DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
The present application, filed 11 July 2024, is a continuation of PCT/JP2023/002009, filed 24 January 2023, which claims foreign priority to Japanese Patent App. No. JP2022-011925, filed 28 January 2022.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a first obtainer that obtains…,” “a detector that detects…,” “a second obtainer that obtains…,” “a calculator that calculates…,” and “an outputter that outputs….”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because they are directed towards an abstract idea without significantly more.
101 Analysis – Step 1
Claims 1-13 are directed towards a method (i.e., a process). Claim 14 is directed towards a device (i.e., a machine). Claim 15 is directed towards a non-transitory computer readable recording medium (i.e., a manufacture). Therefore, claim 1-15 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite a mental process (emphasized below) and will be used as the representative claim for the remainder of the 35 U.S.C. 101 rejection. Claim 1 recites:
An information output method in an informative output device, the information output method comprising:
Obtaining first information regarding a movement of a first mobile object operated by autonomous mobility and remote control;
Detecting, based on the first information, an event that occurs while the first mobile object is moving;
Obtaining second information regarding an operator who intervenes an operation of the first mobile object;
Calculating an importance level of the event based on at least one of the first information or the second information; and
Outputting control information for controlling a second mobile object to prevent occurrence of the event whose importance level calculated satisfies a predetermined condition.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process”, because under its broadest reasonable interpretation, the claim covers actions capable of being performed in the human mind. Specifically, the examiner asserts that the limitations of “detecting … an event…” and “calculating an importance level…” amount to a mere mental judgement as to the presence and importance of an event, respectively. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra-solution activity, or generally linking the use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations”, while the bolded portions continue to represent the “abstract idea”):
An information output method in an informative output device, the information output method comprising:
Obtaining first information regarding a movement of a first mobile object operated by autonomous mobility and remote control;
Detecting, based on the first information, an event that occurs while the first mobile object is moving;
Obtaining second information regarding an operator who intervenes an operation of the first mobile object;
Calculating an importance level of the event based on at least one of the first information or the second information; and
Outputting control information for controlling a second mobile object to prevent occurrence of the event whose importance level calculated satisfies a predetermined condition.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the limitations of “obtaining first information…” and “obtaining second information…,” the examiner asserts that these limitations amount to insignificant, extra-solution activities in the form of mere data gathering. Regarding the limitation of “outputting control information…,” the examiner asserts that this limitation amounts to insignificant, extra-solution activity in the form of mere data transmission.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above, with respect to determining that the claim does not integrate the abstract idea into a practical application. Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, and conventional activity in the field. The additional limitations of “obtaining first information…,” “obtaining second information…,” and “outputting control information” are well-understood, routine, and conventional activities because MPEP § 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, independent claim 1 is not patent eligible.
Claims 14 and 15 are similar in scope to claim 1, and are similarly not patent eligible.
Regarding dependent claim 2, dependent claim 2 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 2 merely recites a further mental process in the form of “referring to an event database,” which the examiner asserts amounts to a mere mental reference based on known information, as well as further insignificant, extra-solution activity in the form of mere data transmission in “outputting the control information…”. Hence, dependent claim 2 is not patent eligible.
Regarding dependent claim 3, dependent claim 3 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 3 merely recites an additional mental process in the form of “generating the event database…,” which the examiner asserts could reasonably be performed in the human mind, or with the assistance of pen and paper. Hence, dependent claim 3 is not patent eligible.
Regarding dependent claim 4, dependent claim 4 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 4 merely provides descriptions of the types of data stored in the event database, as well as providing further description of the mental process of “generating the event database,” insufficient to bring the claim into patent eligibility. Hence, dependent claim 4 is not patent eligible.
Regarding dependent claim 5, dependent claim 5 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 5 merely provides descriptions of the types of data stored in the event database, as well as providing further description of the mental process of “generating the event database,” insufficient to bring the claim into patent eligibility. Hence, dependent claim 5 is not patent eligible.
Regarding dependent claim 6, dependent claim 6 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 6 merely provides descriptions of the types of data stored in the event database, as well as providing further description of the mental process of “generating the event database,” insufficient to bring the claim into patent eligibility. Hence, dependent claim 6 is not patent eligible.
Regarding dependent claim 7, dependent claim 7 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 7 merely provides further description of the well-understood, routine, and conventional activity of “outputting the control information,” insufficient to bring the claim into patent eligibility. Hence, dependent claim 7 is not patent eligible.
