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 .
Response to Amendment
Applicant' s amendment and response filed 1/22/2026 has been entered and made record. This application contains 12 pending claims.
Claims 1-10 have been amended.
Response to Arguments
Claims 1-10 have been amended, and the amended claims limitations overcome the claims limitation interpretation under 35 U.S.A. 112(f). Therefore, claims 1-10 are no longer interpreted under 35 U.S.A. 112(f).
Applicant’s arguments filed 1/22/2026 regarding claims rejections under 35 U.S.C. 101 in claim 1-12 have been fully considered but they are not persuasive.
The applicant argues on pages 14-15 of the remark filed on 1/22/2026 that “… Thirdly, the Office's assertion of the claims being directed to "mathematical concept/mental process" characterization overlooks that the claimed steps are not abstract mathematics performed in isolation, but rather specific signal processing operations tied to physical sensor arrangements and motor hardware. For example, the "dividing" and "specifying" steps operate on actual analog/digital signals from physical magnetic sensors arranged in specific configurations relative to the rotor and magnet. Further, the claimed features cannot practically be performed mentally because they require processing signals from multiple magnetic sensors (N1, N2, N3, or N4 sensors) with specific phase differences, detecting zero-cross points and intersection points in real-time sensor data. Moreover, the mathematical operations are not ends in themselves but are specifically designed to correlate sensor signals with physical pole pair positions of a motor rotor. …”.
The Examiner respectfully disagrees applicant’s argument. The steps of “estimating the rotational position of the rotor on a basis of the learning data”, “dividing a learning period into a plurality of quadrants having digital values of N1 bits different from one another on a basis of the N1 digital signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”, and “dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”, “specifying a current quadrant from among the plurality of quadrants on a basis of the N1 digital signals acquired in the seventh step” and “specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired” are mathematical concepts, therefore, they are considered to be an abstract idea. The step of “determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea. A human mind can observe and evaluate of the acquired signal using a mathematical concept, and make determination, judgment and have opinion about a pole pair number corresponding to a segment number associated with the current section included in the current quadrant based on the learning data. Thus, the claims are directed to an abstract idea.
The applicant argues on pages 13-14 of the remark filed that “…Further, the Office asserted that the additional elements are not sufficient to integrate the limitation into a practical application because they are merely necessary data gathering, insignificant extra-solution activities, or generic computer elements. ... Applicant respectfully disagrees. Firstly, the claims are directed to a specific technical improvement. In the conventional position estimation method described in the specification ... Therefore, it has been difficult to apply the position estimation method to applications in which preliminary operation for rotating the rotor for estimation of the initial position is not allowed driving motors such as, for example, robots, unmanned transport vehicles, and sewing devices. (Specification, para. [0004]). The claimed method/device solves this problem. … Since the motor 200 does not need a preliminary rotation operation for origin point adjustment, the driving time and power consumption required for the preliminary rotation operation can be reduced. (Specification, para. [0065]). One of ordinary skill in the art would consider this as a concrete technical improvement in motor control technology, not an abstract idea.”
The Examiner respectfully disagrees applicant’s argument. Practical application can be demonstrated by additional elements that are sufficient to integrate the judicial exception into a practical application. The additional elements “acquiring learning data necessary for estimation of the rotational position”; “the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor”, “acquiring N1 (N1 is an integer of 3 or more) digital signals having levels inverted every time the magnet rotates by 180° and having a first phase difference from one another, by using N1 first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”, “acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor, on a basis of the N2 analog signals obtained in the learning period”, “acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position”, and “the estimating the rotational position of the rotor” are not sufficient to integrate the abstract idea into a practical application. The additional elements “acquiring the N1 digital signals by using the N1 first magnetic sensors”, and “acquiring the N2 analog signals by using the N2 second magnetic sensors” are considered necessary data gathering and thus, not sufficient to integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), necessary data gathering (i.e., acquiring signal) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
Reducing the driving time and power consumption required for the preliminary rotation operation is routine in monitoring and estimating a rotational position of a motor. The alleged improvement of the motor not needing a preliminary rotation operation for origin point adjustment relates to improvement to the abstract idea itself. Therefore, the current claims do not recite additional elements that are indicative of integration of an abstract idea into a practical application.
The applicant argues on page 14 of the remark filed that “… Secondly, the Office's own prior art analysis undermines the § 101 rejection. Significantly, the Office found that the prior arts of record, alone or in combination, do not fairly teach or suggest " acquiring N1 (N1 is an integer of 3 or more) digital signals having levels inverted every time the magnet rotates by 1800 and having a first phase difference from one another, by using N1 first magnetic sensors opposed to the magnet" and the other claimed limitations. This finding undermines the Office's Step 2B analysis. If the claimed elements were merely "well-understood, routine, conventional" activities, the prior art would have disclosed them. The Office's acknowledgment that the claims are novel over the prior art confirms that the claims recite an unconventional technical approach rather than routine or conventional activity.”
