DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE10 2022 001 720.2, filed on 05/17/2022.
Status of Claims
Claims 14-15, 17-25, and 27-35 filed on 04/02/2026 are presently examined. Claims 1-13, 16, and 26 are cancelled. Claims 27-35 are new. Claim 14 is amended.
Response to Arguments
Regarding previous claim objections, the amendments overcome the objection.
Regarding 35 USC 101, Applicant's arguments filed 04/02/2026 have been fully considered but they are not persuasive. Retrieving data within a network collected by sensors to perform an otherwise abstract idea is not significantly more. “Collection and categorization of the variables” is not significantly more. Collection of variables is merely done with generic sensor components. Categorization of variables is merely a mental process performed on a generic computer. Transmission of data within a network is routine and ordinary as stated by the courts.
Regarding 35 USC 103 Applicant’s arguments with respect to claims have been considered but are moot because the amendment changed the scope of the invention and required a new ground of rejection. New reference Max (2) teaches the newly claimed limitations.
Max (2) teaches identifying, by the first vehicle, another variable for the information relevant to the vehicle-external service, wherein the another variable is a variable, corresponding to the variable of the first vehicle, of a second vehicle from the class of the similar vehicles; transmitting, by the first vehicle, a request for the vehicle-external service that includes the another variable for the information relevant to the vehicle-external service instead of the variable of the first vehicle for the information relevant to the vehicle-external service ([column 10, lines 40-52] “the vehicle 10 receives group information from other vehicles 63 by means of the first communication module 20 and by means of vehicle-to-vehicle communication. Therein, the group information is transmitted to the motor vehicle 10 only by such vehicles 63 that also transmit vehicle data sets to the network server 70. The group information contains, for each additional vehicle 63, information regarding the location, the speed and the direction of travel of this vehicle 63. The group information is also used to determine traffic flow data, in particular a traffic flow forecast in the vicinity of vehicle 10 is calculated or estimated.” [column 1, lines 27-29] “various services, for example, the provision of information data on traffic, weather, traffic jams”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Max (2)’s teaching of collecting similar data of at least one second vehicle and transmitting it to a vehicle-external service. One would be motivated, with reasonable expectation of success, to do so in order to use the anonymized data for receiving services that are still valuable with anonymized data (Max (2) [column 11, lines 47-52] “Examples of data that are locally concealed are rain and weather data, which can be captured for a weather service, for example. Such data can be integrated into a km grid, for example, without the value of the data being excessively reduced. Still, nevertheless, it is no longer possible to use said data for drawing conclusions about personal data.”).
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims and 15 and 28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth 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.
Claims 15 and 28 recite the term "the requesting vehicle." There is insufficient antecedent basis for this term in the claim.
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 14-26 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claims 14-15, 17-25, and 27-35 are directed to a method. Therefore, all the claims are within at least one of the four statutory categories.
Regarding claims 14-15 and 17-25:
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Claim 14 is recited below and limitations that recite an abstract idea are emphasized in bolding below:
A method for anonymizing vehicle data for using vehicle-external services, the method comprising:
recording, for each vehicle within a fleet of vehicles of a same type with a diverse group of vehicle users, a first variable, which is at least indirectly dependent on an integer number n of recorded vehicle sensor values, and a second variable, which is at least indirectly dependent on an integer number m of current vehicle sensor values;
determining similarities for at least one of the first and second variables of several or all of the vehicles in the fleet;
categorizing, after determining the similarities, the several or the all of the vehicles in the fleet into one of a class that is similar in terms of similarity of the at least one of the first and second variables and a dissimilar class;
determining, by a first vehicle of the several or the all of the vehicles in the fleet, that a vehicle-external service is to be requested;
identifying, by the first vehicle, information relevant to the vehicle-external service;
determining, by the first vehicle, a variable of the first vehicle for the information relevant to the vehicle-external service;
identifying, by the first vehicle, another variable for the information relevant to the vehicle-external service, wherein the another variable is a variable, corresponding to the variable of the first vehicle, of a second vehicle from the class of the similar vehicles; and
transmitting, by the first vehicle, a request for the vehicle-external service that includes the another variable for the information relevant to the vehicle- external service instead of the variable of the first vehicle for the information relevant to the vehicle-external service.
The examiner submits that the above bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. The bolded limitations in the context of this claim encompasses a person mentally, or with pen and paper, recording variables that are indirectly dependent on sensor values, determining similarities between the variables, and categorizing the vehicles into classes based on the similarity. 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 practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for anonymizing vehicle data for using vehicle-external services, the method comprising:
recording, for each vehicle within a fleet of vehicles of a same type with a diverse group of vehicle users, a first variable, which is at least indirectly dependent on an integer number n of recorded vehicle sensor values, and a second variable, which is at least indirectly dependent on an integer number m of current vehicle sensor values;
determining similarities for at least one of the first and second variables of several or all of the vehicles in the fleet;
categorizing, after determining the similarities, the several or the all of the vehicles in the fleet into one of a class that is similar in terms of similarity of the at least one of the first and second variables and a dissimilar class;
determining, by a first vehicle of the several or the all of the vehicles in the fleet, that a vehicle-external service is to be requested;
identifying, by the first vehicle, information relevant to the vehicle-external service;
determining, by the first vehicle, a variable of the first vehicle for the information relevant to the vehicle-external service;
identifying, by the first vehicle, another variable for the information relevant to the vehicle-external service, wherein the another variable is a variable, corresponding to the variable of the first vehicle, of a second vehicle from the class of the similar vehicles; and
transmitting, by the first vehicle, a request for the vehicle-external service that includes the another variable for the information relevant to the vehicle- external service instead of the variable of the first vehicle for the information relevant to the vehicle-external service.
