DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner Notes
2. The Examiner has cited particular paragraphs or columns and line numbers in the references applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested of the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure (see MPEP §2163.06). Applicant is reminded that the Examiner is entitled to give the Broadest Reasonable Interpretation (BRI) of the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims. SEE MPEP 2141.02 [R-07.2015] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert, denied, 469 U.S. 851 (1984). See also MPEP §2123.
Claim Interpretation
3. 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.
4. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
5. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
6. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
7. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
8. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
9. 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:
“first acquisition unit” in claim 10. The corresponding structure according to the Applicant’s disclosure is “the program causes a computer to function as: a first acquisition unit” ([0030]). As such, the structure of the first acquisition unit is a generic computer.
“second acquisition unit” in claim 10. The corresponding structure according to the Applicant’s disclosure is “the program causes a computer to function as: … a second acquisition unit” ([0030]). As such, the structure of the second acquisition unit is a generic computer.
“third acquisition unit” in claim 10. The corresponding structure according to the Applicant’s disclosure is “the program causes a computer to function as: … a third acquisition unit” ([0030]). As such, the structure of the third acquisition unit is a generic computer.
“detection unit” in claim 10. The corresponding structure according to the Applicant’s disclosure is “the program causes a computer to function as: … a detection unit” ([0030]). As such, the structure of the detection unit is a generic computer.
10. 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.
11. 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 § 112
12. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
13. Claim 4, 6-7, 14-18, and 20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
14. Claim 4 recites the limitation “the type” in line 3. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “a type”.
15. Claim 6 recites the limitation “the vehicles” in lines 5-6. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “the vehicle”.
16. Claim 7 recites the limitation “the type” in line 6. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “a type”.
17. Claim 15 recites the limitation “the vehicles” in line 4. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “the vehicle”.
18. Claim 16 recites the limitation “the vehicles” in line 4. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “the vehicle”.
19. Claim 17 recites the limitation “the vehicles” in line 4. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “the vehicle”.
20. Claim 18 recites the limitation “the vehicles” in line 4. There is insufficient antecedent basis for this limitation in the claim. For the purpose of prior art rejection, the limitation was interpreted as “the vehicle”.
21. Claim(s) 14, 17, and 20 rejected by virtue of its/their dependency on claim 4.
Claim Rejections - 35 USC § 101
22. 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.
23. Claim(s) 1-20
is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
24. The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
25. Claim(s) 1-8, and 11-20 is/are directed to a device or a system (i.e. an apparatus).
Therefore, claim(s) 1-8, and 11-20 is/are within at least one of the four statutory categories.
26. Claim(s) 9 is/are directed to a method (i.e. a process).
Therefore, claim(s) 9 is/are within at least one of the four statutory categories.
27. Claim(s) 10 is/are directed to a non-transitory storage medium (i.e. an article of manufacture).
Therefore, claim(s) 10 is/are within at least one of the four statutory categories.
Representative Claim
28. Claim 1 will be used as a representative claim for the remainder of the 101 rejection. The recited abstract ideas are emphasized below [with the category of abstract idea in brackets] and they are analyzed in 101 Analysis – Step 2A, Prong I section. The additional limitations beyond the abstract ideas are underlined below [with a description of the additional limitations in brackets]) and they are analyzed in 101 Analysis – Step 2A, Prong II section. Claim 1 recites:
A detection device comprising a processor programmed to perform operations [applying the abstract idea using generic computing module, Apply it 2106.05(f)] comprising:
acquiring, from a vehicle, vehicle information regarding the vehicle, the vehicle information including identification information of the vehicle [pre-solution activity (data gathering), 2106.05(g) using generic sensors];
acquiring a collection record of the vehicle information [pre-solution activity (data gathering), 2106.05(g) using generic sensors];
acquiring an operation plan for the vehicle corresponding to the identification information [pre-solution activity (data gathering), 2106.05(g) using generic sensors]; and
detecting an abnormality related to the vehicle, based on the collection record and the operation plan [mental process/step].
101 Analysis – Step 2A, Prong I
29. 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. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c).
30. Independent claim(s) 1, and 8-10 include(s) limitations that recite an abstract idea. As mentioned above, claim 1 will be used as a representative claim for the remainder of the 101 rejection.
31. The Examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers steps that could be carried out in the human mind. For example, “detecting an abnormality related to the vehicle, based on the collection record and the operation plan” step(s) encompass(es) a user making observation, evaluation or judgement about occurring abnormalities, could all be carried out in one’s mind. The same user looking at the data collected, could form a simple judgement and conclude whether the data indicates an abnormality in the operation of the vehicle. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
32. Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). 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 use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
33. 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.
34. Regarding the additional limitations of “a processor programmed to perform operations” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (vehicle controller) to perform the process. The “a processor programmed to perform operations” is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of data gathering and detecting abnormalities) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
35. Regarding the additional limitation(s) of “acquiring, from a vehicle, vehicle information regarding the vehicle, the vehicle information including identification information of the vehicle” “acquiring a collection record of the vehicle information” and “acquiring an operation plan for the vehicle corresponding to the identification information” the examiner submits that this/these limitation(s) is/are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the “acquiring, from a vehicle, vehicle information regarding the vehicle, the vehicle information including identification information of the vehicle” “acquiring a collection record of the vehicle information” and “acquiring an operation plan for the vehicle corresponding to the identification information” step(s) is/are recited at a high level of generality (i.e. as a general means of gathering data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
36. 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. see MPEP § 2106.05. Accordingly, the additional limitation(s) 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
37. Regarding Step 2B of the Revised Guidance, 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. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “a processor programmed to perform operations” amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As discussed above, in regard to the additional limitations of “acquiring, from a vehicle, vehicle information regarding the vehicle, the vehicle information including identification information of the vehicle” “acquiring a collection record of the vehicle information” and “acquiring an operation plan for the vehicle corresponding to the identification information” the examiner submits that these limitations are insignificant extra-solution activities.