Regarding dependent claim 8, dependent claim 8 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 8 merely provides further description of the well-understood, routine, and conventional activity of “outputting the control information,” insufficient to bring the claim into patent eligibility. Hence, dependent claim 8 is not patent eligible.
Regarding dependent claim 9, dependent claim 9 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 9 merely provides further description of the well-understood, routine, and conventional activity of “outputting the control information,” insufficient to bring the claim into patent eligibility. Hence, dependent claim 9 is not patent eligible.
Regarding dependent claim 10, dependent claim 10 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 10 merely provides further description of the well-understood, routine, and conventional activity of “outputting the control information,” insufficient to bring the claim into patent eligibility. Hence, dependent claim 10 is not patent eligible.
Regarding dependent claim 11, dependent claim 11 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 11 merely recites an additional mental process in the form of “calculating… an estimated importance,” as well as further description of the well-understood, routine, and conventional activity of “outputting the control information,” insufficient to bring the claim into patent eligibility. Regarding the limitation of “wherein the operator intervenes in operations of one or more second mobile objects each of which moves according to the control information outputted,” the examiner notes that under the broadest reasonable interpretation of the claims as presented, the above limitation could be interpreted as a method of organizing human activity (i.e., instructing the operator to intervene in the operation of the second mobile objects) or the limitation may be interpreted as a passive statement (i.e., the method is not actively reciting the intervening of the second mobile objects by the operator, externally to the information output method). Hence, dependent claim 11 is not patent eligible.
Regarding dependent claim 12, dependent claim 12 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 12 merely provides further description of the well-understood, routine, and conventional activity of “outputting the control information,” insufficient to bring the claim into patent eligibility. Hence, dependent claim 12 is not patent eligible.
Regarding dependent claim 13, dependent claim 13 does not include additional limitations that would cause the claim to be patent eligible. Specifically, dependent claim 13 merely recites an additional well-understood, routine, and conventional activity in the form of “outputting presentation information,” as well as a mere description of the “presentation information” to be output/transferred. Hence, dependent claim 13 is not patent eligible.
Claim Rejections - 35 USC § 102
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 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 1, 2, 6, 9-11 and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gross (US 20230138112 A1, previously published as WO2021177964A1 on 10 September 2021).
Regarding claim 1, Gross teaches an information output method in an informative output device, the information output method comprising:
Obtaining first information regarding a movement of a first mobile object operated by autonomous mobility and remote control (0186, The depicted method continues at 1604 with the RMCC determining the locations of the multiple autonomous vehicles based on the sensor data received from the multiple vehicles. At 1606, the RMCC determines the locations of objects based on the sensor data. Using the sensor data, the RMCC determines the vehicle speeds at 1608. At 1610, the RMCC determines the incident risk for each of the multiple vehicles, based on the vehicle locations, object locations, and vehicle speed. In some examples, the incident risk may be determined as a function of whether an object or vehicle is of interest to one or more of the multiple vehicles.);
Detecting, based on the first information, an event that occurs while the first mobile object is moving (0186, The depicted method continues at 1604 with the RMCC determining the locations of the multiple autonomous vehicles based on the sensor data received from the multiple vehicles. At 1606, the RMCC determines the locations of objects based on the sensor data. Using the sensor data, the RMCC determines the vehicle speeds at 1608. At 1610, the RMCC determines the incident risk for each of the multiple vehicles, based on the vehicle locations, object locations, and vehicle speed. In some examples, the incident risk may be determined as a function of whether an object or vehicle is of interest to one or more of the multiple vehicles.);
Obtaining second information regarding an operator who intervenes an operation of the first mobile object (0189, If the RMCC determines at step 1618 the incident risk level is not dangerous, the RMCC determines at step 1622 if the incident risk level for each vehicle is unsafe, based on the incident risk evaluated for each vehicle as a function of an incident risk margin determined by artificial intelligence configured with historical sensor data. If the RMCC determines at 1622 the incident risk level for at least one vehicle is unsafe, the RMCC at 1624 takes control of the at least one vehicle, and the method continues at 1602 with the processor 503 capturing sensor data from multiple autonomous vehicles.);
Calculating an importance level of the event based on at least one of the first information or the second information (0188, At step 1612, if the incident risk level is not safe, the RMCC determines at step 1618 if the incident risk level is dangerous, based on the incident risk evaluated for each vehicle as a function of an incident risk margin determined by artificial intelligence configured with historical sensor data. If the RMCC determines at step 1618 the incident risk level for one or more vehicle is dangerous, the RMCC processes an advisory to generate a recommendation message sent to the one or more vehicle at 1620. In various examples, the advisory recommendation may include a suggestion to the autonomous vehicle to reduce speed, or may include an image of an object, for example a pedestrian occluded from the vehicle's field of view.); and
Outputting control information for controlling a second mobile object to prevent occurrence of the event whose importance level calculated satisfies a predetermined condition (0188, At step 1612, if the incident risk level is not safe, the RMCC determines at step 1618 if the incident risk level is dangerous, based on the incident risk evaluated for each vehicle as a function of an incident risk margin determined by artificial intelligence configured with historical sensor data. If the RMCC determines at step 1618 the incident risk level for one or more vehicle is dangerous, the RMCC processes an advisory to generate a recommendation message sent to the one or more vehicle at 1620. In various examples, the advisory recommendation may include a suggestion to the autonomous vehicle to reduce speed, or may include an image of an object, for example a pedestrian occluded from the vehicle's field of view.).