For the following reason(s), the examiner submits that the above underlined additional limitations do not integrate the above-noted abstract idea into a practical application.
The examiner submits that these additional limitations merely use a sensors to perform an insignificant extra-solution activity of transmission of data within a network amounting to merely transmitting/outputting a result determined in an otherwise mental 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 nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional 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 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor or generic computer components to gather data and perform the otherwise mental judgements amounts to nothing more than applying the exception using generic computer components. Generally applying an exception using a generic computer component cannot provide an inventive concept. Further the additional limitations are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, merely use generic computer components in their ordinary capacity to perform an otherwise mental process or judgement, and do not amount to significantly more. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Dependent claims 15-25 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, merely use generic computer components in their ordinary capacity to perform an otherwise mental process or judgement, data gathering, or transmission of data within a network equivalent to outputting a result, and do not amount to significantly more.
Regarding claims 27-35:
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Claim 27 is recited below and limitations that recite an abstract idea are emphasized in bolding below:
A method for anonymizing vehicle data for using vehicle-external services, the method comprising:
recording, for each vehicle within a fleet of vehicles of a same type with a diverse group of vehicle users, a first variable, which is at least indirectly dependent on an integer number n of recorded vehicle sensor values, and a second variable, which is at least indirectly dependent on an integer number m of current vehicle sensor values;
determining similarities for at least one of the first and second variables of several or all of the vehicles in the fleet;
categorizing, after determining the similarities, the several or the all of the vehicles in the fleet into one of a class that is similar in terms of similarity of the at least one of the first and second variables and a dissimilar class;
determining, by a first vehicle of the several or the all of the vehicles in the fleet, that a vehicle-external service is to be requested;
identifying, by the first vehicle, information relevant to the vehicle-external service;
determining, by the first vehicle, a relevance of the information to the vehicle-external services;
determining, by the first vehicle, a variable of the first vehicle for the information relevant to the vehicle-external service;
identifying, by the first vehicle, based on the determined relevance of the information relevant to the vehicle-external services, another variable for the information relevant to the vehicle-external service, wherein the another variable is at least one of the following:
a computed variable from variables of several vehicles, corresponding to the variable of the first vehicle, from the class of the similar vehicles, and
an artificially generated variable similar to the variable of the first vehicle; and
transmitting, by the first vehicle, a request for the vehicle-external service that includes the another variable for the information relevant to the vehicle-external service instead of the variable of the first vehicle for the information relevant to the vehicle- external service.
The examiner submits that the above bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. The bolded limitations in the context of this claim encompasses a person mentally, or with pen and paper, recording variables that are indirectly dependent on sensor values, determining similarities between the variables, and categorizing the vehicles into classes based on the similarity. 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 practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for anonymizing vehicle data for using vehicle-external services, the method comprising:
recording, for each vehicle within a fleet of vehicles of a same type with a diverse group of vehicle users, a first variable, which is at least indirectly dependent on an integer number n of recorded vehicle sensor values, and a second variable, which is at least indirectly dependent on an integer number m of current vehicle sensor values;
determining similarities for at least one of the first and second variables of several or all of the vehicles in the fleet;
categorizing, after determining the similarities, the several or the all of the vehicles in the fleet into one of a class that is similar in terms of similarity of the at least one of the first and second variables and a dissimilar class;
determining, by a first vehicle of the several or the all of the vehicles in the fleet, that a vehicle-external service is to be requested;
identifying, by the first vehicle, information relevant to the vehicle-external service;
determining, by the first vehicle, a relevance of the information to the vehicle-external services;
determining, by the first vehicle, a variable of the first vehicle for the information relevant to the vehicle-external service;
identifying, by the first vehicle, based on the determined relevance of the information relevant to the vehicle-external services, another variable for the information relevant to the vehicle-external service, wherein the another variable is at least one of the following:
a computed variable from variables of several vehicles, corresponding to the variable of the first vehicle, from the class of the similar vehicles, and
an artificially generated variable similar to the variable of the first vehicle; and
transmitting, by the first vehicle, a request for the vehicle-external service that includes the another variable for the information relevant to the vehicle-external service instead of the variable of the first vehicle for the information relevant to the vehicle- external service.
For the following reason(s), the examiner submits that the above underlined additional limitations do not integrate the above-noted abstract idea into a practical application.
