DETAILED ACTION
This office action is responsive to the amendment in the above identified application filed September 1, 2026. Claims 1, 4-6, 8, 9, 12-14, 16, 17, 19 and 24-31 are pending, all examined and rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Claims 1, 4-6, 8, 9, 12-14, 16, 17, 19 and 24-31 are rejected under 35 U.S.C. 103 as being unpatentable over Xu, U.S. Patent #10,671,068, issued June 2, 2020
in view of Drayna, U.S. PG Publication #2019/0191424, filed December 23, 2019
With regard to Independent Claim 1,
Xu teaches a method performed by a first apparatus, wherein the first apparatus is a sensor and wherein the method comprises:
obtaining first information comprising environment information of a terminal in which the first apparatus is located, wherein the environment information is detected by the first apparatus. See e.g., Col. 5:29-33 (“Next, the specification describes a control system that may receive sensor data from different sensors and share sensor data across processing pipelines. An example control system, providing autonomous navigation for a vehicle is then described.”) See also Col. 7:30-32 (“External sensors may be sensors that can monitor one or more aspects of an external environment relative to vehicle…”). The examiner notes that a “vehicle” is a “terminal.”
Xu further teaches receiving, from a second apparatus, second information indicating at least one of region information of the terminal, wherein the second apparatus is an electronic control unit, domain control unit, multi-domain controller or in vehicle terminal. See e.g., Col. 7:32-39, (discussing various types of sensors, including GPS devices which provide “region information” of the terminal.) See also Col. 4:12-22, (sensor pipelines which pass data back and forth can be implemented on CPUs).
Xu further teaches outputting first sensed information for the terminal based on the first information and the second information. See e.g., Fig. 1 (showing raw sensor data from various sensors – which as explained above can include the claimed “first information” and “second sensors” being passed into processing pipelines (which include various algorithms). At step 182, the processed sensor data from one sensor’s pipeline can be shared with others. Thus, as the pipeline progresses along, the data that is ultimately output is based on both the claimed “first information” and “second information.” Further see Col. 13:1-15, (discussing that any or all of the invention of Xu can be performed on a system including a camera which is a sensor).
Xu does not explicitly disclose wherein the second information is time information or wherein the region information comprises a continent name, a country name, a region name, a province, or a city, wherein the time information comprises time system information and wherein the time system information comprises a season, a holiday, a solar term, or a seasonal timekeeping practice.
In an analogous art, Drayna teaches wherein the second information is region information, wherein the region information comprises a city or time information, wherein the time information comprises time system information and wherein the time system information comprises a season. See e.g., [0019], (detect and deploy models for different locations, e.g., cities), [0020], (“For example, the other data may include sensor data captured by the sensor package (e.g., vibrational and/or movement data along the travel route), time of day, season, weather conditions, ambient or artificial lighting, acceleration parameters of the fleet vehicle, and/or crowd congestion models and parameters.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the fusion system and neural networks of Xu with the ability to be trained specifically on region and time information as discussed by Drayna. One would be motivated to do so to better allow determination and recognition of operational surfaces on which the vehicles are operated. See generally Drayna, [0020].
With respect to Dependent Claim 4,
As discussed above, Xu-Drayna teaches all the limitations of Claim 1. Xu-Drayna further teaches outputting the first sensed information comprises outputting the first sensed information based on a first neural network model, wherein the first neural network model is based on the region information or the time information. See e.g., Xu, Fig. 4, Col. 9:45-55, (discussing using a neural network in the processing pipelines which includes the region information, thus the information output would be based on a neural network model.) See Drayna, [0020], (input data for neural network can be based on movement data along travel route, time of day, season, etc.) See also [0067], (discussing training data may be generated using GNSS – global navigation system, which see [0030] includes a GPS.)
With respect to Dependent Claim 5,
As discussed above, Xu-Drayna teaches all the limitations of Claim 1. Xu-Drayna further teaches wherein outputting the first sensed information comprises sending the first sensed information to a fusion unit. See e.g., Xu, Fig. 1, (first sensed information is sent to “final decision processing 170” which is a fusion unit.)