38. As established above claim 1 is representative of all independent claims and therefore claim(s) 8-10 is/are rejected for the same reason.
39. Dependent claim(s) 2-7, and 11-20 does/do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-7, and 11-20 are not patent eligible under the same rationale as provided for in the rejection of 1.
40. Therefore, claim(s) 1-20 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
41. 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.
42. Claim(s) 1-2, 5-6, 8-10, 12, 15, and 18
is/are rejected under 35 U.S.C. 103 as being unpatentable over Misawa (US-20230406357-A1) in view of Yamada (JP-2009044672-A).
In regard to claim 1
, Misawa discloses a detection device comprising a processor programmed to perform operations comprising (See at least Figs. 1-2 and [0032 & 0045]: the autonomous driving system 100 includes an on-board device 10 mounted to an autonomous driving vehicle and an autonomous driving control device 20 [i.e., a detection device] installed in an autonomous driving assistance center. The autonomous driving control device 20 includes a CPU 21 [i.e., a processor], a memory 22, an operating unit 23, a display unit 24, a storage unit and a communication unit 26):
acquiring, from a vehicle, vehicle information regarding the vehicle, (See at least Fig. 1-2, and [0057-0058]: the CPU 21 of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving data collection unit 21A regularly collects driving data including own-state quantities from the on-board device 10 of the autonomous driving vehicle [i.e., acquiring, from a vehicle, vehicle information regarding the vehicle], and stores the collected driving data in the data/model storage database 25B);
acquiring a collection record of the vehicle information (See at least Fig. 1-2, and [0057-0058]: the CPU 21 of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving data collection unit 21A regularly collects driving data [i.e., acquiring a collection record of the vehicle information] including own-state quantities from the on-board device 10 of the autonomous driving vehicle, and stores the collected driving data in the data/model storage database 25B. Examiner notes, as mentioned above, the driving data, or the vehicle information, is collected and stored on a regular basis. The stored data over a period of time is the collection record of the vehicle information);
acquiring an operation plan for the vehicle corresponding to the identification information (See at least [0060]: the driving trajectory model generation unit 21B calculates, as the specific values of own-state quantities, offset values representing time series deviations of the candidate driving trajectory or actual driving trajectory from a reference trajectory planned in advance on a predefined digital map [i.e., an operation plan], and then aggregates the calculated offset values within a predetermined region to thereby calculate a representative value of the own-state quantities. Examiner notes, the reference trajectory planned in advance is the operation plan of the vehicle and it must be necessarily acquired); and
detecting an abnormality related to the vehicle, based on the collection record and the operation plan (See at least Figs. 1-2, [0059 & 0068]: the driving trajectory model generation unit 21B generates a driving trajectory model that represents specific values of own-state quantities in a time series based on the driving data stored in the data/model storage DB 25B. The abnormality level determination unit 21C compares the reference driving trajectory acquired from the driving trajectory model generated by the driving trajectory model generation unit 21B and the driving trajectory under evaluation for the autonomous driving vehicle, and determines the abnormality level [i.e., detecting an abnormality] expressed as a degree of deviation between the reference driving trajectory [i.e., the operation plan] and the driving trajectory under evaluation [i.e., the collection record]. Examiner notes, as mentioned above, the driving trajectory model generation unit 21B generates a driving trajectory model based on the driving data stored in the data/model storage DB 25B. The stored driving data is the collection record which gathered by the driving data collection unit 21A).
While Misawa discloses the reference driving trajectory acquired from the driving trajectory model of an autonomous driving vehicle (e.g., vehicle ID=1) and the driving trajectory under evaluation of the autonomous driving vehicle (e.g., vehicle ID=1) are compared for a certain segment (See at least Fig. 7, and [0086]), which suggests each vehicle has an identifier associated with it, Misawa does not explicitly disclose the vehicle information including identification information of the vehicle.
However, Yamada teaches the in-vehicle communication device transmits identification information [i.e., identification information of the vehicle] for identifying the in-vehicle communication device, and the roadside communication device receives the identification information transmitted from the in-vehicle communication device. The roadside communication device's storage means stores identification information for identifying an in-vehicle communication device that transmits abnormal in-vehicle device information, and the determination means determines whether or not there is an abnormality in the in-vehicle device by comparing the received identification information with the identification information stored in the storage means (See at least [0023]). Examiner notes, the identification information for identifying the in-vehicle communication device necessarily identifies the vehicle as well.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Misawa, by incorporating the teachings of Yamada, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – vehicles and detecting abnormalities, such that the autonomous driving control device of Misawa acquires the identification information for identifying the in-vehicle communication device from the vehicle and uses the identification information for identifying the vehicle.
The motivation to modify is that, as acknowledged by Yamada, supporting safe driving and improving transportation efficiency (See at least [0002]) which one of ordinary skill would have recognized allows the vehicle to become more reliable.
