Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed of CN Application No. 202210913779.0, filed on 07/29/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/24/2025 and 06/17/2026 are being considered by the examiner.
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 18-22 and 24-37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e., a mental process) without significantly more.
Claim 18. A cruise route map generation method, comprising:
in response to a route learning request of a user, determining a target learning route corresponding to the route learning request [mental process];
obtaining ambient environment information of a current vehicle during a process in which the current vehicle drives along the target learning route [insignificant extra-solution activity i.e. data collection];
generating mapping information corresponding to the target learning route based on the ambient environment information [mental process]; and
generating a cruise route map corresponding to the target learning route based on the mapping information [mental process].
101 Analysis – Step 1: Statutory Category – Yes
Claim 18 is directed to a method. Therefore, claim 18 is within at least one of the four statutory categories.
Step 2A, Prong one evaluation: Judicial Exception – Yes – Mental Processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The examiner submits that the limitations
in response to a route learning request of a user, determining a target learning route corresponding to the route learning request
generating mapping information corresponding to the target learning route based on the ambient environment information
generating a cruise route map corresponding to the target learning route based on the mapping information
constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. For example,
A person can mentally determine a route based on the driver requesting help navigating from his home to the grocery store.
A person can mentally generate landmarks like buildings and street names that are along the way from the driver’s home to the grocery store.
A person can then create a mental map for the route between the driver’s home and the grocery store based on the buildings and street names.
Step 2A Prong two evaluation: Practical Application – No
In Step 2A, Prong two of the 2019 PEG, a claim is to be 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 such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In the present case, the examiner submits that the foregoing underlined limitations recite additional elements that do not integrate the recited judicial exception into a practical application.
Regarding the additional limitations of “obtaining ambient environment information of a current vehicle during a process in which the current vehicle drives along the target learning route” the examiner submits that this limitation is merely data gathering.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitation on practicing the abstract idea.
Step 2B evaluation: Inventive concept – No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts 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 merely using a computer to implement an abstract idea. The same analysis applies here in 2B, i.e., merely using a computer to implement an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Thus, the claim 18 is ineligible.
Independent medium claim 32 and apparatus claim 35 recite similar limitations performed by the method of claim 18. Therefore, claims 32 and 35 are rejected under the same rationales used in the rejection of claim 18 as outlined above.
Dependent claims 19-22, 24-31, 33-34, and 35-37 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
Therefore, dependent claims 19-22, 24-31, 33-34, and 35-37 are not patent eligible under the same rationale as provided for in the rejection of independent claim 18. Therefore, claims 18-22 and 24-37 are ineligible under 35 USC § 101.
Examiner’s note: Claim 23 is eligible under 35 USC § 101, because it does integrate the mental process into a practical application by reciting an active control step of “performing cruise control on the current vehicle based on the crise route map corresponding to the target learning route”.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 18-19, 23, 31-33, and 35-36 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fujita (JP2019197453A).
Regarding claim 18, Fujita discloses A cruise route map generation method [Fujita ¶ 0001 “an information processing device that generates map information for autonomous driving.”], comprising:
in response to a route learning request of a user, determining a target learning route corresponding to the route learning request [Fujita ¶ 0033 "step S101, the control unit 31 determines whether or not to collect the information acquired by the sensor 4 … Step S101 can be determined, for example, by having the passenger (driver) of vehicle C choose whether or not to collect the data when the vehicle starts moving."];
obtaining ambient environment information of a current vehicle during a process in which the current vehicle drives along the target learning route [Fujita ¶ 0034 "step S102, the control unit 31 starts collecting route information, sensor information, and environmental information."];
generating mapping information corresponding to the target learning route based on the ambient environment information [Fujita ¶ 0050 "step S206, the control unit 11 joins together point cloud information from multiple runs" and ¶ 0051 "step S207, the control unit 11 extracts features (buildings, road signs, etc.) from the results of step S206 and plots them."]; and
generating a cruise route map corresponding to the target learning route based on the mapping information [Fujita ¶ 0052 "steps S202 to S208, the control unit 11 functions as a generation unit that generates map information for autonomous driving by a vehicle with autonomous driving capabilities, based on the route information and sensor information collected by the collection unit."].
