DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2021-037570, filed on 03/09/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted was on 09/07/2023. 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 Objections
Claims 1 and 14 are objected to because of the following informalities:
Claims 1 and 14 detail the limitation “a second acquiring unit that acquires, when the reception intensity peak exceeds a second threshold as the threshold different from the first threshold” which should read “a second acquiring unit that acquires, when the reception intensity peak exceeds a second threshold as a threshold different from the first threshold” as it is detailed as a different threshold, thus the correct antecedent basis should be “a” rather than “the” for “threshold different from the first threshold”.
Appropriate correction is required.
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: positional information acquiring unit (Claim 1, 14), object tracking unit (Claim 1, 14), state identifying unit (Claim 1, 14), first acquiring unit (Claim 1, 14), second acquiring unit (Claim 1, 14), first tracking processing unit (Claim 1-4, 10, 12-14), second tracking processing unit (Claim 1-4, 10-14), probability setting unit (Claim 5-9), difference extracting unit (Claim 11)
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. The positional information acquiring unit, object tracking unit, state identifying unit, first acquiring unit, second acquiring unit, first tracking processing unit, second tracking processing unit, probability setting unit, and difference extracting unit are interpreted as being a computer processor as detailed in [0111]-[0117].
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 § 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 1-14 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 14 details the limitation “a positional information acquiring unit configured to acquire, when a reception intensity peak of reflection waves contained in an object detection signal exceeds a threshold, positional information corresponding to the reception intensity peak”. Claim 1 further details the limitations “a first observation information as the positional information corresponding to the reception intensity peak” and “a second observation information as the positional information corresponding to the reception intensity peak”. The two additional limitation of the first observation information and the second observation information are detailed as being the same positional information corresponding to the reception intensity peak. It is not clear nor distinct whether the first observation information and the second observation information are supposed to be the same positional information corresponding to the reception intensity peak or different positional information corresponding to the reception intensity peak.
Examiner interprets the first observation information and the second observation information as being different positional information corresponding to the reception intensity peak.
Claims 2-13 are rejected due to dependence on Claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sakamaki (US20200312156) in view of Suzuki (JP2007232409A).
As best understood, in regards to Claims 1, Sakamaki teaches “a positional information acquiring unit configured to acquire positional information, the object detection signal being generated by an object sensor mounted on the vehicle, the object sensor emitting probing waves as electromagnetic waves and receiving reflection waves of the probing waves which are reflected at the object (“the sensor devices 102 - 112 can determine location parameters associated with the target object 130 based on a reflection from the target object 130 of a radar signal transmitted by a radar” – [0051]; sensor devices are implemented, integrated, or mounted on vehicle – [0052]);
an object tracking unit that executes, based on the positional information acquired by the positional information acquiring unit, a tracking process of the object (each sensor includes an interactive multiple model filter which can include multiple filters for tracking a target object by using sensor data from the sensors and respective target models to generate respective state estimates and error covariances for the target object – [0053]); and
a state identifying unit that identifies a state of the object based on a result of the tracking process executed by the object tracking unit (“Moreover, the filters 116 in the IMMF 114 of a sensor device can process measurements or observations from the sensor device (e.g., 102 - 112 ) and use the filters 116 and respective target models to calculate a motion or location of the target object (e.g., 130 ), estimate or predict the target object's states (e.g., position, velocity, trajectory, acceleration, angle, altitude, etc.), calculate one or more tracks (e.g., estimated states and error covariances) for the target object, etc. The filters 116 can produce various results, which may depend on one or more factors such as the suitability of their respective target models for a particular type of motion of the target object (e.g., 130 )” – [0054]),
the object tracking unit comprises:
a first tracking processing unit that executes the tracking process (“Each of the sensor devices 102 - 112 can include an interactive multiple model filter (IMMF) 114 , which can include multiple filters 116 , such as Kalman filters, for tracking a target object (e.g., 130 ) by using sensor data from the sensor devices 102 - 112 and respective target models to generate respective state estimates and error covariances for the target object (e.g., 130 ). In some cases, the filters 116 in the IMMF 114 can run in parallel. Moreover, the different filters 116 in an IMMF 114 can implement different target models for tracking a target object (e.g., 130 ), such as a nearly-constant velocity model, a nearly-constant acceleration model, a dynamic model, a random acceleration or velocity model, a linear model, a non-linear model, etc. The different target models can capture the various dynamics of the target object”– [0053]; first filter in the plurality of filters would correspond to the first tracking processing unit); and
a second tracking processing unit that executes the tracking process (“Each of the sensor devices 102 - 112 can include an interactive multiple model filter (IMMF) 114 , which can include multiple filters 116 , such as Kalman filters, for tracking a target object (e.g., 130 ) by using sensor data from the sensor devices 102 - 112 and respective target models to generate respective state estimates and error covariances for the target object (e.g., 130 ). In some cases, the filters 116 in the IMMF 114 can run in parallel. Moreover, the different filters 116 in an IMMF 114 can implement different target models for tracking a target object (e.g., 130 ), such as a nearly-constant velocity model, a nearly-constant acceleration model, a dynamic model, a random acceleration or velocity model, a linear model, a non-linear model, etc. The different target models can capture the various dynamics of the target object”– [0053]; second filter in the plurality of filters would correspond to the second tracking processing unit),
either one of the first tracking processing unit or the second tracking processing unit has anti-clutter characteristics higher than that of the other one (Moreover, the different filters 116 in an IMMF 114 can implement different target models for tracking a target object (e.g., 130 ), such as a nearly-constant velocity model, a nearly-constant acceleration model, a dynamic model, a random acceleration or velocity model, a linear model, a non-linear model, etc. The different target models can capture the various dynamics of the target object”– [0053]; the different filters being a linear model or a non-linear model would thus have different anti-clutter characteristics where one is higher than the other); and
the state identifying unit identifies the state of the object based on a result of the tracking process executed by the first tracking processing unit and the second tracking processing unit (“Moreover, the filters 116 in the IMMF 114 of a sensor device can process measurements or observations from the sensor device (e.g., 102 - 112 ) and use the filters 116 and respective target models to calculate a motion or location of the target object (e.g., 130 ), estimate or predict the target object's states (e.g., position, velocity, trajectory, acceleration, angle, altitude, etc.), calculate one or more tracks (e.g., estimated states and error covariances) for the target object, etc. The filters 116 can produce various results, which may depend on one or more factors such as the suitability of their respective target models for a particular type of motion of the target object (e.g., 130 )” – [0054]).”
Sakamaki is silent with regards to the language of “a positional information acquiring unit configured to acquire, when a reception intensity peak of reflection waves contained in an object detection signal exceeds a threshold, positional information corresponding to the reception intensity peak, the object detection signal being generated by an object sensor mounted on the vehicle, the object sensor emitting probing waves as electromagnetic waves and receiving reflection waves of the probing waves which are reflected at the object; wherein the positional information acquiring unit comprises: a first acquiring unit that acquires, when the reception intensity peak exceeds a first threshold as the threshold, a first observation information as the positional information corresponding to the reception intensity peak, a second acquiring unit that acquires, when the reception intensity peak exceeds a second threshold as the threshold different from the first threshold, a second observation information as the positional information corresponding to the reception intensity peak; a first tracking processing unit that executes the tracking process based on the first observation information; a second tracking processing unit that executes the tracking process based on the second observation information.”
Suzuki teaches “a positional information acquiring unit configured to acquire, when a reception intensity peak of reflection waves contained in an object detection signal exceeds a threshold, positional information corresponding to the reception intensity peak, the object detection signal being generated by an object sensor mounted on the vehicle, the object sensor emitting probing waves as electromagnetic waves and receiving reflection waves of the probing waves which are reflected at the object (target detection device that detects a target by transmitting a signal wave and receiving a reflected wave of the transmitted signal wave, where the detection means for detecting a target located in a first detection region when the intensity of the signal wave is equal to or greater than a first threshold – [0008]; target detection device is mounted on a vehicle – [0020]);
wherein the positional information acquiring unit comprises:
a first acquiring unit that acquires, when the reception intensity peak exceeds a first threshold as the threshold, a first observation information as the positional information corresponding to the reception intensity peak (“the invention described in claim 1 is a target detection device that detects a target by transmitting a signal wave and receiving a reflected wave of the transmitted signal wave, characterized in that it comprises detection means for detecting a target located in a first detection region when the intensity of the signal wave is equal to or greater than a first threshold [i.e. second observation information], and for detecting a target located in a second detection region that is wider than the first detection region and includes the first detection region when the intensity of the signal wave is equal to or greater than a second threshold that is less than the first threshold [i.e. first observation information]” – [0008]);
a second acquiring unit that acquires, when the reception intensity peak exceeds a second threshold as the threshold different from the first threshold, a second observation information as the positional information corresponding to the reception intensity peak (“the invention described in claim 1 is a target detection device that detects a target by transmitting a signal wave and receiving a reflected wave of the transmitted signal wave, characterized in that it comprises detection means for detecting a target located in a first detection region when the intensity of the signal wave is equal to or greater than a first threshold [i.e. second observation information], and for detecting a target located in a second detection region that is wider than the first detection region and includes the first detection region when the intensity of the signal wave is equal to or greater than a second threshold that is less than the first threshold [i.e. first observation information]” – [0008]);
a first tracking processing unit that executes the tracking process based on the first observation information (first target tracking processing means that tracks movement of the target from the first detection area based on the relationship between the peak value of the intensity of the reflected wave reflected by the target – [0010]);
a second tracking processing unit that executes the tracking process based on the second observation information (second target tracking processing means to track movement of the target from the second detection region based on the relationship between the peak value of the intensity of the reflected wave reflected by the target and the first and second thresholds – [0012])”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki to incorporate the teaching of Suzuki to utilize the peak intensity of the reflected waves and to determine information in the first and second detection region based on thresholds of the intensity of the reflected signal. By utilizing the peak intensity of the reflected signals for the tracking determination in the first and second target detection area, this is an improvement that yields predictable results when tracking moving objects from a vehicle.
In regards to Claim 2, Sakamaki in view of Suzuki discloses the claimed invention as detailed above. Sakamaki further teaches “the first tracking processing unit has anti-clutter characteristics higher than that of the second tracking processing unit (Moreover, the different filters 116 in an IMMF 114 can implement different target models for tracking a target object (e.g., 130 ), such as a nearly-constant velocity model, a nearly-constant acceleration model, a dynamic model, a random acceleration or velocity model, a linear model, a non-linear model, etc. The different target models can capture the various dynamics of the target object”– [0053]; the different filters being a linear model or a non-linear model would thus have different anti-clutter characteristics where one is higher than the other).”
Sakamaki is silent with regards to the language of “the first threshold is lower than the second threshold.”
Suzuki further teaches “the first threshold is lower than the second threshold (“the invention described in claim 1 is a target detection device that detects a target by transmitting a signal wave and receiving a reflected wave of the transmitted signal wave, characterized in that it comprises detection means for detecting a target located in a first detection region when the intensity of the signal wave is equal to or greater than a first threshold [i.e. second threshold], and for detecting a target located in a second detection region that is wider than the first detection region and includes the first detection region when the intensity of the signal wave is equal to or greater than a second threshold that is less than the first threshold [i.e. first threshold]” – [0008]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki to incorporate the further teaching of Suzuki to utilize thresholds being different. By utilizing different thresholds, this is an improvement that yields predictable results when tracking moving objects from a vehicle.
In regards to Claim 3, Sakamaki in view of Suzuki discloses the claimed invention as detailed above. Sakamaki is silent with regards to the language of “the first tracking processing unit executes the tracking process based on the first observation information that satisfies a condition in which the positional information indicates that it is in a first region; the second tracking processing unit executes the tracking process based on the second observation information that satisfies a condition in which the positional information indicates that it is in a second region; and the second region includes a nearer distance region than the first region.”
Suzuki further teaches “the first tracking processing unit executes the tracking process based on the first observation information that satisfies a condition in which the positional information indicates that it is in a first region (second target tracking processing means to track movement of the target from the second detection region based on the relationship between the peak value of the intensity of the reflected wave reflected by the target and the first and second thresholds – [0012]);
the second tracking processing unit executes the tracking process based on the second observation information that satisfies a condition in which the positional information indicates that it is in a second region (first target tracking processing means that tracks movement of the target from the first detection area based on the relationship between the peak value of the intensity of the reflected wave reflected by the target – [0010]); and
the second region includes a nearer distance region than the first region (“the first detection region is set centered on the transmission direction of the signal wave, and the second detection region is a region in which the intensity of the transmitted signal is smaller than that of the first detection region.” – [0009]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki to incorporate the further teaching of Suzuki to utilize thresholds being different. By utilizing different thresholds for positional information and tracking of objects, this is an improvement that yields predictable results when tracking moving objects from a vehicle.
As best understood, in regards to Claim 14, Sakamaki teaches “a positional information acquiring unit configured to acquire positional information from an object sensor (“the sensor devices 102 - 112 can determine location parameters associated with the target object 130 based on a reflection from the target object 130 of a radar signal transmitted by a radar” – [0051]; sensor devices are implemented, integrated, or mounted on vehicle – [0052]);
an object tracking unit that executes, based on the positional information acquired by the positional information acquiring unit, a tracking process of the object (each sensor includes an interactive multiple model filter which can include multiple filters for tracking a target object by using sensor data from the sensors and respective target models to generate respective state estimates and error covariances for the target object – [0053]); and
a state identifying unit that identifies a state of the object based on a result of the tracking process executed by the object tracking unit (“Moreover, the filters 116 in the IMMF 114 of a sensor device can process measurements or observations from the sensor device (e.g., 102 - 112 ) and use the filters 116 and respective target models to calculate a motion or location of the target object (e.g., 130 ), estimate or predict the target object's states (e.g., position, velocity, trajectory, acceleration, angle, altitude, etc.), calculate one or more tracks (e.g., estimated states and error covariances) for the target object, etc. The filters 116 can produce various results, which may depend on one or more factors such as the suitability of their respective target models for a particular type of motion of the target object (e.g., 130 )” – [0054]),
the object tracking unit comprises:
a first tracking processing unit that executes the tracking process (“Each of the sensor devices 102 - 112 can include an interactive multiple model filter (IMMF) 114 , which can include multiple filters 116 , such as Kalman filters, for tracking a target object (e.g., 130 ) by using sensor data from the sensor devices 102 - 112 and respective target models to generate respective state estimates and error covariances for the target object (e.g., 130 ). In some cases, the filters 116 in the IMMF 114 can run in parallel. Moreover, the different filters 116 in an IMMF 114 can implement different target models for tracking a target object (e.g., 130 ), such as a nearly-constant velocity model, a nearly-constant acceleration model, a dynamic model, a random acceleration or velocity model, a linear model, a non-linear model, etc. The different target models can capture the various dynamics of the target object”– [0053]; first filter in the plurality of filters would correspond to the first tracking processing unit); and
a second tracking processing unit that executes the tracking process (“Each of the sensor devices 102 - 112 can include an interactive multiple model filter (IMMF) 114 , which can include multiple filters 116 , such as Kalman filters, for tracking a target object (e.g., 130 ) by using sensor data from the sensor devices 102 - 112 and respective target models to generate respective state estimates and error covariances for the target object (e.g., 130 ). In some cases, the filters 116 in the IMMF 114 can run in parallel. Moreover, the different filters 116 in an IMMF 114 can implement different target models for tracking a target object (e.g., 130 ), such as a nearly-constant velocity model, a nearly-constant acceleration model, a dynamic model, a random acceleration or velocity model, a linear model, a non-linear model, etc. The different target models can capture the various dynamics of the target object”– [0053]; second filter in the plurality of filters would correspond to the second tracking processing unit),
either one of the first tracking processing unit or the second tracking processing unit has anti-clutter characteristics higher than that of the other one (Moreover, the different filters 116 in an IMMF 114 can implement different target models for tracking a target object (e.g., 130 ), such as a nearly-constant velocity model, a nearly-constant acceleration model, a dynamic model, a random acceleration or velocity model, a linear model, a non-linear model, etc. The different target models can capture the various dynamics of the target object”– [0053]; the different filters being a linear model or a non-linear model would thus have different anti-clutter characteristics where one is higher than the other); and
the state identifying unit identifies the state of the object based on a result of the tracking process executed by the first tracking processing unit and the second tracking processing unit (“Moreover, the filters 116 in the IMMF 114 of a sensor device can process measurements or observations from the sensor device (e.g., 102 - 112 ) and use the filters 116 and respective target models to calculate a motion or location of the target object (e.g., 130 ), estimate or predict the target object's states (e.g., position, velocity, trajectory, acceleration, angle, altitude, etc.), calculate one or more tracks (e.g., estimated states and error covariances) for the target object, etc. The filters 116 can produce various results, which may depend on one or more factors such as the suitability of their respective target models for a particular type of motion of the target object (e.g., 130 )” – [0054]).”
Sakamaki is silent with regards to the language of “a positional information acquiring unit configured to acquire positional information when an intensity of an object detection signal acquired from an object sensor exceeds a predetermined threshold; wherein the positional information acquiring unit comprises: a first acquiring unit that acquires, when the object detection signal exceeds a first threshold as the threshold, a first observation information as the positional information corresponding to the object detection signal; a second acquiring unit that acquires, when the object detection signal exceeds a second threshold as the threshold different from the first threshold, a second observation information as the positional information corresponding to object detection signal; a first tracking processing unit that executes the tracking process based on the first observation information; a second tracking processing unit that executes the tracking process based on the second observation information.”
Suzuki teaches “a positional information acquiring unit configured to acquire positional information when an intensity of an object detection signal acquired from an object sensor exceeds a predetermined threshold (target detection device that detects a target by transmitting a signal wave and receiving a reflected wave of the transmitted signal wave, where the detection means for detecting a target located in a first detection region when the intensity of the signal wave is equal to or greater than a first threshold – [0008]; target detection device is mounted on a vehicle – [0020]);
wherein the positional information acquiring unit comprises:
a first acquiring unit that acquires, when the object detection signal exceeds a first threshold as the threshold, a first observation information as the positional information corresponding to the object detection signal (“the invention described in claim 1 is a target detection device that detects a target by transmitting a signal wave and receiving a reflected wave of the transmitted signal wave, characterized in that it comprises detection means for detecting a target located in a first detection region when the intensity of the signal wave is equal to or greater than a first threshold [i.e. second observation information], and for detecting a target located in a second detection region that is wider than the first detection region and includes the first detection region when the intensity of the signal wave is equal to or greater than a second threshold that is less than the first threshold [i.e. first observation information]” – [0008]);
