Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant’s amendments dated 11/26/2025 have been filed.
Response to Arguments
Applicant’s arguments regarding the 101 rejection are considered but are found unpersuasive. While the examiner recognizes that the intent is that the prediction model be performing calculations that may be beyond the limitations of a human mind, the claims do not currently capture this limitation. It should be noted that the prediction map is generated based on a prediction model, but it is not explicitly stated that the prediction model generate the prediction map. Only that the map is based on the model in some way. A human could interpret outputs from the model and then generate a prediction map based on that. As such the actual action occurring could be done by a human, who is using the model as a reference. Language the specifically states that the prediction map is a direct output of the prediction model, or that the prediction model generates the prediction map would address this issue. Additional details about how complex the model is and what it’s training data is irrelevant if the model is not generating the map itself.
Arguments that claims integrate the concept into a practical application is also unpersuasive. As it is understood, while generating a route that is optimal in some way could be useful information, it is not considered to be integrating an exception into a practical application. Displaying or presenting a route also does not integrate the exception into a practical application. If there was a specific control step (controlling a vehicle to drive along the path) that would do so. The examiner does not believe that the claims themselves are directed towards an improvement in computer functionality, but a method of determining an optimal route that may be implemented by a computer. There is nothing that improves the functionality of a computer itself, merely a method to prevent a computer from entering an area of low connectivity. The examiner believes that the subject of the claims presented are generally well understood, routine or conventional. The use of models with inputs to determine connection maps, and using connection maps to determine optimal routes based on such maps is broadly known in the art.
Applicant’s arguments regarding the rejections under U.S.C. 103 are considered but found unpersuasive. The term “estimated period of travel between a third geolocation and a fourth geolocation along the path” may be broader than intended. Both Xu and Swar make use of segments for the overall routes. While neither list a precise time period for that segment of the route (for example they both broadly consider the time of the entire trip vs a specific time the vehicle would be at a specific segment), they do consider an estimated period of travel for each of the segments. Even if this is broad, it would technically meet the limitation. For Xu:
[0099] The scores may be limited to a particular time. For example, the scores may be valid at the time the routing request is received, but may expire when new wireless performance, probe, or traffic data is received or a period of time has elapsed, after which the expired scores may be updated based on the new data.
[0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.
For Swar:
[0037] The communication monitoring system may also include determining a forecasted change in a weather condition in the different areas along the route. The communication heat map may update the spatial distribution of the wireless communication signal strength based on the forecasted change in the weather condition. Additionally, based on the forecasted weather change, the operation of the second system may be changed as well. For example, if the forecasted whether condition for a given area is heavy cloud cover that may impact communication, the controller may update the communication heat map for the time(s) at which the adverse weather event may be expected.
[0038] The communication monitoring system may monitor present and historical weather conditions in the different areas along the route. The controller may cross-reference the wireless communication signal strengths with changes in the weather conditions to more accurately reflect the wireless communication signal strength. For example, the controller may receive an input indicative of a weak wireless communication signal strength at a location X at a time Y, but the controller may have received an input indicative of a strong wireless communication signal strength at location X at a time Z. Based on the historical and present monitoring of the weather, the controller may determine that the weak wireless communication signal strength at time Y was the result of a weather event, such heavy cloud cover or rain.
While the “estimated period of time” is fairly broad (current time or a broad trip time), the examiner does believe this does read on the limitation. Both references also refer to adjusting the signal strength when certain situations occur (high traffic, weather situations, etc) which would require an estimated time to be at the segment, even if that time is a current time. Because this is understood to be performed all segments, it would meet the limitations of doing it for a third and fourth location and between them. Please refer to the rejections below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 5-10, 13-17, 20-26 are rejected under 35 U.S.C. 101 because they are directed towards a mental process without significantly more.
Claim 1 recites:
A computer-implemented method comprising:
receiving user-input requesting a route from a first geolocation to a second geolocation;
accessing, in response to the user-input, a prediction map comprising predicted signal strengths associated with a wireless network at a plurality of geolocations between the first geolocation and the second geolocation, the prediction map accounting for expected weather conditions and predicted signal sgrenths for an estimated period of travel between a third geolocation and a fourth geolocation located along a path between the first geolocation and the second geolocation; and
determining, based on the prediction map, the route from the first geolocation to the second geolocation, wherein the route is determined such that a signal strength along the route during the estimated period of travel between the third geolocation and the fourth geolocation is above a first threshold signal value,
wherein the prediction map is generated based on a prediction model that is trained on training data that includes signal strength corresponding to various geological locations collected from a plurality of user-devices and corresponding weather conditions.
Step 2A prong one evaluation: Judicial Exception – Yes – Mental Processes
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental.
The claims recite determining based on the prediction route the route from a first location to another, where the signal strength along the route is above a threshold value. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could mentally consider a signal strength map, and determine a route in which the strength stays a certain strength. Thus this step is directed to a mental process.
The claim recites that the prediction map is generated based on a prediction model that is trained on training data that includes signal strength and weather data . This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “a computing device,” “prediction model”, “non transitory computer readable storage medium” “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could consider the historic strength of signals at locations, and then consider another few maps that have different weather conditions. They could recognize how rainy, sunny, or snowy conditions appear to change signal strength at locations. It should also be noted that this limitation does not explicitly state that the model generates the prediction map. Merely that it is based upon the prediction model. The model could output information that a human could consider when making their map. A person could do this if using a pen and paper. Thus this step is directed to a mental process.
Step 2A Prong Two evaluations
Claims are evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea or adding/performing insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The claims recite receiving a user requested route and accessing a prediction map. The above listed actions are recited at a high level of generality. These actions amount to nothing more than mere data receiving and gathering, which are considered extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims recite determining a route based on signal strengths and generating a prediction map using a model using a processor, a memory, and a non-transitory computer readable storage medium. The above listed actions are recited at a high level of generality. The computer/circuitry that facilitate the steps are described by the specification at a high level of generality. The generically recited computer merely describes how to generally “apply” the otherwise mental/extra solution processes using a generic or general-purpose processor. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim is not patent eligible.
2B Evaluation: Inventive Concept – No
Claims are evaluated as to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than possible uses for the output of the abstract idea. The same analysis applies here in 2B, i.e., possible uses for information or mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus the claims are not patent eligible.
Claim 9 recites:
A system comprising:
one or more processors; and
one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform operations comprising:
receiving user-input requesting a route from a first geolocation to a second geolocation;
accessing, in response to the user-input, a prediction map comprising predicted signal strengths associated with a wireless network at a plurality of geolocations between the first geolocation and the second geolocation, the prediction map accounting for expected weather conditions and predicted signal sgrenths for an estimated period of travel between a third geolocation and a fourth geolocation located along a path between the first geolocation and the second geolocation; and
determining, based on the prediction map, the route from the first geolocation to the second geolocation, wherein the route is determined such that a signal strength along the route during the estimated period of travel between the third geolocation and the fourth geolocation is above a first threshold signal value,
wherein the prediction map is generated based on a prediction model that is trained on training data that includes signal strength corresponding to various geological locations collected from a plurality of user-devices and corresponding weather conditions.
Step 2A prong one evaluation: Judicial Exception – Yes – Mental Processes
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental.
The claims recite determining based on the prediction route the route from a first location to another, where the signal strength along the route is above a threshold value. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could mentally consider a signal strength map, and determine a route in which the strength stays a certain strength. Thus this step is directed to a mental process.
The claim recites that the prediction map is generated based on a prediction model that is trained on training data that includes signal strength and weather data . This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “a computing device,” “prediction model”, “non transitory computer readable storage medium” “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could consider the historic strength of signals at locations, and then consider another few maps that have different weather conditions. They could recognize how rainy, sunny, or snowy conditions appear to change signal strength at locations. It should also be noted that this limitation does not explicitly state that the model generates the prediction map. Merely that it is based upon the prediction model. The model could output information that a human could consider when making their map. A person could do this if using a pen and paper. Thus this step is directed to a mental process.
Step 2A Prong Two evaluations
Claims are evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea or adding/performing insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The claims recite receiving a user requested route and accessing a prediction map. The above listed actions are recited at a high level of generality. These actions amount to nothing more than mere data receiving and gathering, which are considered extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims recite determining a route and based on signal strengths and generating a prediction map based on a model using a processor, a memory, and a non-transitory computer readable storage medium. The above listed actions are recited at a high level of generality. The computer/circuitry that facilitate the steps are described by the specification at a high level of generality. The generically recited computer merely describes how to generally “apply” the otherwise mental/extra solution processes using a generic or general-purpose processor. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim is not patent eligible.
2B Evaluation: Inventive Concept – No
Claims are evaluated as to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than possible uses for the output of the abstract idea. The same analysis applies here in 2B, i.e., possible uses for information or mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus the claims are not patent eligible.
Claim 16 recites:
A non-transitory computer-readable medium storing instructions that are executable by a processing device, and upon such execution cause the processing device to perform operations comprising:
receiving user-input requesting a route from a first geolocation to a second geolocation;
accessing, in response to the user-input, a prediction map comprising predicted signal strengths associated with a wireless network at a plurality of geolocations between the first geolocation and the second geolocation, the prediction map accounting for expected weather conditions and predicted signal sgrenths for an estimated period of travel between a third geolocation and a fourth geolocation located along a path between the first geolocation and the second geolocation; and
determining, based on the prediction map, the route from the first geolocation to the second geolocation, wherein the route is determined such that a signal strength along the route during the estimated period of travel between the third geolocation and the fourth geolocation is above a first threshold signal value,
wherein the prediction map is generated based on a prediction model that is trained on training data that includes signal strength corresponding to various geological locations collected from a plurality of user-devices and corresponding weather conditions.
Step 2A prong one evaluation: Judicial Exception – Yes – Mental Processes
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental.
The claims recite determining based on the prediction route the route from a first location to another, where the signal strength along the route is above a threshold value. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could mentally consider a signal strength map, and determine a route in which the strength stays a certain strength. Thus this step is directed to a mental process.
The claim recites that the prediction map is generated based on a prediction model that is trained on training data that includes signal strength and weather data . This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “a computing device,” “prediction model”, “non transitory computer readable storage medium” “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could consider the historic strength of signals at locations, and then consider another few maps that have different weather conditions. They could recognize how rainy, sunny, or snowy conditions appear to change signal strength at locations. It should also be noted that this limitation does not explicitly state that the model generates the prediction map. Merely that it is based upon the prediction model. The model could output information that a human could consider when making their map. A person could do this if using a pen and paper. Thus this step is directed to a mental process.
Step 2A Prong Two evaluations
Claims are evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea or adding/performing insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The claims recite receiving a user requested route and accessing a prediction map. The above listed actions are recited at a high level of generality. These actions amount to nothing more than mere data receiving and gathering, which are considered extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims recite determining a route based on signal strengths and generating a prediction map using a model using a processor, a memory, and a non-transitory computer readable storage medium. The above listed actions are recited at a high level of generality. The computer/circuitry that facilitate the steps are described by the specification at a high level of generality. The generically recited computer merely describes how to generally “apply” the otherwise mental/extra solution processes using a generic or general-purpose processor. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim is not patent eligible.
2B Evaluation: Inventive Concept – No
Claims are evaluated as to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than possible uses for the output of the abstract idea. The same analysis applies here in 2B, i.e., possible uses for information or mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus the claims are not patent eligible.
Claim 2 recites:
The method of claim 1, further comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device.
Claim 5 recites:
The method of claim 1, the method comprising generating the prediction map comprising:
obtaining data representing the expected weather conditions for the plurality of geolocations between the first geolocation and the second geolocation during the estimated period of travel; and
executing the prediction model to determine the predicted signal strengths at the plurality of geolocations at estimated time points determined based on expected period of travel between the first geolocation and each of the respective geolocations.
Claim 6 recites:
The method of claim 1, comprising:
receiving information about one or more parameters likely to affect signal strength along the route during the estimated period of travel;
based on the received information, determining that the signal strength is predicted to fall below the first threshold signal value for at least a portion of the route; and
in response to the determination, determining a revised route such that the signal strength remains above the first threshold signal value along the revised route during a revised estimated period of travel between the first geolocation and the second geolocation according to the revised route.