The examiner submits that these additional limitations merely use a sensors to perform an insignificant extra-solution activity of transmission of data within a network amounting to merely transmitting/outputting a result determined in an otherwise mental 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 nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional 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 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor or generic computer components to gather data and perform the otherwise mental judgements amounts to nothing more than applying the exception using generic computer components. Generally applying an exception using a generic computer component cannot provide an inventive concept. Further the additional limitations are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, merely use generic computer components in their ordinary capacity to perform an otherwise mental process or judgement, and do not amount to significantly more. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Dependent claims 28-35 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, merely use generic computer components in their ordinary capacity to perform an otherwise mental process or judgement, data gathering, or transmission of data within a network equivalent to outputting a result, and do not amount to significantly more.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 27, 28, 32, 34, and 35 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Max et al. (US 20180173895 A1), hereinafter referred to as Max.
Regarding claim 27, Max discloses A method for anonymizing vehicle data for using vehicle-external services ([0045] “in FIG. 5 and FIG. 6, the data are anonymized in the backend. In FIG. 7 and FIG. 8, the data are anonymized in the vehicle itself.” [0012] “As vehicle networking increases, there is an interest in the data captured by the vehicle being used for further evaluation, e.g., for capturing traffic data or weather data.” See at least [0058] for uses of the data.), the method comprising:
recording, for each vehicle within a fleet of vehicles of a same type with a diverse group of vehicle users, a first variable, which is at least indirectly dependent on an integer number n of recorded vehicle sensor values, and a second variable, which is at least indirectly dependent on an integer number m of current vehicle sensor values ([0056] “time-oriented and location-oriented masking is to achieve group anonymity … data can now be assigned only to a sufficiently large group of vehicles … On a freeway during the day, a measured value is normally captured by a multiplicity of vehicles in a short time … At night on a back road with little use, a measured value is sometimes captured only by a single vehicle. In this case, extensive masking is required. This circumstance can be taken into account by the anonymization unit 43 by taking into consideration data pertaining to the flow of traffic.” [0059] “All the vehicle sensors 46, such as, e.g., camera, radar, ultrasonic, temperature or climate sensors, report the measured data to a communication unit 47.”);
determining similarities for at least one of the first and second variables of several or all of the vehicles in the fleet; categorizing, after determining the similarities, the several or the all of the vehicles in the fleet into one of a class that is similar in terms of similarity of the at least one of the first and second variables and a dissimilar class ([0056] “The aim of the time-oriented and location-oriented masking is to achieve group anonymity.” [0059] “The anonymized data, which also have vehicle-specific mobile-radio-based connection data, are sent by mobile radio to the reception system 41, in which a pseudonymization unit 42 isolates the data from the vehicle-specific connection data and provides them with a pseudo-ID. In this case, the vehicle 40 is registered by a group certificate, which is identical in all vehicles. The aim in this case is to hamper upload for unauthorized parties without revealing the ID of the vehicle 40.”);
determining, by a first vehicle of the several or the all of the vehicles in the fleet, that a vehicle-external service is to be requested ([0047] “backend 45 requests data from the vehicle 40, which data are only then transmitted from the vehicle 40 to the backend 45.”);
identifying, by the first vehicle, information relevant to the vehicle-external service; determining, by the first vehicle, a relevance of the information to the vehicle- external services; determining, by the first vehicle, a variable of the first vehicle for the information relevant to the vehicle-external service ([0051] “The order memory 44 contains all the active measurement orders commissioned by the backend for defined positions. The vehicle 40 can report to the order memory 44 with its ego position via the pseudonymization unit 42. The order memory 44 then checks whether the ego position fits an open measurement order. If this is the case, the order memory 44 requests the measurement data from the vehicle 40 via the pseudonymization unit 42. The measurement data are then transmitted from the vehicle 40 to the reception system 41 in return.”);
identifying, by the first vehicle, based on the determined relevance of the information relevant to the vehicle-external services, another variable for the information relevant to the vehicle-external service, wherein the another variable is at least one of the following: a computed variable from variables of several vehicles, corresponding to the variable of the first vehicle, from the class of the similar vehicles, and an artificially generated variable similar to the variable of the first vehicle; and transmitting, by the first vehicle, a request for the vehicle-external service that includes the another variable for the information relevant to the vehicle-external service instead of the variable of the first vehicle for the information relevant to the vehicle- external service (([0060] “from the data 52 pertaining to detected vehicles in the surroundings, a traffic flow forecast in the surroundings of the vehicle is calculated … Based on the input variables, the anonymization unit 43 then shifts the data in accordance with the time stamp or the location stamp. In the event of a shift in the time stamp, the time interval needed and the possible distribution are first of all computed on the basis of the parameters. Thereafter, the shift to the measurement time is computed by a random algorithm and added to the measurement data. In the event of a shift in the location stamp, the shift interval needed and the possible distribution are first of all determined from the parameters. Thereafter, the shift on the location stamp is computed by a random algorithm and added to the measurement data.”)).
