Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 therefore, subject to the conditions and requirements of this title.
The USPTO “Interim Guidelines for Examination of Patent Applications for Patent Subject Matter Eligibility” (Official Gazette notice of 23 February 2010), Annex IV, reads as follows:
The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. § 101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. § 101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation "non-transitory" to the claim. Cf. Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. § 101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matters occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473(Fed. Cir. 1998).
Claim(s) 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 12 defines a “program product” embodying functional descriptive material. However, the claim does not define a non-transitory computer-readable medium or memory and is thus non-statutory for that reason (i.e., “examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In see Official Gazette Notice 1351 OG212, February 23,2010). That is, the scope of the presently claimed “computer program product ” typically covers forms of non-transitory tangible media and transitory propagating signals per se. The examiner suggests amending the claim to embody the program on a “computer readable medium” and adding the limitation ”non-transitory ” to the claim or equivalent in order to make the claim statutory. Any amendment to the claim should be commensurate with its corresponding disclosure.
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.
Claim limitation “a self-position estimating unit, an output self-position determiner, an environmental information acquiring unit ” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a linking word “ configured to” coupled with functional language respectively recited after each of the aforementioned claim limitations, without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 1-4; 6-8;11-12 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: see figure 1 and corresponding text. If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f).
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Claim(s) 1-10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kai et al (US 20230009012) in view of Kato et al ( 2020/0309963).
As to claim 1, Kai et al teaches the localization estimation apparatus for a mobile object equipped with a plurality of sensors, each of which is configured to measure, as a measurement result, surrounding environments around the mobile object, the self-localization estimation apparatus comprising: a self-position estimating unit (the self-position estimation apparatus 10 ) configured to perform a plurality of self-position estimation tasks corresponding to the respective sensors with respective estimation accuracies, each of the estimation accuracies depending on a measurement characteristic of the corresponding one of the sensors (The first estimation part 13 estimates the position of the mobile object from sensor data containing information that changes according to the position of the mobile object and the information in the environmental data storage part. As for this sensor data, sensor data obtained by a sensor 17 provided in the self-position estimation apparatus 10 or information acquired by the camera part 11 may be used, depending on the information held in the environmental data storage part, paragraph [0032]); and an output self-position determiner configured to determine (The first estimation part 13 estimates the position of the mobile object from sensor data containing information that changes according to the position of the mobile object and the information in the environmental data storage part. As for this sensor data, sensor data obtained by a sensor 17 provided in the self-position estimation apparatus 10 or information acquired by the camera part 11 may be used, depending on the information held in the environmental data storage part, paragraph [0043]). While Kai teaches the limitation above, Kai fails to teach “based on analysis of estimation results and the estimation accuracies of the respective self-position estimation tasks, a self-position of the mobile object estimated by a selected one of the self-position estimation tasks as an output self-localization position of the mobile object. “. Kato et al teaches the sensor acquires surrounding information. The map location estimation unit estimates a self-location in a local map on the basis of an output of the sensor. The GNSS reception unit receives global satellite navigation system (GNSS) positioning information using a first carrier phase distance. The absolute location estimation unit estimates a self-absolute location on the basis of the GNSS positioning information using the first carrier phase distance, GNSS positioning information using a second carrier phase distance, and a relative location, the GNSS positioning information using the second carrier phase distance being received by another mobile object (abstract). Kato teaches The output control unit 105 controls output of various kinds of information to the passenger of the own car or to an outside of the own car. For example, the output control unit 105 generates an output signal that includes at least one of visual information (such as image data) or audio information (such as sound data), supplies the output signal to the output unit 106, and thereby controls output of the visual information and the audio information from the output unit 106. Specifically, for example, the output control unit 105 combines pieces of image data captured by different imaging apparatuses included in the data acquisition unit 102, generates a bird's-eye image, a panoramic image, or the like, and supplies an output signal including the generated image to the output unit 106. In addition, for example, the output control unit 105 generates sound data including warning sound, a warning message, or the like with regard to danger such as collision, contact, or entrance into a danger zone, and supplies an output signal including the generated sound data to the output unit 106 (paragraph [0102-0103], note that the vehicle exterior information detection unit 141 supplies data indicating results of the detection processes to the self-location estimation unit 132, a map analysis unit 151, a traffic rule recognition unit 152, and a situation recognition unit 153 of the situation analysis unit 133, an emergency event avoiding unit 171 of the behavior control unit 135, paragraph [0111]). It would have been obvious to one skilled in the art before filing of the claimed invention to use Kato invention in order to perform a high-accuracy self-location estimation at high speed by using GNSS positioning including a carrier phase. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 2, Kato et al teaches the self-localization estimation apparatus according to claim 1, wherein: the output self-position determiner is configured to:
determine whether a highest estimation accuracy among the estimation accuracies of the respective self-position estimation tasks satisfies a reliability condition previously determined for the highest estimation accuracy ( the vehicle condition detection unit 143; paragraph [0110-0113]); and determine, upon determination that the highest estimation accuracy satisfies the reliability condition, a first self-position of the mobile object estimated by the self-position estimation task that has the highest estimation accuracy as the output self-localization position of the mobile object (the self-location estimation map may be a high-accuracy map using a technology such as simultaneous localization and mapping (SLAM). The self-location estimation unit 132 supplies data indicating a result of the estimation process to the map analysis unit 151, the traffic rule recognition unit 152, and the situation recognition unit 153 of the situation analysis unit 133, and the like, paragraph [0114]).
