DETAILED ACTION
This communication is a Non-Final Office Action on the Merits. Claims 1-9 and 15-18 as originally filed are pending and have been considered as follows.
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 .
Specification
Applicant is reminded of proper language and format for an abstract (see MPEP § 608.01(b)):
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because: the Abstract includes, according to MS Word, 156 words exceeding the maximum of 150 words; and “The invention relates to” is a phrase which can be implied and should be avoided. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa (US Pub. No. 2014/0379128) in view of Haddadin (US Pub. No. 2018/0169864).
As per Claim 1, Ishikawa discloses a method (Fig. 11) for detection of ratcheting (as per S29 via S27) during operation of a robot (100) in relation to at least one joint (111 to 116) of the robot (100) on the basis of operating data (as per 10, 16) of the robot (100) by means of a ratcheting detecting process (as per Fig. 11) (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83), comprising the steps of:
providing operating data (as per 10, 16) in relation to a time behavior (as per Figs. 5-6) of a motor (1) of the robot (100) (Figs. 1-6, 10, 11; ¶30-38, 40-43, 45-58, 75-83); and
detecting ratcheting (as per S29 via S27) of a joint (111 to 116) of the robot (100) by evaluation of the operating data (as per 10, 16) as indirect or direct input data (as per 405, 421; as per S26 via S21) for a ratcheting detecting process system (201, 204, 421) yielding output data (as per 311, S28/S29) (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83) (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83),
wherein the output data (as per 311, S28/S29) of the ratcheting detecting process system (201, 204, 421) comprises an indicator (as per 311) which indicates whether ratcheting has occurred (as per S28 or S29) in relation to the joint (111 to 116) (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83).
Ishikawa does not expressly disclose wherein the ratcheting detecting process involves artificial intelligence.
Haddadin discloses a robot joint modeled as a controller, physical model, and sensor system (Fig. 3.2; ¶261). Fault modeling is performed (¶268) with modeled faults including: sensor faults (¶239-250, 269); and mechanical faults (¶224-231, 291-296). Modeled mechanical faults (¶224-231, 291-296) include gear train faults involving the ratcheting effect (¶229). In various embodiment, symptoms of modeled faults are classified using geometric spacing and probability methods, artificial neural networks, and/or fuzzy clustering (¶217). If more is known about the relationship between symptom and fault, symptoms of modeled faults are classified using a decision tree involving simple IF-THEN rules (¶217). Like Ishikawa, Haddadin is concerned with robot control systems.
Therefore, from these teachings of Ishikawa and Haddadin, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Haddadin to the system of Ishikawa since doing would enhance the system by adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault. Applying the teachings of Haddadin to the system of Ishikawa would result in a system that operates “wherein the ratcheting detecting process involves artificial intelligence” in that the ratcheting detecting process system (201, 204, 421) of Ishikawa would be adapted to operate using an artificial neural network as per Haddadin.
As per Claim 3, the combination of Ishikawa and Haddadin teaches or suggests all limitations of Claim 1. Ishikawa further discloses wherein the output data (as per 311, S28/S29) further indicate an extent of ratcheting (as per “’no ratcheting’” in step S28 or occurrence of ratcheting in step S29) that has occurred (Figs. 10-11; ¶43, 75-83).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa (US Pub. No. 2014/0379128) in view of Haddadin (US Pub. No. 2018/0169864), further in view of Inagaki (US Pub. No. 2017/0031329).
As per Claim 2, the combination of Ishikawa and Haddadin teaches or suggests all limitations of Claim 1. Ishikawa does not expressly disclose wherein the artificial intelligence uses a technique of supervised or semi-supervised learning.
See rejection of Claim 1 for discussion of teachings of Haddadin.
Inagaki discloses a robot (2) having: a sensor (11) that outputs state information about the robot (2) to a machine learning device (5) that operates to discover a fault of the robot (2); and a robot controller (31) that includes a fault determination unit (31) for determining a fault of the robot (2) (Fig. 1; 37-41). The machine learning unit (5) includes: a determination data obtaining unit (51) that obtains determination data from the fault determination unit (31) (Fig. 1; ¶39, 45); and a state observation unit (52) that observes a state variable as an input value for machine learning based on state information from the sensor (11) (Fig. 1; ¶53). The machine learning device (5) further includes a learning unit (53) that learns fault conditions in accordance with a training data set generated based on a combination of the state variable output from the state observation unit (52) and the determination data output from the determination data obtaining unit (51) (Fig. 2; ¶49). The learning unit (53) learns fault conditions in accordance with a neural network model featuring supervised learning (Fig. 3; ¶52). In this way, accuracy of fault prediction is enhanced (¶8, 73). Like Ishikawa, Inagaki is concerned with robot control systems.
Therefore, from these teachings of Ishikawa, Haddadin, and Inagaki, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Haddadin and Inagaki to the system of Ishikawa since doing so would enhance the system by: adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault; and improving accuracy of fault prediction. Applying the teachings of Haddadin and Inagaki to the system of Ishikawa would result in a system that operates: “wherein the artificial intelligence uses a technique of supervised or semi-supervised learning” in that the ratcheting detecting process system (201, 204, 421) of Ishikawa would be adapted to detect faults through supervised learning as per Inagaki and operate using an artificial neural network as per Haddadin.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa (US Pub. No. 2014/0379128) in view of Haddadin (US Pub. No. 2018/0169864), further in view of Johnson (US Pub. No. 2010/0101346).