Claims 14 and 15 are similar in scope to claim 1, and are similarly rejected.
Regarding claim 2, Gross teaches the information output method according to claim 1, and further teaches wherein the outputting of the control information includes:
Referring to an event database in which the event and the importance level calculated for the event are associated with each other (0185, In some embodiments, an incident risk margin may be determined by artificial intelligence configured with historical sensor data. For example, a test vehicle equipped with sensors may permit a neural network or a decision tree to be trained based on sensor data representative of vehicle travels on particular roads, or under certain conditions. Such test data could be used to predict a minimum safe risk margin threshold differential with respect to the incident risk determined by the RMCC, for various live autonomous vehicle driving conditions); and
Outputting the control information for controlling the second mobile object to prevent occurrence of the event associated with an importance level satisfying the predetermined condition in the event database (0188, At step 1612, if the incident risk level is not safe, the RMCC determines at step 1618 if the incident risk level is dangerous, based on the incident risk evaluated for each vehicle as a function of an incident risk margin determined by artificial intelligence configured with historical sensor data. If the RMCC determines at step 1618 the incident risk level for one or more vehicle is dangerous, the RMCC processes an advisory to generate a recommendation message sent to the one or more vehicle at 1620. In various examples, the advisory recommendation may include a suggestion to the autonomous vehicle to reduce speed, or may include an image of an object, for example a pedestrian occluded from the vehicle's field of view.).
Regarding claim 6, Gross teaches the information output method according to claim 2, and further teaches wherein in the event database, the event detected, the importance level calculated for the event, and one or more condition parameters regarding an occurrence condition of the event are associated with one another (0185, In some embodiments, an incident risk margin may be determined by artificial intelligence configured with historical sensor data. For example, a test vehicle equipped with sensors may permit a neural network or a decision tree to be trained based on sensor data representative of vehicle travels on particular roads, or under certain conditions. Such test data could be used to predict a minimum safe risk margin threshold differential with respect to the incident risk determined by the RMCC, for various live autonomous vehicle driving conditions. In an illustrative example, various embodiment RMCC may determine the incident risk for each vehicle as safe, dangerous, or unsafe, characterized as a function of an incident risk, incident risk margin, and a minimum safe risk margin threshold. 0186, The depicted method continues at 1604 with the RMCC determining the locations of the multiple autonomous vehicles based on the sensor data received from the multiple vehicles. At 1606, the RMCC determines the locations of objects based on the sensor data. Using the sensor data, the RMCC determines the vehicle speeds at 1608. At 1610, the RMCC determines the incident risk for each of the multiple vehicles, based on the vehicle locations, object locations, and vehicle speed. In some examples, the incident risk may be determined as a function of whether an object or vehicle is of interest to one or more of the multiple vehicles.), and
The outputting of the control information includes outputting the control information for controlling the second mobile object to have a condition with one or more condition parameters that do not at least partially match the one or more condition parameters associated with the event whose importance level calculated satisfies the predetermined condition (0191, At step 1708, the RMCC determines the per-vehicle incident risk for each of the multiple autonomous vehicles, based on the artificial intelligence and the live and historical sensor data. If the RMCC determines at step 1710 the incident risk level for all vehicles is not dangerous or unsafe, the process ends, otherwise, the RMCC mitigates the dangerous or unsafe incident risk for at least one of the multiple autonomous vehicles by choosing at 1712 an appropriate safety measure determined by artificial intelligence. At step 1714 the RMCC selects a vehicle to implement the chosen safety measure at 1716. In various examples, the safety measure may include automatic vehicle braking, reducing speed, or steering away from a potential collision.).