Claim 7 recites:
The method of claim 6, wherein the one or more parameters comprise at least one of: weather condition parameters, signal status information, or information indicative of obstacles along the route.
Claim 8 recites:
The method of claim 1, comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device;
in response to receiving a notification that the user-device has a signal strength below a second signal threshold value, determining a new route to the second geolocation, wherein the new route is determined such that a signal strength along the new route during a revised estimated period of travel is above the first threshold signal value; and
providing a geolocation map of an area including the new route for display at the user-interface of the user-device.
Claim 10 recites:
The system of claim 9, wherein the computer-readable memories further store instructions that are executable by the one or more processors to perform operations comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device.
Claim 13 recites:
The system of claim 9, wherein generating the prediction map comprising:
obtaining data representing the expected weather conditions for the plurality of geolocations between the first geolocation and the second geolocation during an estimated period of travel; and
executing the prediction model to determine the predicted signal strengths at the plurality of geolocations at estimated time points determined based on expected period of travel between the first geolocation and each of the respective geolocations.
Claim 14 recites:
The system of claim 9, wherein the computer-readable memories further store instructions that are executable by the one or more processors to perform operations comprising:
receiving information about one or more parameters likely to affect signal strength along the route during the estimated period of travel;
based on the received information, determining that the signal strength is predicted to fall below the first threshold signal value for at least a portion of the route; and
in response to the determination, determining a revised route such that the signal strength remains above the first threshold signal value along the revised route during a revised estimated period of travel between the first geolocation and the second geolocation according to the revised route.
Claim 15 recites:
The system of claim 14, wherein the one or more parameters comprise at least one of: weather condition parameters, signal status information, or information indicative of obstacles along the route.
Claim 17 recites:
The non-transitory computer-readable medium of claim 16, wherein the computer-readable medium further store instructions that are executable by the processing device to perform operations comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device.
Claim 20 recites:
The non-transitory computer-readable medium of claim 16, wherein generating the prediction map comprising:
obtaining data representing the expected weather conditions for the plurality of geolocations between the first geolocation and the second geolocation during an estimated period of travel; and
executing the prediction model to determine the predicted signal strengths at the plurality of geolocations at estimated time points determined based on expected period of travel between the first geolocation and each of the respective geolocations.
Claim 21 recites:
The non-transitory computer readable medium of claim 16, wherein the computer-readable medium further store instructions that are executable by the processing device to perform operations comprising:
receiving information about one or more parameters likely to affect signal strength along the route during the estimated period of travel;
based on the received information, determining that the signal strength is predicted to fall below the first threshold signal value for at least a portion of the route; and
in response to the determination, determining a revised route such that the signal strength remains above the first threshold signal value along the revised route during a revised estimated period of travel between the first geolocation and the second geolocation according to the revised route.
Claim 22 recites:
The non-transitory computer-readable medium of claim 21, wherein the one or more parameters comprise at least one of: weather condition parameters, signal status information, or information indicative of obstacles along the route.
Claim 23 recites:
The non-transitory computer-readable medium of claim 16, wherein the computer-readable medium further store instructions that are executable by the processing device to perform operations comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device;
in response to receiving a notification that the user-device has a signal strength below a second signal threshold value, determining a new route to the second geolocation, wherein the new route is determined such that a signal strength along the new route during a revised estimated period of travel is above the first threshold signal value; and
providing a geolocation map of an area including the new route for display at the user-interface of the user-device.
Claim 24 recites:
The method of claim 1, wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), channel state information (CSI) collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
Claim 25 recites:
The system of claim 9, wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), channel state information (CSI) collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
Claim 24 recites:
The non-transitory computer readable medium of claim 16, wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), channel state information (CSI) collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
Step 2A prong one evaluation: Judicial Exception – Yes – Mental Processes
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental.
Claims 5, 13, and 20 recite determining the predicted signal strengths at time points based on periods of travel at the geolocations . This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “a computing device,” “prediction model”, “non transitory computer readable storage medium” “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could consider the historic strength of signals at locations, and then consider another few maps that have different weather conditions. They could recognize how rainy, sunny, or snowy conditions appear to change signal strength at locations. They could then use that information to determine predicted signal strengths at certain times when the weather will be at certain conditions. A person could do this if using a pen and paper. Thus this step is directed to a mental process.
Claims 6, 14, 21 recite determining that the signal strength will fall below a threshold for a portion of the route. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “a computing device,” “prediction model”, “non transitory computer readable storage medium” “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could consider the signal strength at different locations and the threshold, and determine that the values are below the threshold. A person could do this if using a pen and paper. Thus this step is directed to a mental process.
Claims 6 and 14 and 21 recite determining a revised route so that the signal strength remains above a threshold. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “a computing device,” “prediction model”, “non transitory computer readable storage medium” “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could consider the signal strength at different locations and the threshold, and determine that a different route has the strength above the threshold. A person could do this if using a pen and paper. Thus this step is directed to a mental process.
Claims 8 and 23 recites determining a new route to the second geolocation in response to receiving a notification that the user device has a strength below a second threshold. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the limitation that processing circuitry be programed to perform the task. That is, other than reciting “a computing device,” “prediction model”, “non transitory computer readable storage medium” “processor”, or “memory”, nothing in the claim precludes the element being done in the mind. A person could in response to a notification, determine a new route so that the strength remains above a threshold. A person could do this if using a pen and paper. Thus this step is directed to a mental process.
Step 2A Prong Two evaluations
Claims are evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea or adding/performing insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The claims recite providing information to a user device, collecting data from user devices, obtaining weather data, receiving information regarding parameters, providing information to a user, and providing a geolocation map to a user. The above listed actions are recited at a high level of generality. These actions amount to nothing more than mere data receiving and gathering, which are considered extra solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims recite determining a route based on signal strengths, generating a prediction model, determining predicted strengths, revising routes, and determining new routes using a processor, a memory, a model, and a non-transitory computer readable storage medium. The above listed actions are recited at a high level of generality. The computer/circuitry that facilitate the steps are described by the specification at a high level of generality. The generically recited computer merely describes how to generally “apply” the otherwise mental/extra solution processes using a generic or general-purpose processor. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim is not patent eligible.
2B Evaluation: Inventive Concept – No
Claims are evaluated as to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than possible uses for the output of the abstract idea. The same analysis applies here in 2B, i.e., possible uses for information or mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus the claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-10, 13-17, 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al (US Pub 2022/0196425 A1), hereafter known as Xu in light of Swar et al (US Pub 2023/0362694 A1), hereafter known as Swar.
For Claim 1, Xu teaches A computer-implemented method comprising:
receiving user-input requesting a route from a first geolocation to a second geolocation; ([0087] At act S101, the controller 800 receives a vehicle routing request. The routing request may be received, for example, via the communication interface 818. The vehicle routing request may be generated by a mobile device 122. In some cases, the vehicle routing request may include a driving assistance capability of a vehicle. The driving assistance capability may indicate which set or level driving assistance features are available on the vehicle. In addition to the capability, the routing request may include a data requirement to enable the driver assistance capability. For example, the data requirement may specify a threshold level of wireless network performance, such as speed, bandwidth, signal strength, or latency, to enable the driver assistance capability. When the wireless network performance is below the threshold, the driving assistance capability may be disabled.
[0095] At act S115, the controller 800 receives a start and end point of a route. In some cases, the start point and end point may be included as part of the vehicle routing request. The start point and end point may define a desired starting and end location for the route. In some cases, only an end point may be received. The start point may be determined based on a location reported by the mobile device, e.g. received by the controller 800 in the probe data.)
accessing, in response to the user-input, a prediction map comprising predicted signal strengths associated with a wireless network at a plurality of geolocations between the first geolocation and the second geolocation, the prediction map also considering for expected weather and predicted signal strengths for an estimated period of travel between a third geolocation and a fourth geolocation located along a path between the first geolocation and the second geolocation; and ([0088] At act S103, the controller 800 receives wireless network performance data. The wireless network performance data may indicate wireless network performance for one or more path segments. Performance of one or more wireless networks (e.g. operated by different providers/operators, or using different standards) may be included in the data. In some cases, the performance data may be generated by one or more mobile devices. For example, the performance data may be generated by the mobile device that generated the routing request, and/or by one or more other mobile devices. In some other cases, the wireless network performance data may be received from an operator of the wireless network.
[0114] The user preference may specify that driving assistance features be enabled in in certain conditions. For example, the preference may indicate that autonomous driving features should be enable when traffic/congestion or a weather event (storms, rain, snow, high winds) are present. Route planning only based on traffic data or weather data may determine a revised route that has less traffic or is less affected by the weather conditions, at the expense of including in the route path segments that have reduced wireless network performance, and, therefore a lesser set of driving assistance features enabled thereon. However, the path segment experiencing congestion or a weather event conditions may be determined to have wireless network performance enabling the desired driving assistance features. As a result, the revised path may still include the path with congestion or inclement weather so long as the wireless network performance data for the path segment enabled the desired driving assistance features.
[0115] The revised route (or the original route) may include a start time or an end time for a trip. For example, the estimated time of arrival at the destination may be updated based on the revised route. In another example, the start time or end time may be based on the user preference for enablement of a set of driving assistance features on the route. For example, the controller 900 may send preference data that autonomous driving features be enabled on the route. Based on current, historical, or predicted future wireless performance data for the path segments, the route or revised route may include a start time so that the preferred autonomous driving features are enabled on the route. For example, the routing request may request a route from an office to a home. It may be determined that current wireless performance data may not support the preferred set of driver assistance features on the path segments. As a result, the routing request may recommend a delay in starting the route until wireless performance increases in the future in order to enable the driving assistance features. In another example, historical wireless performance data collected over time may indicate a reduction in wireless performance during a date and time (such as during the weekdays at 17:30, due to rush hour). The historical performance data may be used to estimate wireless performance data for path segments of the route. Based on the predicted wireless performance data for the path segments, the route or revised route may include a recommendation to start the route before or at a particular time, or after a particular time, e.g. to avoid times that path segments in the route have wireless performance that fails to enabled the preferred driving assistance features.
[0116] The revised route may include less than all of the path segments of the first route; one or more of the path segments included in the first route may be excluded from the revised route. In this way, a mobile device 122 traversing a planned route may receive an updated route responsive to changes in wireless performance data. Additionally or alternatively, the revised route may indicate that a set of driver assistance features for a path segment in the first route are newly enabled or no longer enabled by the updated wireless performance data.
[0125] FIG. 12 illustrates components of a road segment data record 980 contained in the geographic database 123 according to one embodiment. The road segment data record 980 may include a segment ID 984(1) by which the data record can be identified in the geographic database 123. Each road segment data record 980 may have associated information (such as “attributes”, “fields”, etc.) that describes features of the represented road segment. The road segment data record 980 may include data 984(2) that indicate the restrictions, if any, on the direction of vehicular travel permitted on the represented road segment. The road segment data record 980 may include data 984(3) that indicate a speed limit or speed category (i.e., the maximum permitted vehicular speed of travel) on the represented road segment. The road segment data record 304 may also include classification data 984(4) indicating whether the represented road segment is part of a controlled access road (such as an expressway), a ramp to a controlled access road, a bridge, a tunnel, a toll road, a ferry, and so on. The road segment data record may include location fingerprint data, for example a set of sensor data for a particular location.
[0126] The geographic database 123 may include road segment data records 980 (or data entities) that describe current, historical, or future wireless network performance data for the road segment. Additional schema may be used to describe road objects. The attribute data may be stored in relation to geographic coordinates (e.g., the latitude and longitude) of the end points of the represented road segment. In one embodiment, the data 984(7) are references to the node data records 986 that represent the nodes corresponding to the end points of the represented road segment.
[0127] The road segment data record 980 may also include or be associated with other data that refer to various other attributes of the represented road segment. The various attributes associated with a road segment may be included in a single road segment record or may be included in more than one type of record which cross-references to each other. For example, the road segment data record may include data identifying what turn restrictions exist at each of the nodes which correspond to intersections at the ends of the road portion represented by the road segment, the name, or names by which the represented road segment is identified, the street address ranges along the represented road segment, and so on.