As to claim 3, Kato et al teaches the self-localization estimation apparatus according to claim 2, wherein: the estimation accuracies (self location estimation unit 132) of the respective self-position estimation tasks additionally include a second highest estimation accuracy there among; and the predetermined reliability condition includes a condition where the first self-position of the mobile object estimated by the self-position estimation task that has the highest estimation accuracy is within an estimation error range of a second self-position of the mobile object estimated by the self-position estimation task that has the second highest estimation accuracy (The self-location estimation unit 132 performs a process of estimating a location, a posture, and the like of the own car on the basis of data or signals from the respective units of the vehicle control system 100 such as the vehicle exterior information detection unit 141 and the situation recognition unit 153 of the situation analysis unit 133. In addition, as necessary, the self-location estimation unit 132 generates a local map (hereinafter, referred to as a self-location estimation map) to be used for estimating a self-location, paragraph [0114])
As to claim 4, Kato et al teaches the self-localization estimation apparatus according to claim 3, wherein: the estimation accuracies (self-location estimation unit 132) of the respective self-position estimation tasks additionally include a third and subsequent highest estimation accuracies there among; and the output self-position determiner is configured to determine, upon determination that the highest estimation accuracy does not satisfy the reliability condition, the second self-position or a further one self- position of the mobile object as the output self-localization position of the mobile object, the further one self-position of the mobile object being estimated by the self-position estimation task that has any one of the third and subsequent highest estimation accuracies(The self-location estimation unit 132 performs a process of estimating a location, a posture, and the like of the own car on the basis of data or signals from the respective units of the vehicle control system 100 such as the vehicle exterior information detection unit 141 and the situation recognition unit 153 of the situation analysis unit 133. In addition, as necessary, the self-location estimation unit 132 generates a local map (hereinafter, referred to as a self-location estimation map) to be used for estimating a self-location, paragraph [0114]).
As to claim 5, Kai et al teaches the self-localization estimation apparatus according to claim 1, further comprising: an environmental information acquiring unit (60) configured to acquire, as surrounding-environment information, information related to surrounding environments around the mobile object (geomagnetic data mapping information that associates geomagnetic data measured in advance with the position information thereof may be used as the environmental data. In this case, the first estimation part 13/103 performs position estimation on the basis of geomagnetic data obtained by a geomagnetic sensor mounted in the mobile object. Then, the second estimation part 14/104 performs position estimation so as to complement the first estimation part 13/103, paragraph [0084]), wherein: the self-position estimating unit comprises an estimation accuracy determiner configured to determine, for each of the self-position estimation tasks, the corresponding one of the estimation accuracies, the estimation accuracy of each of the self-position estimation tasks being previously determined based on at least one of the measurement result and previous measurement results of the corresponding one of the sensor ; the estimation accuracy determiner is configured to adjust, for each of the self-position estimation tasks, a value of the corresponding one of the estimation accuracies based on the surrounding-environment information; and the output self-position determiner is configured to determine, based on the analysis of the estimation results and the adjusted values of the estimation accuracies of the respective self-position estimation tasks, the self-position of the mobile object estimated by the selected one of the self-position estimation tasks as the output self-localization position of the mobile object ( the procedures described in the first to the fourth example embodiments above can be implemented by a program causing a computer (9000 in FIG. 8) that functions as the self-position estimation apparatus to realize the functions as the self-position estimation apparatus. FIG. 8 illustrates, as an example, such a computer configured to comprise a CPU (Central Processing Unit) 9010, a communication interface 9020, a memory 9030, and an auxiliary storage device 9040. In other words, the CPU 9010 in FIG. 8 executes a position estimation program and a self-position calculation program to perform updating each computation parameter held by the auxiliary storage device 9040, paragraph [0088-0096]).