The Examiner respectfully disagrees applicant’s argument. Significantly more can be demonstrated by additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application. However, the claims do not recite them. The limitations of “acquiring learning data necessary for estimation of the rotational position”; “the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor”, “acquiring N1 (N1 is an integer of 3 or more) digital signals having levels inverted every time the magnet rotates by 180° and having a first phase difference from one another, by using N1 first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”, “acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor, on a basis of the N2 analog signals obtained in the learning period”, “acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position”, and “the estimating the rotational position of the rotor” are well-understood and conventional. Therefore, the claims 1-3, and 6-8 do not contain additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application.
Dependent claims 4-5 and 9-12 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application. Therefore, claims 4-5 and 9-12 are also patent ineligible.
Hence, the Examiner submits that the rejections of Claims 1-12 are proper.
Claim Objections
Claim 1 recites the limitation:
“the seventh step” in line 34,
“the eighth step” in line 36.
Claim 2 recites the limitation:
“the seventh step” in line 33.
Claim 3 recites the limitation:
“the seventh step” in line 36,
“the eighth step” in line 38.
There are insufficient antecedent basis for this limitation in the claim. The claims use a definite article “the”, however, the claim 1 does not recite the claim limitations of “a seventh step”, and “an eighth step”; the claim 2 does not recite the claim limitation of “a seventh step”; and the claim 3 does not recite the claim limitations of “a seventh step”, and “an eighth step”.
Appropriate correction is required.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As to claim 1, the claim recites “A position estimation method for estimating a rotational position of a motor including a rotor having P (P is an integer of 2 or more) magnetic pole pairs, the position estimation method comprising:
acquiring learning data necessary for estimation of the rotational position; and
estimating the rotational position of the rotor on a basis of the learning data, wherein
the acquiring learning data includes
rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor,
acquiring N1 (N1 is an integer of 3 or more) digital signals having levels inverted every time the magnet rotates by 180° and having a first phase difference from one another, by using N1 first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet,
acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor,
dividing a learning period into a plurality of quadrants having digital values of N1 bits different from one another on a basis of the N1 digital signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle,
on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections, and
acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position, and
the estimating the rotational position of the rotor includes
acquiring the N1 digital signals by using the N1 first magnetic sensors,
acquiring the N2 analog signals by using the N2 second magnetic sensors,
specifying a current quadrant from among the plurality of quadrants on a basis of the N1 digital signals acquired in the seventh step,
specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired in the eighth step, and
determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.”
As to claim 2, the claim recites “A position estimation method for estimating a rotational position of a motor including a rotor having P (P is an integer of 2 or more) magnetic pole pairs, the position estimation method comprising:
acquiring learning data necessary for estimation of the rotational position; and
estimating the rotational position of the rotor on a basis of the learning data, wherein
the acquiring learning data includes
rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor,
acquiring N3 (N3 is an integer of 2 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a third phase difference from each other, by using N3 third magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet,
acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor,
calculating time series data of a mechanical angle in a learning period on a basis of the N3 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle,
on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections, and
acquiring, as the learning data, data indicating a correspondence relationship between the time series data of the mechanical angle and the pole pair number, and the estimating the rotational position of the rotor includes
acquiring the N3 analog signals by using the N3 third magnetic sensors,
calculating a current value of the mechanical angle on a basis of the N3 analog signals acquired, and
determining, as an initial position of the rotor, a pole pair number corresponding to the current value of the mechanical angle on a basis of the learning data.”
As to claim 3, the claim recites “A position estimation method for estimating a rotational position of a motor including a rotor having P (P is an integer of 2 or more) magnetic pole pairs, the position estimation method comprising:
acquiring learning data necessary for estimation of the rotational position; and
estimating the rotational position of the rotor on a basis of the learning data, wherein
the acquiring learning data includes
rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor,
acquiring N4 (N4 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a fourth phase difference from one another, by using N4 fourth magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet,
acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor,
dividing a learning period into a plurality of quadrants on a basis of the N4 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle,
on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections, and
acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position, and
the estimating the rotational position of the rotor includes
acquiring the N4 analog signals by using the N4 fourth magnetic sensors,
acquiring the N2 analog signals by using the N2 second magnetic sensors,
specifying a current quadrant from among the plurality of quadrants on a basis of the N4 analog signals acquired in the seventh step,
specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired in the eighth step, and
determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.”