With respect to Independent Claim 6,
Xu teaches a method performed by a third apparatus, wherein the third apparatus is a fusion unit, and wherein the method comprises:
receiving first information from a first apparatus, wherein the first apparatus is a sensor wherein the first information comprises environment information of a terminal in which the first apparatus is located, and wherein the first information comprises one or more pieces of information from at least one of the sensor. See e.g., Col. 5:29-33 (“Next, the specification describes a control system that may receive sensor data from different sensors and share sensor data across processing pipelines. An example control system, providing autonomous navigation for a vehicle is then described.”) See also Col. 7:30-32 (“External sensors may be sensors that can monitor one or more aspects of an external environment relative to vehicle…”). The examiner notes that a “vehicle” is a “terminal.”
Xu further teaches receiving, from a second apparatus of the terminal, second information indicating at least one of region information of the terminal, wherein the second apparatus is an electronic control unit, domain control unit, multi-domain controller or in vehicle terminal, wherein the second apparatus determined the second information based on information from a first map module. See e.g., Col. 7:32-39, (discussing various types of sensors, including GPS devices (map module) which provide “region information” of the terminal.) See also Col. 4:12-22, (sensor pipelines which pass data back and forth can be implemented on CPUs).
Xu further teaches inputting the first information and the second information to a predefined algorithm of multiple predefined algorithms to obtain fused information for the terminal and outputting the fused information. See e.g., Fig. 1 (showing raw sensor data from various sensors – which as explained above can include the claimed “first information” and “second sensors” being passed into processing pipelines (which include various algorithms). At step 182, the processed sensor data from one sensor’s pipeline can be shared with others. Thus, as the pipeline progresses along, the data that is ultimately output is based on both the claimed “first information” and “second information.” See e.g., Xu, Fig. 1, (first sensed information is sent to “final decision processing 170” which is a fusion unit based on a “predefined algorithm.”) Note also that the different processing pipelines, corresponding to different sensors are “predefined algorithms.”
Xu does not explicitly disclose wherein the second information is time information or wherein the region information comprises a continent name, a country name, a region name, a province, or a city, wherein the time information comprises time system information and wherein the time system information comprises a season, a holiday, a solar term, or a seasonal timekeeping practice.
In an analogous art, Drayna teaches wherein the second information is region information, wherein the region information comprises a city or time information, wherein the time information comprises time system information and wherein the time system information comprises a season. See e.g., [0019], (detect and deploy models for different locations, e.g., cities), [0020], (“For example, the other data may include sensor data captured by the sensor package (e.g., vibrational and/or movement data along the travel route), time of day, season, weather conditions, ambient or artificial lighting, acceleration parameters of the fleet vehicle, and/or crowd congestion models and parameters.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the fusion system and neural networks of Xu with the ability to be trained specifically on region and time information as discussed by Drayna. One would be motivated to do so to better allow determination and recognition of operational surfaces on which the vehicles are operated. See generally Drayna, [0020].
With respect to Dependent Claim 8,
As discussed above, Xu-Drayna teaches all the limitations of Claim 6. Xu-Drayna further teaches wherein each of the multiple predefined algorithms in the predefined algorithm set further correspond to different sensors. See Xu, Fig. 1, showing various sensors with separate processing pipelines
With regard to Dependent Claim 9,
As discussed above, Xu-Drayna teaches all the limitations of Claim 6. Xu-Drayna further teaches wherein each of the multiple predefined algorithms is a neural network model. See e.g., Xu, Fig. 4, Col. 9:45-55, (discussing using a neural network in the processing pipelines which includes the region information, thus the information output would be based on a neural network model.) See also Drayna, [0019], (discussing different NNs for different locations.)
With regard to Dependent Claim 12,
As discussed above, Xu-Drayna teaches all the limitations of Claim 6. Xu-Drayna further teaches wherein the first apparatus is a sensor and wherein the sensor comprises a camera apparatus, a lidar… See Xu, Col. 7:26-46 (describing several sensor types including cameras, lidar.)
