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 .
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.
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.
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.
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.
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.
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.
Claim 6 contains the following limitations that invoke 35 U.S.C. 112(f): “traveling road history acquisition means,” “road information acquisition means,” and “prediction means.”
Claim 8 contains the following limitations that invoke 35 U.S.C. 112(f): “vehicle data acquisition means for acquiring vehicle data . . . ,” “geographic information acquisition means for acquiring geographic information about the road . . . ,” and “classification generation means for generating a road classification . . . .”
Notably, there appears to be no description of the components comprising these means in the Specification of this application. In fact, there are only nine mentions of the term “means” in the entire Specification- none of which continue to detail the structure of any of the claimed means. Therefore, the broadest reasonable interpretation of each limitation is any way to accomplish the described functions.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5 and 10-11 are rejected under 35 U.S.C. 101 because they are directed toward a judicial exception without significantly more.
The Examiner will now proceed through the two-prong test laid out in MPEP § 2106 on claim 1 to illustrate how the broadest reasonable interpretation of the claims is directed toward the judicial exception. However, the other independent and dependent claims are also directed to a judicial exception unless otherwise specified.
Firstly, the broadest reasonable interpretation (BRI) of the present claim is a way of determining an internal anomaly of a vehicle using data acquired from various databases.
Regarding Step 1, claim 1 is directed to a system and a device because they describe parts and their functions. The analysis proceeds to Step 2A.
Regarding Step 2A, claim 1 recites a judicial exception because it is (1) directed to an abstract idea; and (2) it does not recite additional elements that integrate the judicial exception into a practical application.
Claim 1 is (1) directed to an abstract idea. An abstract idea is any concept that could be interpreted as being performed by the human mind or by a human mind with a physical aid. MPEP § 2106.04(a)(2)(III).
Claim 1 recites the following limitations that are directed to an abstract idea:
“ . . . predicts an internal anomaly of the target vehicle based on the road information about the road included in the traveling road history of the target vehicle.”
These limitations, when broadly interpreted, do not preclude a human from, in their mind or with a pen and piece of paper, making any kind of prediction about any kind of vehicle anomaly based on where the vehicle has been. Therefore, the claim is directed to a mental process because of the high level of generality with which the limitations are recited, and analysis proceeds to step (2).
Claim 1 also (2) fails to integrate the judicial exception into a practical application. In a computing environment, an abstract idea may be integrated into a practical application where the claim goes “beyond generally linking the use of the judicial exception to a particular technological environment . . . .” MPEP § 2106.04(d)(1). Claim 1 attempts to integrate the exception into a practical application with the following limitations:
“An anomaly prediction system comprising:
a road information database that stores road information in which position information about a road is associated with a road classification into which the road is classified according to an influence on an internal anomaly of a vehicle; and
an anomaly prediction device capable of communicating with the road information database ...”
However, this does not appear to the Examiner as more than generally linking the abstract idea defined in step 2A to a data storage, collection, and computing environment. Therefore, the present claim does not integrate the abstract idea into a practical application.
Claim 1 reciting an abstract idea generically applied on computer hardware, the analysis proceeds to Step 2B.
Regarding Step 2B, claim 1 does not recite additional elements that amount to significantly more than the judicial exception. Additional elements of computer components to an abstract idea do not amount to significantly more than the judicial exception when, considered as a whole, the claim appears to be “[s]imply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.” MPEP § 2106.05(I)(A). Claim 1 recites the following additional elements:
“ . . . acquires a traveling road history of a target vehicle for which an internal anomaly is to be predicted,
acquires the road information from information database for a road included in the traveling road history . . . ”
However, these elements appear to be appending the well-understood, routine, conventional activity of gathering data, at a high level of generality, to the mental process of the present claim. Therefore, claim 1 does not recite significantly more than the judicial exception.
The Examiner notes that while the above analysis was applied to claim in particular, further steps recited in the other independent and dependent claims all feature similar issues that bar them from being considered eligible subject matter unless specified below.
Claims 6-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims recite means for performing different functions without further describing the structure of the means. For example, claim 8 recites that the classification generation device comprises vehicle data acquisition means, geographic information acquisition means, and classification generation means. However, neither the Claims nor the Specification of this invention describe the structure of such means, as noted in the Claim Interpretation section above. In the absence of any structural components, claims 6-9 do not fall under one of the statutory categories.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 6, 10, and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20210056778 A1 to Wylie, Stephen et al. (“Wylie”).
