DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to Applicant's Amendment and Remarks filed on 11/6/2025. This Action is made FINAL.
Claims 2, 7-9 were canceled.
Claims 1, 3-6, 10-15 are pending for examination.
Response to Arguments
(A) Applicant’s arguments filed “Since the claims have been amended to expressly recite that the steps are performed "by the at least one processor," including specifying ranges for sensor values, counting frequency values, inputting frequency values to a classifier, acquiring output data, associating information of the traveling situation with corresponding traveling-section information, storing the associated information in vehicle memory, and transmitting stored combinations to a device outside the vehicle, are explicitly limited to processor execution. Therefore, the claims do not encompass implementation by a user or in the human mind, and therefore do not fall under a judicial exception to eligibility.” on 11/6/2025, have been fully considered but they are not persuasive.
As to point (A), the examiner respectfully disagrees. The examiner further notes the limitations of “by the at least one processor” are mere instructions to apply the above noted abstract idea by merely using a computer to perform the process (MPEP § 2106.05). In particular, processor recited at a high-level of generality (i.e., as processor performing a generic computer function of computing result) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
(B) Applicant's arguments filed “Amended claim 1 expressly requires that the classifier is a learned machine learning model. The learned machine learning model receives, as input data, values acquired from at least one vehicle-mounted sensor, and outputs, as output data, traveling-situation category information corresponding to the input data. The claimed classifier is generated by machine learning using learning data. Therefore, the claims are directed to a specific machine-trained model processing real-world sensor inputs, and are not directed to a mere data analysis concept or observation-based mental process.” on 11/6/2025 have been fully considered but they are not persuasive.
As to point (B), the examiner respectfully disagrees. The examiner further notes the limitations of “learned machine learning model” are mere instructions to apply the above noted abstract idea by merely using a computer to perform the process (MPEP § 2106.05). In particular, learned machine learning model recited at a high-level of generality (i.e., as computer model on a computer performing a generic computer function of outputting corresponding result based on the input) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
(C) Applicant's arguments filed “the method recited in claim 1 includes associating the traveling-situation category output from the machine learning model with the corresponding traveling-section information and storing this association in vehicle memory. The method further requires transmitting one or more combinations of this information to a device located outside the vehicle such that the device can utilize the information of the traveling situation category and the information of the traveling section. Thus, the method recited in claim 1 includes providing the combination of the information of the traveling situation category and the corresponding information of the traveling section to an external device for real-world utilization. The method therefore implements a practical application of machine learning in a vehicular environment and is not directed to mere simple data analysis” on 11/6/2025 have been fully considered but they are not persuasive.
As to point (C), the examiner respectfully disagrees. The examiner further notes “associating the traveling-situation category output from the machine learning model with the corresponding traveling-section information and storing this association” and “transmitting one or more combinations of this information to a device” does not result in a specific, actionable, or transformed output beyond generic display of information. The specification lacked details regarding transforming the output. Furthermore, Fig. 8 of the drawings indicted a map with labeled road sections which amounts no more than a step of outputting data in a claimed process.
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, 3-6, 10-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis-Step 1
Claims 1, 3-6, 10-15 are directed to A method of estimating a traveling situation of a vehicle (i.e., a process). Therefore, claims 1, 3-6, 10-15 are within at least one of the four statutory categories.
