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 .
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 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: “respective detection unit”, “determination unit” and “checking unit” in claim 8; “respective preprocessing unit” in claim 9; and “receiver unit” and “control unit” in claim 14.
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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 and 8-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
[101 Analysis Step 1]
Step 1, of the 2019 Guidance, first looks to whether the claimed invention is directed to a statutory category, namely a process, machine, manufactures, and compositions of mater.
The claim 1 is directed to a method for reliably detecting objects in the surroundings of a motor vehicle (i.e. process) and claim 8 is directed to a system for reliably detecting objects in the surroundings of a motor vehicle (i.e. machine). Thus, claims 1 and 8 are one of four the statutory categories (Step 1: YES).
[101 Analysis Step 2A, Prong I]
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim(s) for the remainder of the 101 rejection. Claim 1 recites:
A method for reliably detecting objects in the surroundings of a motor vehicle, wherein the motor vehicle includes a plurality of different surroundings sensors, comprising:
acquiring sensor data by at least one of the plurality of different surroundings sensors;
for each of the at least one of the plurality of different surroundings sensors, detecting objects in the corresponding acquired sensor data;
determining at least one distinctive feature during object detection based on the detected objects; and
for each of the at least one distinctive features, checking the corresponding distinctive feature in order to reliably detect objects in the surroundings of the motor vehicle.
The examiner submits that the foregoing bolded limitations(s) constitute a “mental process” because under its broadest reasonable interpretations, the claim covers performance of the limitation in the human mind. For example, “detecting…”, “determining…” and “checking…” in the context of the claim encompasses a person looking at and using the image data collected to formulating a judgement. 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 reliably detecting objects in the surroundings of a motor vehicle, wherein the motor vehicle includes a plurality of different surroundings sensors, comprising:
acquiring sensor data by at least one of the plurality of different surroundings sensors;
for each of the at least one of the plurality of different surroundings sensors, detecting objects in the corresponding acquired sensor data;
determining at least one distinctive feature during object detection based on the detected objects; and
for each of the at least one distinctive features, checking the corresponding distinctive feature in order to reliably detect objects in the surroundings of the motor vehicle.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract into a practical applications.
Regarding the additional limitations of “acquiring sensor data by at least one of the plurality of different surroundings sensors” the examiner submits that these limitations are insignificant extra-solution activities that merely using sensors to perform the process. In particular, the acquiring step can be performed via sensors are recited at a high level of generality (i.e. as a general means of gathering surrounding information for use in the detecting, determining and checking steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Lastly, the “a plurality of different surroundings sensors” and “motor vehicle” are recited at a high-level of generality (i.e. as sensors and vehicle) such that it amounts no more than mere instructions to apply the exception using a generic computer component and sensors.
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 filed, 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 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 Revised Guidance, representative independent claims 1 and 8 do 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, information from the additional elements of using sensors to perform the steps of “detecting…”, “determining…” and “checking…” amounts to nothing more than mere instructions to apply the exception using a generic sensor in the vehicle. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “acquiring,…” the examiner submits that these limitations are insignificant extra-solution activities. Hence, the claims are not patent eligible.
Dependent claims 2-6 and 9-13 do not recite any further limitations that cause the claims to be directed towards statutory subject matter. The claims merely recite: abstract idea. Each of the further limitations expound upon the abstract ideas and do not recite additional elements integrating the abstract ideas into a practical application or additional elements that are not well-understood, routine or conventional. Therefore, dependent claims 2-6 and 9-13 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claims 1-6 and 8-13 is/are ineligible under 35 USC §101 and the examiner suggest amending the claims to include the recitations (e.g. “controlling at least one function of the motor vehicle…) from claims 7 and 14 in order to overcome the rejection above.
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.
Claim(s) 1, 3, 5-6, 8, 10 and 12-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pub No. JP 2006234513 A to Shirodono et. al. (Shirodono).
Examiner’s Note: Machine translation of Pub No. JP 2006234513 A will be used in the rejection below.
In Reference to Claim 1
A method for reliably detecting objects in the surroundings of a motor vehicle, wherein the motor vehicle includes a plurality of different surroundings sensors (101, 102R, 102L, 202) (see at least Shirodono Figs.4 and 8 and paragraph [0039] “In the first embodiment, an apparatus configuration using a millimeter wave radar, a stereo camera, and a vehicle speed sensor is illustrated. FIG. 4 shows a logical configuration of the obstacle detection apparatus 100 according to the first embodiment. The millimeter wave radar 101 that provides necessary search data (received signal) to the obstacle detection processing unit 190 constituting the detection processing system for processing the millimeter wave signal includes M antenna elements constituting the array antenna. The output is input to the frequency analysis means 121 as a reception signal of the millimeter wave radar according to the first embodiment. The number M of antenna elements may be an appropriate number of 2 or more. Usually, an appropriate number within the range of about 3 to 30 is selected. The frequency analysis unit 121 performs a well-known frequency analysis in the FM-CW method”), comprising:
acquiring sensor data by at least one of the plurality of different surroundings sensors (101, 102R, 102L, 202) (see at least Shirodono Fig.4 and paragraphs [0039], [0042] “The output is input to the frequency analysis means 121 as a reception signal of the millimeter wave radar according to the first embodiment” and “On the other hand, the three-dimensional object detection unit 110 of the stereo image processing system is a detection unit that three-dimensionally detects a target obstacle based on parallax for both left and right imaging data obtained from the stereo camera 102”);
for each of the at least one of the plurality of different surroundings sensors (101, 102R, 102L, 202), detecting objects in the corresponding acquired sensor data (see at least Shirodono Fig.5 and paragraphs [0045], [0046] “FIG. 5 illustrates a control procedure of the obstacle detection apparatus 100 described above. In the stereo image processing system (three-dimensional object detection means 110), first, in step 31 of FIG. 5, the images obtained by the stereo camera 102 (the left and right cameras 102L and 102R) are stored in a predetermined storage area. Store. Next, in step 32, the same obstacle is extracted from both the left and right images stored in the storage area, and the difference between the positions of the obstacle on the left and right images is extracted. Based on this extraction result, the distance and direction to the obstacle are calculated” and “On the other hand, in the observation system on the millimeter wave radar side, first, in step 33 of FIG. 5, a reception signal from the millimeter wave radar (millimeter wave radar 101) is acquired, and the received signal is subjected to frequency analysis (frequency analysis means 121). ). Next, in step 34, a target (target obstacle) is detected. That is, a peak having a strong reflection intensity is detected from the result of the frequency analysis (first peak detecting means 131), and the distance, relative speed, and direction to the obstacle are detected (distance / direction / relative speed detecting means 150)”);
determining at least one distinctive feature during object detection based on the detected objects (see at least Shirodono Fig.5 and paragraphs [0048] “On the other hand, if the obstacle detected by the stereo image processing is not detected by the millimeter wave radar, the process proceeds to step 36 and adaptive control of the millimeter wave radar (hereinafter, this may be referred to as gaze control). Execute. The execution command to this adaptive control (gaze control) that proceeds to step 36 corresponds to the feedback signal S”); and
for each of the at least one distinctive features, checking the corresponding distinctive feature in order to reliably detect objects in the surroundings of the motor vehicle (see at least Shirodono Figs.5-7 and 10 and paragraphs [0052], [0064] “(Step 43) Based on the image analysis result Y, the obstacle search range in the analysis result (distance-reception intensity data) of the frequency analysis with respect to the reception signal of the millimeter wave radar is limited, and the beam width and the estimation are performed. The detection threshold is also adaptively controlled according to the reflection characteristics of the obstacle. Here, when limiting the search range, attention should be paid to detection errors in the image analysis result. In particular, in stereo detection in an in-vehicle camera, there is a limit to securing parallax, and thus the detection error tends to be larger in the distance direction than in the angle direction. (Step 44) An obstacle within the designated search range is searched based on the designated detection threshold. As shown in FIG. 7B, the obstacle can be recognized with high accuracy from the characteristics of the reception intensity and the distance in which the directivity is adaptively controlled. (Step 45) The reliability of the obstacle presence is calculated based on the received intensity and the peak shape of the frequency analysis result. Thereafter, control is transferred to step 37 in FIG. 5, and the position of the obstacle and its certainty are determined by fusing the above gaze control result (detection result including reliability) and the stereo detection result (obstacle determination). 180)” and “(Step 61) The detection result of the radar sensor is acquired. (Step 62) The detection result of the radar sensor is converted into the coordinate system of the stereo camera to limit the image range to be searched. (Step 63) The determined image range is investigated and appropriate image correction processing (gamma correction, edge enhancement, contrast correction, etc.) is performed. (Step 64) The corresponding point search of the left and right images is performed again within the limited image range. At this time, the window size, the detection threshold, and the like are adaptively changed. (Step 65) The obstacle is detected from the result of the re-search, and the reliability of the obstacle existence is calculated based on the number of pixels having parallax corresponding to the obstacle’).
In Reference to Claim 3
The method according to claim 1 (see rejection to claim 1 above), wherein the step of determining at least one distinctive feature during object detection based on the detected objects comprises determining at least one distinctive feature based on the detected objects and further information describing the situation (see at least Shirodono Figs. 4-10 and paragraphs [0006], [0013], [0024] “In the detection device, external environment information acquisition means for estimating or detecting a state around the vehicle, directivity variable control means for variably controlling directivity based on external world information provided from the external environment information acquisition means, and directivity variable control Weighting for each received signal received by each antenna element of the array antenna, the obstacle position estimating means for obtaining the position of the obstacle based on the received signal of the millimeter wave radar, the directivity of which is adaptively controlled by the means This is to variably control the directivity of the millimeter wave radar”, “According to a fifth means of the present invention, in the fourth means, the in-vehicle camera is a stereo camera, and the external information acquisition means is configured to determine the distance to the obstacle detected by the in-vehicle camera. Distance calculation means for calculating based on the parallax of the camera is provided, and at least the azimuth and distance are included in the external environment information, and the azimuth and distance and the received signal received by the millimeter wave radar by the obstacle position estimation means. Is to determine the position of the obstacle” and “In addition, according to any one of the fourth to sixth means of the present invention, real-time imaging data around the site is collected by the in-vehicle camera, and therefore based on the imaging data or the image analysis result for the imaging data. In other words, by including such information in the above-mentioned external environment information and limiting the detection range to an appropriate peak search method, it is possible to accurately extract reflected signals from pedestrians with low reflection intensity It becomes”).
In Reference to Claim 5
The method according to claim 1 (see rejection to claim 1 above), wherein the step of, for each of the at least one distinctive feature, checking the corresponding distinctive feature comprises applying an event camera to check the corresponding distinctive feature and/or refining an object detection underlying the corresponding distinctive feature and checking the corresponding distinctive feature based on the refined object detection and/or detecting objects in sensor data acquired by another one of the plurality of different surroundings sensors to check the corresponding distinctive feature (see at least Shirodono Figs. 4-10 and paragraphs [0052] and [0057] “(Step 43) Based on the image analysis result Y, the obstacle search range in the analysis result (distance-reception intensity data) of the frequency analysis with respect to the reception signal of the millimeter wave radar is limited, and the beam width and the estimation are performed. The detection threshold is also adaptively controlled according to the reflection characteristics of the obstacle. Here, when limiting the search range, attention should be paid to detection errors in the image analysis result. In particular, in stereo detection in an in-vehicle camera, there is a limit to securing parallax, and thus the detection error tends to be larger in the distance direction than in the angle direction. (Step 44) An obstacle within the designated search range is searched based on the designated detection threshold. As shown in FIG. 7B, the obstacle can be recognized with high accuracy from the characteristics of the reception intensity and the distance in which the directivity is adaptively controlled. (Step 45) The reliability of the obstacle presence is calculated based on the received intensity and the peak shape of the frequency analysis result. Thereafter, control is transferred to step 37 in FIG. 5, and the position of the obstacle and its certainty are determined by fusing the above gaze control result (detection result including reliability) and the stereo detection result (obstacle determination). 180)” and “Further, according to the apparatus configuration of the first embodiment, the reliability of the detection result is calculated in the above procedure (step 45) even when only one of the information on the rising modulation interval and the falling modulation interval is obtained. The information can be used as a material for the occasion. At this time, compared with the case where the target object can be accurately detected in the two sections, the rising modulation section and the falling modulation section, the reliability of detection is lower, but the radio wave reflection intensity like a pedestrian. For a target that fluctuates from moment to moment, increase the material (: available information) when calculating the reliability as described above to improve detection stability compared to the conventional method. Can do. These pieces of information can be used to limit the obstacle detection range, or can be used to calculate the reliability of the obstacle detection result”).
In Reference to Claim 6
The method according to claim 1 (see rejection to claim 1 above), wherein the plurality of different surroundings sensors includes a video sensor and/or a LIDAR sensor and/or a radar sensor (101) and/or an ultrasonic sensor (see at least Shirodono Figs. 4-10 and paragraphs 39, 46).
In Reference to Claim 8
A system for reliably detecting objects in the surroundings of a motor vehicle, comprising:
a plurality of different surroundings sensors (101, 102R, 102L, 202), and wherein the system is designed to acquire sensor data by means way of at least one of the plurality of different surroundings sensors (101, 102R, 102L, 202) (see at least Shirodono Figs.4 and 8 and paragraphs [0039] and [0042] “In the first embodiment, an apparatus configuration using a millimeter wave radar, a stereo camera, and a vehicle speed sensor is illustrated. FIG. 4 shows a logical configuration of the obstacle detection apparatus 100 according to the first embodiment. The millimeter wave radar 101 that provides necessary search data (received signal) to the obstacle detection processing unit 190 constituting the detection processing system for processing the millimeter wave signal includes M antenna elements constituting the array antenna. The output is input to the frequency analysis means 121 as a reception signal of the millimeter wave radar according to the first embodiment. The number M of antenna elements may be an appropriate number of 2 or more. Usually, an appropriate number within the range of about 3 to 30 is selected. The frequency analysis unit 121 performs a well-known frequency analysis in the FM-CW method” and “On the other hand, the three-dimensional object detection unit 110 of the stereo image processing system is a detection unit that three-dimensionally detects a target obstacle based on parallax for both left and right imaging data obtained from the stereo camera 102”)),
for each of the at least one of the plurality of different surroundings sensors (101, 102R, 102L, 202), a respective detection unit (100, 200) which is designed to detect objects in the corresponding acquired sensor data (see at least Shirodono Fig.5 and paragraphs [0045], [0046] “FIG. 5 illustrates a control procedure of the obstacle detection apparatus 100 described above. In the stereo image processing system (three-dimensional object detection means 110), first, in step 31 of FIG. 5, the images obtained by the stereo camera 102 (the left and right cameras 102L and 102R) are stored in a predetermined storage area. Store. Next, in step 32, the same obstacle is extracted from both the left and right images stored in the storage area, and the difference between the positions of the obstacle on the left and right images is extracted. Based on this extraction result, the distance and direction to the obstacle are calculated” and “On the other hand, in the observation system on the millimeter wave radar side, first, in step 33 of FIG. 5, a reception signal from the millimeter wave radar (millimeter wave radar 101) is acquired, and the received signal is subjected to frequency analysis (frequency analysis means 121). ). Next, in step 34, a target (target obstacle) is detected. That is, a peak having a strong reflection intensity is detected from the result of the frequency analysis (first peak detecting means 131), and the distance, relative speed, and direction to the obstacle are detected (distance / direction / relative speed detecting means 150)”),
a determination unit (100, 200) which is designed to determine at least one distinctive feature during object detection based on detected objects (see at least Shirodono Fig.5 and paragraphs [0048] “On the other hand, if the obstacle detected by the stereo image processing is not detected by the millimeter wave radar, the process proceeds to step 36 and adaptive control of the millimeter wave radar (hereinafter, this may be referred to as gaze control). Execute. The execution command to this adaptive control (gaze control) that proceeds to step 36 corresponds to the feedback signal S”), and
a checking unit (100, 200) which is designed to check the corresponding distinctive feature for each of the at least one distinctive features in order to reliably detect objects in the surroundings of the motor vehicle (see at least Shirodono Figs.5-7 and 10 and paragraphs [0052], [0064] “(Step 43) Based on the image analysis result Y, the obstacle search range in the analysis result (distance-reception intensity data) of the frequency analysis with respect to the reception signal of the millimeter wave radar is limited, and the beam width and the estimation are performed. The detection threshold is also adaptively controlled according to the reflection characteristics of the obstacle. Here, when limiting the search range, attention should be paid to detection errors in the image analysis result. In particular, in stereo detection in an in-vehicle camera, there is a limit to securing parallax, and thus the detection error tends to be larger in the distance direction than in the angle direction. (Step 44) An obstacle within the designated search range is searched based on the designated detection threshold. As shown in FIG. 7B, the obstacle can be recognized with high accuracy from the characteristics of the reception intensity and the distance in which the directivity is adaptively controlled. (Step 45) The reliability of the obstacle presence is calculated based on the received intensity and the peak shape of the frequency analysis result. Thereafter, control is transferred to step 37 in FIG. 5, and the position of the obstacle and its certainty are determined by fusing the above gaze control result (detection result including reliability) and the stereo detection result (obstacle determination). 180)” and “(Step 61) The detection result of the radar sensor is acquired. (Step 62) The detection result of the radar sensor is converted into the coordinate system of the stereo camera to limit the image range to be searched. (Step 63) The determined image range is investigated and appropriate image correction processing (gamma correction, edge enhancement, contrast correction, etc.) is performed. (Step 64) The corresponding point search of the left and right images is performed again within the limited image range. At this time, the window size, the detection threshold, and the like are adaptively changed. (Step 65) The obstacle is detected from the result of the re-search, and the reliability of the obstacle existence is calculated based on the number of pixels having parallax corresponding to the obstacle’).
In Reference to Claim 10
The system according to claim 8 (see rejection to claim 8 above), wherein the determination unit (100, 200) is designed to determine the at least one distinctive feature based on the detected objects and further information describing the situation (see at least Shirodono Figs. 4-10 and paragraphs [0006], [0013], [0024] “In the detection device, external environment information acquisition means for estimating or detecting a state around the vehicle, directivity variable control means for variably controlling directivity based on external world information provided from the external environment information acquisition means, and directivity variable control Weighting for each received signal received by each antenna element of the array antenna, the obstacle position estimating means for obtaining the position of the obstacle based on the received signal of the millimeter wave radar, the directivity of which is adaptively controlled by the means This is to variably control the directivity of the millimeter wave radar”, “According to a fifth means of the present invention, in the fourth means, the in-vehicle camera is a stereo camera, and the external information acquisition means is configured to determine the distance to the obstacle detected by the in-vehicle camera. Distance calculation means for calculating based on the parallax of the camera is provided, and at least the azimuth and distance are included in the external environment information, and the azimuth and distance and the received signal received by the millimeter wave radar by the obstacle position estimation means. Is to determine the position of the obstacle” and “In addition, according to any one of the fourth to sixth means of the present invention, real-time imaging data around the site is collected by the in-vehicle camera, and therefore based on the imaging data or the image analysis result for the imaging data. In other words, by including such information in the above-mentioned external environment information and limiting the detection range to an appropriate peak search method, it is possible to accurately extract reflected signals from pedestrians with low reflection intensity It becomes”).
In Reference to Claim 12
The system according to claim 8 (see rejection to claim 8 above), wherein the checking unit (100, 200) is designed to utilize, for each of the at least one distinctive features, an event camera for checking the corresponding distinctive feature and/or to refine an object detection underlying the corresponding distinctive feature and to check the corresponding distinctive feature based on the refined object detection and/or to check the corresponding distinctive feature by detecting objects in sensor data acquired by another one of the plurality of different surroundings sensors (101, 102R, 102L, 202) (see at least Shirodono Figs. 4-10 and paragraphs [0052] and [0057] “(Step 43) Based on the image analysis result Y, the obstacle search range in the analysis result (distance-reception intensity data) of the frequency analysis with respect to the reception signal of the millimeter wave radar is limited, and the beam width and the estimation are performed. The detection threshold is also adaptively controlled according to the reflection characteristics of the obstacle. Here, when limiting the search range, attention should be paid to detection errors in the image analysis result. In particular, in stereo detection in an in-vehicle camera, there is a limit to securing parallax, and thus the detection error tends to be larger in the distance direction than in the angle direction. (Step 44) An obstacle within the designated search range is searched based on the designated detection threshold. As shown in FIG. 7B, the obstacle can be recognized with high accuracy from the characteristics of the reception intensity and the distance in which the directivity is adaptively controlled. (Step 45) The reliability of the obstacle presence is calculated based on the received intensity and the peak shape of the frequency analysis result. Thereafter, control is transferred to step 37 in FIG. 5, and the position of the obstacle and its certainty are determined by fusing the above gaze control result (detection result including reliability) and the stereo detection result (obstacle determination). 180)” and “Further, according to the apparatus configuration of the first embodiment, the reliability of the detection result is calculated in the above procedure (step 45) even when only one of the information on the rising modulation interval and the falling modulation interval is obtained. The information can be used as a material for the occasion. At this time, compared with the case where the target object can be accurately detected in the two sections, the rising modulation section and the falling modulation section, the reliability of detection is lower, but the radio wave reflection intensity like a pedestrian. For a target that fluctuates from moment to moment, increase the material (: available information) when calculating the reliability as described above to improve detection stability compared to the conventional method. Can do. These pieces of information can be used to limit the obstacle detection range, or can be used to calculate the reliability of the obstacle detection result”).
In Reference to Claim 13
The system according to claim 8 (see rejection to claim 8 above), wherein the plurality of different surroundings sensors include a video sensor and/or a LIDAR sensor and/or a radar sensor (101) and/or an ultrasonic sensor (see at least Shirodono Figs. 4-10 and paragraphs 39, 46).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2, 4, 9 and 11 and are rejected under 35 U.S.C. 103 as being unpatentable over Shirodono in view of Pub No. US 2023/0113547 A1 Tamaoki (Tamaoki).
In Reference to Claim 2
Shirodono teaches (except for the bolded and italic recitations below):
The method according to claim 1 (see rejection to claim 1 above), further comprising:
for each of the at least one of the plurality of different surroundings sensors, pre-processing the corresponding acquired sensor data,
wherein the step of, for each of the at least one of the plurality of different surroundings sensors (101, 102R, 102L, 202), respectively detecting objects in the corresponding acquired sensor data comprises detecting objects in the corresponding pre-processed sensor data (see at least Shirodono Figs. 4-10 and paragraphs 39, 42, 45-48, 52 and 64).
Shirodono do not explicitly teaches (bolded and italic recitations above) as to pre-processing the corresponding acquired sensor data and detecting objects in the corresponding pre-processed sensor data. However, it is known in the art before the effective filing date of the claimed invention to preform the step of pre-processing the corresponding acquired sensor data and detecting objects in the corresponding pre-processed sensor data. For example, Tamaoki teaches to pre-processing the corresponding acquired sensor data and detecting objects in the corresponding pre-processed sensor data. Tamaoki teaches that performing such steps provide improvement of the accuracy of the image (see at least Tamaoki Figs.1-4 and paragraphs 16, 33, 46-51). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Shirodono with the steps of pre-processing the corresponding acquired sensor data and detecting objects in the corresponding pre-processed sensor data as taught by Tamaoki in order to improve the accuracy of the image.
In Reference to Claim 4
Shirodono teaches (except for the bolded and italic recitations below):
The method according to claim 1 (see rejection to claim 1 above), wherein the step of determining at least one distinctive feature during object detection based on the detected objects comprises applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects (see at least Shirodono Figs. 4-10 and paragraphs 39, 42, 45-48, 52 and 64).
Shirodono do not explicitly teaches (bolded and italic recitations above) as to applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects. However, it is known in the art before the effective filing date of the claimed invention to applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects. For example, Tamaoki teaches to applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects. Tamaoki implicitly teaches that performing such step would provide accurate determination of the distinctive features (see at least Tamaoki Figs.1-4 and paragraphs 35-36). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Shirodono to perform the step of applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects as taught by Tamaoki in order to provide accurate determination of the distinctive features.
In Reference to Claim 9
Shirodono teaches (except for the bolded and italic recitations below):
The system according to claim 8 (see rejection to claim 8 above), further comprising:
for each of the at least one of the plurality of different surroundings sensors, a respective preprocessing unit (100, 200) which is designed to preprocess the corresponding acquired sensor data,
wherein, for each of the at least one of the plurality of different surroundings sensors (101, 102R, 102L, 202), the detection unit (100, 200) is configured to detect objects in the corresponding preprocessed sensor data (see at least Shirodono Figs. 4-10 and paragraphs 39, 42, 45-48, 52 and 64).
Shirodono do not explicitly teaches (bolded and italic recitations above) as to preprocess the corresponding acquired sensor data and detecting objects in the corresponding preprocessed sensor data. However, it is known in the art before the effective filing date of the claimed invention to preform the step of preprocess the corresponding acquired sensor data and detecting objects in the corresponding preprocessed sensor data. For example, Tamaoki teaches to preprocess the corresponding acquired sensor data and detecting objects in the corresponding preprocessed sensor data. Tamaoki teaches that performing such steps provide improvement of the accuracy of the image (see at least Tamaoki Figs.1-4 and paragraphs 16, 33, 46-51). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Shirodono with the steps of preprocess the corresponding acquired sensor data and detecting objects in the corresponding preprocessed sensor data as taught by Tamaoki in order to improve the accuracy of the image.
In Reference to Claim 11
Shirodono teaches (except for the bolded and italic recitations below):
The system according to claim 8 (see rejection to claim 8 above), wherein the determination unit (100, 200) is designed to determine the at least one distinctive feature by applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects (see at least Shirodono Figs. 4-10 and paragraphs 39, 42, 45-48, 52 and 64).
Shirodono do not explicitly teaches (bolded and italic recitations above) as to applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects. However, it is known in the art before the effective filing date of the claimed invention to applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects. For example, Tamaoki teaches to applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects. Tamaoki implicitly teaches that performing such step would provide accurate determination of the distinctive features (see at least Tamaoki Figs.1-4 and paragraphs 35-36). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Shirodono to perform the step of applying a machine learning algorithm which is trained to determine distinctive features during object detection based on information about detected objects as taught by Tamaoki in order to provide accurate determination of the distinctive features.
Claim(s) 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shirodono in view of Pub No. US 2022/0374638 A1 Felner et. al. (Felner).
In Reference to Claim 7
Shirodono teaches (except for the bolded and italic recitations below):
A method for controlling at least one function of a motor vehicle based on objects detected in the surroundings of the motor vehicle, comprising:
detecting objects in the surroundings of the motor vehicle by way of the method for reliably detecting objects in the surroundings of the motor vehicle according to claim 1 (see rejection to claim 1 above); and
controlling at least one function of the motor vehicle based on the detected objects (see at least Shirodono Figs. 4-10 and paragraphs 39, 42, 45-48, 52 and 64).
Shirodono do not explicitly teaches (bolded and italic recitations above) as to controlling at least one function of the motor vehicle based on the detected objects. However, it is known in the art before the effective filing date of the claimed invention to controlling at least one function of the motor vehicle based on the detected objects. For example, Felner teaches to control at least one function of the motor vehicle based on the detected objects. Felner further teaches that performing such step provides improved safety of the vehicle (see at least Felner Figs. 1-10 and paragraphs 1, 17, 41-46). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Shirodono to perform the step of controlling at least one function of the motor vehicle based on the detected objects as taught by Felner in order to improved safety of the vehicle.
In Reference to Claim 14
Shirodono teaches (except for the bolded and italic recitations below):
A system for controlling at least one function of a motor vehicle based on objects detected in the surroundings of the motor vehicle, comprising:
a receiver unit (100, 200) configured to receive information about objects detected in the surroundings of the motor vehicle, wherein the objects have been detected by the system for reliably detecting objects in the surroundings of the motor vehicle according to claim 8 (see rejection to claim 8 above), and
a control unit (100, 200) which is designed to control the at least one function based on the provided information (see at least Shirodono Figs. 4-10 and paragraphs 39, 42, 45-48, 52 and 64).
Shirodono do not explicitly teaches (bolded and italic recitations above) as to a control unit (100, 200) which is designed to control the at least one function based on the provided information. However, it is known in the art before the effective filing date of the claimed invention to the control unit which is designed to control the at least one function based on the provided information. For example, Felner teaches to the control unit which is designed to control the at least one function based on the provided information. Felner further teaches that having such structure provides improved safety of the vehicle (see at least Felner Figs. 1-10 and paragraphs 1, 17, 41-46). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the control system of Shirodono to perform the function of controlling at least one function of the motor vehicle based on the detected objects as taught by Felner in order to improved safety of the vehicle.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patent No. US 12,001,519 B2 to Yamazaki et. al. (Yamazaki) teaches to check the corresponding distinctive feature from image sensors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON DONGPA LEE whose telephone number is (571)270-3525. The examiner can normally be reached Monday - Friday, 8:00 am - 5:00 pm.
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/BRANDON D LEE/Primary Examiner, Art Unit 3662 July 20, 2026
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