As to claim 6, the claim recites “A position estimation device that estimates a rotational position of a motor including a rotor having P (P is an integer of 2 or more) magnetic pole pairs, the position estimation device comprising:
a magnet having one magnetic pole pair and sharing a rotation axis with the rotor;
N1 (N1 is an integer of 3 or more) first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet;
N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor; and
a signal processing device that processes output signals of the first magnetic sensor and the second magnetic sensor, wherein
the signal processing device includes
a processor that executes learning processing of acquiring learning data necessary for estimation of the rotational position and position estimation processing of estimating a rotational position of the rotor on a basis of the learning data, and
a memory that stores the learning data,
the processor executes, as the learning processing,
first processing of rotating the magnet together with the rotor,
second processing of acquiring N1 digital signals having levels inverted every time the magnet rotates by 1800 and having a first phase difference from one another, via the N1 first magnetic sensors,
third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors,
fourth processing of dividing a learning period into a plurality of quadrants having digital values of N1 bits different from one another on a basis of the N1 digital signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle,
fifth processing of, on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections, and
sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number representing the pole pair position, and
the processor executes, as the position estimation processing,
seventh processing of acquiring the N1 digital signals via the N1 first magnetic sensors,
eighth processing of acquiring the N2 analog signals via the N2 second magnetic sensors,
ninth processing of specifying a current quadrant from among the plurality of quadrants on a basis of the N1 digital signals acquired in the seventh processing,
tenth processing of specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired in the eighth processing, and
eleventh processing of determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.”
As to claim 7, the claim recites “A position estimation device that estimates a rotational position of a motor including a rotor having P (P is an integer of 2 or more) magnetic pole pairs, the position estimation device comprising:
a magnet having one magnetic pole pair and sharing a rotation axis with the rotor;
N1 (N1 is an integer of 3 or more) first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet;
N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor; and
a signal processing device that processes output signals of the first magnetic sensor and the second magnetic sensor, wherein
the signal processing device includes
a processor that executes learning processing of acquiring learning data necessary for estimation of the rotational position and position estimation processing of estimating a rotational position of the rotor on a basis of the learning data, and
a memory that stores the learning data,
the processor executes, as the learning processing,
first processing of rotating the magnet together with the rotor,
second processing of acquiring N1 digital signals having levels inverted every time the magnet rotates by 1800 and having a first phase difference from one another, via the N1 first magnetic sensors,
third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors,
fourth processing of dividing a learning period into a plurality of quadrants having digital values of N1 bits different from one another on a basis of the N1 digital signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle,
fifth processing of, on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections, and
sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number representing the pole pair position, and
the processor executes, as the position estimation processing,
seventh processing of acquiring the N1 digital signals via the N1 first magnetic sensors,
eighth processing of acquiring the N2 analog signals via the N2 second magnetic sensors,
ninth processing of specifying a current quadrant from among the plurality of quadrants on a basis of the N1 digital signals acquired in the seventh processing,
tenth processing of specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired in the eighth processing, and
eleventh processing of determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.
As to claim 8, the claim recites “A position estimation device that estimates a rotational position of a motor including a rotor having P (P is an integer of 2 or more) magnetic pole pairs, the position estimation device comprising:
a magnet having one magnetic pole pair and sharing a rotation axis with the rotor;
N4 (N4 is an integer of 3 or more) fourth magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet;
N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along the rotation direction of the rotor; and
a signal processing device that processes output signals of the second magnetic sensors and the fourth magnetic sensors, wherein
the signal processing device includes
a processor that executes learning processing of acquiring learning data necessary for estimation of the rotational position and position estimation processing of estimating a rotational position of the rotor on a basis of the learning data, and
a memory that stores the learning data,
the processor executes, as the learning processing,
first processing of rotating the magnet together with the rotor,
second processing of acquiring N4 analog signals having electric signals that fluctuate according to magnetic field strength and having a fourth phase difference from one another, via the N4 fourth magnetic sensors,
third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors,
fourth processing of dividing a learning period into a plurality of quadrants on a basis of the N4 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle,
fifth processing of, on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections, and
sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number representing the pole pair position, and
the processor executes, as the position estimation processing,
seventh processing of acquiring the N4 digital signals via the N4 fourth magnetic sensors,
eighth processing of acquiring the N2 analog signals via the N2 second magnetic sensors,
ninth processing of specifying a current quadrant from among the plurality of quadrants on a basis of the N4 digital signals acquired in the seventh processing,
tenth processing of specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired in the eighth processing, and
eleventh processing of determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.”
Under the Step 1 of the eligibility analysis, we determine whether the claims are directed to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category (process for claim 1-3, and apparatus for claims 6-8).
Under the Step 2A, Prong One, we consider whether the claims recite a judicial exception (abstract idea). In the above claims, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, they recite limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes (concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions).