Regarding claim 9, Gross teaches the information output method according to claim 6, and further teaches wherein the one or more condition parameters indicate a traveling history of the first mobile object (0116, The functionality of the AI system 100 is possible because the RMCC 109 is context aware. Context awareness is the capability of the AI system 100 to be aware of its physical environment or situation and respond proactively and intelligently based on that awareness. The RMCC 109 can be aware of the GPS positioning of the vehicles 103a, 103b, and 103c, and, for example, when any of the vehicles 103a, 103b, or 103c enters an area that has previously been learned, that area's contextual information will be relayed to the processing circuitry or computer inside the vehicle 103a, 103b, or 103c during autonomous driving and to the user during manual driving. When routes are shared, the RMCC 109 will also record the time taken driving the route as well as the time when the route was driven, not only when the route is initially recorded, but also during every subsequent time that custom route is driven. Using that semantic data, the AI system 100 will be able to choose a preferred route during autonomous driving and prioritize suggested routes to give users during manual driving.), and
The outputting of the control information includes outputting the control information for controlling the second mobile object to do traveling different from the traveling history indicated by the information included in the one or more condition parameters associated with the event whose importance level calculated satisfies the predetermined condition (0116, The functionality of the AI system 100 is possible because the RMCC 109 is context aware. Context awareness is the capability of the AI system 100 to be aware of its physical environment or situation and respond proactively and intelligently based on that awareness. The RMCC 109 can be aware of the GPS positioning of the vehicles 103a, 103b, and 103c, and, for example, when any of the vehicles 103a, 103b, or 103c enters an area that has previously been learned, that area's contextual information will be relayed to the processing circuitry or computer inside the vehicle 103a, 103b, or 103c during autonomous driving and to the user during manual driving. When routes are shared, the RMCC 109 will also record the time taken driving the route as well as the time when the route was driven, not only when the route is initially recorded, but also during every subsequent time that custom route is driven. Using that semantic data, the AI system 100 will be able to choose a preferred route during autonomous driving and prioritize suggested routes to give users during manual driving.).
Regarding claim 10, Gross teaches the information output method according to claim 6, wherein the one or more condition parameters include information indicating hours during which the first mobile object moves (0114, The geolocation and networking capabilities of current mobile devices can be used to provide real-time information to the RMCC 109. The AI system 100 can then use the user information to determine locations that may be most likely to have parking spaces available at a certain time based on success rate data shared by users of the AI system 100. The AI system 100 is context aware, meaning it is aware of various factors related to its operation, and able to act upon that awareness. For example, the AI system 100 may be aware of the GPS positioning of the user, the position of the user's destination, and the time, date, and day of the week in which shared locations have been used. When a user or an autonomous vehicle utilizes shared information from RMCC 109 for navigating, the resulting usage data corresponding to that information is captured and saved by the RMCC 109. For instance, the captured data can include whether or not open parking spaces were found at shared locations or how long it took to traverse a certain route, as well as the day and time when those usage instances occurred. All that contextual information can be used by the AI system 100 to determine which location will be most likely to have free parking.), and
The outputting of the control information includes outputting the control information for controlling the second mobile object during hours different from the hours indicated by the information included in the one or more condition parameters associated with the event whose importance level calculated satisfies the predetermined condition (0114, The geolocation and networking capabilities of current mobile devices can be used to provide real-time information to the RMCC 109. The AI system 100 can then use the user information to determine locations that may be most likely to have parking spaces available at a certain time based on success rate data shared by users of the AI system 100. The AI system 100 is context aware, meaning it is aware of various factors related to its operation, and able to act upon that awareness. For example, the AI system 100 may be aware of the GPS positioning of the user, the position of the user's destination, and the time, date, and day of the week in which shared locations have been used. When a user or an autonomous vehicle utilizes shared information from RMCC 109 for navigating, the resulting usage data corresponding to that information is captured and saved by the RMCC 109. For instance, the captured data can include whether or not open parking spaces were found at shared locations or how long it took to traverse a certain route, as well as the day and time when those usage instances occurred. All that contextual information can be used by the AI system 100 to determine which location will be most likely to have free parking.).