[0099] The scores may be limited to a particular time. For example, the scores may be valid at the time the routing request is received, but may expire when new wireless performance, probe, or traffic data is received or a period of time has elapsed, after which the expired scores may be updated based on the new data.
[0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.
)
determining, based on the prediction map, the route from the first geolocation to the second geolocation, wherein the route is determined such that a signal strength along the route during the estimated period of travel between the third geolocation and the fourth geolocation during the estimated period of travel is above a first threshold signal value, ([0057] The route composer 213 may score the path segments based on whether or not the wireless performance data 201 for the segment meets or exceeds a driver assistance threshold. In some cases, the path segment may be scored based on the highest or more sophisticated set or level of driver assistance features enabled by the wireless network performance data 201. For example, where autonomous driver assistance features require a 5G connection, path segments may be scored based on whether or not (or to what extent) a 5G network is available as indicated by the wireless network performance data 201 for the path segments. In another example a “hands-off” set of driver assistance features may require a 4G connection or above to be enabled. Path segments having 4G or 5G network coverage may be scored as enabling the “hands-off” driver assistance features because those networks meet or exceed the required threshold of network performance. Though examples are given using network standards as the requirement for driver assistance features, the requirements may specify a minimum or minimum bandwidth, speed, latency, or signal strength required to enable the driver assistance features.
[0101] At act S123, the controller 800 determines the vehicle route. The vehicle route may be determined based on the score of the path segments. For example, the vehicle route may be based on or include path segments that have scores meeting or exceeding the data requirement to enable a set of driving assistance features on the path segment.
[0114] The user preference may specify that driving assistance features be enabled in in certain conditions. For example, the preference may indicate that autonomous driving features should be enable when traffic/congestion or a weather event (storms, rain, snow, high winds) are present. Route planning only based on traffic data or weather data may determine a revised route that has less traffic or is less affected by the weather conditions, at the expense of including in the route path segments that have reduced wireless network performance, and, therefore a lesser set of driving assistance features enabled thereon. However, the path segment experiencing congestion or a weather event conditions may be determined to have wireless network performance enabling the desired driving assistance features. As a result, the revised path may still include the path with congestion or inclement weather so long as the wireless network performance data for the path segment enabled the desired driving assistance features.
[0115] The revised route (or the original route) may include a start time or an end time for a trip. For example, the estimated time of arrival at the destination may be updated based on the revised route. In another example, the start time or end time may be based on the user preference for enablement of a set of driving assistance features on the route. For example, the controller 900 may send preference data that autonomous driving features be enabled on the route. Based on current, historical, or predicted future wireless performance data for the path segments, the route or revised route may include a start time so that the preferred autonomous driving features are enabled on the route. For example, the routing request may request a route from an office to a home. It may be determined that current wireless performance data may not support the preferred set of driver assistance features on the path segments. As a result, the routing request may recommend a delay in starting the route until wireless performance increases in the future in order to enable the driving assistance features. In another example, historical wireless performance data collected over time may indicate a reduction in wireless performance during a date and time (such as during the weekdays at 17:30, due to rush hour). The historical performance data may be used to estimate wireless performance data for path segments of the route. Based on the predicted wireless performance data for the path segments, the route or revised route may include a recommendation to start the route before or at a particular time, or after a particular time, e.g. to avoid times that path segments in the route have wireless performance that fails to enabled the preferred driving assistance features.
[0116] The revised route may include less than all of the path segments of the first route; one or more of the path segments included in the first route may be excluded from the revised route. In this way, a mobile device 122 traversing a planned route may receive an updated route responsive to changes in wireless performance data. Additionally or alternatively, the revised route may indicate that a set of driver assistance features for a path segment in the first route are newly enabled or no longer enabled by the updated wireless performance data.
[0099] The scores may be limited to a particular time. For example, the scores may be valid at the time the routing request is received, but may expire when new wireless performance, probe, or traffic data is received or a period of time has elapsed, after which the expired scores may be updated based on the new data.
[0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
Wherein the prediction map uses data collected from a plurality of user devices. [0045] The network performance data 201 may be aggregated from multiple mobile devices 122. The network performance data 201 may be aggregated across a particular service, platform, and application. For example, multiple mobile devices 122 may be in communication with a platform server associated with a particular entity. For example, a map provider may collect network performance data 201 using an application (e.g., navigation application, mapping application running) running on the mobile device 122.
Xu does not teach the prediction map accounting for expected weather conditions at the plurality of geolocations for the signal strength.
wherein the prediction map is generated based on a prediction model that is trained on training data that includes signal strength corresponding to various geological locations collected from a plurality of user-devices and corresponding weather conditions.
Swar, however, does teach the prediction map accounting for expected weather conditions at the plurality of geolocations for the signal strength. ([0037] The communication monitoring system may also include determining a forecasted change in a weather condition in the different areas along the route. The communication heat map may update the spatial distribution of the wireless communication signal strength based on the forecasted change in the weather condition. Additionally, based on the forecasted weather change, the operation of the second system may be changed as well. For example, if the forecasted whether condition for a given area is heavy cloud cover that may impact communication, the controller may update the communication heat map for the time(s) at which the adverse weather event may be expected.
[0044] The method may also include determining a forecasted change in a weather condition of the different areas along the route. The spatial distribution may be updated based on the forecasted weather condition. The method may also include matching changes in the signal strengths with changes in the weather conditions and modifying the spatial distribution and operation of the second vehicle system accordingly.
[0045] The method may include monitoring weather conditions in the different areas over time. The method may include matching changes in the wireless communication signal strengths with changes in the weather conditions that may be monitored. The spatial distribution of the wireless communication signal strengths may be determined based on the values of the wireless communication signal strengths that were measured, the locations where the wireless communication signal strengths were measured, and the changes in the weather conditions. The operation of the second system may be changed based on the spatial distribution that may be determined and a weather condition in which the second system may travel through.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar such that the prediction map accounting for expected weather conditions at the plurality of geolocations for the signal strength. It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in this way because if weather would affect signal strength, and signal strength is an important factor while routing, then understanding how the signal strength might be stronger or weaker for certain routes would be useful in ensuring that routes maintain the necessary amount of signal strength. This allows certain routes to open up on days in which the weather is unusually helpful for signal strength, and warns the system away from routes that are no acceptable when weather conditions are not good.
Swar, however, does teach wherein the prediction map is generated based on a prediction model that is trained on training data that includes signal strength corresponding to various geological locations collected from a plurality of user-devices and corresponding weather conditions. ([0044] The method may also include determining a forecasted change in a weather condition of the different areas along the route. The spatial distribution may be updated based on the forecasted weather condition. The method may also include matching changes in the signal strengths with changes in the weather conditions and modifying the spatial distribution and operation of the second vehicle system accordingly.
[0045] The method may include monitoring weather conditions in the different areas over time. The method may include matching changes in the wireless communication signal strengths with changes in the weather conditions that may be monitored. The spatial distribution of the wireless communication signal strengths may be determined based on the values of the wireless communication signal strengths that were measured, the locations where the wireless communication signal strengths were measured, and the changes in the weather conditions. The operation of the second system may be changed based on the spatial distribution that may be determined and a weather condition in which the second system may travel through.
[0051] In one embodiment, the monitoring system may have a local data collection system deployed that may use machine learning to enable derivation-based learning outcomes. The controller may learn from and make decisions on a set of data (including data provided by the various sensors), by making data-driven predictions and adapting according to the set of data. For example, the controller can use artificial intelligence or machine learning to examine signal strengths and associated locations where the signal strengths were measured for identifying or predicting areas where communication may be likely to be poor and/or wayside repeaters are needed. The output from the controller (e.g., the areas that are identified based on signal strengths and locations) can be examined and compared to additional measurements to determine if those areas do, in fact, have reduced signal strengths. Based on this comparison, differences between the output from the controller and the actual measured signal strengths (e.g., differences in areas where signal strengths are calculated or predicted to be poor versus areas where the signal strengths actually were poor) can be identified. These differences can then be used to train the monitoring system (e.g., via back-propagation or other machine learning training techniques).)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar such that a machine learning prediction model is trained on signal strength data corresponding to collected data and weather conditions because a machine learning model would be expected to be successful at taking in information regarding signal strength and historic weather, and determining the relationship for the signal strength and weather patterns. This information could then be used to interpret the signal strength based on weather patterns, which would be useful at accurately ensuring that the system does not route users through areas of low signal strength and allowing them access to as many possible routes as is possible.
For Claim 2, Xu teaches The method of claim 1, further comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device. ([0130] The controller 800 or 900 may include a routing module including an application specific module or processor that calculates routing between an origin and destination. The routing module is an example means for generating a route in response to the anonymized data to the destination. The routing command may be a driving instruction (e.g., turn left, go straight), which may be presented to a driver or passenger, or sent to an assisted driving system. The display 914 is an example means for displaying the routing command. The mobile device 122 may generate a routing instruction based on the anonymized data.
[0131] The routing instructions may be provided by display 914. The mobile device 122 may be configured to execute routing algorithms to determine an optimum route to travel along a road network from an origin location to a destination location in a geographic region. Using input(s) including map matching values from the server 125, a mobile device 122 examines potential routes between the origin location and the destination location to determine the optimum route. The mobile device 122, which may be referred to as a navigation device, may then provide the end user with information about the optimum route in the form of guidance that identifies the maneuvers required to be taken by the end user to travel from the origin to the destination location. Some mobile devices 122 show detailed maps on displays outlining the route, the types of maneuvers to be taken at various locations along the route, locations of certain types of features, and so on. Possible routes may be calculated based on a Dijkstra method, an A-star algorithm or search, and/or other route exploration or calculation algorithms that may be modified to take into consideration assigned cost values of the underlying road segments.)
For Claim 5, Xu teaches The method of claim 1, the method comprising generating the prediction map comprising:
Xu does not teach obtaining data representing the expected weather conditions for the plurality of geolocations between the first geolocation and the second geolocation during the estimated period of travel; and
executing the prediction model to determine the predicted signal strengths at the plurality of geolocations at estimated time points determined based on expected period of travel between the first geolocation and each of the respective geolocations.
Swar, however, does teach obtaining data representing the expected weather conditions for the plurality of geolocations between the first geolocation and the second geolocation during the estimated period of travel; and ([0044-0045], [0051], [0020])
executing the prediction model to determine the predicted signal strengths at the plurality of geolocations at estimated time points determined based on expected period of travel between the first geolocation and each of the respective geolocations. ([0044-0045], [0051], [0020])
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar such that a machine learning prediction model is trained on signal strength data corresponding to collected data and weather conditions because a machine learning model would be expected to be successful at taking in information regarding signal strength and historic weather, and determining the relationship for the signal strength and weather patterns. This information could then be used to interpret the signal strength based on weather patterns, which would be useful at accurately ensuring that the system does not route users through areas of low signal strength and allowing them access to as many possible routes as is possible. Using expected or historic data and then comparing it to other data sets is a known and expected to be successful at training a machine learning model. Providing that model with the input data of signal strength and weather would be expected to then be successful at allowing the model to output the expected signal strength considering the weather.