In claim 1, the steps of “estimating the rotational position of the rotor on a basis of the learning data”,
“dividing a learning period into a plurality of quadrants having digital values of N1 bits different from one another on a basis of the N1 digital signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”, and
“dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”,
“specifying a current quadrant from among the plurality of quadrants on a basis of the N1 digital signals acquired” and
“specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired” are mathematical concepts, therefore, they are considered to be an abstract idea.
The steps of “determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
In claim 2, the steps of “estimating the rotational position of the rotor on a basis of the learning data”,
“calculating time series data of a mechanical angle in a learning period on a basis of the N3 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”, and
“calculating a current value of the mechanical angle on a basis of the N3 analog signals acquired” are mathematical concepts, therefore, they are considered to be an abstract idea.
The step of “determining, as an initial position of the rotor, a pole pair number corresponding to the current value of the mechanical angle on a basis of the learning data.” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
In claim 3, the steps of “estimating the rotational position of the rotor on a basis of the learning data”,
“dividing a learning period into a plurality of quadrants on a basis of the N4 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”,
“specifying a current quadrant from among the plurality of quadrants on a basis of the N4 analog signals acquired”, and
“specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired” are mathematical concepts, therefore, they are considered to be an abstract idea.
The step of “determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
In claim 6, the steps of “estimation of the rotational position and position estimation processing of estimating a rotational position of the rotor on a basis of the learning data”,
“dividing a learning period into a plurality of quadrants having digital values of N1 bits different from one another on a basis of the N1 digital signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”,
“specifying a current quadrant from among the plurality of quadrants on a basis of the N1 digital signals acquired in the seventh processing”, and
“specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired in the eighth processing” are mathematical concepts, therefore, they are considered to be an abstract idea.
The step of “determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
In claim 7, the steps of “estimation of the rotational position and position estimation processing of estimating a rotational position of the rotor on a basis of the learning data”,
“calculating time series data of a mechanical angle in a learning period on a basis of the N3 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”, and
“calculating a current value of the mechanical angle on a basis of the N3 analog signals acquired in the seventh processing”, are mathematical concepts, therefore, they are considered to be an abstract idea.
The step of “determining, as an initial position of the rotor, a pole pair number corresponding to the current value of the mechanical angle on a basis of the learning data stored in the memory” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
In claim 8, the steps of “estimation of the rotational position and position estimation processing of estimating a rotational position of the rotor on a basis of the learning data”,
“dividing a learning period into a plurality of quadrants on a basis of the N4 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”,
“specifying a current quadrant from among the plurality of quadrants on a basis of the N4 digital signals acquired in the seventh processing”, and
“specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired in the eighth processing” are mathematical concepts, therefore, they are considered to be an abstract idea.
The step of “determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
Next, under the Step 2A, Prong Two, we consider whether the claims that recite a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claims recite additional elements that integrate the exception into a practical application of that exception.
The claims comprise the following additional elements:
Claim 1: acquiring learning data necessary for estimation of the rotational position; the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor, acquiring N1 (N1 is an integer of 3 or more) digital signals having levels inverted every time the magnet rotates by 180° and having a first phase difference from one another, by using N1 first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet, acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor, on a basis of the N2 analog signals obtained in the learning period, acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position, and the estimating the rotational position of the rotor includes acquiring the N1 digital signals by using the N1 first magnetic sensors, acquiring the N2 analog signals by using the N2 second magnetic sensors.
The additional elements “acquiring learning data necessary for estimation of the rotational position”; “the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor”, “acquiring N1 (N1 is an integer of 3 or more) digital signals having levels inverted every time the magnet rotates by 180° and having a first phase difference from one another, by using N1 first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”, “acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor, on a basis of the N2 analog signals obtained in the learning period”, “acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position”, and “the estimating the rotational position of the rotor” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional elements “acquiring the N1 digital signals by using the N1 first magnetic sensors”, and “acquiring the N2 analog signals by using the N2 second magnetic sensors” represent necessary data gathering and do not integrate the limitation into a practical application.
Claim 2: acquiring learning data necessary for estimation of the rotational position; the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor, acquiring N3 (N3 is an integer of 2 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a third phase difference from each other, by using N3 third magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet, acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor, on a basis of the N2 analog signals obtained in the learning period, and acquiring, as the learning data, data indicating a correspondence relationship between the time series data of the mechanical angle and the pole pair number, and the estimating the rotational position of the rotor includes acquiring the N3 analog signals by using the N3 third magnetic sensors.