101 Analysis-Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for reminder of the 101 rejection. Claim 1 recites:
A method of estimating a traveling situation of a vehicle,
the method comprising:
by at least one processor,
receiving sensor values, obtained by at least one sensor mounted on the vehicle, plural times while the vehicle is traveling a traveling section;
by the at least one processor, specifying, in a specifying step, which of predetermined M ranges each of the received sensor values belongs to, M being an integer of two or more;
by the at least one processor, counting, as M frequency values, the numbers of times of the execution of the specifying step for the respective ranges;
by the at least one processor, inputting, to a classifier as input data, the M frequency values respectively corresponding to the M ranges, the classifier being a learned machine learning model that receives the M frequency values as the input data and outputs, as output data, traveling situation category information that corresponds to the M frequency values and indicates any of K types of traveling situation categories which are predetermined and respectively indicate traveling situations of the vehicle, K being an integer of two or more;
by the at least one processor, acquiring from the classifier, the output data indicting any of K types of traveling situation categories, as a situation estimation result indicating the traveling situation of the vehicle in the traveling section;
by the at least one processor, associating information of a traveling situation category acquired from the output data of the classifier with information of the traveling section corresponding to the input data of the classifier, and storing the information of the traveling situation category and the information of the traveling section in a memory of the vehicle; and
by the at least one processor, transmitting, to a device located outside the vehicle, one or more of combinations of the information of the traveling situation category and the information of the traveling section accumulated in the memory, the device utilizing the combinations of the information of the traveling situation category and the information of the traveling section, wherein:
by the at least one processor, the classifier specifies a representative vector closest to a vector constituted by the M frequency values from among K representative vectors respectively associated with the K types of traveling situation categories;
the classifier outputs a traveling situation category of the K types of traveling situation categories corresponding to the specified representative vector; and
the K representative vectors of the classifier are obtained by k-means including
(i) classifying X vectors into K clusters, the X vectors respectively corresponding to X traveling sections, X being an integer of two or more, each of the X vectors being constituted by the M frequency values, K being the number of types of the traveling situation categories,
(ii) obtaining K centers of the K clusters,
(iii) obtaining distances between the K centers and the X vectors and classifying again each of the X vectors into the cluster corresponding to the center closest to the vector, and
(iv) repeating (ii) and (iii).
The examiner submits that the foregoing bolded limitation(s) constitute a "mental process" and/or “certain methods of organizing human activity” because under its broadest reasonable interpretation, the claim covers performance of the limitation by a user or in the human mind. For example, “specifying, in a specifying step, which of predetermined M ranges each of the received sensor values belongs to, M being an integer of two or more” in the context of this claim encompasses the user mentally determining range. Similarly, the limitation of "counting, as M frequency values, the numbers of times of the execution of the specifying step for the respective ranges" in the context of this claim encompasses the user mentally counting. The limitation of "associating information of a traveling situation category acquired from the output data of the classifier with information of the traveling section corresponding to the input data of the classifier" in the context of this claim encompasses the user mentally associating information. Furthermore, the limitation of “specifies a representative vector closest to a vector constituted by the M frequency values from among K representative vectors respectively associated with the K types of traveling situation categories” in the context of this claim encompasses the user mentally comparing and determining closest vector. Lastly, the limitation of “the K representative vectors of the classifier are obtained by k-means including (i) classifying X vectors into K clusters, the X vectors respectively corresponding to X traveling sections, X being an integer of two or more, each of the X vectors being constituted by the M frequency values, K being the number of types of the traveling situation categories, (ii) obtaining K centers of the K clusters, (iii) obtaining distances between the K centers and the X vectors and classifying again each of the X vectors into the cluster corresponding to the center closest to the vector, and (iv) repeating (ii) and (iii).” in the context of this claim encompasses the user mentally organizing and analyzing data. Accordingly, the claim recites at least one abstract idea.