For Claim 6, Xu teaches The method of claim 1, comprising:
receiving information about one or more parameters likely to affect signal strength along the route during the estimated period of travel; ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
based on the received information, determining that the signal strength is predicted to fall below the first threshold signal value for at least a portion of the route; and ([0081] The incident data 502, vehicle probe data 504, and wireless performance data 506 may be updated. The updates may allow for dynamic route planning based on current conditions. In one example, a traffic jam happens on a busy road due to an accident. The accident and traffic jam on the road may be included in the incident data. Additionally, probe data 504 from vehicles 512 (e.g. autonomous vehicles or non-autonomous vehicles) in the traffic jam may be collected. The probe data 504, for example, may indicate the extent of congestion (e.g. if probe data indicates many slow-moving vehicles close together). The wireless performance data 506 for the road may also be collected. In some cases, the incident data 502, probe data 504, and wireless performance data 506 may be matched to the road segment (e.g. included in the HD map data 508) based on, for example, coordinates in the probe data 504. While, in normal traffic, wireless performance on the road may enable autonomous driving assistance features, the wireless performance may degrade with a large number of vehicles on the road. By collecting the various sources of information, route planning to enable a level or set of driver assistance features may avoid heavy traffic and/or atypical wireless performance caused by the traffic jam or another event. Additionally, the route may be updated based on a position of the vehicle 512 that requested the vehicle route.)
in response to the determination, determining a revised route such that the signal strength remains above the first threshold signal value along the revised route during a revised estimated period of travel between the first geolocation and the second geolocation according to the revised route. ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
For Claim 7, Xu teaches The method of claim 6, wherein the one or more parameters comprise at least one of: weather condition parameters, signal status information, or information indicative of obstacles along the route. ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
For Claim 8, Xu teaches The method of claim 1, comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device; ([0131] The routing instructions may be provided by display 914. The mobile device 122 may be configured to execute routing algorithms to determine an optimum route to travel along a road network from an origin location to a destination location in a geographic region. Using input(s) including map matching values from the server 125, a mobile device 122 examines potential routes between the origin location and the destination location to determine the optimum route. The mobile device 122, which may be referred to as a navigation device, may then provide the end user with information about the optimum route in the form of guidance that identifies the maneuvers required to be taken by the end user to travel from the origin to the destination location. Some mobile devices 122 show detailed maps on displays outlining the route, the types of maneuvers to be taken at various locations along the route, locations of certain types of features, and so on. Possible routes may be calculated based on a Dijkstra method, an A-star algorithm or search, and/or other route exploration or calculation algorithms that may be modified to take into consideration assigned cost values of the underlying road segments.)
in response to receiving a notification that the user-device has a signal strength below a second signal threshold value, determining a new route to the second geolocation, wherein the new route is determined such that a signal strength along the new route during a revised estimated period of travel is above the first threshold signal value; and ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.
[0071] The route planning process may iteratively consider all neighboring nodes to a current node. At the beginning, the start node is also the current node. A score for the nodes neighboring the current node may be determined. For example, the score of the neighboring nodes may be determined based on the wireless network performance for the path segment connecting the current node to the neighboring nodes. In the example of FIG. 4, the path segment L1 connects the current node at (1,0) to a neighboring node at (1,4). The score for the neighboring node may be set based on the wireless performance data for the segment L2. Similarly, a neighboring node at (4,4) is connected to the current node by path segment L2 and may be scored based on the wireless performance data for the segment L2.
It should be noted that according to applicant’s specification, the second threshold may be the same as the first threshold. Additionally, Xu states that the current node is part of the route, so if the revised routing occurs when a node’s strength is too low, then a current node would yield the result as well.)
providing a geolocation map of an area including the new route for display at the user-interface of the user-device. ([0131] The routing instructions may be provided by display 914. The mobile device 122 may be configured to execute routing algorithms to determine an optimum route to travel along a road network from an origin location to a destination location in a geographic region. Using input(s) including map matching values from the server 125, a mobile device 122 examines potential routes between the origin location and the destination location to determine the optimum route. The mobile device 122, which may be referred to as a navigation device, may then provide the end user with information about the optimum route in the form of guidance that identifies the maneuvers required to be taken by the end user to travel from the origin to the destination location. Some mobile devices 122 show detailed maps on displays outlining the route, the types of maneuvers to be taken at various locations along the route, locations of certain types of features, and so on. Possible routes may be calculated based on a Dijkstra method, an A-star algorithm or search, and/or other route exploration or calculation algorithms that may be modified to take into consideration assigned cost values of the underlying road segments.)
For Claim 9, Xu teaches A system comprising:
one or more processors; and ([0082] FIG. 6 illustrates an example server 125 for the system of FIG. 1. The server 125 may include a bus 810 that facilitates communication between a controller (e.g., the routing controller 121) that may be implemented by a processor 801 and/or an application specific controller 802, which may be referred to individually or collectively as controller 800, and one or more other components including a database 803, a memory 804, a computer readable medium 805, a display 814, a user input device 816, and a communication interface 818 connected to the internet and/or other networks 820. The contents of database 803 are described with respect to database 123. The server-side database 803 may be a master database that provides data in portions to the database 903 of the mobile device 122. Additional, different, or fewer components may be included.)
one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform operations comprising: ([0082] FIG. 6 illustrates an example server 125 for the system of FIG. 1. The server 125 may include a bus 810 that facilitates communication between a controller (e.g., the routing controller 121) that may be implemented by a processor 801 and/or an application specific controller 802, which may be referred to individually or collectively as controller 800, and one or more other components including a database 803, a memory 804, a computer readable medium 805, a display 814, a user input device 816, and a communication interface 818 connected to the internet and/or other networks 820. The contents of database 803 are described with respect to database 123. The server-side database 803 may be a master database that provides data in portions to the database 903 of the mobile device 122. Additional, different, or fewer components may be included.)
receiving user-input requesting a route from a first geolocation to a second geolocation; ([0087] At act S101, the controller 800 receives a vehicle routing request. The routing request may be received, for example, via the communication interface 818. The vehicle routing request may be generated by a mobile device 122. In some cases, the vehicle routing request may include a driving assistance capability of a vehicle. The driving assistance capability may indicate which set or level driving assistance features are available on the vehicle. In addition to the capability, the routing request may include a data requirement to enable the driver assistance capability. For example, the data requirement may specify a threshold level of wireless network performance, such as speed, bandwidth, signal strength, or latency, to enable the driver assistance capability. When the wireless network performance is below the threshold, the driving assistance capability may be disabled.
[0095] At act S115, the controller 800 receives a start and end point of a route. In some cases, the start point and end point may be included as part of the vehicle routing request. The start point and end point may define a desired starting and end location for the route. In some cases, only an end point may be received. The start point may be determined based on a location reported by the mobile device, e.g. received by the controller 800 in the probe data.)
accessing, in response to the user-input, a prediction map comprising predicted signal strengths associated with a wireless network at a plurality of geolocations between the first geolocation and the second geolocation, the prediction map also considering for expected weather and predicted signal strengths for an estimated period of travel between a third geolocation and a fourth geolocation located along a path between the first geolocation and the second geolocation; and ([0088] At act S103, the controller 800 receives wireless network performance data. The wireless network performance data may indicate wireless network performance for one or more path segments. Performance of one or more wireless networks (e.g. operated by different providers/operators, or using different standards) may be included in the data. In some cases, the performance data may be generated by one or more mobile devices. For example, the performance data may be generated by the mobile device that generated the routing request, and/or by one or more other mobile devices. In some other cases, the wireless network performance data may be received from an operator of the wireless network.
[0114] The user preference may specify that driving assistance features be enabled in in certain conditions. For example, the preference may indicate that autonomous driving features should be enable when traffic/congestion or a weather event (storms, rain, snow, high winds) are present. Route planning only based on traffic data or weather data may determine a revised route that has less traffic or is less affected by the weather conditions, at the expense of including in the route path segments that have reduced wireless network performance, and, therefore a lesser set of driving assistance features enabled thereon. However, the path segment experiencing congestion or a weather event conditions may be determined to have wireless network performance enabling the desired driving assistance features. As a result, the revised path may still include the path with congestion or inclement weather so long as the wireless network performance data for the path segment enabled the desired driving assistance features.
[0115] The revised route (or the original route) may include a start time or an end time for a trip. For example, the estimated time of arrival at the destination may be updated based on the revised route. In another example, the start time or end time may be based on the user preference for enablement of a set of driving assistance features on the route. For example, the controller 900 may send preference data that autonomous driving features be enabled on the route. Based on current, historical, or predicted future wireless performance data for the path segments, the route or revised route may include a start time so that the preferred autonomous driving features are enabled on the route. For example, the routing request may request a route from an office to a home. It may be determined that current wireless performance data may not support the preferred set of driver assistance features on the path segments. As a result, the routing request may recommend a delay in starting the route until wireless performance increases in the future in order to enable the driving assistance features. In another example, historical wireless performance data collected over time may indicate a reduction in wireless performance during a date and time (such as during the weekdays at 17:30, due to rush hour). The historical performance data may be used to estimate wireless performance data for path segments of the route. Based on the predicted wireless performance data for the path segments, the route or revised route may include a recommendation to start the route before or at a particular time, or after a particular time, e.g. to avoid times that path segments in the route have wireless performance that fails to enabled the preferred driving assistance features.
[0116] The revised route may include less than all of the path segments of the first route; one or more of the path segments included in the first route may be excluded from the revised route. In this way, a mobile device 122 traversing a planned route may receive an updated route responsive to changes in wireless performance data. Additionally or alternatively, the revised route may indicate that a set of driver assistance features for a path segment in the first route are newly enabled or no longer enabled by the updated wireless performance data.
[0125] FIG. 12 illustrates components of a road segment data record 980 contained in the geographic database 123 according to one embodiment. The road segment data record 980 may include a segment ID 984(1) by which the data record can be identified in the geographic database 123. Each road segment data record 980 may have associated information (such as “attributes”, “fields”, etc.) that describes features of the represented road segment. The road segment data record 980 may include data 984(2) that indicate the restrictions, if any, on the direction of vehicular travel permitted on the represented road segment. The road segment data record 980 may include data 984(3) that indicate a speed limit or speed category (i.e., the maximum permitted vehicular speed of travel) on the represented road segment. The road segment data record 304 may also include classification data 984(4) indicating whether the represented road segment is part of a controlled access road (such as an expressway), a ramp to a controlled access road, a bridge, a tunnel, a toll road, a ferry, and so on. The road segment data record may include location fingerprint data, for example a set of sensor data for a particular location.
[0126] The geographic database 123 may include road segment data records 980 (or data entities) that describe current, historical, or future wireless network performance data for the road segment. Additional schema may be used to describe road objects. The attribute data may be stored in relation to geographic coordinates (e.g., the latitude and longitude) of the end points of the represented road segment. In one embodiment, the data 984(7) are references to the node data records 986 that represent the nodes corresponding to the end points of the represented road segment.
[0127] The road segment data record 980 may also include or be associated with other data that refer to various other attributes of the represented road segment. The various attributes associated with a road segment may be included in a single road segment record or may be included in more than one type of record which cross-references to each other. For example, the road segment data record may include data identifying what turn restrictions exist at each of the nodes which correspond to intersections at the ends of the road portion represented by the road segment, the name, or names by which the represented road segment is identified, the street address ranges along the represented road segment, and so on.
[0099] The scores may be limited to a particular time. For example, the scores may be valid at the time the routing request is received, but may expire when new wireless performance, probe, or traffic data is received or a period of time has elapsed, after which the expired scores may be updated based on the new data.
[0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
determining, based on the prediction map, the route from the first geolocation to the second geolocation, wherein the route is determined such that a signal strength along the route during the estimated period of travel between the third geolocation and the fourth geolocation during the estimated period of travel is above a first threshold signal value, ([0057] The route composer 213 may score the path segments based on whether or not the wireless performance data 201 for the segment meets or exceeds a driver assistance threshold. In some cases, the path segment may be scored based on the highest or more sophisticated set or level of driver assistance features enabled by the wireless network performance data 201. For example, where autonomous driver assistance features require a 5G connection, path segments may be scored based on whether or not (or to what extent) a 5G network is available as indicated by the wireless network performance data 201 for the path segments. In another example a “hands-off” set of driver assistance features may require a 4G connection or above to be enabled. Path segments having 4G or 5G network coverage may be scored as enabling the “hands-off” driver assistance features because those networks meet or exceed the required threshold of network performance. Though examples are given using network standards as the requirement for driver assistance features, the requirements may specify a minimum or minimum bandwidth, speed, latency, or signal strength required to enable the driver assistance features.
[0101] At act S123, the controller 800 determines the vehicle route. The vehicle route may be determined based on the score of the path segments. For example, the vehicle route may be based on or include path segments that have scores meeting or exceeding the data requirement to enable a set of driving assistance features on the path segment.
[0114] The user preference may specify that driving assistance features be enabled in in certain conditions. For example, the preference may indicate that autonomous driving features should be enable when traffic/congestion or a weather event (storms, rain, snow, high winds) are present. Route planning only based on traffic data or weather data may determine a revised route that has less traffic or is less affected by the weather conditions, at the expense of including in the route path segments that have reduced wireless network performance, and, therefore a lesser set of driving assistance features enabled thereon. However, the path segment experiencing congestion or a weather event conditions may be determined to have wireless network performance enabling the desired driving assistance features. As a result, the revised path may still include the path with congestion or inclement weather so long as the wireless network performance data for the path segment enabled the desired driving assistance features.