The additional elements “acquiring learning data necessary for estimation of the rotational position; the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor”, “acquiring N3 (N3 is an integer of 2 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a third phase difference from each other, by using N3 third magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”, “acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor, on a basis of the N2 analog signals obtained in the learning period”, and “acquiring, as the learning data, data indicating a correspondence relationship between the time series data of the mechanical angle and the pole pair number, and the estimating the rotational position of the rotor” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional element “acquiring the N3 analog signals by using the N3 third magnetic sensors” represents necessary data gathering and does not integrate the limitation into a practical application.
Claim 3: acquiring learning data necessary for estimation of the rotational position; the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor, acquiring N4 (N4 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a fourth phase difference from one another, by using N4 fourth magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet, on a basis of the N2 analog signals obtained in the learning period, acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position, and the estimating the rotational position of the rotor includes acquiring the N4 analog signals by using the N4 fourth magnetic sensors, acquiring the N2 analog signals by using the N2 second magnetic sensors, specifying a current quadrant from among the plurality of quadrants on a basis of the N4 analog signals acquired, specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired.
The additional elements “acquiring learning data necessary for estimation of the rotational position”; “the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor”, “acquiring N4 (N4 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a fourth phase difference from one another, by using N4 fourth magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet, on a basis of the N2 analog signals obtained in the learning period”, “acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position”, and “the estimating the rotational position of the rotor”; “specifying a current quadrant from among the plurality of quadrants on a basis of the N4 analog signals acquired”, “specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired.” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional elements “acquiring the N4 analog signals by using the N4 fourth magnetic sensors”, and “acquiring the N2 analog signals by using the N2 second magnetic sensors” represent necessary data gathering and do not integrate the limitations into a practical application.
Claim 6: a magnet having one magnetic pole pair and sharing a rotation axis with the rotor; N1 (N1 is an integer of 3 or more) first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet; N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor; and a signal processing device that processes output signals of the first magnetic sensor and the second magnetic sensor, wherein the signal processing device includes a processor that executes learning processing of acquiring learning data necessary, a memory that stores the learning data, the processor executes, as the learning processing, first processing of rotating the magnet together with the rotor, second processing of acquiring N1 digital signals having levels inverted every time the magnet rotates by 1800 and having a first phase difference from one another, via the N1 first magnetic sensors, third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors, fifth processing of, on a basis of the N2 analog signals obtained in the learning period, sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number representing the pole pair position, and the processor executes, as the position estimation processing, seventh processing of acquiring the N1 digital signals via the N1 first magnetic sensors, and eighth processing of acquiring the N2 analog signals via the N2 second magnetic sensors.
The additional elements “a magnet having one magnetic pole pair and sharing a rotation axis with the rotor”; “N1 (N1 is an integer of 3 or more) first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet; N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor”; “a signal processing device that processes output signals of the first magnetic sensor and the second magnetic sensor”, “wherein the signal processing device includes a processor that executes learning processing of acquiring learning data necessary”, “a memory that stores the learning data,
the processor executes, as the learning processing”, “first processing of rotating the magnet together with the rotor”, “second processing of acquiring N1 digital signals having levels inverted every time the magnet rotates by 1800 and having a first phase difference from one another, via the N1 first magnetic sensors”, “third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors”, “fifth processing of, on a basis of the N2 analog signals obtained in the learning period”, “sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number representing the pole pair position”, “the processor executes, as the position estimation processing”, “seventh processing of acquiring the N1 digital signals via the N1 first magnetic sensors”, and “eighth processing of acquiring the N2 analog signals via the N2 second magnetic sensors” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional elements “acquiring the N1 digital signals via the N1 first magnetic sensors”, and “acquiring the N2 analog signals via the N2 second magnetic sensors” represent necessary data gathering and do not integrate the limitations into a practical application.
Claim 7: a magnet having one magnetic pole pair and sharing a rotation axis with the rotor; N3 (N3 is an integer of 3 or more) third magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet; N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor; a signal processing device that processes output signals of the second magnetic sensors and the third magnetic sensors, wherein the signal processing device includes a processor that executes learning processing of acquiring learning data necessary, a memory that stores the learning data, the processor executes, as the learning processing, first processing of rotating the magnet together with the rotor, second processing of acquiring N3 analog signals having electric signals that fluctuate according to magnetic field strength and having a third phase difference from each other, via the N3 third magnetic sensors, third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors, fifth processing of, on a basis of the N2 analog signals obtained in the learning period”, “sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the time series data of the mechanical angle and the pole pair number”, and “the processor performs, as the position estimation processing”.