a second acquiring unit that acquires, when the object detection signal exceeds a second threshold as the threshold different from the first threshold, a second observation information as the positional information corresponding to object detection signal (“the invention described in claim 1 is a target detection device that detects a target by transmitting a signal wave and receiving a reflected wave of the transmitted signal wave, characterized in that it comprises detection means for detecting a target located in a first detection region when the intensity of the signal wave is equal to or greater than a first threshold [i.e. second observation information], and for detecting a target located in a second detection region that is wider than the first detection region and includes the first detection region when the intensity of the signal wave is equal to or greater than a second threshold that is less than the first threshold [i.e. first observation information]” – [0008]);
a first tracking processing unit that executes the tracking process based on the first observation information (first target tracking processing means that tracks movement of the target from the first detection area based on the relationship between the peak value of the intensity of the reflected wave reflected by the target – [0010]);
a second tracking processing unit that executes the tracking process based on the second observation information (second target tracking processing means to track movement of the target from the second detection region based on the relationship between the peak value of the intensity of the reflected wave reflected by the target and the first and second thresholds – [0012])”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki to incorporate the teaching of Suzuki to utilize the peak intensity of the reflected waves and to determine information in the first and second detection region based on thresholds of the intensity of the reflected signal. By utilizing the peak intensity of the reflected signals for the tracking determination in the first and second target detection area, this is an improvement that yields predictable results when tracking moving objects from a vehicle.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Sakamaki in view of Suzuki as applied to claim 1 above, and further in view of Wu (CN109447145A).
In regards to Claim 4, Sakamaki in view of Suzuki discloses the claimed invention as detailed above. Sakamaki in view of Suzuki is silent with regards to the language of “the first tracking processing unit and/or the second tracking processing unit executes the tracking process based on random finite set theory.”
Wu teaches “the first tracking processing unit and/or the second tracking processing unit executes the tracking process based on random finite set theory (tracking based on the theory of random finite sets – [0002]).”
It would have been obvious to one of ordinary skill in the art to modify Sakamaki in view of Suzuki to incorporate the teaching of Wu to utilize the theory of random finite sets. By tracking based on random finite set theory this is an improvement that yields predictable results to the computational efficiency and preventing target miss detection.
Allowable Subject Matter
Claims 5-13 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
In regards to Claim 5, Sakamaki in view of Suzuki and Wu discloses the claimed invention as detailed above. Sakamaki in view of Suzuki is silent with regards to the language of “a probability setting unit that sets an erroneous detection probability as a setting parameter in the random finite theory depending on the threshold.”
Wu further teaches “a probability setting unit that sets an erroneous detection probability as a setting parameter (threshold filtering is utilized with the random finite theory with the Gaussian component matrix – [0019]; probability density function of the Gaussian distribution with mean m and covariance P – [0056]).”
Sakamaki in view of Suzuki and Wu are silent with regards to the language of “a probability setting unit that sets an erroneous detection probability as a setting parameter in the random finite theory depending on the threshold.”
Claims 5-9 are dependent on claim 5.
In regards to Claim 10, Sakamaki in view of Suzuki discloses the claimed invention as detailed above. Sakamaki in view of Suzuki are silent with regards to the language of “the second tracking processing unit executes the tracking process based on a result of the tracking process executed by the first tracking processing unit.”
Claims 11-13 are dependent on Claim 10.
Examiner’s Note
The following prior art, while not detailed in the 35 U.S.C. 103 rejection of Claims above, is of interest.
Miyazaki (JP2019196994A) teaches a radar device and target detection method utilizing tracking filters.
Eimiya (JP2014115119A) teaches an object detector using electromagnetic waves and a tracking filter with thresholds.
Sugaya (JP2018115870) teaches an object detection using LIDAR with evaluation of the reflection intensity values compared to thresholds.
Tamaoki (US20230113547) teaches plural sensors including radar to perform object detection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOSSEF KORANG-BEHESHTI whose telephone number is (571)272-3291. The examiner can normally be reached Monday - Friday 10:00 am - 6:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/YOSSEF KORANG-BEHESHTI/ Examiner, Art Unit 2857
Prosecution Insights