[0115] The revised route (or the original route) may include a start time or an end time for a trip. For example, the estimated time of arrival at the destination may be updated based on the revised route. In another example, the start time or end time may be based on the user preference for enablement of a set of driving assistance features on the route. For example, the controller 900 may send preference data that autonomous driving features be enabled on the route. Based on current, historical, or predicted future wireless performance data for the path segments, the route or revised route may include a start time so that the preferred autonomous driving features are enabled on the route. For example, the routing request may request a route from an office to a home. It may be determined that current wireless performance data may not support the preferred set of driver assistance features on the path segments. As a result, the routing request may recommend a delay in starting the route until wireless performance increases in the future in order to enable the driving assistance features. In another example, historical wireless performance data collected over time may indicate a reduction in wireless performance during a date and time (such as during the weekdays at 17:30, due to rush hour). The historical performance data may be used to estimate wireless performance data for path segments of the route. Based on the predicted wireless performance data for the path segments, the route or revised route may include a recommendation to start the route before or at a particular time, or after a particular time, e.g. to avoid times that path segments in the route have wireless performance that fails to enabled the preferred driving assistance features.
[0116] The revised route may include less than all of the path segments of the first route; one or more of the path segments included in the first route may be excluded from the revised route. In this way, a mobile device 122 traversing a planned route may receive an updated route responsive to changes in wireless performance data. Additionally or alternatively, the revised route may indicate that a set of driver assistance features for a path segment in the first route are newly enabled or no longer enabled by the updated wireless performance data.
[0099] The scores may be limited to a particular time. For example, the scores may be valid at the time the routing request is received, but may expire when new wireless performance, probe, or traffic data is received or a period of time has elapsed, after which the expired scores may be updated based on the new data.
[0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
Wherein the prediction map uses data collected from a plurality of user devices. [0045] The network performance data 201 may be aggregated from multiple mobile devices 122. The network performance data 201 may be aggregated across a particular service, platform, and application. For example, multiple mobile devices 122 may be in communication with a platform server associated with a particular entity. For example, a map provider may collect network performance data 201 using an application (e.g., navigation application, mapping application running) running on the mobile device 122.
Xu does not teach the prediction map accounting for expected weather conditions at the plurality of geolocations for the signal strength.
wherein the prediction map is generated based on a prediction model that is trained on training data that includes signal strength corresponding to various geological locations collected from a plurality of user-devices and corresponding weather conditions.
Swar, however, does teach the prediction map accounting for expected weather conditions at the plurality of geolocations for the signal strength. ([0037] The communication monitoring system may also include determining a forecasted change in a weather condition in the different areas along the route. The communication heat map may update the spatial distribution of the wireless communication signal strength based on the forecasted change in the weather condition. Additionally, based on the forecasted weather change, the operation of the second system may be changed as well. For example, if the forecasted whether condition for a given area is heavy cloud cover that may impact communication, the controller may update the communication heat map for the time(s) at which the adverse weather event may be expected.
[0044] The method may also include determining a forecasted change in a weather condition of the different areas along the route. The spatial distribution may be updated based on the forecasted weather condition. The method may also include matching changes in the signal strengths with changes in the weather conditions and modifying the spatial distribution and operation of the second vehicle system accordingly.
[0045] The method may include monitoring weather conditions in the different areas over time. The method may include matching changes in the wireless communication signal strengths with changes in the weather conditions that may be monitored. The spatial distribution of the wireless communication signal strengths may be determined based on the values of the wireless communication signal strengths that were measured, the locations where the wireless communication signal strengths were measured, and the changes in the weather conditions. The operation of the second system may be changed based on the spatial distribution that may be determined and a weather condition in which the second system may travel through.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar such that the prediction map accounting for expected weather conditions at the plurality of geolocations for the signal strength. It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in this way because if weather would affect signal strength, and signal strength is an important factor while routing, then understanding how the signal strength might be stronger or weaker for certain routes would be useful in ensuring that routes maintain the necessary amount of signal strength. This allows certain routes to open up on days in which the weather is unusually helpful for signal strength, and warns the system away from routes that are no acceptable when weather conditions are not good.
Swar, however, does teach wherein the prediction map is generated based on a prediction model that is trained on training data that includes signal strength corresponding to various geological locations collected from a plurality of user-devices and corresponding weather conditions. ([0044] The method may also include determining a forecasted change in a weather condition of the different areas along the route. The spatial distribution may be updated based on the forecasted weather condition. The method may also include matching changes in the signal strengths with changes in the weather conditions and modifying the spatial distribution and operation of the second vehicle system accordingly.
[0045] The method may include monitoring weather conditions in the different areas over time. The method may include matching changes in the wireless communication signal strengths with changes in the weather conditions that may be monitored. The spatial distribution of the wireless communication signal strengths may be determined based on the values of the wireless communication signal strengths that were measured, the locations where the wireless communication signal strengths were measured, and the changes in the weather conditions. The operation of the second system may be changed based on the spatial distribution that may be determined and a weather condition in which the second system may travel through.
[0051] In one embodiment, the monitoring system may have a local data collection system deployed that may use machine learning to enable derivation-based learning outcomes. The controller may learn from and make decisions on a set of data (including data provided by the various sensors), by making data-driven predictions and adapting according to the set of data. For example, the controller can use artificial intelligence or machine learning to examine signal strengths and associated locations where the signal strengths were measured for identifying or predicting areas where communication may be likely to be poor and/or wayside repeaters are needed. The output from the controller (e.g., the areas that are identified based on signal strengths and locations) can be examined and compared to additional measurements to determine if those areas do, in fact, have reduced signal strengths. Based on this comparison, differences between the output from the controller and the actual measured signal strengths (e.g., differences in areas where signal strengths are calculated or predicted to be poor versus areas where the signal strengths actually were poor) can be identified. These differences can then be used to train the monitoring system (e.g., via back-propagation or other machine learning training techniques).)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar such that a machine learning prediction model is trained on signal strength data corresponding to collected data and weather conditions because a machine learning model would be expected to be successful at taking in information regarding signal strength and historic weather, and determining the relationship for the signal strength and weather patterns. This information could then be used to interpret the signal strength based on weather patterns, which would be useful at accurately ensuring that the system does not route users through areas of low signal strength and allowing them access to as many possible routes as is possible.
For Claim 10, Xu teaches The system of claim 9, wherein the computer-readable memories further store instructions that are executable by the one or more processors to perform operations comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device. ([0130] The controller 800 or 900 may include a routing module including an application specific module or processor that calculates routing between an origin and destination. The routing module is an example means for generating a route in response to the anonymized data to the destination. The routing command may be a driving instruction (e.g., turn left, go straight), which may be presented to a driver or passenger, or sent to an assisted driving system. The display 914 is an example means for displaying the routing command. The mobile device 122 may generate a routing instruction based on the anonymized data.
[0131] The routing instructions may be provided by display 914. The mobile device 122 may be configured to execute routing algorithms to determine an optimum route to travel along a road network from an origin location to a destination location in a geographic region. Using input(s) including map matching values from the server 125, a mobile device 122 examines potential routes between the origin location and the destination location to determine the optimum route. The mobile device 122, which may be referred to as a navigation device, may then provide the end user with information about the optimum route in the form of guidance that identifies the maneuvers required to be taken by the end user to travel from the origin to the destination location. Some mobile devices 122 show detailed maps on displays outlining the route, the types of maneuvers to be taken at various locations along the route, locations of certain types of features, and so on. Possible routes may be calculated based on a Dijkstra method, an A-star algorithm or search, and/or other route exploration or calculation algorithms that may be modified to take into consideration assigned cost values of the underlying road segments.)
For Claim 13, Xu teaches The system of claim 9, wherein generating the prediction map comprising:
Xu does not teach obtaining data representing the expected weather conditions for the plurality of geolocations between the first geolocation and the second geolocation during an estimated period of travel; and
executing the prediction model to determine the predicted signal strengths at the plurality of geolocations at estimated time points determined based on expected period of travel between the first geolocation and each of the respective geolocations.
Swar, however, does teach obtaining data representing the expected weather conditions for the plurality of geolocations between the first geolocation and the second geolocation during an estimated period of travel; and ([0044-0045], [0051], [0020])
executing the prediction model to determine the predicted signal strengths at the plurality of geolocations at estimated time points determined based on expected period of travel between the first geolocation and each of the respective geolocations. ([0044-0045], [0051], [0020])
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar such that a machine learning prediction model is trained on signal strength data corresponding to collected data and weather conditions because a machine learning model would be expected to be successful at taking in information regarding signal strength and historic weather, and determining the relationship for the signal strength and weather patterns. This information could then be used to interpret the signal strength based on weather patterns, which would be useful at accurately ensuring that the system does not route users through areas of low signal strength and allowing them access to as many possible routes as is possible. Using expected or historic data and then comparing it to other data sets is a known and expected to be successful at training a machine learning model. Providing that model with the input data of signal strength and weather would be expected to then be successful at allowing the model to output the expected signal strength considering the weather.
For Claim 14, Xu teaches The system of claim 9, wherein the computer-readable memories further store instructions that are executable by the one or more processors to perform operations comprising:
receiving information about one or more parameters likely to affect signal strength along the route during the estimated period of travel; ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
based on the received information, determining that the signal strength is predicted to fall below the first threshold signal value for at least a portion of the route; and ([0081] The incident data 502, vehicle probe data 504, and wireless performance data 506 may be updated. The updates may allow for dynamic route planning based on current conditions. In one example, a traffic jam happens on a busy road due to an accident. The accident and traffic jam on the road may be included in the incident data. Additionally, probe data 504 from vehicles 512 (e.g. autonomous vehicles or non-autonomous vehicles) in the traffic jam may be collected. The probe data 504, for example, may indicate the extent of congestion (e.g. if probe data indicates many slow-moving vehicles close together). The wireless performance data 506 for the road may also be collected. In some cases, the incident data 502, probe data 504, and wireless performance data 506 may be matched to the road segment (e.g. included in the HD map data 508) based on, for example, coordinates in the probe data 504. While, in normal traffic, wireless performance on the road may enable autonomous driving assistance features, the wireless performance may degrade with a large number of vehicles on the road. By collecting the various sources of information, route planning to enable a level or set of driver assistance features may avoid heavy traffic and/or atypical wireless performance caused by the traffic jam or another event. Additionally, the route may be updated based on a position of the vehicle 512 that requested the vehicle route.)
in response to the determination, determining a revised route such that the signal strength remains above the first threshold signal value along the revised route during a revised estimated period of travel between the first geolocation and the second geolocation according to the revised route. ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
For Claim 15, Xu teaches The system of claim 14, wherein the one or more parameters comprise at least one of: weather condition parameters, signal status information, or information indicative of obstacles along the route. ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
For Claim 16, Xu teaches A non-transitory computer-readable medium storing instructions that are executable by a processing device, and upon such execution cause the processing device to perform operations comprising: ([0082] FIG. 6 illustrates an example server 125 for the system of FIG. 1. The server 125 may include a bus 810 that facilitates communication between a controller (e.g., the routing controller 121) that may be implemented by a processor 801 and/or an application specific controller 802, which may be referred to individually or collectively as controller 800, and one or more other components including a database 803, a memory 804, a computer readable medium 805, a display 814, a user input device 816, and a communication interface 818 connected to the internet and/or other networks 820. The contents of database 803 are described with respect to database 123. The server-side database 803 may be a master database that provides data in portions to the database 903 of the mobile device 122. Additional, different, or fewer components may be included.)
receiving user-input requesting a route from a first geolocation to a second geolocation; ([0087] At act S101, the controller 800 receives a vehicle routing request. The routing request may be received, for example, via the communication interface 818. The vehicle routing request may be generated by a mobile device 122. In some cases, the vehicle routing request may include a driving assistance capability of a vehicle. The driving assistance capability may indicate which set or level driving assistance features are available on the vehicle. In addition to the capability, the routing request may include a data requirement to enable the driver assistance capability. For example, the data requirement may specify a threshold level of wireless network performance, such as speed, bandwidth, signal strength, or latency, to enable the driver assistance capability. When the wireless network performance is below the threshold, the driving assistance capability may be disabled.