Regarding claim 11, Gross teaches the information output method according to claim 1, and further teaches wherein the operator intervenes in operations of one or more second mobile objects each of which moves according to the control information outputted, the one or more second mobile objects each being the second mobile object (0191-0192, At step 1708, the RMCC determines the per-vehicle incident risk for each of the multiple autonomous vehicles, based on the artificial intelligence and the live and historical sensor data. If the RMCC determines at step 1710 the incident risk level for all vehicles is not dangerous or unsafe, the process ends, otherwise, the RMCC mitigates the dangerous or unsafe incident risk for at least one of the multiple autonomous vehicles by choosing at 1712 an appropriate safety measure determined by artificial intelligence. At step 1714 the RMCC selects a vehicle to implement the chosen safety measure at 1716. In various examples, the safety measure may include automatic vehicle braking, reducing speed, or steering away from a potential collision. At 1718 the RMCC determines if more of the autonomous vehicles need to implement safety measures to reduce the incident risk level to a safe margin. If more vehicles need safety measures, the RMCC continues selecting a vehicle at 1714 to implement the safety measure at 1716. If all vehicles have implemented safety measures, the RMCC determines at 1720 if the incident risk level for all vehicles has been reduced to a safe margin. If the RMCC determines at 1720 all vehicles are not operating at a safe risk incident level, the process continues at 1708.), and
The outputting of the control information includes:
Calculating, for each of the one or more second mobile objects, an estimated importance level of an anticipated event of the second mobile object when moving according to the control information to be outputted (0191, At step 1708, the RMCC determines the per-vehicle incident risk for each of the multiple autonomous vehicles, based on the artificial intelligence and the live and historical sensor data. If the RMCC determines at step 1710 the incident risk level for all vehicles is not dangerous or unsafe, the process ends, otherwise, the RMCC mitigates the dangerous or unsafe incident risk for at least one of the multiple autonomous vehicles by choosing at 1712 an appropriate safety measure determined by artificial intelligence.); and
Outputting the control information for controlling the second mobile object to prevent a sum of estimated importance levels calculated for all the one or more second mobile objects from exceeding an importance-level threshold set for the operator (0192, At 1718 the RMCC determines if more of the autonomous vehicles need to implement safety measures to reduce the incident risk level to a safe margin. If more vehicles need safety measures, the RMCC continues selecting a vehicle at 1714 to implement the safety measure at 1716. If all vehicles have implemented safety measures, the RMCC determines at 1720 if the incident risk level for all vehicles has been reduced to a safe margin. If the RMCC determines at 1720 all vehicles are not operating at a safe risk incident level, the process continues at 1708.).
Regarding claim 13, Gross teaches the information output method according to claim 1, further comprising:
Outputting presentation information for presenting, to the operator, an expected occurrence time of an anticipated event of the second mobile object when moving according to the control information to be outputted (0165, From the location of the object, a safety concern can be evaluated at 903 by the receiving vehicle. In one embodiment, the receiving vehicle computes an expected collision point, D, between the object and the receiving vehicle as seen in Fig. 10. 0166, Based on the second location of the object, a safety measure can be implemented in the receiving vehicle as indicated at 904. For example, assuming an unexpected collision point exists, a safety concern can be raised and a warning can be issued to the driver of the receiving vehicle. The warning can be issued at a fixed interval (e.g., 5 seconds) before an anticipated collision. The warning may a visual, audible and/or haptic indicator. In response to a raised safety issue, the receiving vehicle may also implement an automated preventive measure, such as automatic braking of the vehicle.), wherein
The presentation information includes the expected occurrence time of the anticipated event and a time range in which it is allowed to change the expected occurrence time of the anticipated event through input from the operator (0165, From the location of the object, a safety concern can be evaluated at 903 by the receiving vehicle. In one embodiment, the receiving vehicle computes an expected collision point, D, between the object and the receiving vehicle as seen in Fig. 10. 0166, Based on the second location of the object, a safety measure can be implemented in the receiving vehicle as indicated at 904. For example, assuming an unexpected collision point exists, a safety concern can be raised and a warning can be issued to the driver of the receiving vehicle. The warning can be issued at a fixed interval (e.g., 5 seconds) before an anticipated collision. The warning may a visual, audible and/or haptic indicator. In response to a raised safety issue, the receiving vehicle may also implement an automated preventive measure, such as automatic braking of the vehicle.).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Gross, and further in view of Simpson (US 20210096581 A1), hereafter Simpson.
Regarding claim 3, Gross teaches the information output method according to claim 2, but fails to teach it further comprising:
Generating the event database by accumulating the following in a storage in association with each other: the event detected based on the first information; and the importance level calculated for the event.