As to claim 6, Kai et al teaches the self-localization estimation apparatus according to claim 5, wherein: the surrounding-environment information includes at least one of: weather information on a weather of a place in which the mobile object is traveling; and time-zone information on a time zone in which the mobile object is traveling(The continuity determination part 107 determines whether or not the position calculated by the self-position calculation part 106 is continuous (i.e., whether or not spatial discontinuity occurs between the positions at consecutive points in time). This continuity determination can be performed by whether or not a position at a time of t and a position at a time of t−1 fall within a predetermined range, paragraph [0044]).
As to claim 7, Kai et al teaches the self-localization estimation apparatus according to claim 1, wherein: the output self-position determiner is configured to: determine whether each of the self-position estimation tasks is successful in a corresponding self-position of the mobile object in accordance with the measurement results of the respective self- position estimation tasks; and determine, upon selected self-position estimation tasks in the self-position estimation tasks being determined to be successful in the corresponding self-positions of the mobile object, one of the self- positions of the mobile object estimated by the respective selected successful self-position estimation tasks (The self-position calculation part 106, which has received the notification of the decrease value Δw.sub.i, recalculates a position x.sub.i′ of the automated guided vehicle (AGV) using a weight w.sub.i−Δw.sub.i. The recalculated position x.sub.i′ is supplied to the continuity determination part 107 and it is again verified whether or not that there is the continuity of the trajectory of the automated guided vehicle (AGV), paragraph [044][0061]).
As to claim 8, Kai et al teaches the self-localization estimation apparatus according to claim 1, wherein: a relationship between various features existing around the mobile object and respectively corresponding positional information items is stored in the mobile object or the self-localization estimation apparatus as map information (geomagnetic data mapping information that associates geomagnetic data measured in advance with the position information thereof may be used as the environmental data. In this case, the first estimation part 13/103 performs position estimation on the basis of geomagnetic data obtained by a geomagnetic sensor mounted in the mobile object. Then, the second estimation part 14/104 performs position estimation so as to complement the first estimation part 13/103, paragraph [0084]) ; and each of the self-position estimation tasks is configured to refer to the map information using specific features included in the measurement result of the corresponding one of the sensors to accordingly perform estimation of a corresponding self-position of the mobile object (The weighting determination part of the self-position estimation apparatus described above may be configured to calculate a weight w to the position estimation result by the first estimation part on the basis of the similarity between the information in the environmental data storage part and the sensor data and set a weight to the position estimation result by the second estimation part to (1−w), paragraph [0091-0093]).
As to claim 9, Kato et al teaches the self-localization estimation apparatus according to claim 8, further comprising: a satellite-system position estimator configured to receive positioning signals from global positioning satellites to accordingly estimate a current position of the mobile object he GNSS reception unit receives global navigation satellite system (GNSS) positioning information using a first carrier phase distance, abstract), wherein: each of the self-position estimation tasks is configured to: perform a first estimation of referring to a first limited range of the map information using first specific features included in a first measurement result as the measurement result of the corresponding one of the sensors(The GNSS reception unit is capable of performing solitary positioning (general GNSS positioning) in addition to the GNSS positioning using a carrier phase distance and capable of estimating a rough location of the mobile object from a result of the positioning. The initial absolute location estimation unit is capable of narrowing potential locations by the GNSS positioning using a carrier phase distance on the basis of the rough location of the mobile object, paragraph [0030]); and estimate, based on the first estimation, the corresponding one of the self-positions of the mobile object as a first self-position of the mobile object, so that the output self-position determiner determines a first final self-localization position of the mobile object as the output self- localization position of the mobile object, the first limited range of the map information for each of the self-position estimation tasks enclosing the current position of the mobile object estimated by the satellite- system position estimator(the server includes a map location estimation unit that estimates a location of the first mobile object in a local map on the basis of an output of the first sensor and estimates a location of the second mobile object in the local map on the basis of an output of the second sensor, a relative location estimation unit that estimates, from the location of the first mobile object in the local map and the location of the second mobile object in the local map, a relative location of the first mobile object with respect to the second mobile object, and an absolute location estimation unit that estimates an absolute location of the first mobile object on the basis of the GNSS positioning information using the first carrier phase distance, the GNSS positioning information using the second carrier phase distance, and the relative location, paragraph [0043]).