Regarding claim 28, Max discloses The method of claim 27, wherein when the vehicle-external service requires further information that less relevant for performing the service and the further information is based on the respective other variable, instead of the respective other variable of the requesting vehicle, at least one of the following is transmitted: the variable, corresponding to the variable of the requesting vehicle, of the vehicle from the class of the dissimilar vehicles, a computed variable from the variables, corresponding to the variable of the requesting vehicle, of the several vehicles from the class of the dissimilar vehicles, or an artificially generated variable similar to the variable of the requesting vehicle ([0060] “from the data 52 pertaining to detected vehicles in the surroundings, a traffic flow forecast in the surroundings of the vehicle is calculated … Based on the input variables, the anonymization unit 43 then shifts the data in accordance with the time stamp or the location stamp. In the event of a shift in the time stamp, the time interval needed and the possible distribution are first of all computed on the basis of the parameters. Thereafter, the shift to the measurement time is computed by a random algorithm and added to the measurement data. In the event of a shift in the location stamp, the shift interval needed and the possible distribution are first of all determined from the parameters. Thereafter, the shift on the location stamp is computed by a random algorithm and added to the measurement data.”).
Regarding claim 32, Max discloses The method of claim 27, wherein the first and second variables are at least partially exchanged between the vehicles and a central data center, wherein the first and second variables are aggregated and analyzed by the central data center ([0059] “transmitting data from a vehicle 40 to a backend 45 in which anonymization is effected in the vehicle 40.” [0029] “the captured data can now be assigned only to a sufficiently large group of vehicles and no longer to a single vehicle or a few vehicles.”).
Regarding claim 34, Max discloses The method of claim 27, wherein, to distinguish between information that is relevant and information that is less relevant to the vehicle-external service, information with a similar first variable and information with a respectively dissimilar second variable is sent from at least some of the vehicles in the fleet to the vehicle- external service, after which the service is then analyzed ([0058] determines a similar first variable being time/location and dissimilar second variable being opposite way driving to prevent accidents or differences in engine fuel consumption to evaluating what is causing said differences between vehicles.).
Regarding claim 35, Max discloses The method of claim 34, wherein individual vehicle sensor values are then logged and other vehicle sensor values are randomized to determine relevant and less relevant vehicle sensor values for the use of the respective vehicle-external service ([0058] Max discloses the comparison of data in regards to relevance in location for it to be useful for external services.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 14-15, 17, 22, and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Max in view of Max (2) (US 11960627 B2), hereinafter referred to as Max (2).
Regarding claim 14, Max discloses A method for anonymizing vehicle data for using vehicle-external services ([0045] “in FIG. 5 and FIG. 6, the data are anonymized in the backend. In FIG. 7 and FIG. 8, the data are anonymized in the vehicle itself.” [0012] “As vehicle networking increases, there is an interest in the data captured by the vehicle being used for further evaluation, e.g., for capturing traffic data or weather data.” See at least [0058] for uses of the data.), the method comprising:
recording, for each vehicle within a fleet of vehicles of a same type with a diverse group of vehicle users, a first variable, which is at least indirectly dependent on an integer number n of recorded vehicle sensor values ([0056] “time-oriented and location-oriented masking is to achieve group anonymity … data can now be assigned only to a sufficiently large group of vehicles … On a freeway during the day, a measured value is normally captured by a multiplicity of vehicles in a short time … At night on a back road with little use, a measured value is sometimes captured only by a single vehicle. In this case, extensive masking is required. This circumstance can be taken into account by the anonymization unit 43 by taking into consideration data pertaining to the flow of traffic.”), and a second variable, which is at least indirectly dependent on an integer number m of current vehicle sensor values ([0059] “All the vehicle sensors 46, such as, e.g., camera, radar, ultrasonic, temperature or climate sensors, report the measured data to a communication unit 47.”);
determining similarities for at least one of the first and second variables of several or all of the vehicles in the fleet; categorizing, after determining the similarities, several or the all of the vehicles in the fleet into one of a class that is similar in terms of similarity of the at least one of the first and second variables and a dissimilar class ([0056] “The aim of the time-oriented and location-oriented masking is to achieve group anonymity.” [0059] “The anonymized data, which also have vehicle-specific mobile-radio-based connection data, are sent by mobile radio to the reception system 41, in which a pseudonymization unit 42 isolates the data from the vehicle-specific connection data and provides them with a pseudo-ID. In this case, the vehicle 40 is registered by a group certificate, which is identical in all vehicles. The aim in this case is to hamper upload for unauthorized parties without revealing the ID of the vehicle 40.”);
determining, by a first vehicle of the several or the all of the vehicles in the fleet, that a vehicle-external service is to be requested ([0047] “backend 45 requests data from the vehicle 40, which data are only then transmitted from the vehicle 40 to the backend 45.”),
identifying, by the first vehicle, information relevant to the vehicle-external service; determining, by the first vehicle, a variable of the first vehicle for the information relevant to the vehicle-external service ([0051] “The order memory 44 contains all the active measurement orders commissioned by the backend for defined positions. The vehicle 40 can report to the order memory 44 with its ego position via the pseudonymization unit 42. The order memory 44 then checks whether the ego position fits an open measurement order. If this is the case, the order memory 44 requests the measurement data from the vehicle 40 via the pseudonymization unit 42. The measurement data are then transmitted from the vehicle 40 to the reception system 41 in return.”);
Max fails to explicitly disclose identifying, by the first vehicle, another variable for the information relevant to the vehicle-external service, wherein the another variable is a variable, corresponding to the variable of the first vehicle, of a second vehicle from the class of the similar vehicles, transmitting, by the first vehicle, a request for the vehicle-external service that includes the another variable for the information relevant to the vehicle-external service instead of the variable of the first vehicle for the information relevant to the vehicle-external service.
