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 .
Status of Claims
This office action is a response to an amendment filed on 12/03/2025. Claims 1-12 are currently pending, all of which are amended.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/17/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s remarks, see page 6, with respect to the rejections under 35 USC 101 have been fully considered. The claims have been amended to be directed to statutory subject matter, therefore the rejections are withdrawn.
Applicant’s remarks, see page 6, with respect to the rejections under 35 USC 112(b) have been fully considered. Regarding claims 1-4 and 7-10, the claims have been amended to clarify the claimed subject matter, therefore, the rejections are withdrawn. Regarding claims 5, 6, 11 and 12, the claims have not been amended, therefore, the rejections are maintained.
Applicant’s remarks, see pages 6-8, with respect to the rejections under 35 USC 102 and 103 have been fully considered but are not persuasive. The rejections are being maintained, with the mapping clarified and updated in view of Applicant’s amendment.
Applicant argues that Schieber does not teach “evaluation is selected on the basis of at least two characteristics”. That is, Schieber does not teach the limitation “selecting an evaluation of the data set on the basis of at least two characteristics of the determined group of characteristics …”, as recited in claim 1. The Examiner respectfully disagrees.
Schieber discloses that the vehicle-external computing device recognizes a data pattern in the vehicle data (data set) and checks whether the recognized data pattern corresponds to a stored reference pattern assigned to a driving maneuver or a vehicle state (characteristics). (Schieber, [0013]-[[014], [0038]).
Schieber further discloses that a predefined quality criterion is chosen and applied to the vehicle data (data set) underlying the recognized data pattern and/or to other vehicle data recorded at the same time, and that the quality criterion is met based on different lighting conditions or a specified minimum diversity of driving situations (characteristics) (Schieber, [0015], [0019], [0040]).
Thus, Schieber’s evaluation is based on at least two characteristics: recognized data pattern characteristics, such as driving maneuver or vehicle state; and quality related characteristics, such as lighting conditions or diversity of driving situations. Schieber selects/determines the evaluation by determining whether the recognized data pattern matches the stored reference pattern, and whether the vehicle data meets the predefined quality criterion. If the recognized data pattern matches the stored reference pattern and the vehicle data (data set) meets the quality criterion, a corresponding message/report of the data analysis is issued (Schieber, [0015]-[0016], [0040]-[0041]. Therefore, Schieber discloses “selecting an evaluation of the data set on the basis of at least two characteristics of the determined group of characteristics”.
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 5, 6, 11 and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 5 recites “the context” and “the suitable information source”. There is insufficient antecedent basis for these limitations, therefore, the claim is rendered indefinite.
Claim 6 recites the limitation “wherein a respective information source in the group respectively assigns a predetermined evaluation to one or more of the previously known data patterns, or is ascertained using an artificial neural network configured and trained to select a suitable information source on the basis of a context or output parameter supplied to the network”. Due to wording, it is unclear what is being ascertained using an artificial neural network. The claim is therefore rendered indefinite.
Claim 11 recites in the preamble, “data set DS” and “data packet D”. Claim 11 also recites “… typical of the data packet DS”. It is unclear if the limitation “typical of the data packet DS” is referring to the data set DS or the data packet D, therefore the claim is rendered indefinite.
Claim 12 is dependent from claim 11 and therefore contain the same indefinite language. As a result, it is rejected under the same rationale as claim 11.
Claim 12 recites “the ascertained data pattern DSMUS” and “the ascertained context DSKXT”. There is insufficient antecedent basis for these elements, therefore the claim is rendered indefinite.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 11 and 12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schieber et al. (DE102019203205A1 – citing the machine translation), hereinafter Schieber. Schieber is cited by Applicant in the IDS filed on 08/02/2023.