Regarding claim 19, Fujita discloses The method according to claim 18, further comprising:
inspecting the cruise route map according to a first preset inspection rule to obtain a first inspection result [Fujita ¶ 0045 "step S203, the control unit 11 determines whether the travel route collected in step S201 has been traveled on the same route more than a predetermined number of times"]; and
in response to that the first inspection result is passed, pushing a cruise function corresponding to the cruise route map to the user [Fujita ¶ 0056 "In this case, the owner of vehicle C must first drive vehicle C manually from their home H to supermarket S a specified number of times (round trip) … After the map data for autonomous driving is stored in the vehicle control device 3, the vehicle C can use its autonomous driving function to autonomously drive at least from home H to supermarket S." The user will not be able to autonomously drive the route until it passes the specified number of times (first inspection result) the route is driven manually.].
Regarding claim 23, Fujita discloses The method according to claim 19, wherein after the in response to that the first inspection result is passed, pushing a cruise function corresponding to the cruise route map to the user, the method further comprises:
in response to a cruise request of the user for the target learning route, performing cruise control on the current vehicle based on the cruise route map corresponding to the target learning route [Fujita ¶ 0056 "In this case, the owner of vehicle C must first drive vehicle C manually from their home H to supermarket S a specified number of times (round trip) … After the map data for autonomous driving is stored in the vehicle control device 3, the vehicle C can use its autonomous driving function to autonomously drive at least from home H to supermarket S."].
Regarding claim 31, Fujita discloses The method according to claim 18, wherein after the generating mapping information corresponding to the target learning route based on the ambient environment information, the method further comprises:
uploading the mapping information to a server, so that the server generates the cruise route map corresponding to the target learning route based on the mapping information [Fujita ¶ 0022 "The control unit 11 functions as the CPU (Central Processing Unit) of the server device 1 and is responsible for the overall control of the server device 1. The control unit 11 generates map data with detailed information sufficient to enable the vehicle C to drive autonomously (drive automatically) based on route information and sensor information transmitted from the vehicle control device 3, and stores it in the storage unit 13 as map data 13a."]; and
receiving and saving the cruise route map returned by the server [Fujita ¶ 0022 "The generated map data is then provided to the vehicle control device 3 (vehicle C)." and ¶ 0027 "Furthermore, the control unit 31 receives the map data generated by the server device 1 and stores it in the storage unit 33 as map data 33a."]
Regarding claims 32-33, all limitations have been examined with respect to the method in claims 18-19. The medium taught/disclosed in claims 32-33 can clearly perform the method of claims 18-19. Therefore, claims 32-33 are rejected under the same rationale.
Regarding claims 35-36, all limitations have been examined with respect to the method in claims 18-19. The method taught/disclosed in claims 18-19 can clearly perform on the system of claims 35-36. Therefore, claims 35-36 are rejected under the same rationale.
Claim Rejections - 35 USC § 103
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 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 20-22, 26, 28, 30, 34, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Fujita in view of Kang (CN 109733383 A).
Regarding claim 20, Fujita teaches claim 19. Fujita does not teach further comprising: in response to that the first inspection result is not passed, saving the target learning route as an uncompleted learning route.
However, in a related field of invention, Kang does teach further comprising:
in response to that the first inspection result is not passed, saving the target learning route as an uncompleted learning route [Kang ¶ 0123 "If the path learning is not 100% complete in a single attempt (within 3 attempts using the product function), the driver can click "Save Incomplete Parking Path" to save the path."].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita with using saving an incomplete path as taught by Kang in order to increase usability.
Regarding claim 21, Fujita and Kang teach claim 20. Kang further teaches wherein after the saving the target learning route as an uncompleted learning route, the method further comprises:
outputting prompt information for continuing training [Kang ¶ 0124 "For parking lots that have completed partial parking path learning, when a driver re-enters the parking lot environment in the future, the vehicle's terminal will promptly display the "Continue learning historical parking paths" icon."].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita with using user prompts as taught by Kang in order to increase usability.
Regarding claim 22, Fujita teaches claim 19. Fujita does not teach wherein after the in response to that the first inspection result is passed, pushing a cruise function corresponding to the cruise route map to the user, the method further comprises: controlling output of introduction information about the cruise function corresponding to the cruise route map.
However, in a related field of invention, Kang teaches wherein after the in response to that the first inspection result is passed, pushing a cruise function corresponding to the cruise route map to the user, the method further comprises:
controlling output of introduction information about the cruise function corresponding to the cruise route map [Kang ¶ 0125 "If the parking path learning reaches 100%, it means that the learning is complete. The system will prompt "Parking path learning is complete; valet parking function is available" on the vehicle terminal."].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita with using user prompts and outputting information as taught by Kang in order to increase usability.