[0095] At act S115, the controller 800 receives a start and end point of a route. In some cases, the start point and end point may be included as part of the vehicle routing request. The start point and end point may define a desired starting and end location for the route. In some cases, only an end point may be received. The start point may be determined based on a location reported by the mobile device, e.g. received by the controller 800 in the probe data.)
accessing, in response to the user-input, a prediction map comprising predicted signal strengths associated with a wireless network at a plurality of geolocations between the first geolocation and the second geolocation, the prediction map also considering for expected weather and predicted signal strengths for an estimated period of travel between a third geolocation and a fourth geolocation located along a path between the first geolocation and the second geolocation; and ([0088] At act S103, the controller 800 receives wireless network performance data. The wireless network performance data may indicate wireless network performance for one or more path segments. Performance of one or more wireless networks (e.g. operated by different providers/operators, or using different standards) may be included in the data. In some cases, the performance data may be generated by one or more mobile devices. For example, the performance data may be generated by the mobile device that generated the routing request, and/or by one or more other mobile devices. In some other cases, the wireless network performance data may be received from an operator of the wireless network.
[0114] The user preference may specify that driving assistance features be enabled in in certain conditions. For example, the preference may indicate that autonomous driving features should be enable when traffic/congestion or a weather event (storms, rain, snow, high winds) are present. Route planning only based on traffic data or weather data may determine a revised route that has less traffic or is less affected by the weather conditions, at the expense of including in the route path segments that have reduced wireless network performance, and, therefore a lesser set of driving assistance features enabled thereon. However, the path segment experiencing congestion or a weather event conditions may be determined to have wireless network performance enabling the desired driving assistance features. As a result, the revised path may still include the path with congestion or inclement weather so long as the wireless network performance data for the path segment enabled the desired driving assistance features.
[0115] The revised route (or the original route) may include a start time or an end time for a trip. For example, the estimated time of arrival at the destination may be updated based on the revised route. In another example, the start time or end time may be based on the user preference for enablement of a set of driving assistance features on the route. For example, the controller 900 may send preference data that autonomous driving features be enabled on the route. Based on current, historical, or predicted future wireless performance data for the path segments, the route or revised route may include a start time so that the preferred autonomous driving features are enabled on the route. For example, the routing request may request a route from an office to a home. It may be determined that current wireless performance data may not support the preferred set of driver assistance features on the path segments. As a result, the routing request may recommend a delay in starting the route until wireless performance increases in the future in order to enable the driving assistance features. In another example, historical wireless performance data collected over time may indicate a reduction in wireless performance during a date and time (such as during the weekdays at 17:30, due to rush hour). The historical performance data may be used to estimate wireless performance data for path segments of the route. Based on the predicted wireless performance data for the path segments, the route or revised route may include a recommendation to start the route before or at a particular time, or after a particular time, e.g. to avoid times that path segments in the route have wireless performance that fails to enabled the preferred driving assistance features.
[0116] The revised route may include less than all of the path segments of the first route; one or more of the path segments included in the first route may be excluded from the revised route. In this way, a mobile device 122 traversing a planned route may receive an updated route responsive to changes in wireless performance data. Additionally or alternatively, the revised route may indicate that a set of driver assistance features for a path segment in the first route are newly enabled or no longer enabled by the updated wireless performance data.
[0125] FIG. 12 illustrates components of a road segment data record 980 contained in the geographic database 123 according to one embodiment. The road segment data record 980 may include a segment ID 984(1) by which the data record can be identified in the geographic database 123. Each road segment data record 980 may have associated information (such as “attributes”, “fields”, etc.) that describes features of the represented road segment. The road segment data record 980 may include data 984(2) that indicate the restrictions, if any, on the direction of vehicular travel permitted on the represented road segment. The road segment data record 980 may include data 984(3) that indicate a speed limit or speed category (i.e., the maximum permitted vehicular speed of travel) on the represented road segment. The road segment data record 304 may also include classification data 984(4) indicating whether the represented road segment is part of a controlled access road (such as an expressway), a ramp to a controlled access road, a bridge, a tunnel, a toll road, a ferry, and so on. The road segment data record may include location fingerprint data, for example a set of sensor data for a particular location.
[0126] The geographic database 123 may include road segment data records 980 (or data entities) that describe current, historical, or future wireless network performance data for the road segment. Additional schema may be used to describe road objects. The attribute data may be stored in relation to geographic coordinates (e.g., the latitude and longitude) of the end points of the represented road segment. In one embodiment, the data 984(7) are references to the node data records 986 that represent the nodes corresponding to the end points of the represented road segment.
[0127] The road segment data record 980 may also include or be associated with other data that refer to various other attributes of the represented road segment. The various attributes associated with a road segment may be included in a single road segment record or may be included in more than one type of record which cross-references to each other. For example, the road segment data record may include data identifying what turn restrictions exist at each of the nodes which correspond to intersections at the ends of the road portion represented by the road segment, the name, or names by which the represented road segment is identified, the street address ranges along the represented road segment, and so on.
[0099] The scores may be limited to a particular time. For example, the scores may be valid at the time the routing request is received, but may expire when new wireless performance, probe, or traffic data is received or a period of time has elapsed, after which the expired scores may be updated based on the new data.
[0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
determining, based on the prediction map, the route from the first geolocation to the second geolocation, wherein the route is determined such that a signal strength along the route during the estimated period of travel between the third geolocation and the fourth geolocation during the estimated period of travel is above a first threshold signal value, ([0057] The route composer 213 may score the path segments based on whether or not the wireless performance data 201 for the segment meets or exceeds a driver assistance threshold. In some cases, the path segment may be scored based on the highest or more sophisticated set or level of driver assistance features enabled by the wireless network performance data 201. For example, where autonomous driver assistance features require a 5G connection, path segments may be scored based on whether or not (or to what extent) a 5G network is available as indicated by the wireless network performance data 201 for the path segments. In another example a “hands-off” set of driver assistance features may require a 4G connection or above to be enabled. Path segments having 4G or 5G network coverage may be scored as enabling the “hands-off” driver assistance features because those networks meet or exceed the required threshold of network performance. Though examples are given using network standards as the requirement for driver assistance features, the requirements may specify a minimum or minimum bandwidth, speed, latency, or signal strength required to enable the driver assistance features.
[0101] At act S123, the controller 800 determines the vehicle route. The vehicle route may be determined based on the score of the path segments. For example, the vehicle route may be based on or include path segments that have scores meeting or exceeding the data requirement to enable a set of driving assistance features on the path segment.
[0114] The user preference may specify that driving assistance features be enabled in in certain conditions. For example, the preference may indicate that autonomous driving features should be enable when traffic/congestion or a weather event (storms, rain, snow, high winds) are present. Route planning only based on traffic data or weather data may determine a revised route that has less traffic or is less affected by the weather conditions, at the expense of including in the route path segments that have reduced wireless network performance, and, therefore a lesser set of driving assistance features enabled thereon. However, the path segment experiencing congestion or a weather event conditions may be determined to have wireless network performance enabling the desired driving assistance features. As a result, the revised path may still include the path with congestion or inclement weather so long as the wireless network performance data for the path segment enabled the desired driving assistance features.
[0115] The revised route (or the original route) may include a start time or an end time for a trip. For example, the estimated time of arrival at the destination may be updated based on the revised route. In another example, the start time or end time may be based on the user preference for enablement of a set of driving assistance features on the route. For example, the controller 900 may send preference data that autonomous driving features be enabled on the route. Based on current, historical, or predicted future wireless performance data for the path segments, the route or revised route may include a start time so that the preferred autonomous driving features are enabled on the route. For example, the routing request may request a route from an office to a home. It may be determined that current wireless performance data may not support the preferred set of driver assistance features on the path segments. As a result, the routing request may recommend a delay in starting the route until wireless performance increases in the future in order to enable the driving assistance features. In another example, historical wireless performance data collected over time may indicate a reduction in wireless performance during a date and time (such as during the weekdays at 17:30, due to rush hour). The historical performance data may be used to estimate wireless performance data for path segments of the route. Based on the predicted wireless performance data for the path segments, the route or revised route may include a recommendation to start the route before or at a particular time, or after a particular time, e.g. to avoid times that path segments in the route have wireless performance that fails to enabled the preferred driving assistance features.
[0116] The revised route may include less than all of the path segments of the first route; one or more of the path segments included in the first route may be excluded from the revised route. In this way, a mobile device 122 traversing a planned route may receive an updated route responsive to changes in wireless performance data. Additionally or alternatively, the revised route may indicate that a set of driver assistance features for a path segment in the first route are newly enabled or no longer enabled by the updated wireless performance data.
[0099] The scores may be limited to a particular time. For example, the scores may be valid at the time the routing request is received, but may expire when new wireless performance, probe, or traffic data is received or a period of time has elapsed, after which the expired scores may be updated based on the new data.
[0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
Wherein the prediction map uses data collected from a plurality of user devices. [0045] The network performance data 201 may be aggregated from multiple mobile devices 122. The network performance data 201 may be aggregated across a particular service, platform, and application. For example, multiple mobile devices 122 may be in communication with a platform server associated with a particular entity. For example, a map provider may collect network performance data 201 using an application (e.g., navigation application, mapping application running) running on the mobile device 122.
Xu does not teach the prediction map accounting for expected weather conditions at the plurality of geolocations for the signal strength.
wherein the prediction map is generated based on a prediction model that is trained on training data that includes signal strength corresponding to various geological locations collected from a plurality of user-devices and corresponding weather conditions.
Swar, however, does teach the prediction map accounting for expected weather conditions at the plurality of geolocations for the signal strength. ([0037] The communication monitoring system may also include determining a forecasted change in a weather condition in the different areas along the route. The communication heat map may update the spatial distribution of the wireless communication signal strength based on the forecasted change in the weather condition. Additionally, based on the forecasted weather change, the operation of the second system may be changed as well. For example, if the forecasted whether condition for a given area is heavy cloud cover that may impact communication, the controller may update the communication heat map for the time(s) at which the adverse weather event may be expected.
[0044] The method may also include determining a forecasted change in a weather condition of the different areas along the route. The spatial distribution may be updated based on the forecasted weather condition. The method may also include matching changes in the signal strengths with changes in the weather conditions and modifying the spatial distribution and operation of the second vehicle system accordingly.
[0045] The method may include monitoring weather conditions in the different areas over time. The method may include matching changes in the wireless communication signal strengths with changes in the weather conditions that may be monitored. The spatial distribution of the wireless communication signal strengths may be determined based on the values of the wireless communication signal strengths that were measured, the locations where the wireless communication signal strengths were measured, and the changes in the weather conditions. The operation of the second system may be changed based on the spatial distribution that may be determined and a weather condition in which the second system may travel through.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar such that the prediction map accounting for expected weather conditions at the plurality of geolocations for the signal strength. It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in this way because if weather would affect signal strength, and signal strength is an important factor while routing, then understanding how the signal strength might be stronger or weaker for certain routes would be useful in ensuring that routes maintain the necessary amount of signal strength. This allows certain routes to open up on days in which the weather is unusually helpful for signal strength, and warns the system away from routes that are no acceptable when weather conditions are not good.
Swar, however, does teach wherein the prediction map is generated based on a prediction model that is trained on training data that includes signal strength corresponding to various geological locations collected from a plurality of user-devices and corresponding weather conditions. ([0044] The method may also include determining a forecasted change in a weather condition of the different areas along the route. The spatial distribution may be updated based on the forecasted weather condition. The method may also include matching changes in the signal strengths with changes in the weather conditions and modifying the spatial distribution and operation of the second vehicle system accordingly.
[0045] The method may include monitoring weather conditions in the different areas over time. The method may include matching changes in the wireless communication signal strengths with changes in the weather conditions that may be monitored. The spatial distribution of the wireless communication signal strengths may be determined based on the values of the wireless communication signal strengths that were measured, the locations where the wireless communication signal strengths were measured, and the changes in the weather conditions. The operation of the second system may be changed based on the spatial distribution that may be determined and a weather condition in which the second system may travel through.
