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 .
Status of Claims
This action is in reply to the communications filed on 01/28/2026.
Claims 1-4, 7-10, and 13-18 have been amended.
Claims 6, 12, and 19 have been canceled.
Claims 1-5, 7-11, and 13-18 are currently pending and have been examined.
Response to Applicant’s Remarks
Applicant’s arguments and remarks filed on 01/28/2026 have been fully considered and each argument will be respectfully addressed in the following final office action.
Response to Claim Objection Remarks
Applicant’s remarks filed on pages 9-12 of the Response concerning the claim objections have been fully considered and are found to be persuasive. In view of the amendments to the claims, the claim objections have been withdrawn herein.
Response to 35 U.S.C. § 112 (b) Remarks
Applicant’s remarks filed on page12 of the Response concerning the §112(b) rejection of claims 6, 12, and 19 have been fully considered and are found to be persuasive. In view of the cancellation of the claims, the §112(b) rejections have been withdrawn herein.
Response to 35 U.S.C. § 101 Remarks
Applicant’s remarks filed on pages 12-16 of the Response concerning the 35 U.S.C. § 101 rejection of claims 1-19 have been fully considered but are found not persuasive and are moot in view of the amended rejection that may be found starting on page 6 of this final office action.
On pages 12-16 of the Response, the Applicant argues that the amended limitations of the independent claims integrate the abstract idea into a practical application and that the independent claims include significantly more than the alleged abstract idea. In particular, the Applicant argues “amended claim 1 is directed to analyzing vehicle driving data, generating a recommendation priority based on the analysis result, and utilizing the recommendation priority in allocating vehicles…an improvement the technology of vehicle allocation based on vehicle positioning information” (see pp. 15-16 of Response).
The Examiner respectfully disagrees that the amended independent claims recite additional elements or features that integrate the recited abstract idea into a practical application or provide significantly more than the judicial exception. The amendments to the independent claims recite features that describe a management strategy for a fleet of vehicles comprising determining a recommendation priority of each vehicle in the fleet based on a safety score of each vehicle, determining candidate vehicles in accordance with a pick-up location indicated in a dispatching request, and selecting a particular vehicle to respond to the dispatching request in accordance with the recommendation priority of each vehicle wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that a candidate vehicle is selected. These amended claim features do not integrate the abstract idea into a practical application because they are considered to be a part of the abstract idea itself. In particular, these limitations recite the performance of observation, collecting information, analyzing information, and making a judgement - which is the abstract idea of mental processes. As currently drafted, a human using mental steps would be capable of performing the limitations involving determining a recommendation priority of vehicles in a fleet based on a safety score, determining candidate vehicles based on a proximity to a pick up location indicated in a dispatching request, and selecting a particular vehicle in accordance with the recommendation priority by merely collecting information, analyzing information, and making a judgment based on the collected/analyzed information. Moreover, these limitations, as whole, which involve steps for selecting vehicles to respond to dispatching requests in accordance with a recommendation priority and relevant safety scores recite concepts of facilitating commercial interactions.
Furthermore, the recited computer tools and instructions of the independent claims are recited at a high level of generality and are merely being utilized in their ordinary capacity to execute the abstract idea. For example, with regard to independent claim 1, the “server” and “on-board computer” of the vehicles are merely described as generic computer tools utilized to receive and transmit driving data, which is not considered a technical improvement to the functioning of a computer or technical field. The Examiner notes the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more” (MPEP 2106.05 (f)).
Independent claims 7 and 14 further recite the use of a “server” and a “processor”, respectively, to the perform the amended claim limitations. Again, these additional elements are recited at a high level of generality such that they are merely considered to be generic computer tools and instructions to apply the abstract idea. The Examiner notes, “Claims can recite a mental process even if they are claimed as being performed on a computer” (see MPEP 2106.04(a)(2)(III)(C). The Examiner further notes “courts have also identified limitations that did not integrate a judicial exception into a practical application: Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea” (MPEP 2106.04(d)(I)) and “Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include […] Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer” (See MPEP 2106.05(I)(A)).
Therefore, the Examiner maintains that the additional elements of the claims, when viewed as a whole/ordered combination, do not integrate the abstract idea into a practical application because they are merely recited as generic computer tools and instructions being utilized in their ordinary capacity to receive/transmit information and apply the abstract idea, i.e., the mental process described above. Furthermore, because merely “applying” the exception using generic computer tools/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Response to 35 U.S.C. § 103 Remarks
Applicant’s remarks filed on pages 16-20 of the Response concerning the 35 U.S.C. § 103 rejection of claims 1-19 have been fully considered but are moot in view of the amended §103 rejection that may be found starting on page 20 of this final office action.
On pages 16-20 of the Response, the Applicant argues that the cited prior art of record does not teach or suggest the features of the amended independent claims. In view of the amendments to the claims, the Examiner has set forth an amended §103 rejection of the independent claims with newly cited prior art.
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 1-5, 7-11, and 13-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
First of all, claims must be directed to one or more of the following statutory categories: a process, a machine, a manufacture, or a composition of matter. Claims 1-5 are directed to a process (“a method”), claims 7-11 and 13 are directed to a machine (“a system”), and claims 14-18 are directed to a machine (“a server”). Thus, claims 1-5, 7-11, and 13-18 satisfy Step One because they are all within one of the four statutory categories of eligible subject matter. Claims 1-5, 7-11, and 13-18, however, are directed to an abstract idea without significantly more.
Regarding independent claim 1, the specific limitations that recite an abstract idea are:
Receiving the objective driving data […];
Detecting whether a dangerous driving behavior exists corresponding to the vehicle in accordance with the objective driving data to generate a detection result; and
Determining a management strategy of the vehicle in accordance with the detection result.
Wherein management strategies of the fleet comprise a recommendation priority of each vehicle of the fleet, the recommendation priority of each vehicle of the fleet is determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet;
The method further comprises: determining a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request; and
Selecting an objective vehicle from the first number of candidate vehicles to respond the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles; wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the objective vehicle.