However, Max (2) teaches identifying, by the first vehicle, another variable for the information relevant to the vehicle-external service, wherein the another variable is a variable, corresponding to the variable of the first vehicle, of a second vehicle from the class of the similar vehicles; transmitting, by the first vehicle, a request for the vehicle-external service that includes the another variable for the information relevant to the vehicle-external service instead of the variable of the first vehicle for the information relevant to the vehicle-external service ([column 10, lines 40-52] “the vehicle 10 receives group information from other vehicles 63 by means of the first communication module 20 and by means of vehicle-to-vehicle communication. Therein, the group information is transmitted to the motor vehicle 10 only by such vehicles 63 that also transmit vehicle data sets to the network server 70. The group information contains, for each additional vehicle 63, information regarding the location, the speed and the direction of travel of this vehicle 63. The group information is also used to determine traffic flow data, in particular a traffic flow forecast in the vicinity of vehicle 10 is calculated or estimated.” [column 1, lines 27-29] “various services, for example, the provision of information data on traffic, weather, traffic jams”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Max (2)’s teaching of collecting similar data of at least one second vehicle and transmitting it to a vehicle-external service. One would be motivated, with reasonable expectation of success, to do so in order to use the anonymized data for receiving services that are still valuable with anonymized data (Max (2) [column 11, lines 47-52] “Examples of data that are locally concealed are rain and weather data, which can be captured for a weather service, for example. Such data can be integrated into a km grid, for example, without the value of the data being excessively reduced. Still, nevertheless, it is no longer possible to use said data for drawing conclusions about personal data.”).
Regarding claim 15, Max discloses The method of claim 14, wherein when the vehicle-external service requires further information that less relevant for performing the service and the further information is based on the respective other variable, instead of the respective other variable of the requesting vehicle, at least one of the following is transmitted: the variable, corresponding to the variable of the requesting vehicle, of the vehicle from the class of the dissimilar vehicles, a computed variable from the variables, corresponding to the variable of the requesting vehicle, of the several vehicles from the class of the dissimilar vehicles, or an artificially generated variable similar to the variable of the requesting vehicle ([0060] “from the data 52 pertaining to detected vehicles in the surroundings, a traffic flow forecast in the surroundings of the vehicle is calculated … Based on the input variables, the anonymization unit 43 then shifts the data in accordance with the time stamp or the location stamp. In the event of a shift in the time stamp, the time interval needed and the possible distribution are first of all computed on the basis of the parameters. Thereafter, the shift to the measurement time is computed by a random algorithm and added to the measurement data. In the event of a shift in the location stamp, the shift interval needed and the possible distribution are first of all determined from the parameters. Thereafter, the shift on the location stamp is computed by a random algorithm and added to the measurement data.”).
Regarding claim 17, Max discloses The method of claim 14, wherein each of the first and second variables is formed as a set of the n or m vehicle sensor values (the sensor transmissions from all vehicles including time/position and the various environmental sensors are collected and anonymized as a set. The distribution used for the method is based on the collected set of variables.).
Regarding claim 22, Max discloses The method of claim 14, wherein the first and second variables are at least partially exchanged between the vehicles and a central data center, wherein the first and second variables are aggregated and analyzed by the central data center ([0059] “transmitting data from a vehicle 40 to a backend 45 in which anonymization is effected in the vehicle 40.” [0029] “the captured data can now be assigned only to a sufficiently large group of vehicles and no longer to a single vehicle or a few vehicles.”).
Regarding claim 24, Max discloses The method of claim 14, wherein, to distinguish between information that is relevant and information that is less relevant to the vehicle-external service, information with a similar first variable and information with a respectively dissimilar second variable is sent from at least some of the vehicles in the fleet to the vehicle- external service, after which the service is then analyzed ([0058] determines a similar first variable being time/location and dissimilar second variable being opposite way driving to prevent accidents or differences in engine fuel consumption to evaluating what is causing said differences between vehicles.).
Regarding claim 25, Max discloses The method of claim 24, wherein individual vehicle sensor values are then logged and other vehicle sensor values are randomized to determine relevant and less relevant vehicle sensor values for the use of the respective vehicle-external service ([0058] Max discloses the comparison of data in regards to relevance in location for it to be useful for external services.).