The additional elements “a magnet having one magnetic pole pair and sharing a rotation axis with the rotor”; “N3 (N3 is an integer of 3 or more) third magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”; “N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor”; and “a signal processing device that processes output signals of the second magnetic sensors and the third magnetic sensors”, “wherein the signal processing device includes a processor that executes learning processing of acquiring learning data necessary”, “a memory that stores the learning data”, “the processor executes, as the learning processing”, “first processing of rotating the magnet together with the rotor”, “second processing of acquiring N3 analog signals having electric signals that fluctuate according to magnetic field strength and having a third phase difference from each other, via the N3 third magnetic sensors”, “third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors”, “fifth processing of, on a basis of the N2 analog signals obtained in the learning period”, “sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the time series data of the mechanical angle and the pole pair number”, and “the processor performs, as the position estimation processing” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional elements “acquiring the N3 analog signals via the N3 third magnetic sensors” represents necessary data gathering and do not integrate the limitations into a practical application.
Claim 8: a magnet having one magnetic pole pair and sharing a rotation axis with the rotor; N4 (N4 is an integer of 3 or more) fourth magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet; N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along the rotation direction of the rotor; a signal processing device that processes output signals of the second magnetic sensors and the fourth magnetic sensors, wherein the signal processing device includes a processor that executes learning processing of acquiring learning data necessary, a memory that stores the learning data, the processor executes, as the learning processing, first processing of rotating the magnet together with the rotor, second processing of acquiring N4 analog signals having electric signals that fluctuate according to magnetic field strength and having a fourth phase difference from one another, via the N4 fourth magnetic sensors, third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors, fifth processing of, on a basis of the N2 analog signals obtained in the learning period, sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number representing the pole pair position, and the processor executes, as the position estimation processing.
The additional elements “a magnet having one magnetic pole pair and sharing a rotation axis with the rotor”; “N4 (N4 is an integer of 3 or more) fourth magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet;
N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along the rotation direction of the rotor”; and “a signal processing device that processes output signals of the second magnetic sensors and the fourth magnetic sensors”, wherein the signal processing device includes a processor that executes learning processing of acquiring learning data necessary”, “a memory that stores the learning data”, “the processor executes, as the learning processing, first processing of rotating the magnet together with the rotor”, “second processing of acquiring N4 analog signals having electric signals that fluctuate according to magnetic field strength and having a fourth phase difference from one another, via the N4 fourth magnetic sensors”, “third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors”, “fifth processing of, on a basis of the N2 analog signals obtained in the learning period”, “sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number representing the pole pair position”, and “the processor executes, as the position estimation processing” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional elements “acquiring the N4 digital signals via the N4 fourth magnetic sensors”, and “eighth processing of acquiring the N2 analog signals via the N2 second magnetic sensors” represent necessary data gathering and do not integrate the limitations into a practical application.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B.
The above claims, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis).
For example, acquiring the N1 digital signals by using the N1 first magnetic sensors, and acquiring the N2 analog signals by using the N2 second magnetic sensors
are considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
For example, rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor is disclosed by “Gopalakrishnan US 20210234447”, [0007], [0033], Claim 1, Claim 8, Claim 15; and “Strothmann US 20020152821”, [0007], [0016], [0040], [0041], Claim 1.
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 4-5, and 9-12 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not patent eligible.
Examiner’s Note
Regarding Claims 1-12, the most pertinent prior arts are “Dawn US 20210215157”, “Gopalakrishnan US 20210234447”, “Liu US 20150295525”, “Ishida US 20160109265”, “Markus DE 102016207643A1”, “Abbott US 20200274431”, “Iwamoto US 20160116304”, and “Weiland US 20200232822”.
As to claim 1, Dawn teaches acquiring learning data necessary for estimation of the rotational position (Dawn, Abstract, [0007], [0012], [0029], [0034], [0084], [0201]); and
estimating the rotational position of the rotor on a basis of the learning data (Dawn, Abstract, [0007], [0012], [0029], [0034], [0084], [0201]).
Gopalakrishnan teaches the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor (Gopalakrishnan, [0007], [0033], Claim 1, Claim 8, Claim 15).
Liu teaches the estimating the rotational position of the rotor includes
acquiring the N1 digital signals by using the N1 first magnetic sensors (Liu, [0019], [0020], [0021], [0027], [0028]),
acquiring the N2 analog signals by using the N2 second magnetic sensors (Liu, [0019], [0020], [0021], [0027], [0030], [0033]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “acquiring N1 (N1 is an integer of 3 or more) digital signals having levels inverted every time the magnet rotates by 180° and having a first phase difference from one another, by using N1 first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”,
“acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor”,
“dividing a learning period into a plurality of quadrants having digital values of N1 bits different from one another on a basis of the N1 digital signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”,
“acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position”,
“specifying a current quadrant from among the plurality of quadrants on a basis of the N1 digital signals acquired”,
“specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired”, and
“determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.” including all limitations as claimed.