As to claim 10, Kato et al teaches the self-localization estimation apparatus according to claim 9, wherein: each of the self-position estimation tasks is configured to: perform a second estimation of referring to a second limited range of the map information using second specific features included in, as the measurement result, a second measurement result as the measurement result of the corresponding one of the sensors to accordingly estimate, as a second self-position of the mobile object, the corresponding one of the self-positions of the mobile object, the second limited range of the map information for each of the self-position estimation tasks enclosing the first final self-localization position of the mobile object(The second map location estimation unit 241 extracts feature points in the second captured image captured by the second image sensor 221 as in the first map location estimation unit 231 and matches the extracted feature points with the feature points included in the local map 236. Moreover, the second map location estimation unit 241 can estimate a second map location from the three-dimensional coordinates of the landmark points M and the change in feature points in association with the movement of the second mobile object 220, paragraph [0147]).
As to claim 11, Kato et al teaches the self-localization estimation apparatus according to claim 1, wherein: the sensors include at least one of: at least one camera configured to capture as the measurement result of the at least one camera, one or more images of a predetermined region located to surround the mobile object (the data acquisition unit 102 includes an imaging apparatus such as a time-of-flight (ToF) camera, a stereo camera, a monocular camera, an infrared camera, or another camera. In addition, for example, the data acquisition unit 102 includes an environment sensor for detecting weather, a meteorological phenomenon, or the like, and a surrounding information detection sensor for detecting objects around the own car. For example, the environment sensor includes a raindrop sensor, a fog sensor, a sunshine sensor, a snow sensor, or the like. The surrounding information detection sensor includes an ultrasonic sensor, a radar, a LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) sensor, a sonar, or the like., paragraph[0095]; and at least one probe sensor configured to: emit probe waves; receive reflections of the emitted probe waves from at least one object located around the mobile object; and analyze the received reflections to accordingly calculate information on the at least one object as the measurement result of the corresponding at least one probe sensor.
The limitation of claim 12 has been addressed above.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Natsuki et al (US 20230009012) in view of Tako et al ( 2020/0309963 ) and further in view of Bruemmer et al. (Hereinafter Bruemmer) (WO 2017139432 A1).
As to claim 11, Kato and Kai teaches the limitation above but fails to teach” at least one probe sensor configured to: emit probe waves; receive reflections of the emitted probe waves from at least one object located around the mobile object; and analyze the received reflections to accordingly calculate information on the at least one object as the measurement result of the corresponding at least one probe sensor.” However, Bruemmer teaches elements of emit probe waves(See at least paragraph 51; As the vehicle travels down the road 205 as shown in Figure 2B, the UWB Radar 202transmits and thereafter receives reflected energy from sequential radar scans. Each scan receives reflected energy, providing range and other information. Sequential scans as shown in Figure 2B provide a presentation over time of features in the roadway environment.); receive reflections of the emitted probe waves from at least one object located around the mobile object(See at least paragraph 75; The invention includes the ability to real-time reconfigure a plurality of UWB Radar elements based on the perceived environment. The rate of change of the environment, or optical flow, may also drive these e configurations. An observation of the rate of change in a point cloud may be used to change the frequency of UWB Radar update rates of individual Radars or the overall configuration itself. Similarly, angular resolution of the Radars can be modified by changing aperture width in response to pose (angular) error.); and analyze the received reflections to accordingly calculate information on the at least one object as the measurement result of the corresponding at least one probe sensor(paragraph 52; By filtering data as a function of time this noise is removed, leaving only features that are solid and
continuous remaining. In other embodiments, transitory objects identified by the UWB Radar -
such as moving vehicles, animals and the like - can be removed from consideration or classified
as a distinct category of object to be evaluated. An edge tracking algorithm identifies those
reflections that have consistently remained in the same location for a predetermined period of
time as "coherent features".) It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the reflection of the probe wave as taught by
Bruemmer in the system of the combination of Kai and Kato, since the claimed invention is
merely a combination of old elements, and in the combination each element merely would have
performed the same function as it did separately, and one of ordinary skill in the art would have
recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicants’
disclosure.
Arnold et al (US 2010/0141479) discloses detecting targets in roadway intersection.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANCY BITAR whose telephone number is (571)270-1041. The examiner can normally be reached Mon-Friday from 8:00 am to 5:00 p.m..
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