Regarding claim 1, Schieber discloses a method for evaluating a data set contained in a data packet made available to a client by a data source (Schieber, [0010]-[0012]: computing/server device (client) evaluates vehicle data (data set) stored in a PDU (data packet) from a vehicle (data source)), the method comprising:
analyzing the data packet using the client to determine a group of characteristics CHAR_i, where i=1, . . . , n and where n≥1, representing the data packet (Schieber, [0013]-[0014], [0016]: evaluating/analyzing the vehicle data to determine if recognized data patterns correspond to reference patterns assigned to driving maneuvers and/or vehicle states (characteristics); [0015], [0019]: evaluating quality-related properties of the same and/or associated vehicle data, such as lighting conditions and diversity of driving situations (characteristics); [0016]: the characteristic data patterns are within the vehicle data, which are organized as PDUs (data packets)); and
selecting the evaluation of the data set (Schieber, [0015]: a quality criterion is selected; [0016], [0041]: if the recognized data pattern corresponds to a stored reference pattern, and the vehicle data meets the quality criterion, a message is issued (i.e., an evaluation is selected)) on the basis of at least two characteristics of the determined group of characteristics (Schieber, [0013], [0014], [0016]: data pattern characteristics such as driving maneuver or vehicle state; [0015], [0019]: quality related characteristics, such as lighting condition or driving situation diversity) with the aid of already available information (Schieber, [0014]: checking whether the recognized data pattern corresponds to a specified reference pattern; [0015]: applying a predefined quality criterion. [reference pattern and quality criterion = already available information]).
Regarding claim 11, Schieber discloses a system for ascertaining an evaluation BEW of a data set DS made available to a client of the system in a data packet D (Schieber, [0010]-[0012]: computing/server device (client) evaluates vehicle data (data set) stored in a PDU (data packet)), the system comprising:
a processor programmed to (Schieber, [0012]: computing device):
analyze the data packet D using the client to determine a group GCHAR of characteristics CHAR_i, where i=1, . . . , n and where n≥1, typical of the data packet DS (Schieber, [0013]-[0014], [0016]: evaluating/analyzing the vehicle data to determine if recognized data patterns correspond to reference patterns assigned to driving maneuvers and/or vehicle states (characteristics); [0015], [0019]: evaluating quality-related properties of the same and/or associated vehicle data, such as lighting conditions and diversity of driving situations (characteristics); [0016]: the characteristic data patterns are within the vehicle data, which are organized as PDUs (data packets)); and
ascertain the evaluation BEW of the data set (Schieber, [0015]: a quality criterion is selected; [0016], [0041]: if the recognized data pattern corresponds to a stored reference pattern, and the vehicle data meets the quality criterion, a message is issued (i.e., an evaluation is selected)) on the basis of at least two characteristics of the determined group of characteristics (Schieber, [0013], [0014], [0016]: data pattern characteristics such as driving maneuver or vehicle state; [0015], [0019]: quality related characteristics, such as lighting condition or driving situation diversity) with the aid of already available information (Schieber, [0014]: checking whether the recognized data pattern corresponds to a specified reference pattern; [0015]: applying a predefined quality criterion. [reference pattern and quality criterion = already available information]).
Regarding claim 12, Schieber discloses further comprising an artificial neural network configured to ascertain the evaluation BEW of the data set DS on the basis of the ascertained data pattern DSMUS and the ascertained context DSKXT (Schieber, [0014]-[0016]: an artificial neural network is used for analyzing the data pattern recognized in the vehicle data (data set), and whether the vehicle data was collected during day, night or both (context)).
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-6 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Schieber in view of Schon (DE102016217191A1 – citing the machine translation). Schon is cited by Applicant in the IDS filed on 08/02/2023.
Regarding claim 2, Schieber discloses wherein:
at least one of the characteristics of the group of characteristics comprises a data pattern DSMUS (Schieber, [0014]);
analysis of the data set by the client includes a pattern identification step V1_MUS with regard to the presence of a particular data pattern DMUS_m (Schieber, [0014]: checking for the presence of a reference pattern).
Schieber does not explicitly disclose from a multiplicity of previously known data patterns GDMUS with the aim of identifying one of the previously known data patterns DMUS_m in the data set.
However, Schon discloses analysis of the data set by the client includes a pattern identification step V1_MUS with regard to the presence of a particular data pattern DMUS_m from a multiplicity of previously known data patterns GDMUS with the aim of identifying one of the previously known data patterns DMUS_m in the data set (Schon, [0010], [0012]: adaptive evaluation device assigns a rating based on the degree of agreement between the pattern formed by attribute values of a data set and comparison patterns (previously known data patterns)).
It would have been obvious to one of ordinary skill in the art, having the teachings of Schieber and Schon before him or her before the effective filing date of the claimed invention, to modify a method in which a server determines if the data pattern recognized in vehicle data (data set) corresponds to a reference pattern as taught by Schieber, to include comparing the data set pattern to multiple comparison/reference patterns as taught by Schon in order to identify the pattern with the best degree of agreement. The motivation for doing so would have been to account for possible variations in the data set.
Regarding claim 3, Schieber discloses wherein:
the data packet comprises the data set and an output parameter of the data source (Schieber, [0015]: vehicle data (data set) and other vehicle data (output parameter));
at least one of the characteristics of the group represents a context of the data set DS (Schieber, [0015]: lighting conditions (characteristics) can be daytime, nighttime or both (context));
the context depends at least in part on the output parameter of the data source (Schieber, [0015]: the other vehicle data (output parameter) indicates if the vehicle data was collected during day, night or both (context)); and
the evaluation of the data set depends on the identified data pattern and the ascertained context (Schieber, [0015]-[0016]).
Regarding claim 4, Schieber discloses wherein:
the data packet comprises the data set and an output parameter of the data source (Schieber, [0015]: vehicle data (data set) and other vehicle data (output parameter)).
Schieber does not explicitly disclose at least one of the characteristics of the group represents an information source for ascertaining the evaluation configured to assign an evaluation to a data pattern, the information source is ascertained on the basis of the output parameter of the data source; and the evaluation of the data set depends on the basis of the identified data pattern and the ascertained information source.
However, Schon discloses
at least one of the characteristics of the group represents an information source for ascertaining the evaluation configured to assign an evaluation to a data pattern (Schon, [0013], [0026]: crawler unit determines a specific converter unit (information source) for sending a data set based on the data source (characteristic); [0009]: the converter unit determines attribute values for the data set; [0010]: the attribute values form a pattern),
the information source is ascertained on the basis of the output parameter of the data source (Schon, [0013], [0026]: depending on the data source (output parameter), the crawler unit sends the data set to a specific converter unit (information source)); and
the evaluation of the data set depends on the basis of the identified data pattern and the ascertained information source (Schon, [0013], [0026]: crawler unit sends the data set to a data source-specific converter unit (information source); [0010], [0012]: The converter unit sends determined attribute values for the data set to an adaptive evaluation device which assigns a rating based on the degree of agreement between the pattern formed by the attribute values of the data set and a comparison pattern (identified pattern)).
It would have been obvious to one of ordinary skill in the art, having the teachings of Schieber and Schon before him or her before the effective filing date of the claimed invention, to modify a method in which a server receives a data set for evaluation from a vehicle as taught by Schieber, to include sending the data set to a data source-specific converter unit as taught by Schon. The motivation for doing so would have been to facilitate quickly converting the incoming data set from a known format to a standard format in order to speed up the overall process of evaluating data sets (Schon, [0013]).
Regarding claim 5, Schieber discloses further comprising:
ascertaining the context of the data set on the basis of the output parameter of the data source (Schieber, [0015]: the other vehicle data (output parameter) indicates if the vehicle data was collected during day, night or both (context)).
Schieber does not explicitly disclose then ascertaining the suitable information source on the basis of the ascertained context.
However, Schon discloses wherein:
ascertaining the context of the data set on the basis of the output parameter of the data source (Schon, [0013]. [0026]: crawler unit receives and sends a data set to a data source-specific converter unit [This implies the crawler unit also receives identification (output parameter) of the data source]. The data set is in a known format (context) for the specific data source); and
then ascertaining the suitable information source on the basis of the ascertained context (Schon, [0013], [0026]: crawler unit sends a data set of a known format (context) to a data source-specific converter unit (information source)).
It would have been obvious to one of ordinary skill in the art, having the teachings of Schieber and Schon before him or her before the effective filing date of the claimed invention, to modify a method in which a server receives a data set for evaluation from a vehicle as taught by Schieber, to include sending the data set to a data source-specific converter unit as taught by Schon. The motivation for doing so would have been to facilitate quickly converting the incoming data set from a known format to a standard format in order to speed up the overall process of evaluating data sets (Schon, [0013]).
Regarding claim 6, Schieber does not explicitly disclose further comprising: selecting the suitable information source, on the basis of the at least one output parameter selected from a predefined group of information sources; and wherein a respective information source in the group respectively assigns a predetermined evaluation to one or more of the previously known data patterns, or is ascertained using an artificial neural network configured and trained to select a suitable information source on the basis of a context or output parameter supplied to the network.
However, Schon discloses further comprising:
selecting the suitable information source, on the basis of the at least one output parameter selected from a predefined group of information sources (Schon, [0026]: the converter unit is selected from N data source-specific converter units (information sources)); and
wherein a respective information source in the group respectively assigns a predetermined evaluation to one or more of the previously known data patterns, or is ascertained using an artificial neural network configured and trained to select a suitable information source on the basis of a context or output parameter supplied to the network (Schon, [0012]: adaptive evaluation device assigns ratings to previous data set patterns; [0028]: adaptive evaluation device consists of multiple artificial neural networks).
It would have been obvious to one of ordinary skill in the art, having the teachings of Schieber and Schon before him or her before the effective filing date of the claimed invention, to modify a method in which a server receives a data set for evaluation from a vehicle as taught by Schieber, to include sending the data set to a data source-specific converter unit as taught by Schon. The motivation for doing so would have been to facilitate quickly converting the incoming data set from a known format to a standard format in order to speed up the overall process of evaluating data sets (Schon, [0013]).
Regarding claim 9, Schieber discloses wherein one of the output parameters comprises a predefined use of the data set or a specific predefined context of the data set (Schieber, [0015]: the other vehicle data (output parameter) indicates if the vehicle data (data set) was collected during day, night or both (predefined context)).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Schieber in view of Schon, further in view of Stevens et al. (US 2019/0129762), hereinafter Stevens. Stevens is cited by Applicant in the IDS filed on 08/02/2023.
Regarding claim 7, Schieber and Schon do not explicitly disclose wherein: one of the output parameters comprises an identity of the data source; the identity of the data source is determined by: checking whether the data source is trustworthy, and, if the data source is trustworthy, ascertaining the identity of the data source on the basis of centrally saved information and/or on the basis of information transmitted by the data source.
However, Stevens discloses wherein:
one of the output parameters comprises an identity of the data source (Stevens, [0105]);
the identity of the data source is determined by: checking whether the data source is trustworthy (Stevens, [0105]), and,
if the data source is trustworthy, ascertaining the identity of the data source on the basis of centrally saved information and/or on the basis of information transmitted by the data source (Stevens, [0105]).
It would have been obvious to one of ordinary skill in the art, having the teachings of Schieber, Schon and Stevens before him or her before the effective filing date of the claimed invention, to modify a method in which a server receives a data set for evaluation from a vehicle as taught by Schieber and Schon, to include determining whether the data source is trustworthy as taught by Stevens. The motivation for doing so would have been to improve security (Stevens, [0098]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Schieber in view of Schon, further in view of Briggs et al. (US 2019/0114692), hereinafter Briggs.
Regarding claim 8, Schieber discloses wherein one of the output parameters is a spatial origin of the data set, wherein the origin of the data set is determined on the basis of centrally saved information and/or on the basis of information transmitted by the data source (Schieber, [0010], [0012]: position data from the storage device of the vehicle (data source)).
Schieber and Schon do not explicitly disclose in particular geo-tagging information.
However, Briggs discloses in particular geo-tagging information (Briggs, [0062]: server receives geotag location data from a vehicle).
It would have been obvious to one of ordinary skill in the art, having the teachings of Schieber, Schon and Briggs before him or her before the effective filing date of the claimed invention, to modify a method in which a server receives a data set for evaluation from a vehicle as taught by Schieber and Schon, to include enabling the vehicle to send geotag location data as taught by Briggs. The motivation for doing so would have been to facilitate enhanced safety and compliance as geotagging enables the tracking of driver behavior, thereby providing insights for coaching and promoting safer driving practices.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Schieber in view of Shiraishi et al. (US 2019/0384870), hereinafter Shiraishi.
Regarding claim 10, Schieber does not explicitly disclose wherein the ascertained evaluation represents a financial value of the data packet.
However, Shiraishi discloses wherein the ascertained evaluation represents a financial value of the data packet (Shiraishi, [0035]: data from a vehicle is used to determine a fair price for an insurance policy).
It would have been obvious to one of ordinary skill in the art, having the teachings of Schieber and Shiraishi before him or her before the effective filing date of the claimed invention, to modify a method in which a server receives a data set for evaluation from a vehicle as taught by Schieber, to include evaluating the data to determine insurance pricing as taught by Shiraishi. The motivation for doing so would have been to facilitate determining a fair price for an insurance policy (Shiraishi, [0035]).
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LESA M KENNEDY whose telephone number is (571)431-0704. The examiner can normally be reached on Monday-Wednesday 9:30 am - 5:30 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Umar Cheema can be reached on (571) 270-3037. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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The examiner also requests, in response to this Office Action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
/LESA M KENNEDY/Primary Examiner, Art Unit 2458
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