Regarding claim 1, Wylie discloses an anomaly prediction system comprising:
a road information database that stores road information in which position information about a road is associated with a road classification into which the road is classified according to an influence on an internal anomaly of a vehicle ([0052]-[0053]: Environmental system 112 retrieves information from datastore 110, which broadly reads on the road information database because it stores road conditions (classification) searchable by location. The road conditions are used to detect an anomaly; this broadly reads on the classification having an influence on an internal anomaly of a vehicle.);
and an anomaly prediction device capable of communicating with the road information database ([0052]-[0053]: Vehicle anomaly detection system 106 sends location data to the environmental system 112 to acquire road conditions.), wherein
the anomaly prediction device
acquires a traveling road history of a target vehicle for which an internal anomaly is to be predicted ([0052]: “The environment system 112 may use the location data to determine weather information, road conditions, traffic, and so forth based on the location data during the time that it was collected.”),
acquires the road information from the road information database for a road included in the traveling road history ([0052]: Acquiring road conditions for a particular location broadly reads on acquiring road information for a road in the traveling road history because the road conditions acquired based on the vehicle’s location at the time are necessarily associated with a road the vehicle was traveling on.), and
predicts an internal anomaly of the target vehicle based on the road information about the road included in the traveling road history of the target vehicle (FIG. 3; [0053]: “At operation 314, the vehicle anomaly detection system 106 may apply a trained model to the information and data and determine a result. For example, the model may indicate a probability that an anomaly is detected and what the anomaly is, e.g., there is a 75% probability that tires are worn, a 95% the breaks are worn, etc.”).
Claim 6 is rejected over similar reasons to claim 1 as applied to an anomaly prediction device performing the same function as the anomaly prediction system of claim 1.
Claim 10 is rejected over similar reasons to claim 1, as applied to an anomaly prediction method for performing the functions of the anomaly prediction system of claim 1.
Claim 11 is rejected over similar reasons to claim 1, as applied to a non-transitory computer-readable medium storing an anomaly prediction program for causing a computer to execute the functions of the anomaly prediction system of claim 1.
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.
Claims 2, 5, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Wylie, further in view of US 20190371093 A1 to Edren, Johannes et al. (“Edren”).
Regarding claim 2, Wylie teaches the anomaly prediction system according to claim 1.
Wylie does not appear to expressly teach the system further comprising a vehicle failure information database that is communicable with the anomaly prediction device and stores vehicle failure information in which traveling road histories of a plurality of vehicles are associated with failure information about each of the plurality of vehicles,
wherein the anomaly prediction device identifies a similar vehicle having a similar traveling road history to that of the target vehicle from among the plurality of vehicles based on the traveling road history of the target vehicle and the road information, and predicts the internal anomaly of the target vehicle based on the failure information about the similar vehicle.
However, Edren teaches the system further comprising a vehicle failure information database that is communicable with the anomaly prediction device ([0027]: The graph 142, stored in a database, is communicable with the vehicle for predicting a vehicle fault.) and stores vehicle failure information in which traveling road histories of a plurality of vehicles are associated with failure information about each of the plurality of vehicles device ([0027]: Each line of graph 142 can illustrate motion data of a vehicle associated with a certain kind of fault.),
wherein the anomaly prediction device identifies a similar vehicle having a similar traveling road history to that of the target vehicle from among the plurality of vehicles based on the traveling road history of the target vehicle and the road information, and predicts the internal anomaly of the target vehicle based on the failure information about the similar vehicle ([0027]: “That is, at operation 132, the vehicle can determine whether the motion of the vehicle (as determined by the sensor data) corresponds to any of the lines on the graph 142. If the motion of the vehicle (as determined by the sensor data) corresponds to a line on the graph 142 that is associated with a source of a fault, the vehicle can diagnose the fault based on the source of the fault corresponding to the line on the graph 142.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that predicts vehicle anomalies of Wylie with the system that predicts vehicle faults by matching the motion of the vehicle with the motion of another vehicle that has a particular fault taught by Edren. Doing so would have improved the accuracy of the fault prediction by providing another way to reach a fault conclusion.
Regarding claim 5, Wylie teaches the anomaly prediction system according to claim 1.
Wylie does not appear to expressly teach the anomaly prediction device
receives an input of interview information including malfunction information about the vehicle, and
predicts the internal anomaly of the vehicle based on the interview information.