[0051] In one embodiment, the monitoring system may have a local data collection system deployed that may use machine learning to enable derivation-based learning outcomes. The controller may learn from and make decisions on a set of data (including data provided by the various sensors), by making data-driven predictions and adapting according to the set of data. For example, the controller can use artificial intelligence or machine learning to examine signal strengths and associated locations where the signal strengths were measured for identifying or predicting areas where communication may be likely to be poor and/or wayside repeaters are needed. The output from the controller (e.g., the areas that are identified based on signal strengths and locations) can be examined and compared to additional measurements to determine if those areas do, in fact, have reduced signal strengths. Based on this comparison, differences between the output from the controller and the actual measured signal strengths (e.g., differences in areas where signal strengths are calculated or predicted to be poor versus areas where the signal strengths actually were poor) can be identified. These differences can then be used to train the monitoring system (e.g., via back-propagation or other machine learning training techniques).)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar such that a machine learning prediction model is trained on signal strength data corresponding to collected data and weather conditions because a machine learning model would be expected to be successful at taking in information regarding signal strength and historic weather, and determining the relationship for the signal strength and weather patterns. This information could then be used to interpret the signal strength based on weather patterns, which would be useful at accurately ensuring that the system does not route users through areas of low signal strength and allowing them access to as many possible routes as is possible.
For Claim 17, Xu teaches The non-transitory computer-readable medium of claim 16, wherein the computer-readable medium further store instructions that are executable by the processing device to perform operations comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device. ([0130] The controller 800 or 900 may include a routing module including an application specific module or processor that calculates routing between an origin and destination. The routing module is an example means for generating a route in response to the anonymized data to the destination. The routing command may be a driving instruction (e.g., turn left, go straight), which may be presented to a driver or passenger, or sent to an assisted driving system. The display 914 is an example means for displaying the routing command. The mobile device 122 may generate a routing instruction based on the anonymized data.
[0131] The routing instructions may be provided by display 914. The mobile device 122 may be configured to execute routing algorithms to determine an optimum route to travel along a road network from an origin location to a destination location in a geographic region. Using input(s) including map matching values from the server 125, a mobile device 122 examines potential routes between the origin location and the destination location to determine the optimum route. The mobile device 122, which may be referred to as a navigation device, may then provide the end user with information about the optimum route in the form of guidance that identifies the maneuvers required to be taken by the end user to travel from the origin to the destination location. Some mobile devices 122 show detailed maps on displays outlining the route, the types of maneuvers to be taken at various locations along the route, locations of certain types of features, and so on. Possible routes may be calculated based on a Dijkstra method, an A-star algorithm or search, and/or other route exploration or calculation algorithms that may be modified to take into consideration assigned cost values of the underlying road segments.)
For Claim 20, Xu teaches The non-transitory computer-readable medium of claim 16, wherein generating the prediction map comprising:
Xu does not teach obtaining data representing the expected weather conditions for the plurality of geolocations between the first geolocation and the second geolocation during an estimated period of travel; and
executing the prediction model to determine the predicted signal strengths at the plurality of geolocations at estimated time points determined based on expected period of travel between the first geolocation and each of the respective geolocations.
Swar, however, does teach obtaining data representing the expected weather conditions for the plurality of geolocations between the first geolocation and the second geolocation during an estimated period of travel; and ([0044-0045], [0051], [0020])
executing the prediction model to determine the predicted signal strengths at the plurality of geolocations at estimated time points determined based on expected period of travel between the first geolocation and each of the respective geolocations. ([0044-0045], [0051], [0020])
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar such that a machine learning prediction model is trained on signal strength data corresponding to collected data and weather conditions because a machine learning model would be expected to be successful at taking in information regarding signal strength and historic weather, and determining the relationship for the signal strength and weather patterns. This information could then be used to interpret the signal strength based on weather patterns, which would be useful at accurately ensuring that the system does not route users through areas of low signal strength and allowing them access to as many possible routes as is possible. Using expected or historic data and then comparing it to other data sets is a known and expected to be successful at training a machine learning model. Providing that model with the input data of signal strength and weather would be expected to then be successful at allowing the model to output the expected signal strength considering the weather.
For Claim 21, Xu teaches The non-transitory computer-readable medium of claim 16, wherein the computer-readable medium further store instructions that are executable by the processing device to perform operations comprising:
receiving information about one or more parameters likely to affect signal strength along the route during the estimated period of travel; ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
based on the received information, determining that the signal strength is predicted to fall below the first threshold signal value for at least a portion of the route; and ([0081] The incident data 502, vehicle probe data 504, and wireless performance data 506 may be updated. The updates may allow for dynamic route planning based on current conditions. In one example, a traffic jam happens on a busy road due to an accident. The accident and traffic jam on the road may be included in the incident data. Additionally, probe data 504 from vehicles 512 (e.g. autonomous vehicles or non-autonomous vehicles) in the traffic jam may be collected. The probe data 504, for example, may indicate the extent of congestion (e.g. if probe data indicates many slow-moving vehicles close together). The wireless performance data 506 for the road may also be collected. In some cases, the incident data 502, probe data 504, and wireless performance data 506 may be matched to the road segment (e.g. included in the HD map data 508) based on, for example, coordinates in the probe data 504. While, in normal traffic, wireless performance on the road may enable autonomous driving assistance features, the wireless performance may degrade with a large number of vehicles on the road. By collecting the various sources of information, route planning to enable a level or set of driver assistance features may avoid heavy traffic and/or atypical wireless performance caused by the traffic jam or another event. Additionally, the route may be updated based on a position of the vehicle 512 that requested the vehicle route.)
in response to the determination, determining a revised route such that the signal strength remains above the first threshold signal value along the revised route during a revised estimated period of travel between the first geolocation and the second geolocation according to the revised route. ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
For Claim 22, Xu teaches The non-transitory computer-readable medium of claim 21, wherein the one or more parameters comprise at least one of: weather condition parameters, signal status information, or information indicative of obstacles along the route. ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.)
For Claim 23, Xu teaches The non-transitory computer-readable medium of claim 16, wherein the computer-readable medium further store instructions that are executable by the processing device to perform operations comprising:
providing information about the route to a user-device for presentation on a user-interface of the user-device; ([0131] The routing instructions may be provided by display 914. The mobile device 122 may be configured to execute routing algorithms to determine an optimum route to travel along a road network from an origin location to a destination location in a geographic region. Using input(s) including map matching values from the server 125, a mobile device 122 examines potential routes between the origin location and the destination location to determine the optimum route. The mobile device 122, which may be referred to as a navigation device, may then provide the end user with information about the optimum route in the form of guidance that identifies the maneuvers required to be taken by the end user to travel from the origin to the destination location. Some mobile devices 122 show detailed maps on displays outlining the route, the types of maneuvers to be taken at various locations along the route, locations of certain types of features, and so on. Possible routes may be calculated based on a Dijkstra method, an A-star algorithm or search, and/or other route exploration or calculation algorithms that may be modified to take into consideration assigned cost values of the underlying road segments.)
in response to receiving a notification that the user-device has a signal strength below a second signal threshold value, determining a new route to the second geolocation, wherein the new route is determined such that a signal strength along the new route during a revised estimated period of travel is above the first threshold signal value; and ([0112] In act S211, the controller 900 receives a revised route. A route may be planned at a first time, e.g. in response to sending a routing request, but the wireless performance data for the path segments may change over time. For example, a detour may suddenly increase traffic on a path segment, thereby reducing the available wireless bandwidth and limiting the driving assistance features enabled on the path segment. Based on updated wireless performance data reflecting the change in available bandwidth (or another performance datum of the wireless performance data), the route may be adjusted or revised, e.g. to avoid path segments with low wireless performance. In another example, wireless performance may change over time or throughout the day. Rush hour traffic, for example, may reduce available bandwidth. If, while traversing the route, rush hour traffic or other congestion is present on a path segment of the route, a revised route may be received excluding or routing around the congested route, for example, to ensure enablement of the driving assistance features (e.g. which may be disabled on the congested route because of reduced wireless network performance), or to lessen the duration of the route.
[0071] The route planning process may iteratively consider all neighboring nodes to a current node. At the beginning, the start node is also the current node. A score for the nodes neighboring the current node may be determined. For example, the score of the neighboring nodes may be determined based on the wireless network performance for the path segment connecting the current node to the neighboring nodes. In the example of FIG. 4, the path segment L1 connects the current node at (1,0) to a neighboring node at (1,4). The score for the neighboring node may be set based on the wireless performance data for the segment L2. Similarly, a neighboring node at (4,4) is connected to the current node by path segment L2 and may be scored based on the wireless performance data for the segment L2.
It should be noted that according to applicant’s specification, the second threshold may be the same as the first threshold. Additionally, Xu states that the current node is part of the route, so if the revised routing occurs when a node’s strength is too low, then a current node would yield the result as well.)
providing a geolocation map of an area including the new route for display at the user-interface of the user-device. ([0131] The routing instructions may be provided by display 914. The mobile device 122 may be configured to execute routing algorithms to determine an optimum route to travel along a road network from an origin location to a destination location in a geographic region. Using input(s) including map matching values from the server 125, a mobile device 122 examines potential routes between the origin location and the destination location to determine the optimum route. The mobile device 122, which may be referred to as a navigation device, may then provide the end user with information about the optimum route in the form of guidance that identifies the maneuvers required to be taken by the end user to travel from the origin to the destination location. Some mobile devices 122 show detailed maps on displays outlining the route, the types of maneuvers to be taken at various locations along the route, locations of certain types of features, and so on. Possible routes may be calculated based on a Dijkstra method, an A-star algorithm or search, and/or other route exploration or calculation algorithms that may be modified to take into consideration assigned cost values of the underlying road segments.)
Claim Rejections - 35 USC § 103
Claims 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al in light of Swar in light of Hwang et al (US Pub 2014/0336923 A1), hereafter known as Hwang.
For Claim 24, Xu teaches The method of claim 1,
Xu does not teach wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), channel state information (CSI) collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
Swar, however, does teach wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
([0016] The controllers onboard the vehicle systems may determine wireless signal characteristics received by the communication devices. These signal characteristics can be referred to as communication data. In one example, the communication data may be measured by one or more sensors and reported to the controller. In another example, the communication data may be characteristics of the information being communicated. The communication data may include indications of whether a given communication channel may be busy, radio receive signal strength indicators (RSSI), cellular data signal strength indicators, Wi-Fi signal strength indicators, Bluetooth signal strength indicators, ambient noise floor levels that may indicate a background or reference noise or signal level, whether messages are repeatedly being sent without being received, whether repeaters are being used to successfully communicate the information, or the like. The RSSI and other signal strength indicators may measure the amount of power a signal. The amount of power present in the signal may be an approximate value for signal strength received by a receiving antenna. Communication data also can be evaluated based on time of day, ambient weather, network congestion, grid power availability, and the like. Based at least in part on this aspect, a route section may be rated higher communication quality/reliability during one period than during another period.
[0030] FIG. 3 shows a communication monitoring method 300, according to one example. The method can represent operations performed by the controller of the communication monitoring system. At step 302, wireless signal characteristics may be measured or received. The wireless signal characteristics may be measured in the different areas along the route. The wireless signal characteristics may be determined by data such as whether a given communication channel is busy, radio receive signal strength indicators (RSSI), reference signal received power (RSRP), reference signal received quality (RSRQ), ambient noise floor levels, whether messages are repeatedly being sent to successfully communicate information, whether repeaters are being used to successfully communicate the signals, or the like. In one example, the wireless signal characteristics may be indicative of a cellular communication strength, a radio communication strength, a Wi-Fi communication strength, or the like. A communication device may send the signal characteristics to the controller.
[0038] The communication monitoring system may monitor present and historical weather conditions in the different areas along the route. The controller may cross-reference the wireless communication signal strengths with changes in the weather conditions to more accurately reflect the wireless communication signal strength. For example, the controller may receive an input indicative of a weak wireless communication signal strength at a location X at a time Y, but the controller may have received an input indicative of a strong wireless communication signal strength at location X at a time Z. Based on the historical and present monitoring of the weather, the controller may determine that the weak wireless communication signal strength at time Y was the result of a weather event, such heavy cloud cover or rain.