Regarding claim 26, Fujita teaches claim 18. Fujita further teaches further comprising:
during the process in which the current vehicle drives along the target learning route, determining whether current learning is successful according to a second preset inspection rule [Fujita ¶ 0043 "step S202, the control unit 11 determines whether the route information and sensor information collected in step S201 were collected under adverse conditions … sensor data collected under unfavorable environmental conditions (surrounding environment information) for the sensor is not used to generate map data."];
Fujita does not teach in response to that the current learning is successful, outputting current learning progress of the target learning route or in response to that the current learning fails, outputting prompt information indicating a failure of the current learning of the target learning route.
However, in a related field of invention, Kang does teach in response to that the current learning is successful, outputting current learning progress of the target learning route [Kang ¶ 0125 "If the parking path learning reaches 100%, it means that the learning is complete. The system will prompt "Parking path learning is complete, valet parking function is available" on the vehicle terminal."]; or in response to that the current learning fails, outputting prompt information indicating a failure of the current learning of the target learning route [Kang ¶ 0125 "If the parking path learning reaches 100%, it means that the learning is complete. The system will prompt "Parking path learning is complete, valet parking function is available" on the vehicle terminal."].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita with using user prompts and outputting information as taught by Kang in order to increase usability.
Regarding claim 28, Fujita and Kang teach claim 26. Kang further teaches wherein the in response to that the current learning is successful, outputting current learning progress of the target learning route comprises:
in response to that the current learning is successful, outputting the current learning progress of the target learning route to a display device, and controlling to display the current learning progress on a display screen of the display device in a preset manner [Kang ¶ 0132 "If the recall path learning reaches 100%, it means that the learning is complete. The system will prompt "Recall path learning is complete, valet recall function is available" on the vehicle terminal. The driver can then click "Recall path storage" to successfully store this path."].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita with using user prompts and outputting information as taught by Kang in order to increase usability.
Regarding claim 30, Fujita teaches claim 18. Fujita does not teach wherein before the in response to a route learning request of a user, determining a target learning route corresponding to the route learning request, the method further comprises: determining the route learning request of the user in response to a selection operation of the user on an uncompleted learning route interface, wherein the uncompleted learning route interface comprises at least one uncompleted learning route.
However, in a related field of invention, Kang does teach wherein before the in response to a route learning request of a user, determining a target learning route corresponding to the route learning request, the method further comprises:
determining the route learning request of the user in response to a selection operation of the user on an uncompleted learning route interface, wherein the uncompleted learning route interface comprises at least one uncompleted learning route [Kang ¶ 0131 "For parking lots that have completed partial recall path learning, the vehicle's terminal will promptly display the "Continue learning historical recall paths" icon before the driver starts driving from the parking space again in the future."].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita with using user prompts and outputting information as taught by Kang in order to increase usability.
Regarding claim 34, all limitations have been examined with respect to the method in claim 20. The medium taught/disclosed in claim 34 can clearly perform the method of claim 20. Therefore, claim 34 is rejected under the same rationale.
Regarding claim 37, all limitations have been examined with respect to the method in claim 20. The method taught/disclosed in claim 20 can clearly perform on the system of claim 37. Therefore, claim 37 is rejected under the same rationale.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Fujita in view of Kang and in further view of Jeon (US 20200369294 A1).
Regarding claim 27, Fujita and Kang teach claim 26. Fujita and Kang do not teach further comprising: obtaining a current quantity of learning failures of the target learning route; and in response to that the current quantity of learning failures is greater than a preset threshold of times, outputting route replacement prompt information.
However, in a related field of invention, Jeon does teach further comprising:
obtaining a current quantity of learning failures of the target learning route [Jeon ¶ 0106 "the state in which a cumulative addition of the trajectory errors (i.e., an accumulated and added value of the trajectory errors within one cycle) is the preset second threshold value or more occurs within a second critical time preset as a value greater than the first critical time, the processor 610 may determine that the autonomous driving control over the ego vehicle is unreliable"]; and
in response to that the current quantity of learning failures is greater than a preset threshold of times, outputting route replacement prompt information [Jeon ¶ 0107 "If it is determined through the aforementioned process that the autonomous driving control over the ego vehicle is unreliable, the processor 610 may output, to the passenger, the warning"].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita and Kang with using a failure threshold as taught by Jeon in order to increase usability.
Claims 24-25 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Fujita in view of Singhal (US 20180143639 A1).
Regarding claim 24, Fujita teaches claim 23. Fujita does not teach wherein the in response to a cruise request of the user for the target learning route, performing cruise control on the current vehicle based on the cruise route map corresponding to the target learning route comprises: obtaining a current traffic situation of the target learning route in response to the cruise request of the user for the target learning route; and in response to that the current traffic situation is normal, performing cruise control on the current vehicle based on the cruise route map corresponding to the target learning route.