Simpson, however, in an analogous field of endeavor, does teach:
Generating the event database by accumulating the following in a storage in association with each other: the event detected based on the first information; and the importance level calculated for the event (0055, The data from sensors 150 is provided to the controller 34 of the autonomous vehicle 22 which shares that data as live event data 246 with the remote monitoring station 30. The data from the sensors is also applied to an important message filter 230 imbedded in the software of the controller 34 and, when an important event is detected, the important event data 248 is transferred to server storage 236. If a critical event is detected by the critical event filter 232 of the controller 34, then the event data 248 is transferred to the server storage 236. In addition, upon detection of a critical event by the critical event filter 232, data stream 250 moves the data from the sensors 150 to a recording storage location 238 in the memory of the server 78. The event data 248 is applied to emergency filters 234 on the server 78 to determine if an emergency condition is detected. If so, alerts 242 are forwarded to the remote monitoring station 30.).
Gross and Simpson are analogous because they are in a similar field of endeavor, e.g., vehicle control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the storage based on the importance level of Simpson in order to provide a means of storing particularly relevant information. The motivation to combine is to ensure that relevant information is stored in a database to be used in the future.
Regarding claim 4, the combination of Gross and Simpson teaches the information output method according to claim 3, and Simpson further teaches wherein in the event database, the event detected, the importance level calculated for the event, and a parameter regarding an occurrence factor of the event are associated with one another (0055, The data from sensors 150 is provided to the controller 34 of the autonomous vehicle 22 which shares that data as live event data 246 with the remote monitoring station 30. The data from the sensors is also applied to an important message filter 230 imbedded in the software of the controller 34 and, when an important event is detected, the important event data 248 is transferred to server storage 236. If a critical event is detected by the critical event filter 232 of the controller 34, then the event data 248 is transferred to the server storage 236. In addition, upon detection of a critical event by the critical event filter 232, data stream 250 moves the data from the sensors 150 to a recording storage location 238 in the memory of the server 78. The event data 248 is applied to emergency filters 234 on the server 78 to determine if an emergency condition is detected. If so, alerts 242 are forwarded to the remote monitoring station 30.), and
The generating of the event database further includes: obtaining a different parameter matching the parameter associated with one event accumulated; and accumulating the following in the storage in association with the different parameter obtained: the one event; and an importance level corresponding to the importance level associated with the one event (0055, The data from sensors 150 is provided to the controller 34 of the autonomous vehicle 22 which shares that data as live event data 246 with the remote monitoring station 30. The data from the sensors is also applied to an important message filter 230 imbedded in the software of the controller 34 and, when an important event is detected, the important event data 248 is transferred to server storage 236. If a critical event is detected by the critical event filter 232 of the controller 34, then the event data 248 is transferred to the server storage 236. In addition, upon detection of a critical event by the critical event filter 232, data stream 250 moves the data from the sensors 150 to a recording storage location 238 in the memory of the server 78. The event data 248 is applied to emergency filters 234 on the server 78 to determine if an emergency condition is detected. If so, alerts 242 are forwarded to the remote monitoring station 30.).
Gross and Simpson are analogous because they are in a similar field of endeavor, e.g., vehicle control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the storage based on the importance level of Simpson in order to provide a means of storing particularly relevant information. The motivation to combine is to ensure that relevant information is stored in a database to be used in the future.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Gross in view of Simpson, and further in view of Inagaki (US 20200134943 A1), hereafter Inagaki.
Regarding claim 5, the combination of Gross and Simpson teaches the information output method according to claim 3, and Gross further teaches wherein in the event database, the event detected and the importance level calculated for the event are associated with one another (0188, At step 1612, if the incident risk level is not safe, the RMCC determines at step 1618 if the incident risk level is dangerous, based on the incident risk evaluated for each vehicle as a function of an incident risk margin determined by artificial intelligence configured with historical sensor data. If the RMCC determines at step 1618 the incident risk level for one or more vehicle is dangerous, the RMCC processes an advisory to generate a recommendation message sent to the one or more vehicle at 1620. In various examples, the advisory recommendation may include a suggestion to the autonomous vehicle to reduce speed, or may include an image of an object, for example a pedestrian occluded from the vehicle's field of view.), and
Storing an importance level corresponding to the importance level associated with the one event (0188, At step 1612, if the incident risk level is not safe, the RMCC determines at step 1618 if the incident risk level is dangerous, based on the incident risk evaluated for each vehicle as a function of an incident risk margin determined by artificial intelligence configured with historical sensor data. If the RMCC determines at step 1618 the incident risk level for one or more vehicle is dangerous, the RMCC processes an advisory to generate a recommendation message sent to the one or more vehicle at 1620. In various examples, the advisory recommendation may include a suggestion to the autonomous vehicle to reduce speed, or may include an image of an object, for example a pedestrian occluded from the vehicle's field of view.).