Therefore, claims 1 and 2-5, by virtue of dependence, recite certain methods of organizing human activity. In particular, the limitations of claim 1 identified above, as a whole, are directed towards determining/detecting dangerous driving behavior of a human driver and determining a management strategy of the vehicle in accordance with the detection, wherein the management strategies involve determining a recommendation priority of each vehicle in a fleet in accordance with a safety score and selecting a vehicle to respond to a dispatch request in accordance with the recommendation priority. This is further evidenced by the Applicant specification at ¶ [0033], ¶ [0045], and ¶ [0066]. Thus, claim 1, as a whole, recites concepts of managing personal behavior and commercial interactions. See MPEP 2106.04(a)(2)(II). Furthermore, the limitations of claim 1 identified above recite concepts of mental processes. In particular, the limitations identified above recite the performance of observation, judgement, collecting information, and analyzing information in a manner that is analogous to human mental work. See MPEP 2106.04(a)(2)(III).
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include a “server” and an “on-board computer configured in a vehicle of a fleet”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Furthermore, the claim recites additional elements involving steps for transmitting information over a network (“the server communicating with an on-board computer“, “the on-board computer acquiring objective driving data from initial driving data of the vehicle and transmitting the objective driving data to the server”, “receiving the objective driving data from the on-board computer”). These additional elements fail to integrate the claim into a practical application because the steps for transmitting information over a network amount to no more than mere data gathering/outputting, which is insignificant extra-solution activity. See MPEP 2106.05(g).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for transmitting information over a network fail to amount to significantly more than the judicial exception because the courts have found transmitting information over a network to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve transmitting information over a network, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 1 is not patent eligible.
Regarding independent claim 7, the specific limitations that recite an abstract idea are:
Receive the objective driving data […];
Detect whether a dangerous driving behavior exists corresponding to a first vehicle of the fleet […] in accordance with the objective driving data to generate a detection result; and
Determine a management strategy of the vehicle in accordance with the detection result;
Wherein management strategies of the fleet comprise a recommendation priority of each vehicle of the fleet, the recommendation priority of each vehicle of the fleet is determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet;
[…] determine a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request; and
Select a second vehicle from the first number of candidate vehicles to respond the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles; wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the second vehicle.
Therefore, claims 7 and 8-11 and 13, by virtue of dependence, recite certain methods of organizing human activity. In particular, the limitations of claim 7 identified above, as a whole, are directed towards determining/detecting dangerous driving behavior of a human driver and determining a management strategy of the vehicle in accordance with the detection, wherein the management strategies involve determining a recommendation priority of each vehicle in a fleet in accordance with a safety score and selecting a vehicle to respond to a dispatch request in accordance with the recommendation priority. This is further evidenced by the Applicant specification at ¶ [0033], ¶ [0045], and ¶ [0066]. Thus, claim 7, as a whole, recites concepts of managing personal behavior and commercial interactions. See MPEP 2106.04(a)(2)(II). Furthermore, the limitations of claim 7 identified above recite concepts of mental processes. In particular, the limitations identified above recite the performance of observation, judgement, collecting information, and analyzing information in a manner that is analogous to human mental work. See MPEP 2106.04(a)(2)(III).
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include “one or more on-board computers and a server, the one or more on-board computers communicating with the server, each of the one or more computers being configured in a vehicle of a fleet”, and “an objective on-board computer”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Furthermore, the claim recites additional elements involving steps for transmitting information over a network (“the one or more on-board computers communicating with the server”, “the one or more onboard computers acquiring objective driving data from initial driving data of a corresponding vehicle and transmitting the objective driving data to the server”, “receive the objective driving data from an objective on-board computer of the on-board computers”). These additional elements fail to integrate the claim into a practical application because the steps for transmitting information over a network amount to no more than mere data gathering/outputting, which is insignificant extra-solution activity. See MPEP 2106.05(g).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for transmitting information over a network fail to amount to significantly more than the judicial exception because the courts have found transmitting information over a network to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve transmitting information over a network, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 7 is not patent eligible.
Regarding independent claim 14, the specific limitations that recite an abstract idea are:
Receive the objective driving data […];
Detect whether a dangerous driving behavior exists corresponding to a first vehicle of the fleet […] in accordance with the objective driving data to generate a detection result; and
Determine a management strategy of the vehicle in accordance with the detection result;
Wherein management strategies of the fleet comprise a recommendation priority of each vehicle of the fleet, the recommendation priority of each vehicle of the fleet is determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet;
[…] determine a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request; and
Select a second vehicle from the first number of candidate vehicles to respond the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles; wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the second vehicle.
Therefore, claims 14 and 15-18, by virtue of dependence, recite certain methods of organizing human activity. In particular, the limitations of claim 14 identified above, as a whole, are directed towards determining/detecting dangerous driving behavior of a human driver and determining a management strategy of the vehicle in accordance with the detection, wherein the management strategies involve determining a recommendation priority of each vehicle in a fleet in accordance with a safety score and selecting a vehicle to respond to a dispatch request in accordance with the recommendation priority. This is further evidenced by the Applicant specification at ¶ [0033], ¶ [0045], and ¶ [0066]. Thus, claim 14, as a whole, recites concepts of managing personal behavior and commercial interactions. See MPEP 2106.04(a)(2)(II). Furthermore, the limitations of claim 14 identified above recite concepts of mental processes. In particular, the limitations identified above recite the performance of observation, judgement, collecting information, and analyzing information in a manner that is analogous to human mental work. See MPEP 2106.04(a)(2)(III).
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include “a server configured for communicating with one or more on-board computers”, “one or more on-board computers being configured in a vehicle of a fleet”, “at least one processor”, “a data storage storing one or more programs which when executed by the at least one processor, cause the processor to…”, and an “objective on-board computer” . The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Furthermore, the claim recites additional elements involving steps for transmitting information over a network (“each of the one or more on-board computers acquiring objective driving data from initial driving data of a corresponding vehicle and transmitting the objective driving data to the server”, “receive the objective driving data from an objective on-board computer of the on-board computers”). These additional elements fail to integrate the claim into a practical application because the steps for transmitting information over a network amount to no more than mere data gathering/outputting, which is insignificant extra-solution activity. See MPEP 2106.05(g).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for transmitting information over a network fail to amount to significantly more than the judicial exception because the courts have found transmitting information over a network to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve transmitting information over a network, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 14 is not patent eligible.