However, Edren teaches the anomaly prediction device
receives an input of interview information including malfunction information about the vehicle ([0023]: The user can input observations about vehicle operations like noise reporting.), and
predicts the internal anomaly of the vehicle based on the interview information ([0023]: “In some examples, a vehicle can determine a fault based on the one or more indications from the user.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the system that determines vehicle anomalies of Wylie with the system that determines vehicle faults based on user input of Edren. Doing so would have improved the reliability of the predictions by increasing the number of ways a fault can be detected.
This combination further teaches predicting the internal anomaly of the vehicle based on the traveling road history, and the road information (Wylie FIG. 3; [0053]: Wylie teaches using the location data of the vehicle at the time to acquire environment information that is used in the anomaly prediction. The examiner notes that Wylie is relied upon to teach the internal anomaly prediction based on these two factors, while Edren is relied upon to teach the anomaly prediction based on the interview information.).
Claim 7 is rejected over similar reasons to claim 2, applied to an anomaly prediction device performing the same function as the anomaly prediction system of claim 1.
Claims 3-4 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Wylie, further in view of US 20210129845 A1to Bonk, Alyson (“Bonk”).
Regarding claim 3, Wylie teaches the anomaly prediction system according to claim 1.
Wylie does not appear to expressly teach a classification generation device capable of communicating with the road information database, wherein
the classification generation device
acquires vehicle data in which the traveling road histories of the plurality of vehicles are associated with a vehicle state of each of the plurality of vehicles during traveling on the road,
acquires geographic information about the road included in the vehicle data from a predetermined map information database, and
generates the road classification based on the vehicle data and the geographic information, and registers the road classification in the road information database.
However, Bonk teaches a classification generation device capable of communicating with the road information database (FIG. 1: Map editor 155 reads on the classification device, communicable with the road anomaly log 134.), wherein
the classification generation device
acquires vehicle data in which the traveling road histories of the plurality of vehicles are associated with a vehicle state of each of the plurality of vehicles during traveling on the road (FIG. 1; [0032]: Log data from each AV comprises acceleration event and other sensor data correlated with where the event occurred.),
acquires geographic information about the road included in the vehicle data from a predetermined map information database (FIG. 1; [0026]: “The localization map editor 155 can access a map database 130 comprising localization maps 132 for the transport service region . . . .”), and
generates the road classification based on the vehicle data and the geographic information, and registers the road classification in the road information database (FIG. 1; [0026]: “The localization map editor 155 can . . . input road anomalies into a road anomaly log 134, locating and classifying road anomalies on the localization maps 132.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that uses a database of road conditions logged with respect to location to predict vehicle anomalies of Wylie with the system that registers road anomalies into a database using map data of Bonk. Doing so would have improved the accuracy of the anomaly prediction system by providing it with up-to-date road condition data.
Regarding claim 4, the above combination of Wylie and Bonk teaches the anomaly prediction system according to claim 3, further comprising an influence information database that is communicable with the anomaly prediction device and the classification generation device and stores influence information in which the road classification is associated with failure tendency information indicating a tendency of a failure that is likely to occur in a vehicle that has traveled on the road (Wylie [0038]; [0033]: The vehicle anomaly detection system 106 includes and selects from amongst machine learning models that are trained to associate environment information 112 (among other variables) with likely vehicle anomalies. Because it performs the functions of storing the trained models, training the models, and selecting a trained model for use, one of ordinary skill in the art would have recognized that the vehicle anomaly detection system acts as the influence information database, classification generation device, and the anomaly prediction device, respectively.), wherein
the classification generation device
acquires the failure information about the plurality of vehicles (Wylie [0033]: “For example, the vehicle anomaly detection system 106 may perform supervised machine-learning by utilizing a dataset including data with known results, e.g., with known anomalies . . . .”),
generates the influence information in which the road classification associated with the road included in the traveling road history is associated with failure tendency information indicating the tendency of the failure that is likely to occur in the vehicle that has traveled on the road based on the traveling road history and the failure information about the plurality of vehicles (Wylie [0033]: “For example, the vehicle anomaly detection system 106 may perform supervised machine-learning by utilizing a dataset including data with known results, e.g., with known anomalies and associated collected (sensor) data, vehicle attributes, and environmental data. Thus, the vehicle anomaly detection system 106 may generate or learn a mapping function based on the input variables collected data, vehicle attributes, and environmental data to determine an output variable, e.g., detected vehicle anomalies.”), and registers the influence information in the road information database (Wylie [0076]: “Embodiments are not limited in this manner, for example, the determination of the machine-learning model may also be selected based on similar and/or same environmental data, e.g., raining vs. dry conditions.” Models are stored associated with environmental data to enable the selection of a suitable model based on that data. This broadly reads on registering the models with certain environmental data.), and
the anomaly prediction device acquires the road classification associated with the road included in the traveling road history of the target vehicle (Wylie [0052]: “The environment system 112 may use the location data to determine weather information, road conditions, traffic, and so forth based on the location data during the time that it was collected.”), and predicts the internal anomaly of the target vehicle based on the influence information associated with the road classification (FIG. 6; [0076]: The system determines which machine learning model to use based on the environment information that includes road conditions. The model determines vehicle anomalies.).