The combination of Gross and Simpson fails to teach, however, wherein the event database includes a parameter regarding an occurrence time of the event; and
When the parameter associated with one event accumulated and the parameter associated with another event accumulated indicate periodicity, the generating of the event database further includes accumulating the following in the storage in association with one another: a different parameter indicating the periodicity for the parameter associated with the one event and the parameter associated with the another event; and the one event.
Inagaki, however, in an analogous field of endeavor, does teach wherein the event database includes a parameter regarding an occurrence time of the event (0213, In addition, if the remote instruction request is made at the counting target location, the request occurring location determination unit 41 stores the location where the remote instruction request was made and the time at which the remote instruction request was made at the location in association with each other. Here, if the remote instruction requests are made a plurality of times at a certain location, the request occurring location determination unit 41 stores the time at which each of the remote instruction requests are made. The request occurring location determination unit 41 can determine the location where the remote instruction requests are transmitted at equal to or higher frequency than the instruction request frequency based on the information formed by the association in this manner.); and
When the parameter associated with one event accumulated and the parameter associated with another event accumulated indicate periodicity, the generating of the event database further includes accumulating the following in the storage in association with one another: a different parameter indicating the periodicity for the parameter associated with the one event and the parameter associated with the another event; and the one event (0213, In addition, if the remote instruction request is made at the counting target location, the request occurring location determination unit 41 stores the location where the remote instruction request was made and the time at which the remote instruction request was made at the location in association with each other. Here, if the remote instruction requests are made a plurality of times at a certain location, the request occurring location determination unit 41 stores the time at which each of the remote instruction requests are made. The request occurring location determination unit 41 can determine the location where the remote instruction requests are transmitted at equal to or higher frequency than the instruction request frequency based on the information formed by the association in this manner.).
Gross, Simpson, and Inagaki are analogous because they are in a similar field of endeavor, e.g., vehicle control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the time information of Inagaki in order to provide further means of safely controlling the second mobile object. The motivation to combine is to allow a mobile object to avoid a potentially indicated dangerous location at a particular time.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Gross, and further in view of Inagaki.
Regarding claim 7, Gross teaches the information output method according to claim 6, but fails to teach wherein the one or more condition parameters include information indicating a route along which the first mobile object moves, and
The outputting of the control information includes outputting the control information for controlling the second mobile object to move along a route different from the route indicated by the information included in the one or more condition parameters associated with the event whose importance level calculated satisfies the predetermined condition.
Inagaki, however, in an analogous field of endeavor, does teach wherein the one or more condition parameters include information indicating a route along which the first mobile object moves (0105, The travel management unit 11 generates the target route based on, for example, the destination, the map information stored in the map database 4, and the position information on the remote autonomous driving vehicle 2. For example, the target route may be generated in consideration of a travel distance to the destination. The target route may be generated in consideration of traffic information such as a traffic congestion. The destination is set according to the service performed by the vehicle remote instruction system 100 using the remote autonomous driving vehicle 2.), and
The outputting of the control information includes outputting the control information for controlling the second mobile object to move along a route different from the route indicated by the information included in the one or more condition parameters associated with the event whose importance level calculated satisfies the predetermined condition (0106, In addition, when the passing-detour location is set in the map information stored in the map database 4, the travel management unit 11 generates a target route avoiding the passing-detour location. That is, the travel management unit 11 generates a detour target route so as not to pass through the passing-detour location. As described above, the travel management unit 11 generates the target route avoiding the passing-detour location as a target route of the remote autonomous driving vehicle 2, based on the passing-detour location and the map information. 0132, As described above, the location from which the remote instruction request is transmitted at equal to or higher frequency than the instruction request frequency is set as the passing-detour location in the map information. In this case, when generating the target route for the remote autonomous driving vehicle 2 next time, the travel management unit 11 generates the target route avoiding the set passing-detour location. For example, as illustrated in FIG. 7, if the location P1 is set as the passing-detour location and a target route from the location A to the location B is generated, the travel management unit 11 can generate a target route L1 avoiding the location P1 as indicated by a broken line in FIG. 7. The target route L indicated by a solid line in FIG. 7 is the target route before the location P1 is set as the passing-detour location.).
Gross and Inagaki are analogous because they are in a similar field of endeavor, e.g., vehicle control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the route information of Inagaki in order to provide further means of safely controlling the second mobile object. The motivation to combine is to allow a mobile object to avoid a potentially indicated dangerous location.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Gross, and further in view of Ishikawa (US 20170178505 A1), hereafter Ishikawa.