With regard to Independent Claim 13,
This claim is similar in scope to Claim 1 and is rejected under a similar rationale.
With regard to Dependent Claim 14,
As discussed above, Xu-Drayna teaches all the limitations of Claim 13. Xu-Drayna further teaches wherein the instructions further cause the first apparatus to detect the environment information of the terminal. See also Xu, Col. 7:30-32 (“External sensors may be sensors that can monitor one or more aspects of an external environment relative to vehicle…”). The examiner notes that a “vehicle” is a “terminal” and a “sensor” is a “first apparatus” located in the terminal under BRI consistent with the specification.
With regard to Dependent Claim 16,
This claim is similar in scope to Claim 4 and is rejected under a similar rationale.
With regard to Dependent Claim 17,
Claim 17 is similar in scope to Claim 5 and is rejected under a similar rationale.
With regard to Dependent Claim 19,
As discussed above, Xu-Drayna teaches all the limitations of Claim 13. Xu-Drayna further teaches wherein the second information indicates the region information and the time information. See e.g., Drayna, [0019], (detect and deploy models for different locations, e.g., cities), [0020], (“For example, the other data may include sensor data captured by the sensor package (e.g., vibrational and/or movement data along the travel route), time of day, season, weather conditions, ambient or artificial lighting, acceleration parameters of the fleet vehicle, and/or crowd congestion models and parameters.”)
With regard to Dependent Claim 24,
As discussed above, Xu-Drayna teaches all the limitations of Claim 1. Xu-Drayna further teaches wherein the second apparatus is included in the terminal. See Xu, Col. 7:25-46, (discussing sensors in the terminal, which is a vehicle.). See also Drayna, [0019], (discussing same.)
With regard to Dependent Claim 25,
This claim is similar in scope to Claim 19 and is rejected under a similar rationale.
With regard to Dependent Claim 26,
As discussed above, Xu-Drayna teaches all the limitations of Claim 1. Xu-Drayna further teaches wherein the first apparatus is a sensor and wherein the sensor comprises a camera apparatus, a lidar… See Xu, Col. 7:26-46 (describing several sensor types including cameras, lidar.).
With regard to Dependent Claim 27,
This claim is similar in scope to Claim 5 and is rejected under a similar rationale.
With regard to Dependent Claim 28,
As discussed above, Xu-Drayna teaches all the limitations of Claim 6. Xu-Drayna further teaches wherein inputting the first information and the second information to the predefined algorithm comprises selecting the predefined algorithm from the multiple predefined algorithms based on the second information. See e.g., Drayna, [0019], [0020], (NNs for different locations), [0063], (NNs based at least in part on time).
With regard to Dependent Claim 29,
As discussed above, Xu-Drayna teaches all the limitations of Claim 6. Xu-Drayna further teaches wherein inputting the first information and the second information to the predefined algorithm comprises selecting the predefined algorithm from the multiple predefined algorithms based on a match between the at least one of region information or time information and the corresponding region or time information of one of the multiple predefined algorithms. See e.g., Drayna, [0019], [0020], (NNs for different locations), [0063], (NNs based at least in part on time). The examiner notes that to use an NN for a specific location necessarily requires a “match.”
With regard to Dependent Claim 30,
This claim is similar in scope to Claim 19 and is rejected under a similar rationale.
With regard to Dependent Claim 31,
This claim is similar in scope to Claim 12 and is rejected under a similar rationale.
Response to Arguments
Applicants’ remarks in view of the 35 USC 103 rejection have been considered but are not persuasive. The remarks are primarily focused on the Kim reference. However, the most recent version of the claims is significantly broader and of different scope than the previous version. As such, Kim is no longer necessary to teach all of the limitations (although the examiner still finds it highly relevant.) Xu in view of Drayna teaches all of the limitations. No substantive remarks were made regarding these references.
Conclusion
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/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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