Regarding claim 8, Wylie teaches a classification generation device ([0052]: The anomaly detection system 106’s training function associates road condition information with vehicle anomalies by training a model that represents the relationship between the two. The model is used with road condition information and vehicle location data to predict an anomaly once trained.)
Wylie does not appear to expressly teach the device comprising:
vehicle data acquisition means for acquiring vehicle data in which traveling road histories of a plurality of vehicles are associated with a vehicle state of each of the plurality of vehicles during traveling on a road;
geographic information acquisition means for acquiring geographic information about the road included in the vehicle data from a predetermined map information database; and
classification generation means for generating a road classification according to an influence on an internal anomaly of the vehicle based on the vehicle data and the geographic information, and registering the road classification in a road information database.
However, Bonk teaches the device comprising:
vehicle data acquisition means for acquiring vehicle data in which traveling road histories of a plurality of vehicles are associated with a vehicle state of each of the plurality of vehicles during traveling on a road (FIG. 1; [0032]: Log data from each AV comprises acceleration event and other sensor data correlated with where the event occurred.);
geographic information acquisition means for acquiring geographic information about the road included in the vehicle data from a predetermined map information database (FIG. 1; [0026]: “The localization map editor 155 can access a map database 130 comprising localization maps 132 for the transport service region . . . .”); and
classification generation means for generating a road classification according to an influence on an internal anomaly of the vehicle based on the vehicle data and the geographic information, and registering the road classification in a road information database (FIG. 1; [0026]: “The localization map editor 155 can . . . input road anomalies into a road anomaly log 134, locating and classifying road anomalies on the localization maps 132.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that uses road conditions stored with locations in a database to predict vehicle anomalies Wylie with the system that registers road anomaly locations into a database using map data of Bonk. Doing so would have improved the accuracy of the anomaly prediction system by providing it with up-to-date road condition data.
Regarding claim 9, the above combination of Wylie and Bonk teaches the classification generation device according to claim 8, further comprising:
failure information acquisition means for acquiring failure information about the plurality of vehicles (Wylie [0033]: “For example, the vehicle anomaly detection system 106 may perform supervised machine-learning by utilizing a dataset including data with known results, e.g., with known anomalies . . . .”); and
influence information generation means for generating influence information in which the road classification associated with the road included in the traveling road history is associated with failure tendency information indicating a tendency of a failure that is likely to occur in the vehicle that has traveled on the road based on the traveling road history and the failure information about the plurality of vehicles (Wylie [0033]: “For example, the vehicle anomaly detection system 106 may perform supervised machine-learning by utilizing a dataset including data with known results, e.g., with known anomalies and associated collected (sensor) data, vehicle attributes, and environmental data. Thus, the vehicle anomaly detection system 106 may generate or learn a mapping function based on the input variables collected data, vehicle attributes, and environmental data to determine an output variable, e.g., detected vehicle anomalies.” The trained model is taken as the influence information.), and registering the influence information in the road information database (Wylie [0076]: “Embodiments are not limited in this manner, for example, the determination of the machine-learning model may also be selected based on similar and/or same environmental data, e.g., raining vs. dry conditions.” Storing the models such that they may be selected based on environmental data that includes road conditions taken as registering the model in the road information database.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ishikawa, Masayoshi et al.. US 10169932 B2. Anomality Candidate Information Analysis Apparatus And Behavior Prediction Device.
Chainer, Timothy et al.. US 20180068495 A1. Detection of Road Surface Defects.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY RICHARD HINTON whose telephone number is (703)756-1051. The examiner can normally be reached Monday-Friday 7:30-4:30.
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, Hunter Lonsberry can be reached at (571) 272-7298. 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.
/HENRY R HINTON/ Examiner, Art Unit 3665
/HUNTER B LONSBERRY/ Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights