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
1. Acknowledgment is made of applicant’s claim foreign priority based on application filed in the Republic of Germany on 04/22/2022.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 12/16/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
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 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.
3. Claim(s) 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xiao (U.S. 2018/0201273) and further in view of RIES et al (US 2021/0009107).
1. As per claims 11,20 Xiao disclosed vehicle comprising: a recommendation system comprising a data collection module, a prediction module and a recommendation module [In automated mode, the process identifies a context in step 808. For example, the same road sign has been identified or that the automated vehicle 100 is merging into light traffic. The process takes the identified context from step 808 and inputs the context with one or more planning paths (e.g., a route) in a machine-learning model in step 810. The machine learning model takes the context and the one or more planned paths (e.g., the user driving to work) to determine a personalization score for the one or more planned paths in step 812. For example, a high personalization score may be determined based on how many times the user has driven to work in various contexts, such as weather, sunny conditions, heavy traffic, time of day, etc.] (Paragraph. 0128), wherein the data collection module is configured to collect vehicle data, surroundings data or environmental data [The database 618 may reside in a storage-area network (“SAN”) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers 604, 608, 612, 614, 616 may be stored locally on the respective computer and/or remotely, as appropriate. The database 618 may be a relational database, such as Oracle 20i®, that is adapted to store, update, and retrieve data in response to SQL-formatted commands] (Paragraph. 0119); wherein the prediction module is configured to read a driver profile from a plurality of driver profiles, wherein each driver profile of the plurality of driver profile comprises a machine learning model trained specifically for the respective driver profile or comprises Hueristic model [At a later point in time, in automated mode, the process identifies a context in step 808. For example, the same road sign has been identified or that the automated vehicle 100 is merging into light traffic. The process takes the identified context from step 808 and inputs the context with one or more planning paths (e.g., a route) in a machine-learning model in step 810. The machine learning model takes the context and the one or more planned paths (e.g., the user driving to work) to determine a personalization score for the one or more planned paths in step 812. For example, a high personalization score may be determined based on how many times the user has driven to work in various contexts, such as weather, sunny conditions, heavy traffic, time of day, etc.] (Paragraph. 0128), wherein the machine learning model or the Hueristic model is configured to read the vehicle data, surroundings data or environmental data at least for a route portion lying ahead of the vehicle [the context data may be based on weather conditions (e.g., snowing, raining, snow packed roads, sunny, foggy, wet roads, etc.) road conditions (e.g., paved roads, dirt roads, rough roads, heavy traffic, light traffic, no traffic, etc.), obstacles in the road, road construction, geographic features (e.g., hills, sharp turns, valleys, plains, etc.), traffic signals (e.g., based on a yellow light), traffic signs (e.g. a no passing zone sign, a yield sign, a stop sign, etc.), a destination, a time (e.g., night versus day), routes, and/or the like] (Paragraph. 0081) and
However Xiao did not explicitly disclose and to issue a prediction indication value as an output variable, wherein the predictive indication value is a numerical value; and wherein the recommendation module is configured to compare the predictive indication value to an indication threshold value and to prompt a recommendation to be issued in the vehicle for a person driving the vehicle to take over manual vehicle control when the predictive indication value compared to the indication threshold is in a first range and prompt a recommendation to be issued in the vehicle for a driver assistance system to take over an at least partially automated vehicle control when the predictive indication value compared to the indication threshold value is in a second range.
In the same field of endeavor RIES disclosed, according to the invention, it is provided that an accuracy is predicted with which the position of the vehicle in the environment map can be determined for a predetermined section of road ahead of the vehicle. Fully automated operation of the vehicle is then enabled, i.e., the vehicle can only be operated in a fully automated manner, when the determined accuracy and the predicted accuracy for the predetermined section of road ahead of the vehicle meet the predetermined requirements, i.e., are greater than at least one accuracy threshold. Preferably, it remains enabled only for as long as the requirements for accuracy are met. That is, the fully automated operation of the vehicle is terminated when the conditions for enabling fully automated operation are no longer met (Paragraph 0005).
It would have been obvious to one having ordinary skill in the art before the effective filing date was made to have incorporated according to the invention, it is provided that an accuracy is predicted with which the position of the vehicle in the environment map can be determined for a predetermined section of road ahead of the vehicle. Fully automated operation of the vehicle is then enabled, i.e., the vehicle can only be operated in a fully automated manner, when the determined accuracy and the predicted accuracy for the predetermined section of road ahead of the vehicle meet the predetermined requirements, i.e., are greater than at least one accuracy threshold. Preferably, it remains enabled only for as long as the requirements for accuracy are met. That is, the fully automated operation of the vehicle is terminated when the conditions for enabling fully automated operation are no longer met as taught by RIES in the method and system of Xiao to increase the efficiency of calculating the predictability factor.
2. As per claim 12 Xiao-RIES disclosed wherein the recommendation system further comprises a driver monitoring module configured to monitor the person driving the vehicle using at least one sensor of the vehicle, determine current control behavior or a current driver state from sensor data generated by the at least one sensor, allocate the person driving the vehicle to one of the plurality of driver profiles depending on the determined current control behavior or the determined current driver state. (Xiao, Paragraph. 0097).
3. As per claim 13 wherein the vehicle monitoring module is further configured to determine a current indication value from the current control behavior or the current driver state, and the prediction module is further configured to read the current control behavior, the current driver state, or the current indication value to further optimize the machine learning model or the Heuristic model (RIES, Paragraph. 0005 & 0007). Claim 13 has the same motivation as to claim 11.
4. As per claim 14 Xiao-RIES disclosed wherein the prediction module is further configured to further train the machine learning model or the Heuristic model, taking into consideration a current implementation of the recommendation made by the recommendation module for taking over the vehicle control (Xiao, Paragraph. 0128).
5. As per claim 15 Xiao-RIES disclosed wherein the recommendation system is configured to receive fleet data, wherein the fleet data comprises an aggregated amount of user-specific control behavior or vehicle states of users of a plurality of fleet vehicles, and configured to derive driver profiles from the fleet data or to update an existing driver profile, wherein control behavior or vehicle states that are similar within set limits are allocated to the same driver profile (Xiao, Paragraph. 0125).
6. As per claim 16 Xiao-RIES disclosed wherein the fleet data further comprises implementation behavior of the recommendations made by the recommendation modules of the fleet vehicles by the users of the fleet vehicles, and the prediction module is furthermore configured to further optimize the machine learning model or the Heuristic model taking into consideration the implementation behavior of the users of the driver profile allocated to the person driving the vehicle. (RIES, Paragraph. 0005 & 0007). Claim 16 has the same motivation as to claim 11.
7. As per claim 17 Xiao-RIES disclosed wherein the machine learning model comprises a neural network or the machine learning model is initially assigned based on Heuristic functions (RIES, Paragraph. 0005 & 0007). Claim 17 has the same motivation as to claim 11.
8. As per claim 18 Xiao-RIES disclosed further comprising: a vehicle control device configured to automatically implement the recommendation made by the recommendation system for the person driving the vehicle or the driver assistance system to take over vehicle control (RIES, Paragraph. 0004). Claim 18 has the same motivation as to claim 11.
9. As per claim 19 Xiao-RIES disclosed wherein the recommendation module is further configured to adjust a height of the indication threshold value depending on a driver profile read by the prediction module, the vehicle data, surroundings data, or environmental data. (RIES, Paragraph. 0005 & 0007). Claim 19 has the same motivation as to claim 11.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims 11-20 in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “module” in claims 11-20.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph limitations: “Figure 1 shows, in a schematic front view, a vehicle 1 according to the invention. The vehicle 1 comprises a recommendation system 2, comprising a data collection module 2.1, a prediction module 2.2, a recommendation module 2.3, and a driver monitoring module 2.4…”, as disclosed in Paragraph.0042 and in Figs 1 and 2of the specification.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Conclusion
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/ADNAN M MIRZA/Primary Examiner, Art Unit 3667