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Max in view of Max (2), further in view of Green et al. (US 20210070286 A1), hereinafter referred to as Green.
Regarding claim 18, Max fails to explicitly disclose The method of claim 14, wherein each of the first and second variables is formed in a n- or m-dimensional space as a vector based on respective vehicle sensor values.
However, Green teaches each of the first and second variables is formed in a n- or m-dimensional space as a vector based on respective vehicle sensor values ([0031] “data 241 or 245 may be presented as one or more vectors including a number of weight factors or parameter values” [0020] “data may include vehicle characterization vectors each including a number of coefficients or parameters describing the corresponding past driving behaviors of corresponding vehicles. An individualized vehicle model may include vehicle characterization vectors describing the past driving behaviors of a vehicle associated with a particular anonymous identifier. A vehicle type model may include vehicle characteristic vectors describing historical driving behaviors associated with a vehicle type (e.g., SUVs, sport cars, buses, sedans, trucks, minivans, etc.).” Green also teaches the anonymization of the collected data [0013] “observe the driving behavior of other vehicles and associate such aggregated and anonymized observations with respective anonymous vehicle identifiers associated with those vehicles.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Green’s teaching of the vehicle data being formed into characterization vectors. One would be motivated, with reasonable expectation of success, to form the vehicle data into vectors in order to use collected vehicle data to train a prediction model based on the anonymized, vectorized data to then allow vehicles to take in anonymized data from other vehicles and use the trained prediction model to anticipate behavior of other vehicles (Green [abstract] “The computing system may identify one or more features associated with the first vehicle of interest based on the sensor data. The computing system may determine a driving behavior model associated with the first vehicle of interest based on the one or more features of the first vehicle of interest. The computing system may predict a driving behavior of the first vehicle of interest based on at least the determined driving behavior model. The computing system may determine a vehicle operation for the vehicle based on at least the predicted driving behavior of the first vehicle of interest.”).
Regarding claim 19, Max fails to explicitly disclose The method of claim 18, wherein the first or second variable is formed as a transformation of the respective vector to a value with a smaller number or at most the same number of dimensions.
However, Green teaches the first or second variable is formed as a transformation of the respective vector to a value with a smaller number or at most the same number of dimensions ([0082] “historical data may include aggregate information generated based on past ride information, which may include any ride information described herein and telemetry data collected by sensors … using historical data, the system 660 in particular embodiments may predict and provide ride suggestions in response to a ride request … the system 660 may use machine-learning, … clustering algorithms … dimensionality-reduction algorithms”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Green’s teaching of the vehicle data being formed into characterization vectors and using machine learning dimensionality-reduction algorithms on the vectors. One would be motivated, with reasonable expectation of success, to transform the vectors with dimensionality reduction for the variables in order to reduce the computational load and memory requirements, which is one of the primary purposes of dimensionality reduction of vectors known to those with ordinary skill in the art. When using the anonymized data to predict and provide ride suggestions, in the case of Green, reducing computation and memory load via dimensionality reduction of the vector data will improve performance.
Claims 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Max in view Max (2), further in view of Joana et al. (DE 102020003188 A1), hereinafter referred to as Joana.
Regarding claim 20, Max fails to explicitly disclose The method of claim 14, wherein machine learning clustering mechanisms are used to determine the classes of similar and dissimilar variables in order to determine a similarity measure, wherein the first and second variables are classified based on a default value and a comparison of the determined similarity measure with the default value.
However, Joana teaches machine learning clustering mechanisms are used to determine the classes of similar and dissimilar variables in order to determine a similarity measure ([0019] “clustering mechanisms of machine learning, such as K-means or expectation maximization, are applied to generate a similarity measure for the current emotional states of the fleet.”), wherein the first and second variables are classified based on a default value and a comparison of the determined similarity measure with the default value ([0017] “instead of the signals actually detected in the vehicle, 11 artificially generated signals are transmitted to Cloud 5, for the generation of which plausible signals are used, which are generated on the basis of similar and/or dissimilar occupant states of the fleet processed in Cloud 5 … subsequently, the fleet data undergoes automatic clustering,” [0018] “Area 29 shows the maximum individual deviations of the data points included in area 29 with similar or dissimilar emotional behavior.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Joana’s teaching of using machine learning clustering mechanisms to determine similarity measures between the collected vehicle data. One would be motivated, with reasonable expectation of success, to use clustering mechanisms in order to prevent synthesis of the data from being detected, which can be provided by the cluster center that represents the optimal group presentations (Joana [0019] “To prevent the synthesis of the data from being detected, a synthetic generation of optimal group presentations is required, which is represented by the cluster center.”).
Regarding claim 21, Max discloses The method of claim 20, wherein the default value is parameterized or the default value is defined as a function of the relevant vehicle sensor value, the absolute value of the relevant vehicle sensor value, or as a function of the vehicle-external service ([0053] “A first method consists in location-oriented masking of the data, i.e., the data are masked in respect of the location of their capture. Examples of data that are masked in this manner are rain and weather data that can be captured, e.g., for a weather service. Such data can be integrated into a km raster, for example, without the value of the data being reduced excessively. Nevertheless, it is thus no longer possible to take the data as a basis for inferring personal data.”).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Max in view of Max (2), further in view of Bai et al. (US 20100250106 A1), hereinafter referred to as Bai.