101 Analysis-Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim as a whole, integrates the abstract into a partial application. As noted in the 2019 PEG, it must be determined whether there are any additional elements recited in the claim beyond the judicial exception(s), and whether those additional elements integrate the exception into a practical application of the exception.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method of estimating a traveling situation of a vehicle,
the method comprising:
by at least one processor,
receiving sensor values, obtained by at least one sensor mounted on the vehicle, plural times while the vehicle is traveling a traveling section;
by the at least one processor, specifying, in a specifying step, which of predetermined M ranges each of the received sensor values belongs to, M being an integer of two or more;
by the at least one processor, counting, as M frequency values, the numbers of times of the execution of the specifying step for the respective ranges;
by the at least one processor, inputting, to a classifier as input data, the M frequency values respectively corresponding to the M ranges, the classifier being a learned machine learning model that receives the M frequency values as the input data and outputs, as output data, traveling situation category information that corresponds to the M frequency values and indicates any of K types of traveling situation categories which are predetermined and respectively indicate traveling situations of the vehicle, K being an integer of two or more;
by the at least one processor, acquiring from the classifier, the output data indicting any of K types of traveling situation categories, as a situation estimation result indicating the traveling situation of the vehicle in the traveling section;
by the at least one processor, associating information of a traveling situation category acquired from the output data of the classifier with information of the traveling section corresponding to the input data of the classifier, and storing the information of the traveling situation category and the information of the traveling section in a memory of the vehicle; and
by the at least one processor, transmitting, to a device located outside the vehicle, one or more of combinations of the information of the traveling situation category and the information of the traveling section accumulated in the memory, the device utilizing the combinations of the information of the traveling situation category and the information of the traveling section, wherein:
by the at least one processor, the classifier specifies a representative vector closest to a vector constituted by the M frequency values from among K representative vectors respectively associated with the K types of traveling situation categories;
the classifier outputs a traveling situation category of the K types of traveling situation categories corresponding to the specified representative vector; and
the K representative vectors of the classifier are obtained by k-means including
(i) classifying X vectors into K clusters, the X vectors respectively corresponding to X traveling sections, X being an integer of two or more, each of the X vectors being constituted by the M frequency values, K being the number of types of the traveling situation categories,
(ii) obtaining K centers of the K clusters,
(iii) obtaining distances between the K centers and the X vectors and classifying again each of the X vectors into the cluster corresponding to the center closest to the vector, and
(iv) repeating (ii) and (iii).
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “by at least one processor”, “by at least one sensor mounted on the vehicle”, “a classifier”, “the classifier being a learned machine learning model that receives the M frequency values as the input data and outputs, as output data, traveling situation category information that corresponds to the M frequency values and indicates any of K types of traveling situation categories which are predetermined and respectively indicate traveling situations of the vehicle, K being an integer of two or more”, the examiner submits that these limitations are mere instructions to apply the above noted abstract idea by merely using a computer to perform the process (MPEP § 2106.05). In particular, processor recited at a high-level of generality (i.e., as processor performing a generic computer function of computing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Sensor recited at a high-level of generality (i.e., as sensor performing a generic computer function of gathering data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Classifier and the classifier being a learned machine learning model recited at a high-level of generality (i.e., as computer model on a computer performing a generic computer function of outputting corresponding result based on the input) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Regarding the additional limitations of “receiving sensor values … plural times while the vehicle is traveling a traveling section”, “inputting, to a classifier as input data, the M frequency values respectively corresponding to the M ranges”, “acquiring from the classifier, the output data indicting any of K types of traveling situation categories, as a situation estimation result indicating the traveling situation of the vehicle in the traveling section”, “storing the information of the traveling situation category and the information of the traveling section in a memory of the vehicle”, “storing the information of the traveling situation category and the information of the traveling section in a memory of the vehicle” and “transmitting, to a device located outside the vehicle, one or more of combinations of the information of the traveling situation category and the information of the traveling section accumulated in the memory, the device utilizing the combinations of the information of the traveling situation category and the information of the traveling section”, the examiner submits that these limitations are mere data gathering in conjunction with a law of nature or abstract idea (MPEP § 2106.05). In particular, “receiving sensor values”, “inputting, to a classifier, the M frequency values”, “acquiring from the classifier, the output data”, “storing the information of the traveling situation”, and “transmitting … one or more of combinations of the information” indicate pre-solution activity such that it amounts no more than a step of gathering data for use in a claimed process.