In regard to claim 2
, Misawa, as modified by Yamada, teaches the detection device according to claim 1, wherein
the operations further comprise acquiring setting information indicating setting contents of the vehicle regarding transmission of the vehicle information, and
the detecting includes detecting an abnormality related to the vehicle, based on the setting information, the collection record, and the operation plan (See at least Figs. 1-2, [0059 & 0068]: the driving trajectory model generation unit 21B generates a driving trajectory model that represents specific values of own-state quantities in a time series based on the driving data stored in the data/model storage DB 25B. The abnormality level determination unit 21C compares the reference driving trajectory acquired from the driving trajectory model generated by the driving trajectory model generation unit 21B and the driving trajectory under evaluation for the autonomous driving vehicle, and determines the abnormality level [i.e., detecting an abnormality] expressed as a degree of deviation between the reference driving trajectory [i.e., the operation plan] and the driving trajectory under evaluation [i.e., the collection record]. Examiner notes, as mentioned above, the driving trajectory model generation unit 21B generates a driving trajectory model based on the driving data stored in the data/model storage DB 25B. The stored driving data is the collection record which gathered by the driving data collection unit 21A).
Further, Yamada teaches the in-vehicle device information includes information indicating at least one of the speed, position, and time of the vehicle equipped with the in-vehicle communication device [i.e., setting information indicating setting contents of the vehicle], and the determination means determines whether or not there is an abnormality [i.e., detecting an abnormality related to the vehicle] by determining whether or not at least one of the speed, position, and time indicated by the in-vehicle device information [i.e., based on the setting information] acquired by the acquisition means is within a predetermined range (See at least Figs. 1-6, and [0008]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Misawa, as modified by Yamada, by further incorporating the teachings of Yamada, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – vehicles and detecting abnormalities, such that the autonomous driving control device of Misawa acquires the time information of the vehicle, as taught by Yamada, and uses the acquired time along with the other acquired information to determine whether or not there is an abnormality.
The motivation to do so is the same as acknowledged by Yamada in regard to claim 1.
In regard to claim 5
, Misawa, as modified by Yamada, teaches the detection device according to claim 1, wherein
the operation plan is divided into a plurality of time periods (See at least [0060]: the driving trajectory model generation unit 21B calculates, as the specific values of own-state quantities, offset values representing time series [i.e., a plurality of time periods] deviations of the candidate driving trajectory or actual driving trajectory from a reference trajectory planned in advance on a predefined digital map [i.e., the operation plan], and then aggregates the calculated offset values within a predetermined region to thereby calculate a representative value of the own-state quantities. Examiner notes, calculating offset values representing time series deviations of actual driving trajectory from a reference trajectory planned in advance necessarily encompasses dividing both trajectories into a time series or a plurality of time periods. The reference trajectory planned in advance is the operation plan. Accordingly, Misawa discloses dividing the operation plan into a plurality of time periods), and
the detecting includes determining, for each time period, whether or not a deviation between the operation plan and the collection record has occurred, and detecting the abnormality, based on occurrence frequency of the deviation in each of the plurality of time periods (See at least [0059-0060 & 0068]: the driving trajectory model generation unit 21B generates a driving trajectory model that represents specific values of own-state quantities in a time series based on the driving data stored in the data/model storage DB 25B. Specifically, the driving trajectory model generation unit 21B aggregates the own-state quantities by time period or by segment, and calculate a representative value of the aggregated own-state quantities. The driving trajectory model generation unit 21B calculates, as the specific values of own-state quantities, offset values representing time series deviations of the candidate driving trajectory or actual driving trajectory from a reference trajectory planned in advance on a predefined digital map, and then aggregates the calculated offset values within a predetermined region to thereby calculate a representative value of the own-state quantities. The abnormality level determination unit 21C compares the reference driving trajectory acquired from the driving trajectory model generated by the driving trajectory model generation unit 21B and the driving trajectory under evaluation for the autonomous driving vehicle, and determines the abnormality level expressed as a degree of deviation between the reference driving trajectory and the driving trajectory under evaluation [i.e., detecting the abnormality, based on occurrence frequency of the deviation in each of the plurality of time periods]. Examiner notes, the driving trajectory model generation unit 21B generates the driving trajectory model which is created based on the collection record. Calculating offset values representing time series deviations of the candidate driving trajectory or actual driving trajectory from a reference trajectory planned in advance on a predefined digital map necessarily encompasses determining, for each time period, whether or not a deviation between the operation plan and the collection record has occurred. Furthermore, the offset value is calculated for a time series. Accordingly, determining an abnormality for one or more time periods is necessarily based on the occurrence frequency of the deviation in each of the plurality of time periods).
In regard to claim 6
, Misawa, as modified by Yamada, teaches the detection device according to claim 1, wherein
the operations further comprise calculating an index value of a deviation between the operation plan and the collection record, and creating, based on the index value, list information of the vehicles that require countermeasures against the abnormalities (See at least Figs. 1-2, and [0068 & 0128]: the abnormality level determination unit 21C compares the reference driving trajectory acquired from the driving trajectory model generated by the driving trajectory model generation unit 21B and the driving trajectory under evaluation for the autonomous driving vehicle, and determines the abnormality level expressed as a degree of deviation [i.e., an index value of a deviation] between the reference driving trajectory [i.e., operation plan] and the driving trajectory under evaluation [i.e., the collection record]. The reference driving trajectory represents a driving trajectory to be used as a reference, and the driving trajectory under evaluation represents a driving trajectory to be evaluated. Specifically, the abnormality level determination unit 21C compares the feature quantities indicating the usual driving trajectory (reference driving trajectory) generated for each grid and the feature quantities indicating the driving trajectory under evaluation, and calculates a level of similarity between them. The feature quantities indicating the driving trajectory under evaluation are feature quantities for the current driving. The CPU 21 of the autonomous driving control device 20 presents a list of a plurality of pieces of abnormality data [i.e., list information of the vehicle] to the operator, and receives a selection of the abnormality data from the operator).