[0018] The geographical location may be obtained by the controller via a Global Navigation Satellite System (GNSS) receiver (such as a global positioning system (GPS) receiver), a wireless triangulation system, operator input, a dead reckoning system, a route database, communication with transponders along the route, or the like. It may be useful to track the communication signal strength with the geographical location obtained to track where communication signal strength may vary along a given trip. Based on the wireless signal characteristics and the location that the wireless signal characteristics were measured, the controller may determine a spatial distribution of wireless signal strength. For example, where the RSSI may indicate a strong signal and no repeater may be needed to successfully communicate information, the wireless signal strength may be determined to be strong. Where the RSSI may indicate a weak signal strength and one or more repeaters may be needed to repeat messages to successfully communicate information, the wireless signal strength may be determined to be weak.
Hwang, however, does teach considering Channel State Information when determining pathing through an environment ([0003] The present disclosure relates to an apparatus and method for computing a vehicle path by considering satellite communication channel states, and particularly, to an apparatus and method for computing a vehicle path by considering satellite communication channel states, capable of acquiring an optimum communication channel from a starting point to a destination by considering electric wave characteristics on a set path.
[0014] Therefore, an aspect of the detailed description is to provide an apparatus and method for computing a vehicle path by considering satellite communication channel states from a starting point to a destination.
[0015] To achieve these and other advantages and in accordance with the purpose of this specification, as embodied and broadly described herein, there is provided a method for computing a vehicle path by considering satellite communication channel states, the method comprising: searching for a plurality of candidate paths, each path connected from a starting point to a destination; analyzing a satellite communication channel state with respect to each of the candidate paths, based on a receiving sensitivity of an electric wave received from a satellite; setting one of the candidate paths as a first path, based on the analyzed satellite communication channel states; and mapping the first path onto a map, and displaying the first path on a display unit.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar and Hwang such that wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), channel state information (CSI) collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in this way because the data listed in this limitation includes data representative of signal strength and stability, as well as factors that would be likely to impact the signal strength (as recognized by Xu and Swar). By utilizing this data as training data, it would allow a model to be trained to recognize links and connections between weather, connection, and signal strength, which would be useful in training a useful model to perform the task with location, weather, and time of day as inputs.
For Claim 25, Xu teaches The system of claim 9,
Xu does not teach wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), channel state information (CSI) collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
Swar, however, does teach wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
([0016] The controllers onboard the vehicle systems may determine wireless signal characteristics received by the communication devices. These signal characteristics can be referred to as communication data. In one example, the communication data may be measured by one or more sensors and reported to the controller. In another example, the communication data may be characteristics of the information being communicated. The communication data may include indications of whether a given communication channel may be busy, radio receive signal strength indicators (RSSI), cellular data signal strength indicators, Wi-Fi signal strength indicators, Bluetooth signal strength indicators, ambient noise floor levels that may indicate a background or reference noise or signal level, whether messages are repeatedly being sent without being received, whether repeaters are being used to successfully communicate the information, or the like. The RSSI and other signal strength indicators may measure the amount of power a signal. The amount of power present in the signal may be an approximate value for signal strength received by a receiving antenna. Communication data also can be evaluated based on time of day, ambient weather, network congestion, grid power availability, and the like. Based at least in part on this aspect, a route section may be rated higher communication quality/reliability during one period than during another period.
[0030] FIG. 3 shows a communication monitoring method 300, according to one example. The method can represent operations performed by the controller of the communication monitoring system. At step 302, wireless signal characteristics may be measured or received. The wireless signal characteristics may be measured in the different areas along the route. The wireless signal characteristics may be determined by data such as whether a given communication channel is busy, radio receive signal strength indicators (RSSI), reference signal received power (RSRP), reference signal received quality (RSRQ), ambient noise floor levels, whether messages are repeatedly being sent to successfully communicate information, whether repeaters are being used to successfully communicate the signals, or the like. In one example, the wireless signal characteristics may be indicative of a cellular communication strength, a radio communication strength, a Wi-Fi communication strength, or the like. A communication device may send the signal characteristics to the controller.
[0038] The communication monitoring system may monitor present and historical weather conditions in the different areas along the route. The controller may cross-reference the wireless communication signal strengths with changes in the weather conditions to more accurately reflect the wireless communication signal strength. For example, the controller may receive an input indicative of a weak wireless communication signal strength at a location X at a time Y, but the controller may have received an input indicative of a strong wireless communication signal strength at location X at a time Z. Based on the historical and present monitoring of the weather, the controller may determine that the weak wireless communication signal strength at time Y was the result of a weather event, such heavy cloud cover or rain.
[0018] The geographical location may be obtained by the controller via a Global Navigation Satellite System (GNSS) receiver (such as a global positioning system (GPS) receiver), a wireless triangulation system, operator input, a dead reckoning system, a route database, communication with transponders along the route, or the like. It may be useful to track the communication signal strength with the geographical location obtained to track where communication signal strength may vary along a given trip. Based on the wireless signal characteristics and the location that the wireless signal characteristics were measured, the controller may determine a spatial distribution of wireless signal strength. For example, where the RSSI may indicate a strong signal and no repeater may be needed to successfully communicate information, the wireless signal strength may be determined to be strong. Where the RSSI may indicate a weak signal strength and one or more repeaters may be needed to repeat messages to successfully communicate information, the wireless signal strength may be determined to be weak.
Hwang, however, does teach considering Channel State Information when determining pathing through an environment ([0003] The present disclosure relates to an apparatus and method for computing a vehicle path by considering satellite communication channel states, and particularly, to an apparatus and method for computing a vehicle path by considering satellite communication channel states, capable of acquiring an optimum communication channel from a starting point to a destination by considering electric wave characteristics on a set path.
[0014] Therefore, an aspect of the detailed description is to provide an apparatus and method for computing a vehicle path by considering satellite communication channel states from a starting point to a destination.
[0015] To achieve these and other advantages and in accordance with the purpose of this specification, as embodied and broadly described herein, there is provided a method for computing a vehicle path by considering satellite communication channel states, the method comprising: searching for a plurality of candidate paths, each path connected from a starting point to a destination; analyzing a satellite communication channel state with respect to each of the candidate paths, based on a receiving sensitivity of an electric wave received from a satellite; setting one of the candidate paths as a first path, based on the analyzed satellite communication channel states; and mapping the first path onto a map, and displaying the first path on a display unit.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar and Hwang such that wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), channel state information (CSI) collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in this way because the data listed in this limitation includes data representative of signal strength and stability, as well as factors that would be likely to impact the signal strength (as recognized by Xu and Swar). By utilizing this data as training data, it would allow a model to be trained to recognize links and connections between weather, connection, and signal strength, which would be useful in training a useful model to perform the task with location, weather, and time of day as inputs.
For Claim 26, Xu teaches The non-transitory computer-readable medium of claim 16,
Xu does not teach wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), channel state information (CSI) collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
Swar, however, does teach wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
([0016] The controllers onboard the vehicle systems may determine wireless signal characteristics received by the communication devices. These signal characteristics can be referred to as communication data. In one example, the communication data may be measured by one or more sensors and reported to the controller. In another example, the communication data may be characteristics of the information being communicated. The communication data may include indications of whether a given communication channel may be busy, radio receive signal strength indicators (RSSI), cellular data signal strength indicators, Wi-Fi signal strength indicators, Bluetooth signal strength indicators, ambient noise floor levels that may indicate a background or reference noise or signal level, whether messages are repeatedly being sent without being received, whether repeaters are being used to successfully communicate the information, or the like. The RSSI and other signal strength indicators may measure the amount of power a signal. The amount of power present in the signal may be an approximate value for signal strength received by a receiving antenna. Communication data also can be evaluated based on time of day, ambient weather, network congestion, grid power availability, and the like. Based at least in part on this aspect, a route section may be rated higher communication quality/reliability during one period than during another period.
[0030] FIG. 3 shows a communication monitoring method 300, according to one example. The method can represent operations performed by the controller of the communication monitoring system. At step 302, wireless signal characteristics may be measured or received. The wireless signal characteristics may be measured in the different areas along the route. The wireless signal characteristics may be determined by data such as whether a given communication channel is busy, radio receive signal strength indicators (RSSI), reference signal received power (RSRP), reference signal received quality (RSRQ), ambient noise floor levels, whether messages are repeatedly being sent to successfully communicate information, whether repeaters are being used to successfully communicate the signals, or the like. In one example, the wireless signal characteristics may be indicative of a cellular communication strength, a radio communication strength, a Wi-Fi communication strength, or the like. A communication device may send the signal characteristics to the controller.
[0038] The communication monitoring system may monitor present and historical weather conditions in the different areas along the route. The controller may cross-reference the wireless communication signal strengths with changes in the weather conditions to more accurately reflect the wireless communication signal strength. For example, the controller may receive an input indicative of a weak wireless communication signal strength at a location X at a time Y, but the controller may have received an input indicative of a strong wireless communication signal strength at location X at a time Z. Based on the historical and present monitoring of the weather, the controller may determine that the weak wireless communication signal strength at time Y was the result of a weather event, such heavy cloud cover or rain.
[0018] The geographical location may be obtained by the controller via a Global Navigation Satellite System (GNSS) receiver (such as a global positioning system (GPS) receiver), a wireless triangulation system, operator input, a dead reckoning system, a route database, communication with transponders along the route, or the like. It may be useful to track the communication signal strength with the geographical location obtained to track where communication signal strength may vary along a given trip. Based on the wireless signal characteristics and the location that the wireless signal characteristics were measured, the controller may determine a spatial distribution of wireless signal strength. For example, where the RSSI may indicate a strong signal and no repeater may be needed to successfully communicate information, the wireless signal strength may be determined to be strong. Where the RSSI may indicate a weak signal strength and one or more repeaters may be needed to repeat messages to successfully communicate information, the wireless signal strength may be determined to be weak.
Hwang, however, does teach considering Channel State Information when determining pathing through an environment ([0003] The present disclosure relates to an apparatus and method for computing a vehicle path by considering satellite communication channel states, and particularly, to an apparatus and method for computing a vehicle path by considering satellite communication channel states, capable of acquiring an optimum communication channel from a starting point to a destination by considering electric wave characteristics on a set path.
[0014] Therefore, an aspect of the detailed description is to provide an apparatus and method for computing a vehicle path by considering satellite communication channel states from a starting point to a destination.
[0015] To achieve these and other advantages and in accordance with the purpose of this specification, as embodied and broadly described herein, there is provided a method for computing a vehicle path by considering satellite communication channel states, the method comprising: searching for a plurality of candidate paths, each path connected from a starting point to a destination; analyzing a satellite communication channel state with respect to each of the candidate paths, based on a receiving sensitivity of an electric wave received from a satellite; setting one of the candidate paths as a first path, based on the analyzed satellite communication channel states; and mapping the first path onto a map, and displaying the first path on a display unit.)
Therefore, it would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in light of Swar and Hwang such that wherein the training data comprises (i) signal indicators including received signal strength indicator (RSSI), channel state information (CSI) collected from a plurality of user devices with respective geolocations, (ii) the corresponding weather conditions, and (iii) device movement patterns tracked over time as the plurality of user devices reposition between geolocations within a network coverage area, the training data being collected by crowdsourcing over a time period.
It would be obvious to one of ordinary skill in the art prior to the effective filing date to modify Xu in this way because the data listed in this limitation includes data representative of signal strength and stability, as well as factors that would be likely to impact the signal strength (as recognized by Xu and Swar). By utilizing this data as training data, it would allow a model to be trained to recognize links and connections between weather, connection, and signal strength, which would be useful in training a useful model to perform the task with location, weather, and time of day as inputs.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al (US Pub 2019/0331501 A1) relates to planning vehicle routes considering signal strengths.
Magzimof et al (US Pub 2019/0383624 A1), relates to evaluating vehicle routes based on network performance.
Song et al (US Pub 2023/0194274 A1) relates to ensuring vehicle routes do not fall below a signal strength.
THIS ACTION IS MADE FINAL. 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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/T.J.G./Examiner, Art Unit 3656
/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656