Regarding claim 8, Gross teaches the information output method according to claim 6, but fails to explicitly teach wherein the one or more condition parameters include information indicating a type of the first mobile object, and
The outputting of the control information includes outputting the control information for controlling the second mobile object whose type is different from the type indicated by the information included in the one or more condition parameters associated with the event whose importance level calculated satisfies the predetermined condition.
Ishikawa, however, in an analogous field of endeavor, does teach wherein the one or more condition parameters include information indicating a type of the first mobile object (0128, Furthermore, the priority assigning module 310 may be operable to assign the priority of each mobile object 10 according to the type, purpose, urgency, and/or destination of each mobile object 10. The priority assigning module 310 may be operable to assign the priority of each mobile object 10 according to characteristics of the routes travelled by the mobile objects 10, e.g. the ability to pass other mobile objects, the number of lanes, the speed limit, the traffic conditions, or the time of day.), and
The outputting of the control information includes outputting the control information for controlling the second mobile object whose type is different from the type indicated by the information included in the one or more condition parameters associated with the event whose importance level calculated satisfies the predetermined condition (0129, The priority assigning module 310 may change the assigned priorities according to the state of the routes travelled by the mobile objects 10 and the state of the mobile objects 10. For example, if a plurality of mobile objects 10 that each have only one passenger are moving within a range of a mobile object 10 having two passengers, the priority assigning module 310 increases the priority of the mobile objects 10 having two passengers. Furthermore, if a plurality of mobile objects 10 that each have three or more passengers are moving in a range of a mobile object 10 having two passengers, the priority assigning module 310 may decrease the priority of the mobile object 10 having two passengers. In this way, the priority assigning module 310 may be operable to change the setting conditions for priority according to the state of the routes.).
Gross and Ishikawa are analogous because they are in a similar field of endeavor, e.g., vehicle control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the type-based control of Ishikawa in order to provide a means of adapting control information to a differently configured mobile object. The motivation to combine is to allow the control information to better perform control of a plurality of types of mobile object.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Gross, and further in view of Kuhara (US 20220326706 A1, previously published as WO2022014174A1 on 20 January 2022), hereafter Kuhara.
Regarding claim 12, Gross teaches the information output method according to claim 2, and further teaches wherein the outputting of the control information includes outputting the control information for controlling the second mobile object to prevent, by referring to the event database, occurrence of the event associated with an importance level satisfying the predetermined condition (0191, At step 1708, the RMCC determines the per-vehicle incident risk for each of the multiple autonomous vehicles, based on the artificial intelligence and the live and historical sensor data. If the RMCC determines at step 1710 the incident risk level for all vehicles is not dangerous or unsafe, the process ends, otherwise, the RMCC mitigates the dangerous or unsafe incident risk for at least one of the multiple autonomous vehicles by choosing at 1712 an appropriate safety measure determined by artificial intelligence. At step 1714 the RMCC selects a vehicle to implement the chosen safety measure at 1716. In various examples, the safety measure may include automatic vehicle braking, reducing speed, or steering away from a potential collision.).
Gross fails to explicitly teach, however, wherein the control information minimizes, based on a quality parameter, an effect on decrease in service quality.
Kuhara, however, in analogous field of endeavor, does teach wherein control information minimizes, based on a quality parameter, an effect on decrease in service quality (0145, For example, in the case of performing driving control such as deceleration or stop on the remote operation target vehicle, there is a possibility that the quality of service such as transporting occupants decreases. To prevent such a decrease in service quality, control other than driving control such as deceleration or stop may be performed on the remote operation target vehicle. For example, the travel route to the destination may be changed so that the remote operation target vehicle can arrive at the destination earlier.).
Gross and Kuhara are analogous because they are in a similar field of endeavor, e.g., vehicle control systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the minimizing of a decrease in service quality of Kuhara in order to provide a means of ensuring that the control is performed to a sufficiently high degree. The motivation to combine is to ensure that a user remains satisfied.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sakuma (US 20210056329 A1) teaches a driving state monitoring device which acquires driving state data and event data while driving, and generates dangerous occasion driving data based on the driving state data and the event data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BLAKE A WOOD whose telephone number is (571)272-6830. The examiner can normally be reached M-F, 8:00 AM to 4:30 PM Eastern.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thomas Worden can be reached at (571) 272-4876. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BLAKE A WOOD/Examiner, Art Unit 3658