In regard to claim 8
, Misawa discloses a detection system, comprising (See at least Figs. 1-2 and [0032]: the autonomous driving system 100 [i.e., a detection system] includes an on-board device 10 mounted to an autonomous driving vehicle and an autonomous driving control device 20 installed in an autonomous driving assistance center):
an in-vehicle device mounted in a vehicle (See at least Figs. 1-2 and [0032]: the autonomous driving system 100 includes an on-board device 10 mounted to an autonomous driving vehicle [i.e., an in-vehicle device] and an autonomous driving control device 20 installed in an autonomous driving assistance center); and
a detection device, wherein the in-vehicle device transmits, to the detection device, vehicle information regarding the vehicle, (See at least Figs. 1-2 and [0032-0034 & 0057-0058]: the autonomous driving system 100 includes an on-board device 10 mounted to an autonomous driving vehicle and an autonomous driving control device 20 [i.e., a detection device] installed in an autonomous driving assistance center. The on-board device 10 and the autonomous driving control device 20 are communicatively connected. the CPU 21 of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving data collection unit 21A regularly collects driving data including own-state quantities from the on-board device 10 of the autonomous driving vehicle [i.e., the in-vehicle device transmits, to the detection device, vehicle information regarding the vehicle], and stores the collected driving data in the data/model storage database 25B. Examiner notes, as mentioned above, the on-board device 10 and the autonomous driving control device 20 are communicatively connected which means they exchange data. The driving data collection unit 21A is a component of the autonomous driving control device 20. That is, the in-vehicle device transmits data to the autonomous driving control device 20 or the detection device), and
the detection device detects an abnormality related to the vehicle, based on a collection record of the vehicle information in the detection device, and an operation plan for the vehicle corresponding to the identification information (See at least Fig. 1-2, and [0057-0060 & 0068]: the CPU 21 of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving data collection unit 21A regularly collects driving data [i.e., acquiring a collection record of the vehicle information] including own-state quantities from the on-board device 10 of the autonomous driving vehicle, and stores the collected driving data in the data/model storage database 25B. The abnormality level determination unit 21C compares the reference driving trajectory acquired from the driving trajectory model generated by the driving trajectory model generation unit 21B and the driving trajectory under evaluation for the autonomous driving vehicle, and determines the abnormality level [i.e., detects an abnormality] expressed as a degree of deviation between the reference driving trajectory and the driving trajectory under evaluation. The driving trajectory model generation unit 21B calculates, as the specific values of own-state quantities, offset values representing time series deviations of the candidate driving trajectory or actual driving trajectory from a reference trajectory planned in advance on a predefined digital map [i.e., an operation plan], and then aggregates the calculated offset values within a predetermined region to thereby calculate a representative value of the own-state quantities. Examiner notes, as mentioned above, the driving data, or the vehicle information, is collected and stored on a regular basis. The stored data over a period of time is the collection record of the vehicle information. The reference trajectory planned in advance is the operation plan of the vehicle and it must be necessarily acquired. Furthermore, as mentioned above, the abnormality is detected by using the deviation between the reference driving trajectory, which is the operation plan, and the driving trajectory under evaluation which is created based on the collected data, or the collection record).
While Misawa discloses the reference driving trajectory acquired from the driving trajectory model of an autonomous driving vehicle (e.g., vehicle ID=1) and the driving trajectory under evaluation of the autonomous driving vehicle (e.g., vehicle ID=1) are compared for a certain segment (See at least Fig. 7, and [0086]), which suggests each vehicle has an identifier associated with it, Misawa does not explicitly disclose the vehicle information including identification information of the vehicle.
However, Yamada teaches the in-vehicle communication device transmits identification information [i.e., identification information of the vehicle] for identifying the in-vehicle communication device, and the roadside communication device receives the identification information transmitted from the in-vehicle communication device. The roadside communication device's storage means stores identification information for identifying an in-vehicle communication device that transmits abnormal in-vehicle device information, and the determination means determines whether or not there is an abnormality in the in-vehicle device by comparing the received identification information with the identification information stored in the storage means (See at least [0023]). Examiner notes, the identification information for identifying the in-vehicle communication device necessarily identifies the vehicle as well.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Misawa, by incorporating the teachings of Yamada, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – vehicles and detecting abnormalities, such that the autonomous driving control device of Misawa acquires the identification information for identifying the in-vehicle communication device from the vehicle and uses the identification information for identifying the vehicle.
The motivation to do so is the same as acknowledged by Yamada in regard to claim 1.