Regarding claim 23, Max fails to explicitly disclose The method of claim 14, wherein the first and second variables are at least partially exchanged, aggregated, and analyzed between the vehicles in the fleet. Although, Max does disclose the ability to determine whether a vehicle is driving the wrong way, but not explicitly that vehicles are communicating directly with each other ([0058] “in the case of hazard locations, for example, accidents or when it has been identified that a driver is traveling the wrong way along a freeway, it may be appropriate to give priority over data protection to the use of the data for preventing accidents.”).
However, Bai teaches the first and second variables are at least partially exchanged, aggregated, and analyzed between the vehicles in the fleet ([0002] “the sensors detect the hazardous road conditions and the probability of the detected condition from a vehicle is aggregated with the probability of the detected condition from other vehicles to provide a distributed aggregation operator that is transmitted to vehicles approaching the road condition.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Bai’s teaching of aggregating data that is exchanged between vehicles and analyzing the aggregated data. One would be motivated, with reasonable expectation of success, to aggregate other vehicle sensor data and analyze it in order to determine the confidence that a condition exists and take action if it is reasonable to do so (Bai [0018] “One or more of the vehicles 44 can then wirelessly transmit the aggregated results (or confidence degree) identifying the level of confidence that fog exists down the roadway 40 to other vehicles 48 that may be approaching the fog area as a warning of a potentially hazardous road condition, where vehicle safety devices on those vehicles can be prepared to take suitable action in the event that fog does occur.”).
Claims 29 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Max in view of Joana.
Regarding claim 29, Max discloses generation of random values using algorithms for the purposes of anonymizing data ([0060] “the shift to the measurement time is computed by a random algorithm and added to the measurement data … the shift on the location stamp is computed by a random algorithm and added to the measurement data.” [0061] “an optimized random distribution for the shift in the data can be used, for example, the Pareto distribution already mentioned above. In this case, the data can be shifted such that the shift is small on average, but has a very high maximum. In this manner, on the one hand, the quality of most data is corrupted only to a small degree, whereas, on the other hand, safe identification is possible only within a large anonymization group as a result of the large maximum shift.” [0070-0077] discloses the generation of the random values. Drawing from distributions is a fundamental aspect of machine learning methods, however Max does not explicitly state that machine learning is being used.).
However, Max fails to explicitly disclose The method of 27, wherein the artificially generated variable is a random variable generated via generative machine learning methods
However, Joana teaches the artificially generated variable is a random variable generated via generative machine learning methods ([0017] “instead of the signals actually detected in the vehicle, 11 artificially generated signals are transmitted to Cloud 5 … the fleet data undergoes automatic clustering, averaging, or temporal pattern modification” [0019] “a synthetic generation of optimal group presentations is required, which is represented by the cluster center. Checking the actual changes to data points 15 and 27 provides a control mechanism to react to temporary changes and restart the masking process with the described clustering.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Joana’s teaching of using machine learning to cluster the collected vehicle data. One would be motivated, with reasonable expectation of success, to use clustering mechanisms in order to prevent synthesis of the data from being detected, which can be provided by the cluster center that represents the optimal group presentations (Joana [0019] “To prevent the synthesis of the data from being detected, a synthetic generation of optimal group presentations is required, which is represented by the cluster center.”).
Regarding claim 31, Max discloses the method of claim 27, wherein the default value is parameterized or the default value is defined as a function of the relevant vehicle sensor value, the absolute value of the relevant vehicle sensor value, or as a function of the vehicle-external service ([0053] “A first method consists in location-oriented masking of the data, i.e., the data are masked in respect of the location of their capture. Examples of data that are masked in this manner are rain and weather data that can be captured, e.g., for a weather service. Such data can be integrated into a km raster, for example, without the value of the data being reduced excessively. Nevertheless, it is thus no longer possible to take the data as a basis for inferring personal data.”).
Max fails to explicitly disclose machine learning clustering mechanisms are used to determine the classes of similar and dissimilar variables in order to determine a similarity measure, wherein the first and second variables are classified based on a default value and a comparison of the determined similarity measure with the default value.
However, Joana teaches machine learning clustering mechanisms are used to determine the classes of similar and dissimilar variables in order to determine a similarity measure ([0019] “clustering mechanisms of machine learning, such as K-means or expectation maximization, are applied to generate a similarity measure for the current emotional states of the fleet.”), wherein the first and second variables are classified based on a default value and a comparison of the determined similarity measure with the default value ([0017] “instead of the signals actually detected in the vehicle, 11 artificially generated signals are transmitted to Cloud 5, for the generation of which plausible signals are used, which are generated on the basis of similar and/or dissimilar occupant states of the fleet processed in Cloud 5 … subsequently, the fleet data undergoes automatic clustering,” [0018] “Area 29 shows the maximum individual deviations of the data points included in area 29 with similar or dissimilar emotional behavior.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Joana’s teaching of using machine learning clustering mechanisms to determine similarity measures between the collected vehicle data. One would be motivated, with reasonable expectation of success, to use clustering mechanisms in order to prevent synthesis of the data from being detected, which can be provided by the cluster center that represents the optimal group presentations (Joana [0019] “To prevent the synthesis of the data from being detected, a synthetic generation of optimal group presentations is required, which is represented by the cluster center.”).