Regarding the additional limitations of “outputs a traveling situation category of the K types of traveling situation categories corresponding to the specified representative vector”, the examiner submits that these limitations are mere data outputting in conjunction with a law of nature or abstract idea (MPEP § 2106.05). In particular, “outputting” indicate post-solution activity such that it amounts no more than a step of outputting data for use in a claimed process.
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 no thing that is nor already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2 106.05). 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
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 elements of “by at least one processor”, “by at least one sensor mounted on the vehicle”, “a classifier”, “the classifier being a learned machine learning model that receives the M frequency values as the input data and outputs, as output data, traveling situation category information that corresponds to the M frequency values and indicates any of K types of traveling situation categories which are predetermined and respectively indicate traveling situations of the vehicle, K being an integer of two or more” amounts to nothing more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component cannot provide an inventive concept.
Furthermore, regarding the additional limitation of “receiving sensor values … plural times while the vehicle is traveling a traveling section”, “inputting, to a classifier as input data, the M frequency values respectively corresponding to the M ranges”, “acquiring from the classifier, the output data indicting any of K types of traveling situation categories, as a situation estimation result indicating the traveling situation of the vehicle in the traveling section”, “storing the information of the traveling situation category and the information of the traveling section in a memory of the vehicle”, “storing the information of the traveling situation category and the information of the traveling section in a memory of the vehicle” and “transmitting, to a device located outside the vehicle, one or more of combinations of the information of the traveling situation category and the information of the traveling section accumulated in the memory, the device utilizing the combinations of the information of the traveling situation category and the information of the traveling section” and “outputs a traveling situation category of the K types of traveling situation categories corresponding to the specified representative vector”, the examiner submits that the limitation merely adds insignificant extra-solution activity to the at least one abstract idea as previously discussed.
Hence the claim is not patent eligible.
Therefore, claim(s) 1 is/are ineligible under 35 U.S.C. 101.
Regarding Claim 3, the claim recites further narrowing limitation on the “the traveling section” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 4, the claim recites further narrowing limitation on the “the sensor values” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 5, the claim recites “associating the traveling situation category with the traveling section” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application. The claim recites further narrowing limitation on the “storing the traveling situation category and the traveling section and displaying a traveling course” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 6, the claim recites “the classifier is generated by” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application.
Regarding Claim 10, the claim recites “the vehicle” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application.
Regarding Claim 11, the claim recites “the traveling situation category” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application.
Regarding Claim 12, the claim recites further narrowing limitation on the “transmitting the output situation estimation result” and “storing the received situation estimation result” which is merely insignificant extra solution activity and fail to integrate the abstract idea into a practical application.
Regarding Claim 13, the claim recites “An estimator” which is mere instructions to apply the exception using a generic computer component and fail to integrate the abstract idea into a practical application.
Regarding Claim 14, the claim recites “a distance of the traveling section” which further narrowing the abstract idea and fail to integrate the abstract idea into a practical application.
Regarding Claim 15 , the claim recites “the classifier” which is mere instructions to apply the exception using a generic computer component and fail to integrate the abstract idea into a practical application.
Allowable Subject Matter
Claim 1, 3-6, 10-15 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
In particular, the limitations of “the K representative vectors of the classifier are obtained by k-means including (i) classifying X vectors into K clusters, the X vectors respectively corresponding to X traveling sections, X being an integer of two or more, each of the X vectors being constituted by the M frequency values, K being the number of types of the traveling situation categories, (ii) obtaining K centers of the K clusters, (iii) obtaining distances between the K centers and the X vectors and classifying again each of the X vectors into the cluster corresponding to the center closest to the vector, and (iv) repeating (ii) and (iii)” recited in independent claim 1 were not uncovered in the prior art teachings.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WENYUAN YANG whose telephone number is (571)272-5455. The examiner can normally be reached Monday - Thursday 9:00AM-5:00PM EST.
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/W.Y./Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
12/2/25