In regard to claim 9
, Misawa discloses a detection method used in a detection device, the method comprising (See at least Figs. 8-9, [0027]: an autonomous driving control method including collecting from an autonomous driving vehicle having a driving trajectory planned in advance):
acquiring, from a vehicle, vehicle information regarding the vehicle, (See at least Fig. 1-2, and [0057-0058]: the CPU 21 of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving data collection unit 21A regularly collects driving data including own-state quantities from the on-board device 10 of the autonomous driving vehicle [i.e., acquiring, from a vehicle, vehicle information regarding the vehicle], and stores the collected driving data in the data/model storage database 25B);
acquiring a collection record of the vehicle information by the detection device (See at least Fig. 1-2, and [0057-0058]: the CPU 21 of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving data collection unit 21A regularly collects driving data [i.e., acquiring a collection record of the vehicle information] including own-state quantities from the on-board device 10 of the autonomous driving vehicle, and stores the collected driving data in the data/model storage database 25B. Examiner notes, as mentioned above, the driving data, or the vehicle information, is collected and stored on a regular basis. The stored data over a period of time is the collection record of the vehicle information);
acquiring an operation plan for the vehicle corresponding to the identification information (See at least [0060]: the driving trajectory model generation unit 21B calculates, as the specific values of own-state quantities, offset values representing time series deviations of the candidate driving trajectory or actual driving trajectory from a reference trajectory planned in advance on a predefined digital map [i.e., an operation plan], and then aggregates the calculated offset values within a predetermined region to thereby calculate a representative value of the own-state quantities. Examiner notes, the reference trajectory planned in advance is the operation plan of the vehicle and it must be necessarily acquired); and
detecting an abnormality related to the vehicle, based on the collection record and the operation plan (See at least Figs. 1-2, [0059 & 0068]: the driving trajectory model generation unit 21B generates a driving trajectory model that represents specific values of own-state quantities in a time series based on the driving data stored in the data/model storage DB 25B. The abnormality level determination unit 21C compares the reference driving trajectory acquired from the driving trajectory model generated by the driving trajectory model generation unit 21B and the driving trajectory under evaluation for the autonomous driving vehicle, and determines the abnormality level [i.e., detecting an abnormality] expressed as a degree of deviation between the reference driving trajectory [i.e., the operation plan] and the driving trajectory under evaluation [i.e., the collection record]. Examiner notes, as mentioned above, the driving trajectory model generation unit 21B generates a driving trajectory model based on the driving data stored in the data/model storage DB 25B. The stored driving data is the collection record which gathered by the driving data collection unit 21A).
While Misawa discloses the reference driving trajectory acquired from the driving trajectory model of an autonomous driving vehicle (e.g., vehicle ID=1) and the driving trajectory under evaluation of the autonomous driving vehicle (e.g., vehicle ID=1) are compared for a certain segment (See at least Fig. 7, and [0086]), which suggests each vehicle has an identifier associated with it, Misawa does not explicitly disclose the vehicle information including identification information of the vehicle.
However, Yamada teaches the in-vehicle communication device transmits identification information [i.e., identification information of the vehicle] for identifying the in-vehicle communication device, and the roadside communication device receives the identification information transmitted from the in-vehicle communication device. The roadside communication device's storage means stores identification information for identifying an in-vehicle communication device that transmits abnormal in-vehicle device information, and the determination means determines whether or not there is an abnormality in the in-vehicle device by comparing the received identification information with the identification information stored in the storage means (See at least [0023]). Examiner notes, the identification information for identifying the in-vehicle communication device necessarily identifies the vehicle as well.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Misawa, by incorporating the teachings of Yamada, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – vehicles and detecting abnormalities, such that the autonomous driving control device of Misawa acquires the identification information for identifying the in-vehicle communication device from the vehicle and uses the identification information for identifying the vehicle.
The motivation to do so is the same as acknowledged by Yamada in regard to claim 1.
In regard to claim 10
, Misawa discloses a non-transitory computer-readable storage medium in which a detection program used in a detection device is stored, the program causing a computer to function as (See at least [0028]: a non-transitory computer readable medium having stored thereon instructions executable by a computer to cause the computer to perform an autonomous driving control method):
a first acquisition unit configured to acquire, from a vehicle, vehicle information regarding the vehicle, (See at least Fig. 1-2, and [0057-0058]: the CPU 21 [i.e., a first acquisition unit, see Claim Rejections - 35 USC § 112 section above] of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving data collection unit 21A regularly collects driving data including own-state quantities from the on-board device 10 of the autonomous driving vehicle [i.e., acquiring, from a vehicle, vehicle information regarding the vehicle], and stores the collected driving data in the data/model storage database 25B);
a second acquisition unit configured to acquire a collection record of the vehicle information by the first acquisition unit (See at least Fig. 1-2, and [0057-0058]: the CPU 21 [i.e., a second acquisition unit, see Claim Rejections - 35 USC § 112 section above] of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving data collection unit 21A regularly collects driving data [i.e., acquiring a collection record of the vehicle information] including own-state quantities from the on-board device 10 of the autonomous driving vehicle, and stores the collected driving data in the data/model storage database 25B. Examiner notes, as mentioned above, the driving data, or the vehicle information, is collected and stored on a regular basis. The stored data over a period of time is the collection record of the vehicle information);
a third acquisition unit configured to acquire an operation plan for the vehicle corresponding to the identification information (See at least Fig. 1-2, and [0057 & 0060]: the CPU 21 [i.e., a third acquisition unit, see Claim Rejections - 35 USC § 112 section above] of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving trajectory model generation unit 21B calculates, as the specific values of own-state quantities, offset values representing time series deviations of the candidate driving trajectory or actual driving trajectory from a reference trajectory planned in advance on a predefined digital map [i.e., an operation plan], and then aggregates the calculated offset values within a predetermined region to thereby calculate a representative value of the own-state quantities. Examiner notes, the reference trajectory planned in advance is the operation plan of the vehicle and it must be necessarily acquired); and
a detection unit configured to detect an abnormality related to the vehicle, based on the collection record and the operation plan (See at least Figs. 1-2, [0057-0059 & 0068]: the CPU 21 [i.e., a detection unit, see Claim Rejections - 35 USC § 112 section above] of the autonomous driving control device serves as a driving data collection unit 21A, a driving trajectory model generation unit 21B, an abnormality level determination unit 21C, a detailed data collection unit 21D, a presentation unit 21E, a driving outcome output unit 21F, a learning unit 21G, and an inference unit 21H. The driving trajectory model generation unit 21B generates a driving trajectory model that represents specific values of own-state quantities in a time series based on the driving data stored in the data/model storage DB 25B. The abnormality level determination unit 21C compares the reference driving trajectory acquired from the driving trajectory model generated by the driving trajectory model generation unit 21B and the driving trajectory under evaluation for the autonomous driving vehicle, and determines the abnormality level [i.e., detect an abnormality] expressed as a degree of deviation between the reference driving trajectory [i.e., the operation plan] and the driving trajectory under evaluation [i.e., the collection record]. Examiner notes, as mentioned above, the driving trajectory model generation unit 21B generates a driving trajectory model based on the driving data stored in the data/model storage DB 25B. The stored driving data is the collection record which gathered by the driving data collection unit 21A).