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Max in view of Green.
Regarding claim 30, Max fails to explicitly disclose The method of claim 27, wherein each of the first and second variables is formed in a n- or m-dimensional space as a vector based on respective vehicle sensor values, and wherein the first or second variable is formed as a transformation of the respective vector to a value with a smaller number or at most the same number of dimensions
However, Green teaches each of the first and second variables is formed in a n- or m-dimensional space as a vector based on respective vehicle sensor values ([0031] “data 241 or 245 may be presented as one or more vectors including a number of weight factors or parameter values” [0020] “data may include vehicle characterization vectors each including a number of coefficients or parameters describing the corresponding past driving behaviors of corresponding vehicles. An individualized vehicle model may include vehicle characterization vectors describing the past driving behaviors of a vehicle associated with a particular anonymous identifier. A vehicle type model may include vehicle characteristic vectors describing historical driving behaviors associated with a vehicle type (e.g., SUVs, sport cars, buses, sedans, trucks, minivans, etc.).” Green also teaches the anonymization of the collected data [0013] “observe the driving behavior of other vehicles and associate such aggregated and anonymized observations with respective anonymous vehicle identifiers associated with those vehicles.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Green’s teaching of the vehicle data being formed into characterization vectors. One would be motivated, with reasonable expectation of success, to form the vehicle data into vectors in order to use collected vehicle data to train a prediction model based on the anonymized, vectorized data to then allow vehicles to take in anonymized data from other vehicles and use the trained prediction model to anticipate behavior of other vehicles (Green [abstract] “The computing system may identify one or more features associated with the first vehicle of interest based on the sensor data. The computing system may determine a driving behavior model associated with the first vehicle of interest based on the one or more features of the first vehicle of interest. The computing system may predict a driving behavior of the first vehicle of interest based on at least the determined driving behavior model. The computing system may determine a vehicle operation for the vehicle based on at least the predicted driving behavior of the first vehicle of interest.”).
However, Green teaches the first or second variable is formed as a transformation of the respective vector to a value with a smaller number or at most the same number of dimensions ([0082] “historical data may include aggregate information generated based on past ride information, which may include any ride information described herein and telemetry data collected by sensors … using historical data, the system 660 in particular embodiments may predict and provide ride suggestions in response to a ride request … the system 660 may use machine-learning, … clustering algorithms … dimensionality-reduction algorithms”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Green’s teaching of the vehicle data being formed into characterization vectors and using machine learning dimensionality-reduction algorithms on the vectors. One would be motivated, with reasonable expectation of success, to transform the vectors with dimensionality reduction for the variables in order to reduce the computational load and memory requirements, which is one of the primary purposes of dimensionality reduction of vectors known to those with ordinary skill in the art. When using the anonymized data to predict and provide ride suggestions, in the case of Green, reducing computation and memory load via dimensionality reduction of the vector data will improve performance.
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Max in view of Bai.
Regarding claim 33, Max fails to explicitly disclose The method of claim 27, wherein the first and second variables are at least partially exchanged, aggregated, and analyzed between the vehicles in the fleet. Although, Max does disclose the ability to determine whether a vehicle is driving the wrong way, but not explicitly that vehicles are communicating directly with each other ([0058] “in the case of hazard locations, for example, accidents or when it has been identified that a driver is traveling the wrong way along a freeway, it may be appropriate to give priority over data protection to the use of the data for preventing accidents.”).
However, Bai teaches the first and second variables are at least partially exchanged, aggregated, and analyzed between the vehicles in the fleet ([0002] “the sensors detect the hazardous road conditions and the probability of the detected condition from a vehicle is aggregated with the probability of the detected condition from other vehicles to provide a distributed aggregation operator that is transmitted to vehicles approaching the road condition.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Max with Bai’s teaching of aggregating data that is exchanged between vehicles and analyzing the aggregated data. One would be motivated, with reasonable expectation of success, to aggregate other vehicle sensor data and analyze it in order to determine the confidence that a condition exists and take action if it is reasonable to do so (Bai [0018] “One or more of the vehicles 44 can then wirelessly transmit the aggregated results (or confidence degree) identifying the level of confidence that fog exists down the roadway 40 to other vehicles 48 that may be approaching the fog area as a warning of a potentially hazardous road condition, where vehicle safety devices on those vehicles can be prepared to take suitable action in the event that fog does occur.”).
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.
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/M.R.H./Examiner, Art Unit 3668 /Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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