Claim 2 recites steps for determining driving environment information and operation information of a vehicle in accordance with objective driving data, and detecting whether dangerous driving behavior exists in accordance with the driving environment information and the operation information of the vehicle to generate the detection result. Thus, claim 2 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 3 further defines the collected objective driving data as images of roads during vehicle driving. Thus, claim 3 further describes the abstract idea.
The claim further introduces the additional elements of a “determining the driving environment information of the vehicle in accordance with the objective driving data further comprises: inputting the images of the roads into a preset image semantic recognition model to obtain the driving environment information of the vehicle”.
Claim 3 does introduce more specific technology, “a preset image semantic recognition model”, but again, this is merely being used as a generic computer tool to implement the abstract idea above. The steps involving the use of the “preset image semantic recognition model” are recited at a high level of generality and do not reflect any type of improvement to the technology itself. As currently drafted, the claim merely involves inputting images to the model and obtaining a result without any further technical details pertaining to the functioning of the image semantic recognition model. Accordingly, the additional elements involving the use of the “preset image semantic recognition model” merely serve as generic computer components/instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 4 further defines the objective driving data as including position information of the vehicle, and determining the driving environment information of the vehicle further comprises obtaining a high-precision map comprising the position information of the vehicle and determining the driving environment information of the vehicle in accordance with the position information and high-precision map. Thus, claim 4 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claims 1-2 from which the claim depends.
Claim 5 further describes that determining the management strategy of the vehicle further comprises determining dangerous driving behaviors of the vehicle within a preset time in accordance with the detection result within the preset time, giving a safety score for the vehicle in accordance with the dangerous driving behaviors and types of dangerous driving behaviors, and determining the management strategy in accordance with the safety score. Thus, claim 5 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 8 recites steps for determining driving environment information and operation information of a vehicle in accordance with objective driving data, and detecting whether the vehicle exists the dangerous driving behavior in accordance with the driving environment information and the operation information of the vehicle to generate the detection result. Thus, claim 8 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 7 from which the claim depends.
Claim 9 further defines the collected objective driving data as images of roads during vehicle driving. Thus, claim 9 further describes the abstract idea.
The claim further introduces the additional elements of a “determining the driving environment information of the vehicle in accordance with the objective driving data further comprises: inputting the images of the roads into a preset image semantic recognition model to obtain the driving environment information of the vehicle”.
Claim 9 does introduce more specific technology, “a preset image semantic recognition model”, but again, this is merely being used as a generic computer tool to implement the abstract idea above. The steps involving the use of the “preset image semantic recognition model” are recited at a high level of generality and do not reflect any type of improvement to the technology itself. As currently drafted, the claim merely involves inputting images to the model and obtaining a result without any further technical details pertaining to the functioning of the image semantic recognition model. Accordingly, the additional elements involving the use of the “preset image semantic recognition model” merely serve as generic computer components/instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 10 further defines the objective driving data as including position information of the vehicle, and determining the driving environment information of the vehicle further comprises obtaining a high-precision map comprising the position information of the vehicle and determining the driving environment information of the vehicle in accordance with the position information and high-precision map. Thus, claim 10 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claims 7-8 from which the claim depends.
Claim 11 further describes that determining the management strategy of the vehicle further comprises determining dangerous driving behaviors of the vehicle within a preset time in accordance with the detection result within the preset time, giving a safety score for the vehicle in accordance with the dangerous driving behaviors and types of dangerous driving behaviors, and determining the management strategy in accordance with the safety score. Thus, claim 11 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 7 from which the claim depends.
Claim 13 further defines the objective driving data as comprising driving data indicating a preset potential dangerous event exists in the initial driving data. Thus, claim 13 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 7 from which the claim depends.
Claim 15 recites steps for determining driving environment information and operation information of a vehicle in accordance with objective driving data, and detecting whether the dangerous driving behavior exists in accordance with the driving environment information and the operation information of the vehicle to generate the detection result. Thus, claim 8 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 14 from which the claim depends.
Claim 16 further defines the collected objective driving data as images of roads during vehicle driving. Thus, claim 16 further describes the abstract idea.
The claim further introduces the additional elements of a “determining the driving environment information of the vehicle in accordance with the objective driving data further comprises: inputting the images of the roads into a preset image semantic recognition model to obtain the driving environment information of the vehicle”.
Claim 16 does introduce more specific technology, “a preset image semantic recognition model”, but again, this is merely being used as a generic computer tool to implement the abstract idea above. The steps involving the use of the “preset image semantic recognition model” are recited at a high level of generality and do not reflect any type of improvement to the technology itself. As currently drafted, the claim merely involves inputting images to the model and obtaining a result without any further technical details pertaining to the functioning of the image semantic recognition model. Accordingly, the additional elements involving the use of the “preset image semantic recognition model” merely serve as generic computer components/instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 17 further defines the objective driving data as including position information of the vehicle, and determining the driving environment information of the vehicle further comprises obtaining a high-precision map comprising the position information of the vehicle and determining the driving environment information of the vehicle in accordance with the position information and high-precision map. Thus, claim 17 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claims 14-15 from which the claim depends.
Claim 18 further describes that determining the management strategy of the vehicle further comprises determining dangerous driving behaviors of the vehicle within a preset time in accordance with the detection result within the preset time, giving a safety score for the vehicle in accordance with the dangerous driving behaviors and types of dangerous driving behaviors, and determining the management strategy in accordance with the safety score. Thus, claim 18 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 14 from which the claim depends.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7-11, and 13-18 are rejected under 35 U.S.C. § 103 as being unpatentable over Kim et al. U.S. Publication No. 2018/0134215, hereafter known as Kim, in view of Shoshan U.S. Patent No. 9,576,480, hereafter known as Shoshan, in further view of Marueli et al. U.S. Publication No. 2020/0404074, hereafter known as Marueli.
Claim 1: Kim teaches the following:
A […] method applied to a server, the server communicating with an on-board computer configured in a vehicle […], the on-board computer acquiring objective driving data from initial driving data of the vehicle and transmitting the objective driving data to the server, the […] method comprising: (¶ [0035]: the device may be a micro server); (¶ [0036]: the device may communicate with a server in order to utilize information generated during running of various vehicles); (¶ [0049]: the device may determine the risk level of the driver in consideration of at least information obtained from the vehicle. The information may include image information generated from a camera included in the vehicle, on-board diagnostics (OBD) (e.g., angle information of the steering wheel, RPM information, etc.), and the like).
Receiving the objective driving data from the on-board computer; (¶ [0049]: the device may determine the risk level of the driver in consideration of at least information obtained from the vehicle. The information may include image information generated from a camera included in the vehicle, on-board diagnostics (OBD) (e.g., angle information of the steering wheel, RPM information, etc.), and the like).
Detecting whether a dangerous driving behavior exists corresponding to the vehicle in accordance with the objective driving data to generate a detection result; and (¶ [0049]: see above); (¶ [0045]: The device may determine a current status of a driver. For example, the device may determine that the driver is in a dangerous state if the vehicle has been driven dangerously. As an example, if the device recognizes that the vehicle shows a running pattern that crosses a lane in which the vehicle is running, the device may determine that the vehicle has been driven dangerously. At this time, the device determines that the driver is in a careless state); (¶ [0046]: the device may generate a notification signal based on the risk level of the driver determined based on the current status of the driver).
Determining a management strategy of the vehicle in accordance with the detection result. (¶ [0046]: the device may generate a notification signal based on the risk level of the driver determined based on the current status of the driver); (¶ [0047]: the notification signal may be generated when the risk level is great than or equal to a threshold); (¶ [0071]: the device may generate the notification signal with sound and/or vibration and output the notification signal. For example, the device may provide a driver with a guidance sound to induce safe driving. The guidance sound may be a voice to encourage stretching, a voice to explain a method of resolving drowsiness, and a voice to guide the driver to a resting place or a parking spot).
Although Kim teaches a system configured to monitor driving behavior corresponding to a vehicle via a device/micro-server, Kim does not explicitly teach that the vehicle belongs to a fleet and that the server manages a fleet of vehicles.
However, Shoshan teaches the following:
A fleet managing method applied to a server, the server communicating with an on-board computer configured in a vehicle of a fleet, the on-board computer acquiring objective driving data from initial driving data of the vehicle and transmitting the objective driving data to the server, the fleet management method comprising: (col. 2, lines 5-6: Fig. 2 is a flowchart of an example method of managing a fleet of vehicles); (col. 1, lines 28-38: the system for managing vehicle sensor data comprises a central server configured to aggregate vehicle condition sensor data by vehicle identifier and vehicle position, and determine a safety condition for the aggregated vehicle condition sensor data); (col. 2, lines 62-67: the vehicles have the ability to connect and share information through a centrally-managed cloud computing system. The cloud aggregates vehicle information to determine safety conditions); (col. 3, lines 25-41: the vehicles are equipped with sensors. Sensors provide specialized vehicle operation information and include standard on-board diagnostic (OBD) sensors, and cameras that capture road imagery. Vehicle condition sensor data can be sent from the vehicle to the cloud); (col. 4, lines 41-50: safety conditions can be vehicle related or location related. For example, dangerous driving behavior can be determined from sensors acquiring data from driver input controls, determining unsafe following distances, and excessive speed. Thus, vehicle related safety conditions pertain to a particular vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Kim with the teachings of Shoshan by incorporating the centrally-managed cloud server configured to aggregate driving sensor data from a plurality of vehicles within a fleet and determine driving behaviors for each particular vehicle, as taught by Shoshan, into the system of Kim comprising a device/micro-server that is configured to collect sensor data from a vehicle to determine driving behaviors. One of ordinary skill in the art would have been motivated to make this combination when one considers the claimed invention is merely a combination of old elements. In combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination are predictable. Furthermore, one of ordinary skill in the art would have been motivated to make this modification when one considers it may further “enable increased environmental awareness for car drivers, increasing safety and reducing traffic congestion” (col. 15, lines 27-29), and when one considers “[a]nother advantage of the described technologies is that vehicle management system can use the aggregated vehicle information to guide traffic and ensure efficient use of available resources, such as road capacity” (col. 15, lines 35-39), as suggested by Shoshan. Furthermore, one of ordinary skill in the art would have recognized that the teachings of Shoshan are compatible with the system of Kim as they share capabilities and characteristics. In particular, they are both systems comprising servers configured to monitor vehicle driving data via on-board sensors and detect dangerous driving behaviors.
Kim/Shoshan does not explicitly teach wherein the management strategies of a fleet comprise a recommendation priority of each vehicle of the fleet, the recommendation priority of each vehicle of the fleet determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet. Furthermore, Kim/Shoshan does not explicitly teach a method further comprising determining a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request, and selecting an objective vehicle from the first number of candidate vehicles to respond the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles, wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the objective vehicle.
However, Marueli teaches the following:
Wherein the management strategies of the fleet comprise a recommendation priority of each vehicle of the fleet, the recommendation priority of each vehicle of the fleet is determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet; (¶ [0012]: a queue of drivers for a particular area is maintained, wherein the queue specifies an order in which drivers are selected for transportation requests for a particular area); (¶ [0067]: the server may determine driver placement within a queue based on characteristics of transportation requests and performance metrics/scores associated with the drivers); (¶ [0078]: performance metrics may include a safety record, e.g., a measure of traffic accidents experienced or traffic tickets received).
The method further comprises: determining a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request; (¶ [0012]: see above); (¶ [0067]: the server may determine driver placement within a queue based on characteristics of transportation requests and performance metrics/scores associated with the drivers); (¶ [0019]: backend system may select a driver based on any suitable factors, including information contained in the transportation request, the proximity of the driver to the passenger, whether the pickup location is located within an area serviced by one or more queues, or any other suitable factors);
Selecting an objective vehicle from the first number of candidate vehicles to respond to the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles; wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the objective vehicle. (¶ [0019]: see above); (¶ [0078]: see above); (¶ [0067]: if a driver has a relatively high performance score indicating that the driver is performing well relative to other drivers, the driver may be placed at a spot in the queue that is higher than the bottom of the queue); (¶ [0012]: a queue of drivers for a particular area is maintained, wherein the queue specifies an order in which drivers are selected for transportation requests for a particular area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system of Kim/Shoshan the ability to determine a recommendation priority of each vehicle of a fleet, the recommendation priority of each vehicle of the fleet determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet; determine a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request; and select an objective vehicle from the first number of candidate vehicles to respond to the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the objective vehicle, as taught by Marueli, since the claimed invention is merely a combination of old elements. In combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination are predictable. Furthermore, one of ordinary skill in the art would have been motivated to make this modification when one considers a “driver could be given a preferred position in the queue […] if the driver has performed well relative to other drivers” (¶ [0014]), as suggested by Marueli, and thus incentivizing drivers to drive safely.
Claim 2: Kim/Shoshan/Marueli teaches the limitations of claim 1. Furthermore, Kim teaches the following:
Wherein detecting whether the dangerous driving behavior exists corresponding to the vehicle in accordance with the objective driving data to generate the detection result further comprises: determining driving environment information and operation information of the vehicle in accordance with the objective driving data; (¶ [0049]: the device may determine the risk level of the driver in consideration of at least information obtained from the vehicle. The information may include image information generated from a camera included in the vehicle, on-board diagnostics (OBD) (e.g., angle information of the steering wheel, RPM information, etc.), and the like); (¶ [0045]: The device may determine a current status of a driver. For example, the device may determine that the driver is in a dangerous state if the vehicle has been driven dangerously. As an example, if the device recognizes that the vehicle shows a running pattern that crosses a lane in which the vehicle is running, the device may determine that the vehicle has been driven dangerously. At this time, the device determines that the driver is in a careless state).
Detecting whether the dangerous driving behavior exists corresponding to the vehicle in accordance with the driving environment information and the operation information of the vehicle, to generate the detection result. (¶ [0045]: see above); (¶ [0046]: the device may generate a notification signal based on the risk level of the driver determined based on the current status of the driver); (¶ [0047]: the notification signal may be generated when the risk level is great than or equal to a threshold).
Claim 3: Kim/Shoshan/Marueli teaches the limitations of claim 2. Furthermore, Kim teaches the following:
Wherein the objective driving data comprises images of roads during the vehicle driving; and(¶ [0049]: the information obtained from the vehicle may include image information generated from a camera included in the vehicle); (¶ [0029]: the device may use information generated during running of a vehicle to determine a status of a driver. The device may determine whether the vehicle is running on a predetermined road. If the vehicle is running on the road, the device may determine a risk level of the driver).
Determining the driving environment information of the vehicle in accordance with the objective driving data further comprises: inputting the images of the roads into a preset image semantic recognition model to obtain the driving environment information of the vehicle. (¶ [0049]: see above); (¶ [0029]: see above); (¶ [0120] -¶ [0124]: A data acquisition unit may obtain sensing data. A preprocessing unit may pre-process the obtained data to use for learning to determine whether the vehicle is running on the highway and learn the risk level. A learning data selection unit may select data required for learning from the preprocessed data and provide the selected data to a model learning unit); (¶ [0125]: the model learning unit may learn a data recognition model used for determining whether the vehicle is running on the highway and a risk level of the driver using the learning data); (¶ [0126]: the data recognition model may be a model based on a neural network such as a deep neural network and recurrent neural network).
Claim 4: Kim/Shoshan/Marueli teaches the limitations of claim 2. Furthermore, Kim teaches the following:
Wherein the objective driving data comprises position information of the vehicle; and (¶ [0053]: the device may determine whether the vehicle is running on the highway, based on information about a current state of the vehicle which is sensed while the vehicle is running); (¶ [0054]: the information about a current state of the vehicle which is sensed may include latitude and longitude information of a GPS sensor); (¶ [0057]: an input value of a data recognition model for determining whether the vehicle is running on the highway may be an input image generated by imaging signals of latitude and longitude information).
Determining the driving environment information of the vehicle in accordance with the objective driving data further comprises: obtaining a high-precision map comprising the position information of the vehicle; and (¶ [0053]: see above); (¶ [0054]: the information about a current state of the vehicle which is sensed may include latitude and longitude information of a GPS sensor); (¶ [0057]: an input value of a data recognition model for determining whether the vehicle is running on the highway may be an input image generated by imaging signals of latitude and longitude information); (¶ [0059]: the device may determine whether the vehicle is running on the highway through a mapping function).
Determining the driving environment information of the vehicle in accordance with the position information of the vehicle and the high-precision map. (¶ [0053]: see above); (¶ [0054]: see above); (¶ [0057]: see above).
Claim 5: Kim/Shoshan/Marueli teaches the limitations of claim 1. Furthermore, Kim teaches the following:
Wherein determining the management strategy of the vehicle in accordance with the detection result further comprises: determining dangerous driving behaviors of the vehicle within a preset time in accordance with the detection result within the preset time; (¶ [0056]: the information that the current state of the vehicle is sensed while the vehicle is running may be a time series signal. Further, the information about the current state of the vehicle may be processed in a form suitable for learning. The device may perform an operation of dividing the time series signal into an appropriate length of time. The appropriate length of time may be a length including a specific running pattern. The appropriate length of time may be set to a predetermined length).
Giving a safety score for the vehicle in accordance with the dangerous driving behaviors of the vehicle within the preset time and types of the dangerous driving behaviors; (¶ [0054]: see above); (¶ [0046]: the device may generate a notification signal based on the risk level of the driver determined based on the current status of the driver); (¶ [0062]: Fig. 5 illustrates an example of determining a risk level of a driver); (¶ [0064]: As illustrated in Fig. 5, for example, if the determines that the driver is currently in a completely alert normal state, the device may use a table to determine that the drowsiness degree of the driver is 1. If the device determines that the driver is currently in a very drowsy state, the device may determine that the drowsiness degree is 4. When the drowsiness degree is above a threshold, the device may output a notification signal).
Determining the management strategy of the vehicle in accordance with the safety score of the vehicle. (¶ [0064]: see above); (¶ [0071]: the device may generate the notification signal with sound and/or vibration and output the notification signal. For example, the device may provide a driver with a guidance sound to induce safe driving. The guidance sound may be a voice to encourage stretching, a voice to explain a method of resolving drowsiness, and a voice to guide the driver to a resting place or a parking spot).
Claim 7: Kim teaches the following:
A […] system, comprising one or more on-board computers and a server, the one or more on-board computers communicating with the server, each of the one or more on-board computers being configured in a vehicle […], each of the one or more on-board computers acquiring objective driving data from initial driving data of a corresponding vehicle and transmitting the objective driving data to the server, wherein the server is configured to: (¶ [0035]: the device may be a micro server); (¶ [0036]: the device may communicate with a server in order to utilize information generated during running of various vehicles); (¶ [0049]: the device may determine the risk level of the driver in consideration of at least information obtained from the vehicle. The information may include image information generated from a camera included in the vehicle, on-board diagnostics (OBD) (e.g., angle information of the steering wheel, RPM information, etc.), and the like).
Receive the objective driving data from an objective on-board computer of the on-board computers; (¶ [0049]: the device may determine the risk level of the driver in consideration of at least information obtained from the vehicle. The information may include image information generated from a camera included in the vehicle, on-board diagnostics (OBD) (e.g., angle information of the steering wheel, RPM information, etc.), and the like).
Detect whether a dangerous driving behavior exists corresponding to a first vehicle of the fleet configured with the objective on-board computer in accordance with the objective driving data to generate a detection result; and (¶ [0049]: see above); (¶ [0045]: The device may determine a current status of a driver. For example, the device may determine that the driver is in a dangerous state if the vehicle has been driven dangerously. As an example, if the device recognizes that the vehicle shows a running pattern that crosses a lane in which the vehicle is running, the device may determine that the vehicle has been driven dangerously. At this time, the device determines that the driver is in a careless state); (¶ [0046]: the device may generate a notification signal based on the risk level of the driver determined based on the current status of the driver).
Determine a management strategy of the vehicle in accordance with the detection result. (¶ [0046]: the device may generate a notification signal based on the risk level of the driver determined based on the current status of the driver); (¶ [0047]: the notification signal may be generated when the risk level is great than or equal to a threshold); (¶ [0071]: the device may generate the notification signal with sound and/or vibration and output the notification signal. For example, the device may provide a driver with a guidance sound to induce safe driving. The guidance sound may be a voice to encourage stretching, a voice to explain a method of resolving drowsiness, and a voice to guide the driver to a resting place or a parking spot).
Although Kim teaches a system configured to monitor driving behavior corresponding to a vehicle via a device/micro-server, Kim does not explicitly teach that the vehicle belongs to a fleet and that the server manages a fleet of vehicles.
However, Shoshan teaches the following:
A fleet management system, comprising one or more on-board computers and a server, the one or more on-board computers communicating with the server, each of the one or more on-board computers being configured in a vehicle of a fleet, each of the one or more on-board computers acquiring objective driving data from initial driving data of a corresponding vehicle and transmitting the objective driving data to the server, wherein the server is configured to: (col. 2, lines 5-6: Fig. 2 is a flowchart of an example method of managing a fleet of vehicles); (col. 1, lines 28-38: the system for managing vehicle sensor data comprises a central server configured to aggregate vehicle condition sensor data by vehicle identifier and vehicle position, and determine a safety condition for the aggregated vehicle condition sensor data); (col. 2, lines 62-67: the vehicles have the ability to connect and share information through a centrally-managed cloud computing system. The cloud aggregates vehicle information to determine safety conditions); (col. 3, lines 25-41: the vehicles are equipped with sensors. Sensors provide specialized vehicle operation information and include standard on-board diagnostic (OBD) sensors, and cameras that capture road imagery. Vehicle condition sensor data can be sent from the vehicle to the cloud); (col. 4, lines 41-50: safety conditions can be vehicle related or location related. For example, dangerous driving behavior can be determined from sensors acquiring data from driver input controls, determining unsafe following distances, and excessive speed. Thus, vehicle related safety conditions pertain to a particular vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Kim with the teachings of Shoshan by incorporating the centrally-managed cloud server configured to aggregate driving sensor data from a plurality of vehicles within a fleet and determine driving behaviors for each particular vehicle, as taught by Shoshan, into the system of Kim comprising a device/micro-server that is configured to collect sensor data from a vehicle to determine driving behaviors. One of ordinary skill in the art would have been motivated to make this combination when one considers the claimed invention is merely a combination of old elements. In combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination are predictable. Furthermore, one of ordinary skill in the art would have been motivated to make this modification when one considers it may further “enable increased environmental awareness for car drivers, increasing safety and reducing traffic congestion” (col. 15, lines 27-29), and when one considers “[a]nother advantage of the described technologies is that vehicle management system can use the aggregated vehicle information to guide traffic and ensure efficient use of available resources, such as road capacity” (col. 15, lines 35-39), as suggested by Shoshan. Furthermore, one of ordinary skill in the art would have recognized that the teachings of Shoshan are compatible with the system of Kim as they share capabilities and characteristics. In particular, they are both systems comprising servers configured to monitor vehicle driving data via on-board sensors and detect dangerous driving behaviors.
Kim/Shoshan does not explicitly teach wherein the management strategies of a fleet comprise a recommendation priority of each vehicle of the fleet, the recommendation priority of each vehicle of the fleet determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet. Furthermore, Kim/Shoshan does not explicitly teach a server further configured to determine a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request, and select a second vehicle from the first number of candidate vehicles to respond the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles, wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the second vehicle.
However, Marueli teaches the following:
Wherein the management strategies of the fleet comprise a recommendation priority of each vehicle of the fleet, the recommendation priority of each vehicle of the fleet is determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet; (¶ [0012]: a queue of drivers for a particular area is maintained, wherein the queue specifies an order in which drivers are selected for transportation requests for a particular area); (¶ [0067]: the server may determine driver placement within a queue based on characteristics of transportation requests and performance metrics/scores associated with the drivers); (¶ [0078]: performance metrics may include a safety record, e.g., a measure of traffic accidents experienced or traffic tickets received).
The server is further configured to: determine a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request; (¶ [0012]: see above); (¶ [0067]: the server may determine driver placement within a queue based on characteristics of transportation requests and performance metrics/scores associated with the drivers); (¶ [0019]: backend system may select a driver based on any suitable factors, including information contained in the transportation request, the proximity of the driver to the passenger, whether the pickup location is located within an area serviced by one or more queues, or any other suitable factors);
Select a second vehicle from the first number of candidate vehicles to respond to the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles; wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the second vehicle. (¶ [0019]: see above); (¶ [0078]: see above); (¶ [0067]: if a driver has a relatively high performance score indicating that the driver is performing well relative to other drivers, the driver may be placed at a spot in the queue that is higher than the bottom of the queue); (¶ [0012]: a queue of drivers for a particular area is maintained, wherein the queue specifies an order in which drivers are selected for transportation requests for a particular area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system of Kim/Shoshan the ability to determine a recommendation priority of each vehicle of a fleet, the recommendation priority of each vehicle of the fleet determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet; determine a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request; and select a second vehicle from the first number of candidate vehicles to respond to the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the second vehicle, as taught by Marueli, since the claimed invention is merely a combination of old elements. In combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination are predictable. Furthermore, one of ordinary skill in the art would have been motivated to make this modification when one considers a “driver could be given a preferred position in the queue […] if the driver has performed well relative to other drivers” (¶ [0014]), as suggested by Marueli, and thus incentivizing drivers to drive safely.
Claim 8: Kim/Shoshan/Marueli teaches the limitations of claim 7. Furthermore, the limitations of claim 8 are substantially analogous to the limitations of claim 2. Accordingly, claim 8 is rejected for substantially the same reasons and rationale as set forth above with regard to claim 2.
Claim 9: Kim/Shoshan/Marueli teaches the limitations of claim 8. Furthermore, the limitations of claim 9 are substantially analogous to the limitations of claim 3. Accordingly, claim 9 is rejected for substantially the same reasons and rationale as set forth above with regard to claim 3.
Claim 10: Kim/Shoshan/Marueli teaches the limitations of claim 8. Furthermore, the limitations of claim 10 are substantially analogous to the limitations of claim 4. Accordingly, claim 10 is rejected for substantially the same reasons and rationale as set forth above with regard to claim 4.
Claim 11: Kim/Shoshan/Marueli teaches the limitations of claim 7. Furthermore, the limitations of claim 11 are substantially analogous to the limitations of claim 5. Accordingly, claim 11 is rejected for substantially the same reasons and rationale as set forth above with regard to claim 5.
Claim 13: Kim/Shoshan/Marueli teaches the limitations of claim 7. Furthermore, Kim teaches the following:
Wherein the objective driving data comprises driving data indicating a preset potential dangerous event exists in the initial driving data. (¶ [0049]: the device may determine the risk level of the driver in consideration of at least information obtained from the vehicle. The information may include image information generated from a camera included in the vehicle, on-board diagnostics (OBD) (e.g., angle information of the steering wheel, RPM information, etc.), and the like); (¶ [0062]: Fig. 5 illustrates an example of determining a risk level of a driver); (¶ [0064]: As illustrated in Fig. 5, for example, if the determines that the driver is currently in a completely alert normal state, the device may use a table to determine that the drowsiness degree of the driver is 1. If the device determines that the driver is currently in a very drowsy state, the device may determine that the drowsiness degree is 4. When the drowsiness degree is above a threshold, the device may output a notification signal).
Claim 14: Kim teaches the following:
A server configured for communicating with one or more on-board computers, each of the one or more on-board computers being configured in a vehicle […] each of the one or more on-board computers acquiring objective driving data from initial driving data of a corresponding vehicle and transmitting the objective driving data to the server, the server comprising: at least one processor; and a data storage storing one or more programs which when executed by the at least one processor, cause the at least one processor to: (¶ [0035]: the device may be a micro server); (¶ [0036]: the device may communicate with a server in order to utilize information generated during running of various vehicles); (¶ [0049]: the device may determine the risk level of the driver in consideration of at least information obtained from the vehicle. The information may include image information generated from a camera included in the vehicle, on-board diagnostics (OBD) (e.g., angle information of the steering wheel, RPM information, etc.), and the like); (¶ [0089]: a controller of the the device comprises one or more processors and executed programing stored in the memory).
Receive the objective driving data from an objective on-board computer of the on-board computers; (¶ [0049]: the device may determine the risk level of the driver in consideration of at least information obtained from the vehicle. The information may include image information generated from a camera included in the vehicle, on-board diagnostics (OBD) (e.g., angle information of the steering wheel, RPM information, etc.), and the like).
Detect whether a dangerous driving behavior exists corresponding to a first vehicle of the fleet configured with the objective on-board computer in accordance with the objective driving data to generate a detection result; and (¶ [0049]: see above); (¶ [0045]: The device may determine a current status of a driver. For example, the device may determine that the driver is in a dangerous state if the vehicle has been driven dangerously. As an example, if the device recognizes that the vehicle shows a running pattern that crosses a lane in which the vehicle is running, the device may determine that the vehicle has been driven dangerously. At this time, the device determines that the driver is in a careless state); (¶ [0046]: the device may generate a notification signal based on the risk level of the driver determined based on the current status of the driver).
Determine a management strategy of the vehicle in accordance with the detection result. (¶ [0046]: the device may generate a notification signal based on the risk level of the driver determined based on the current status of the driver); (¶ [0047]: the notification signal may be generated when the risk level is great than or equal to a threshold); (¶ [0071]: the device may generate the notification signal with sound and/or vibration and output the notification signal. For example, the device may provide a driver with a guidance sound to induce safe driving. The guidance sound may be a voice to encourage stretching, a voice to explain a method of resolving drowsiness, and a voice to guide the driver to a resting place or a parking spot).
Although Kim teaches a system configured to monitor driving behavior corresponding to a vehicle via a device/micro-server, Kim does not explicitly teach that the vehicle belongs to a fleet and that the server manages a fleet of vehicles.
However, Shoshan teaches the following:
A server configured for communicating with one or more on-board computers, each of the one or more on-board computers being configured in a vehicle of a fleet, each of the one or more on-board computers acquiring objective driving data from initial driving data of a corresponding vehicle and transmitting the objective driving data to the server, the server comprising: at least one processor; and a data storage storing one or more programs which when executed by the at least one processor, cause the at least one processor to:: (col. 2, lines 5-6: Fig. 2 is a flowchart of an example method of managing a fleet of vehicles); (col. 1, lines 28-38: the system for managing vehicle sensor data comprises a central server configured to aggregate vehicle condition sensor data by vehicle identifier and vehicle position, and determine a safety condition for the aggregated vehicle condition sensor data); (col. 2, lines 62-67: the vehicles have the ability to connect and share information through a centrally-managed cloud computing system. The cloud aggregates vehicle information to determine safety conditions); (col. 3, lines 25-41: the vehicles are equipped with sensors. Sensors provide specialized vehicle operation information and include standard on-board diagnostic (OBD) sensors, and cameras that capture road imagery. Vehicle condition sensor data can be sent from the vehicle to the cloud); (col. 4, lines 41-50: safety conditions can be vehicle related or location related. For example, dangerous driving behavior can be determined from sensors acquiring data from driver input controls, determining unsafe following distances, and excessive speed. Thus, vehicle related safety conditions pertain to a particular vehicle); (col. 13, lines 28-47: the computing system comprises a memory and processor configured to execute computer readable instructions stored in the memory).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Kim with the teachings of Shoshan by incorporating the centrally-managed cloud server configured to aggregate driving sensor data from a plurality of vehicles within a fleet and determine driving behaviors for each particular vehicle, as taught by Shoshan, into the system of Kim comprising a device/micro-server that is configured to collect sensor data from a vehicle to determine driving behaviors. One of ordinary skill in the art would have been motivated to make this combination when one considers the claimed invention is merely a combination of old elements. In combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination are predictable. Furthermore, one of ordinary skill in the art would have been motivated to make this modification when one considers it may further “enable increased environmental awareness for car drivers, increasing safety and reducing traffic congestion” (col. 15, lines 27-29), and when one considers “[a]nother advantage of the described technologies is that vehicle management system can use the aggregated vehicle information to guide traffic and ensure efficient use of available resources, such as road capacity” (col. 15, lines 35-39), as suggested by Shoshan. Furthermore, one of ordinary skill in the art would have recognized that the teachings of Shoshan are compatible with the system of Kim as they share capabilities and characteristics. In particular, they are both systems comprising servers configured to monitor vehicle driving data via on-board sensors and detect dangerous driving behaviors.
Kim/Shoshan does not explicitly teach wherein the management strategies of a fleet comprise a recommendation priority of each vehicle of the fleet, the recommendation priority of each vehicle of the fleet determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet. Furthermore, Kim/Shoshan does not explicitly teach a processor further caused to determine a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request, and select a second vehicle from the first number of candidate vehicles to respond the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles, wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the second vehicle.
However, Marueli teaches the following:
Wherein the management strategies of the fleet comprise a recommendation priority of each vehicle of the fleet, the recommendation priority of each vehicle of the fleet is determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet; (¶ [0012]: a queue of drivers for a particular area is maintained, wherein the queue specifies an order in which drivers are selected for transportation requests for a particular area); (¶ [0067]: the server may determine driver placement within a queue based on characteristics of transportation requests and performance metrics/scores associated with the drivers); (¶ [0078]: performance metrics may include a safety record, e.g., a measure of traffic accidents experienced or traffic tickets received).
The at least one processor is further caused to: determine a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request; (¶ [0012]: see above); (¶ [0067]: the server may determine driver placement within a queue based on characteristics of transportation requests and performance metrics/scores associated with the drivers); (¶ [0019]: backend system may select a driver based on any suitable factors, including information contained in the transportation request, the proximity of the driver to the passenger, whether the pickup location is located within an area serviced by one or more queues, or any other suitable factors);
Select a second vehicle from the first number of candidate vehicles to respond to the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles; wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the second vehicle. (¶ [0019]: see above); (¶ [0078]: see above); (¶ [0067]: if a driver has a relatively high performance score indicating that the driver is performing well relative to other drivers, the driver may be placed at a spot in the queue that is higher than the bottom of the queue); (¶ [0012]: a queue of drivers for a particular area is maintained, wherein the queue specifies an order in which drivers are selected for transportation requests for a particular area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system of Kim/Shoshan the ability to determine a recommendation priority of each vehicle of a fleet, the recommendation priority of each vehicle of the fleet determined in accordance with a safety score determined by dangerous driving behaviors of each vehicle of the fleet; determine a first number of candidate vehicles from a second number of vehicles of the fleet in accordance with a pick-up location indicated by a received vehicle dispatching request; and select a second vehicle from the first number of candidate vehicles to respond to the received vehicle dispatching request in accordance with the recommendation priority of each of the first number of candidate vehicles wherein the higher the recommendation priority of the candidate vehicle is, the greater the probability that the candidate vehicle is selected as the second vehicle, as taught by Marueli, since the claimed invention is merely a combination of old elements. In combination, each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination are predictable. Furthermore, one of ordinary skill in the art would have been motivated to make this modification when one considers a “driver could be given a preferred position in the queue […] if the driver has performed well relative to other drivers” (¶ [0014]), as suggested by Marueli, as thus incentivizing drivers to drive safely.
Claim 15: Kim/Shoshan/Marueli teaches the limitations of claim 14. Furthermore, the limitations of claim 15 are substantially analogous to the limitations of claim 2. Accordingly, claim 15 is rejected for substantially the same reasons and rationale as set forth above with regard to claim 2.
Claim 16: Kim/Shoshan/Marueli teaches the limitations of claim 15. Furthermore, the limitations of claim 16 are substantially analogous to the limitations of claim 3. Accordingly, claim 9 is rejected for substantially the same reasons and rationale as set forth above with regard to claim 3.
Claim 17: Kim/Shoshan/Marueli teaches the limitations of claim 15. Furthermore, the limitations of claim 17 are substantially analogous to the limitations of claim 4. Accordingly, claim 17 is rejected for substantially the same reasons and rationale as set forth above with regard to claim 4.
Claim 18: Kim/Shoshan/Marueli teaches the limitations of claim 14. Furthermore, the limitations of claim 18 are substantially analogous to the limitations of claim 5. Accordingly, claim 18 is rejected for substantially the same reasons and rationale as set forth above with regard to claim 5.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE G DEL TORO-ORTEGA whose telephone number is (571)272-5319. The examiner can normally be reached Monday-Friday 9:00AM-6:00PM.
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/JORGE G DEL TORO-ORTEGA/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628