While Misawa discloses the reference driving trajectory acquired from the driving trajectory model of an autonomous driving vehicle (e.g., vehicle ID=1) and the driving trajectory under evaluation of the autonomous driving vehicle (e.g., vehicle ID=1) are compared for a certain segment (See at least Fig. 7, and [0086]), which suggests each vehicle has an identifier associated with it, Misawa does not explicitly disclose the vehicle information including identification information of the vehicle.
However, Yamada teaches the in-vehicle communication device transmits identification information [i.e., identification information of the vehicle] for identifying the in-vehicle communication device, and the roadside communication device receives the identification information transmitted from the in-vehicle communication device. The roadside communication device's storage means stores identification information for identifying an in-vehicle communication device that transmits abnormal in-vehicle device information, and the determination means determines whether or not there is an abnormality in the in-vehicle device by comparing the received identification information with the identification information stored in the storage means (See at least [0023]). Examiner notes, the identification information for identifying the in-vehicle communication device necessarily identifies the vehicle as well.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Misawa, by incorporating the teachings of Yamada, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – vehicles and detecting abnormalities, such that the autonomous driving control device of Misawa configures the CPU as the first acquisition unit to acquire the identification information for identifying the in-vehicle communication device from the vehicle and to use the identification information for identifying the vehicle.
The motivation to do so is the same as acknowledged by Yamada in regard to claim 1.
In regard to claim 12
, Misawa, as modified by Yamada, teaches the detection device according to claim 2.
Claim 12 recites a device having substantially the same features of claim 5 above, therefore claim 12 is rejected for the same reasons as claim 5.
In regard to claim 15
, Misawa, as modified by Yamada, teaches the detection device according to claim 2.
Claim 15 recites a device having substantially the same features of claim 6 above, therefore claim 15 is rejected for the same reasons as claim 6.
In regard to claim 18
, Misawa, as modified by Yamada, teaches the detection device according to claim 5.
Claim 18 recites a device having substantially the same features of claim 6 above, therefore claim 18 is rejected for the same reasons as claim 6.
43. Claim(s) 3-4, 11, 13-14, 16-17, and 19-20
is/are rejected under 35 U.S.C. 103 as being unpatentable over Misawa (US-20230406357-A1) in view of Yamada (JP-2009044672-A) and further in view of Aiba et al. (WO-2018230324-A1) and further in view of Fushiki et al. (JP-2011201336-A).
In regard to claim 3
, Misawa, as modified by Yamada, teaches the detection device according to claim 2, accordingly the rejection of claim 2 is incorporated.
Misawa, as modified by Yamada, is silent on wherein
the setting information includes at least one of: a transmission cycle of the vehicle information by the vehicle; and
a type of the vehicle information transmitted by the vehicle.
However, Aiba teaches the abnormality detection unit 31c determines whether there is an abnormality in the vehicle 1 if it does not receive location information from the vehicle 1 for a predetermined period [i.e., a transmission cycle of the vehicle information by the vehicle] of, for example, several minutes to several tens of minutes. Then, the abnormality detection unit 31c determines that there is a possibility of an abnormality occurring in the vehicle 1 (See at least Figs. 1-10, and [0057]).
Fushiki teaches a mobile body abnormality detection support system having a condition monitoring device that monitors the status of various devices installed on a mobile body, and more particularly to a system that provides support for abnormality detection of a mobile body by a ground system. When a malfunction occurs, status data of the mobile unit is transmitted to the ground, and the ground side provides advanced support to appropriately predict the possibility of the malfunction leading to an abnormality by referring to past cases, thereby reducing the probability of false detections that lead to the stopping of the mobile unit and aiming to improve the operational rate of the mobile unit. The detailed status data 202 includes a status data header, a fault detection time, and a status data column. The status data header contains various basic information related to the status data, such as the type of status data (basic information such as the time of the status data sequence and the address of the status data) [i.e., a type of the vehicle information] (See at least [0001 & 0008 & 0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Misawa, as modified by Yamada, by incorporating the teachings of Aiba and Fushiki, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – detecting abnormality of moving objects, such that the vehicle transmits the type of status data and its location to the autonomous driving control device of Misawa periodically, and the autonomous driving control system acquires the time period between two consecutive transmission from the vehicle as part of acquiring the vehicle settings and then if the autonomous driving control device does not receives location information from the vehicle for a time period longer than the acquired time period, it is determined that there is a possibility of an abnormality occurring in the vehicle.
The motivation to modify is that, as acknowledged by Aiba, to automatically notify the nearest police or fire department via an emergency call center in the event of a vehicle accident or other emergency (See at least [0003]) which one of ordinary skill would have recognized keeps the occupants of the vehicle safe.
The motivation to modify is that, as acknowledged by Fushiki, to easily and quickly detect anomalies without requiring the mobile unit to have high-performance equipment such as an anomaly detection condition extraction function (See at least [0003]) which one of ordinary skill would have recognized allows the final cost of the mobile unit to come down without downgrading its capabilities.
In regard to claim 4
, Misawa, as modified by Yamada, teaches the detection device according to claim 2, wherein
the operations further comprises acquiring transmission location information (See at least [0077]: when the abnormality level determination unit 21C determines that the abnormality level indicates the presence of an abnormality, the detailed data collection unit 21D collects image data, sensor data, and recognition results as detailed data from the on-board unit 10. Specifically, since the autonomous driving vehicle, the location [i.e., acquiring transmission location information], and the time when the abnormality level was determined can be identified, the on-board device 10 of the identified autonomous driving vehicle is instructed to upload the detailed data in the related grid)., and
when the abnormality has been detected, the detecting includes specifying a location where the abnormality has occurred, based on the collection record, (See at least [0077]: when the abnormality level determination unit 21C determines that the abnormality level indicates the presence of an abnormality, the detailed data collection unit 21D collects image data, sensor data, and recognition results as detailed data from the on-board unit 10. Specifically, since the autonomous driving vehicle, the location, and the time when the abnormality level was determined can be identified [i.e., when the abnormality has been detected, the detecting includes specifying a location where the abnormality has occurred], the on-board device 10 of the identified autonomous driving vehicle is instructed to upload the detailed data in the related grid).
Misawa, as modified by Yamada, is silent on wherein
the setting information includes the type of the vehicle information transmitted by the vehicle,
indicating correspondence between the type,
the type included in the setting information.
However, Aiba teaches the abnormality detection unit 31c determines whether there is an abnormality in the vehicle 1 if it does not receive location information from the vehicle 1 for a predetermined period of, for example, several minutes to several tens of minutes. When the abnormality detection unit 31c determines that there is a possibility of an abnormality occurring in the vehicle 1 (See at least Figs. 1-10, and [0057]). Examiner notes, as mentioned above, the vehicle sends its location to the abnormality detection unit 31c periodically.
Fushiki teaches a mobile body abnormality detection support system having a condition monitoring device that monitors the status of various devices installed on a mobile body, and more particularly to a system that provides support for abnormality detection of a mobile body by a ground system. When a malfunction occurs, status data of the mobile unit is transmitted to the ground, and the ground side provides advanced support to appropriately predict the possibility of the malfunction leading to an abnormality by referring to past cases, thereby reducing the probability of false detections that lead to the stopping of the mobile unit and aiming to improve the operational rate of the mobile unit. The detailed status data 202 includes a status data header, a fault detection time, and a status data column. The status data header contains various basic information related to the status data, such as the type of status data (basic information such as the time of the status data sequence and the address of the status data) [i.e., the type of the vehicle information] (See at least [0001 & 0008 & 0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Misawa, as modified by Yamada, by incorporating the teachings of Aiba and Fushiki, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – detecting abnormality of moving objects, such that the vehicle transmits the type of status data and its location to the autonomous driving control device of Misawa periodically, and when the autonomous driving control device does not receives location information from the vehicle for a time period longer than time period, it is determined that there is a possibility of an abnormality occurring in the vehicle and specified the vicinity of the last acquired location of the vehicle as the location that the abnormality occurred.
The motivation to do so is the same as acknowledged by Aiba in regard to claim 3.
The motivation to do so is the same as acknowledged by Fushiki in regard to claim 3.
In regard to claim 11
, Misawa, as modified by Yamada and Aiba and Fushiki, teaches the detection device according to claim 3.
Claim 11 recites a device having substantially the same features of claim 4 above, therefore claim 11 is rejected for the same reasons as claim 4.
In regard to claim 13
, Misawa, as modified by Yamada and Aiba and Fushiki, teaches the detection device according to claim 3.
Claim 13 recites a device having substantially the same features of claim 5 above, therefore claim 13 is rejected for the same reasons as claim 5.
In regard to claim 14
, Misawa, as modified by Yamada and Aiba and Fushiki, teaches the detection device according to claim 4.
Claim 14 recites a device having substantially the same features of claim 5 above, therefore claim 14 is rejected for the same reasons as claim 5.
In regard to claim 16
, Misawa, as modified by Yamada and Aiba and Fushiki, teaches the detection device according to claim 3.
Claim 16 recites a device having substantially the same features of claim 6 above, therefore claim 16 is rejected for the same reasons as claim 6.
In regard to claim 17
, Misawa, as modified by Yamada and Aiba and Fushiki, teaches the detection device according to claim 4.
Claim 17 recites a device having substantially the same features of claim 6 above, therefore claim 17 is rejected for the same reasons as claim 6.
In regard to claim 19
, Misawa, as modified by Yamada and Aiba and Fushiki, teaches the detection device according to claim 3, wherein
the setting information includes the type of the vehicle information to be transmitted by the vehicle, and
the detecting includes performing a process of displaying, on a screen, a content based on the type of the vehicle information in which the abnormality has occurred.
Further, Fushiki teaches a mobile body abnormality detection support system having a condition monitoring device that monitors the status of various devices installed on a mobile body, and more particularly to a system that provides support for abnormality detection of a mobile body by a ground system. When a malfunction occurs, status data of the mobile unit is transmitted to the ground, and the ground side provides advanced support to appropriately predict the possibility of the malfunction leading to an abnormality by referring to past cases, thereby reducing the probability of false detections that lead to the stopping of the mobile unit and aiming to improve the operational rate of the mobile unit. The detailed status data 202 includes a status data header, a fault detection time, and a status data column. The status data header contains various basic information related to the status data, such as the type of status data (basic information such as the time of the status data sequence and the address of the status data) [i.e., the type of the vehicle information]. The status data column is a time-series data of status information, containing the time, status information, and an anomaly flag. The time when the anomaly flag was set is stored corresponding to the detection time. The abnormal flag is the result of determining whether the status data at that time is abnormal or not, and is the result of the abnormality diagnosis unit 112's diagnosis. The abnormality flags included in the status data column 203 displays status data where an abnormality has occurred. The anomaly occurrence prediction unit is equipped with a monitor [i.e., a screen] that displays the anomaly detection result. The status monitoring device 104 displays status data [i.e., a content based on the type of the vehicle information in which the abnormality has occurred] and status codes sent from the
measuring device 103 on the monitor 106 (See at least [0001 & 0008 & 0013 & 0020 & 0029-0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Misawa, as modified by Yamada and Aiba and Fushiki, by further incorporating the teachings of Fushiki, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – detecting abnormality of a moving object, such that the vehicle transmits status data, including the type of status data, to the autonomous driving control device of Misawa and then the abnormality is displayed on a screen based on its type.
The motivation to do so is the same as acknowledged by Fushiki in regard to claim 3.
In regard to claim 20
, Misawa, as modified by Yamada and Aiba and Fushiki, teaches the detection device according to claim 4.
Claim 20 recites a device having substantially the same features of claim 19 above, therefore claim 20 is rejected for the same reasons as claim 19.
44. Claim(s) 7
is/are rejected under 35 U.S.C. 103 as being unpatentable over Misawa (US-20230406357-A1) in view of Yamada (JP-2009044672-A) and further in view of Fushiki et al. (JP-2011201336-A).
In regard to claim 7
, Misawa, as modified by Yamada, teaches the detection device according to claim 2, accordingly the rejection of claim 2 is incorporated.
Misawa, as modified by Yamada, is silent on wherein
the setting information includes the type of the vehicle information to be transmitted by the vehicle, and
the detecting includes performing a process of displaying, on a screen, a content based on the type of the vehicle information in which the abnormality has occurred.
However, Fushiki teaches a mobile body abnormality detection support system having a condition monitoring device that monitors the status of various devices installed on a mobile body, and more particularly to a system that provides support for abnormality detection of a mobile body by a ground system. When a malfunction occurs, status data of the mobile unit is transmitted to the ground, and the ground side provides advanced support to appropriately predict the possibility of the malfunction leading to an abnormality by referring to past cases, thereby reducing the probability of false detections that lead to the stopping of the mobile unit and aiming to improve the operational rate of the mobile unit. The detailed status data 202 includes a status data header, a fault detection time, and a status data column. The status data header contains various basic information related to the status data, such as the type of status data (basic information such as the time of the status data sequence and the address of the status data) [i.e., the type of the vehicle information]. The status data column is a time-series data of status information, containing the time, status information, and an anomaly flag. The time when the anomaly flag was set is stored corresponding to the detection time. The abnormal flag is the result of determining whether the status data at that time is abnormal or not, and is the result of the abnormality diagnosis unit 112's diagnosis. The abnormality flags included in the status data column 203 displays status data where an abnormality has occurred. The anomaly occurrence prediction unit is equipped with a monitor [i.e., a screen] that displays the anomaly detection result. The status monitoring device 104 displays status data [i.e., a content based on the type of the vehicle information in which the abnormality has occurred] and status codes sent from the
measuring device 103 on the monitor 106 (See at least [0001 & 0008 & 0013 & 0020 & 0029-0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the invention of Misawa, as modified by Yamada, by incorporating the teachings of Fushiki, with a reasonable expectation of success, as all inventions are directed to the same field of endeavor – detecting abnormality of a moving object, such that the vehicle transmits status data, including the type of status data, to the autonomous driving control device of Misawa and then the abnormality is displayed on a screen based on its type.
The motivation to do so is the same as acknowledged by Fushiki in regard to claim 3.
Conclusion
45. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tsuyunashi et al. (JP-2020038409-A) teaches an in-vehicle device which detects an abnormality of the own vehicle.
46. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Preston J Miller whose telephone number is (703)756-1582. The examiner can normally be reached Monday through Friday 7:30 AM - 4:30 PM EST.
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48. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramya P Burgess can be reached at (571) 272-6011. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/P.J.M./Examiner, Art Unit 3661
/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
