0067].
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Weir wherein the processor is further configured to: generate recommended route information using the type of the moving means of the second person. Doing so generates the most appropriate route for the second person based on user preferences and abilities, as stated above by Weir.
Examiner’s Note
17. The Examiner has cited particular paragraphs or columns and line numbers in the
references applied to the claims above for the convenience of the Applicant. Although the
specified citations are representative of the teachings of the art and are applied to specific
limitations within the individual claim, other passages and figures may apply as well. It is
respectfully requested of the Applicant in preparing responses, to fully consider the references
in their entirety as potentially teaching all or part of the claimed invention, as well as the
context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP
2141.02 [R-07.2015] VI. A prior art reference must be considered in its entirety, i.e., as a whole,
including portions that would lead away from the claimed Invention. W.L. Gore & Associates,
Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert, denied, 469 U.S. 851
(1984). See also MPEP §2123.
Conclusion
18. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BYRON XAVIER KASPER/Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657