However, in a related field of invention, Singhal does teach wherein the in response to a cruise request of the user for the target learning route, performing cruise control on the current vehicle based on the cruise route map corresponding to the target learning route comprises:
obtaining a current traffic situation of the target learning route in response to the cruise request of the user for the target learning route [Singhal ¶ 0113 “The dynamic route characteristics 932-940 can be such things as the amount of traffic currently on the route”]; and
in response to that the current traffic situation is normal, performing cruise control on the current vehicle based on the cruise route map corresponding to the target learning route [Singhal ¶ 0126 “The route engine 804 may then select the best route out of the different options and provide that information to the navigation system 302. The selected route can be the route with the highest score that correlates to the route that is best for the current driving context.”].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita with accounting for current traffic conditions when determining a level of autonomy allowed for a route as taught by Singhal in order to increase usability and accuracy.
Regarding claim 25, Fujita and Singhal teach claim 24. Singhal further teaches further comprising:
outputting prompt information in response to that the current traffic situation is abnormal; or in response to that the current traffic situation is abnormal, obtaining a first route that has already been learned by other users and that has a start point and an end point same as those of the target learning route [Singhal ¶ 0123 "Similarly, the learning component 812 can receive dynamic characteristics 928 for routes, in step 1012. The learning component 812 may receive information 928 from sensors 304, from other vehicles, or from a third-party supplier 356A of real-time traffic or other dynamic information through the communication system 350."];
pushing the first route to the user in response to that a current traffic situation of the first route is normal [Singhal ¶ 0126 “The route engine 804 may then select the best route out of the different options and provide that information to the navigation system 302. The selected route can be the route with the highest score that correlates to the route that is best for the current driving context.”];
in response to use confirmation information of the user for the first route, performing cruise control on the current vehicle based on a cruise route map corresponding to the first route [Singhal ¶ 0126 "The route engine 804, which provided the different routes to the autonomy determination component 808, can then receive the information about the autonomy level or best fit for each route from the autonomy determination component 808. The route engine 804 may then select the best route out of the different options and provide that information to the navigation system 302. The selected route can be the route with the highest score that correlates to the route that is best for the current driving context. The navigation system 302 may then change or select the route presented to the user and then control the vehicle 348 based on the amount of autonomy allowed for or other characteristics associated with the route."]; and
outputting prompt information in response to that the current traffic situation of the first route is abnormal [Singhal ¶ 0126 “The navigation system 302 may then change or select the route presented to the user and then control the vehicle 348 based on the amount of autonomy allowed for or other characteristics associated with the route.”].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita with accounting for current traffic conditions when determining a level of autonomy allowed for a route as taught by Singhal in order to increase usability and accuracy.
Regarding claim 29, Fujita teaches claim 18. Fujita does not teach wherein before the in response to a route learning request of a user, determining a target learning route corresponding to the route learning request, the method further comprises: controlling display of a cruise interface in response to a request to enter the cruise interface of the user; controlling display of a map interface in response to a new route creation request of the user on the cruise interface; and determining the route learning request of the user in response to a target start point and a target end point that are selected on the map interface by the user.
However, in a related field of invention, Singhal does teach wherein before the in response to a route learning request of a user, determining a target learning route corresponding to the route learning request, the method further comprises:
controlling display of a cruise interface in response to a request to enter the cruise interface of the user [Singhal ¶ 0053 "the vehicle 100 may include a number of user interface devices. The user interface devices receive and translate human input into a mechanical movement or electrical signal or stimulus. The human input may be one or more of motion (e.g., body movement, body part movement, in two-dimensional or three-dimensional space, etc.), voice, touch, and/or physical interaction with the components of the vehicle 100."];
controlling display of a map interface in response to a new route creation request of the user on the cruise interface [Singhal ¶ 0126 "The navigation system 302 may then change or select the route presented to the user and then control the vehicle 348 based on the amount of autonomy allowed for or other characteristics associated with the route."]; and
determining the route learning request of the user in response to a target start point and a target end point that are selected on the map interface by the user [Singhal ¶0018 “The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy.”].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine the information processing device that can generate autonomously navigable map information according to individual needs as taught by Fujita with a user display that takes in input when determining a level of autonomy allowed for a route as taught by Singhal in order to increase usability.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPHINE RICH whose telephone number is (571)272-6384. The examiner can normally be reached M-F 8-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Browne can be reached at (571) 270-0151. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.E.R./Examiner, Art Unit 3666
/JESS WHITTINGTON/Primary Examiner, Art Unit 3666c
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