As to claim 2, Dawn teaches acquiring learning data necessary for estimation of the rotational position (Dawn, Abstract, [0007], [0012], [0029], [0034], [0084], [0201]); and
estimating the rotational position of the rotor on a basis of the learning data (Dawn, Abstract, [0007], [0012], [0029], [0034], [0084], [0201]).
Gopalakrishnan teaches acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor (Gopalakrishnan, [0007], [0033], Claim 1, Claim 8, Claim 15).
Markus teaches acquiring, as the learning data, data indicating a correspondence relationship between the time series data of the mechanical angle and the pole pair number (Markus, [0012], [0016], [0017], [0020], [0021], [0025], [0039], FIG. 2).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “acquiring N3 (N3 is an integer of 2 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a third phase difference from each other, by using N3 third magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”,
“acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor”,
“calculating time series data of a mechanical angle in a learning period on a basis of the N3 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”, and
“acquiring the N3 analog signals by using the N3 third magnetic sensors”,
“calculating a current value of the mechanical angle on a basis of the N3 analog signals acquired”, and
“determining, as an initial position of the rotor, a pole pair number corresponding to the current value of the mechanical angle on a basis of the learning data.” including all limitations as claimed.
As to claim 3, Dawn teaches acquiring learning data necessary for estimation of the rotational position (Dawn, Abstract, [0007], [0012], [0029], [0034], [0084], [0201]); and
estimating the rotational position of the rotor on a basis of the learning data (Dawn, Abstract, [0007], [0012], [0029], [0034], [0084], [0201]).
Gopalakrishnan teaches the acquiring learning data includes rotating, together with the rotor, a magnet having one magnetic pole pair and sharing a rotation axis with the rotor (Gopalakrishnan, [0007], [0033], Claim 1, Claim 8, Claim 15).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “acquiring N4 (N4 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a fourth phase difference from one another, by using N4 fourth magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”,
“acquiring N2 (N2 is an integer of 3 or more) analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, by using N2 second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor”,
“dividing a learning period into a plurality of quadrants on a basis of the N4 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”, and
“acquiring, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number indicating the pole pair position”, and
“acquiring the N4 analog signals by using the N4 fourth magnetic sensors”,
“acquiring the N2 analog signals by using the N2 second magnetic sensors”,
“specifying a current quadrant from among the plurality of quadrants on a basis of the N4 analog signals acquired”,
“specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired”, and
“determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.” including all limitations as claimed.
As to claim 6, Gopalakrishnan teaches a magnet having one magnetic pole pair and sharing a rotation axis with the rotor (Gopalakrishnan, [0007], [0033], Claim 1, Claim 8, Claim 15).
Liu teaches N1 (N1 is an integer of 3 or more) first magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet (Liu, [0019], [0020], [0021], [0027], [0028]),
a signal processing device that processes output signals of the first magnetic sensor and the second magnetic sensor (Liu, [0019], [0020], [0021], [0027], [0030], [0033]).
Dawn teaches a memory that stores the learning data (Dawn, [0168], [0176]),
the processor executes, as the learning processing (Dawn, [0033], [0161], [0169]),
first processing of rotating the magnet together with the rotor (Dawn, [0138], [0181], [0190], [0194], [0218]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor”,
“wherein the signal processing device includes a processor that executes learning processing of acquiring learning data necessary for estimation of the rotational position and position estimation processing of estimating a rotational position of the rotor on a basis of the learning data”, and
“second processing of acquiring N1 digital signals having levels inverted every time the magnet rotates by 1800 and having a first phase difference from one another, via the N1 first magnetic sensors”,
“third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors”,
“fourth processing of dividing a learning period into a plurality of quadrants having digital values of N1 bits different from one another on a basis of the N1 digital signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“fifth processing of, on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”, and
“sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number representing the pole pair position”, and
“seventh processing of acquiring the N1 digital signals via the N1 first magnetic sensors”,
“eighth processing of acquiring the N2 analog signals via the N2 second magnetic sensors”,
“ninth processing of specifying a current quadrant from among the plurality of quadrants on a basis of the N1 digital signals acquired in the seventh processing”,
“tenth processing of specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired in the eighth processing”, and
“eleventh processing of determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.” including all limitations as claimed.
As to claim 7, Gopalakrishnan teaches a magnet having one magnetic pole pair and sharing a rotation axis with the rotor (Gopalakrishnan, [0007], [0033], Claim 1, Claim 8, Claim 15).
a signal processing device that processes output signals of the second magnetic sensors and the third magnetic sensors (Gopalakrishnan, [0004], [0028], [0034], [0040], Claim 1, Claim 8, Claim 15, Claim17).
Dawn teaches wherein the signal processing device includes a processor that executes learning processing of acquiring learning data necessary for estimation of the rotational position and position estimation processing of estimating a rotational position of the rotor on a basis of the learning data (Dawn, Abstract, [0003], [0013], [0029], [0169] , [0201]), and
a memory that stores the learning data (Dawn, [0168], [0176]),
the processor executes, as the learning processing (Dawn, [0033], [0161], [0169]),
first processing of rotating the magnet together with the rotor (Dawn, [0138], [0181], [0190], [0194], [0218]),
the processor performs, as the position estimation processing (Dawn, [0013], [0030], [0039], [0043], [0079]).
Markus teaches sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the time series data of the mechanical angle and the pole pair number (Markus, [0012], [0016], [0017], [0020], [0021], [0025], [0039], FIG. 2).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “N3 (N3 is an integer of 3 or more) third magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”;
“N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along a rotation direction of the rotor”;
“second processing of acquiring N3 analog signals having electric signals that fluctuate according to magnetic field strength and having a third phase difference from each other, via the N3 third magnetic sensors”,
“third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors”,
“fourth processing of calculating time series data of a mechanical angle in a learning period on a basis of the N3 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“fifth processing of, on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”,
“seventh processing of acquiring the N3 analog signals via the N3 third magnetic sensors”,
“eighth processing of calculating a current value of the mechanical angle on a basis of the N3 analog signals acquired in the seventh processing”, and
“ninth processing of determining, as an initial position of the rotor, a pole pair number corresponding to the current value of the mechanical angle on a basis of the learning data stored in the memory” including all limitations as claimed.
As to claim 8, Gopalakrishnan teaches a magnet having one magnetic pole pair and sharing a rotation axis with the rotor (Gopalakrishnan, [0007], [0033], Claim 1, Claim 8, Claim 15).
a signal processing device that processes output signals of the second magnetic sensors and the fourth magnetic sensors (Gopalakrishnan, [0004], [0028], [0034], [0040], Claim 1, Claim 8, Claim 15, Claim17).
Dawn teaches wherein the signal processing device includes a processor that executes learning processing of acquiring learning data necessary for estimation of the rotational position and position estimation processing of estimating a rotational position of the rotor on a basis of the learning data (Dawn, Abstract, [0003], [0013], [0029], [0169] , [0201]), and
a memory that stores the learning data (Dawn, [0168], [0176]),
the processor executes, as the learning processing (Dawn, [0033], [0161], [0169]),
first processing of rotating the magnet together with the rotor (Dawn, [0138], [0181], [0190], [0194], [0218]),
the processor executes, as the position estimation processing (Dawn, [0033], [0161], [0169]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “N4 (N4 is an integer of 3 or more) fourth magnetic sensors opposed to the magnet and arranged along a rotation direction of the magnet”;
“N2 (N2 is an integer of 3 or more) second magnetic sensors opposed to the rotor and arranged along the rotation direction of the rotor”;
“second processing of acquiring N4 analog signals having electric signals that fluctuate according to magnetic field strength and having a fourth phase difference from one another, via the N4 fourth magnetic sensors”,
“third processing of acquiring N2 analog signals having electric signals that fluctuate according to magnetic field strength and having a second phase difference from one another, via the N2 second magnetic sensors”,
“fourth processing of dividing a learning period into a plurality of quadrants on a basis of the N4 analog signals obtained in the learning period corresponding to one cycle in terms of a mechanical angle”,
“fifth processing of, on a basis of the N2 analog signals obtained in the learning period, dividing the learning period into P pole pair regions associated with pole pair numbers representing pole pair positions of the P magnetic pole pairs, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections”,
“sixth processing of storing, into the memory, as the learning data, data indicating a correspondence relationship between the segment number associated with the section included in each of the plurality of quadrants and the pole pair number representing the pole pair position”,
“seventh processing of acquiring the N4 digital signals via the N4 fourth magnetic sensors”,
“eighth processing of acquiring the N2 analog signals via the N2 second magnetic sensors”,
“ninth processing of specifying a current quadrant from among the plurality of quadrants on a basis of the N4 digital signals acquired in the seventh processing”,
“tenth processing of specifying a current section from among the plurality of sections on a basis of the N2 analog signals acquired in the eighth processing”, and
“eleventh processing of determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant on a basis of the learning data.” including all limitations as claimed.
Dependent claims 4-5 and 9-12 are also distinguish over the prior art for at least the same reason as claims 1 and 6.
Examiner notes, however, that claims 1-12 are rejected under 35 U.S.C. 101, and therefore, not patent eligible.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAL CE MANG whose telephone number is (571)272-0370. The examiner can normally be reached Monday to Friday- 8:30-12:00, 1:00-5:30 EST.
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 T 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.
/LAL CE MANG/Examiner, Art Unit 2857
Prosecution Insights