As per Claim 4, the combination of Ishikawa and Haddadin teaches or suggests all limitations of Claim 3. Ishikawa does not expressly disclose a step of calculating at least one of a corrected trajectory and a corrected control instruction based on the extent of ratcheting indicated by the output data.
See rejection of Claim 1 for discussion of teachings of Haddadin.
Johnson discloses a robotic arm that includes joints (10, 11, 12, 13) and a gripper (16) for picking up and manipulating a specified object (Fig. 2; ¶17-18). A controller (40) reads both motor and rotational states of the robotic arm and performs operations including reading a joint angle sensor (90), determining whether the joint is at a desired joint angle (91), and determining whether the joint is on a proper trajectory (95) (Fig. 7; ¶27-28). If the joint is not at the desired angle (No at 91) or if the joint is not on the proper trajectory (No at 95), a drive motor correction is applied (92) (Fig. 7; ¶28). In this way, the system compensates for detected joint slip (¶15). Like Ishikawa, Johnson is concerned with robot control systems.
Therefore, from these teachings of Ishikawa, Haddadin, and Johnson, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Haddadin and Johnson to the system of Ishikawa since doing would enhance the system by: adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault; and compensating for detected joint slip. Applying the teachings of Haddadin and Johnson to the system of Ishikawa would result in a system that operates with “a step of calculating at least one of a corrected trajectory and a corrected control instruction based on the extent of ratcheting indicated by the output data” in that the ratcheting detecting process system (201, 204, 421) of Ishikawa would be adapted to compensate for joint slip as per Johnson.
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa (US Pub. No. 2014/0379128) in view of Nishimura (US Pub. No. 2021/0053219), further in view of Haddadin (US Pub. No. 2018/0169864).
As per Claim 5, Ishikawa discloses a robot (100) (Fig. 1; ¶31), comprising:
a robotic device (101, 102, 103) having a joint (111 to 116) operable to be moved by a motor (1) (Figs. 1-2; ¶31-35); and
a motor control unit (201, 313) for controlling the motor (1) (Figs. 1-3; ¶31, 34-35, 40-44),
wherein the robot (100) is further configured to use a ratcheting detecting process system (201, 204, 421) to detect ratcheting (as per S29 via S27) of the joint (111 to 116) by evaluating operating data (as per 10, 16) in relation to a time behavior (as per Figs. 5-6) of the motor (1) as indirect or direct input data (as per 405, 421; as per S26 via S21) for the ratcheting detecting process system (201, 204, 421) (Figs. 1-6, 10-11; ¶30-38, 40-43, 45-53, 75-83),
wherein the operating data (as per 10, 16) comprises current flow data (as per “input pulse signal” in ¶36, as per “output pulse signal” in ¶38) (Figs. 1-4, 10; ¶31-38, 40-43, 45-58, 75-78).
Ishikawa does not expressly disclose:
wherein the robot is collaborative; and
wherein the ratcheting detecting process involves artificial intelligence.
Nishimura discloses a robot system (1) that includes three robots (10) and a controller (100) (Fig. 1; ¶21). In one embodiment, the robot system (1) is a human-collaborative system that controls the robots (10) to cooperate with a person in which working is performed in a space where the person is not restricted from entering (¶34). In this way, a variety of human related works can be supported (¶164). Like Ishikawa, Nishimura is concerned with robot control systems.
See rejection of Claim 1 for discussion of teachings of Haddadin.
Therefore, from these teachings of Ishikawa, Nishimura, and Haddadin, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Nishimura and Haddadin to the system of Ishikawa since doing so would enhance the system by: adapting the system to support a variety of human related works; and adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault. Applying the teachings of Nishimura and Haddadin to the system of Ishikawa would result in a system that operates:
“wherein the robot is collaborative” in that the robot (100) of Ishikawa would be configured to cooperate with a person as per Nishimura; and
“wherein the ratcheting detecting process involves artificial intelligence” that the ratcheting detecting process system (201, 204, 421) of Ishikawa would be adapted to operate using an artificial neural network as per Haddadin.
As per Claim 6, the combination of Ishikawa, Nishimura, and Haddadin teaches or suggests all limitations of Claim 5. Ishikawa further discloses wherein the ratcheting detecting process system (201, 204, 421) is designed to provide output data (as per 311, S28/S29) which comprise an indicator (311) which indicates whether ratcheting has occurred (as per “’no ratcheting’” in step S28 or occurrence of ratcheting in step S29) in relation to the joint (111-116) (Figs. 10-11; ¶43, 75-83).
Ishikawa does not expressly disclose wherein the ratcheting detecting process system involves artificial intelligence.
See rejection of Claim 1 for discussion of teachings of Haddadin.
Therefore, from these teachings of Ishikawa, Nishimura, and Haddadin, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Nishimura and Haddadin to the system of Ishikawa since doing so would enhance the system by: adapting the system to support a variety of human related works; and adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault. Applying the teachings of Nishimura and Haddadin to the system of Ishikawa would result in a system that operates “wherein the ratcheting detecting process involves artificial intelligence” in that the ratcheting detecting process system (201, 204, 421) of Ishikawa would be adapted to operate using an artificial neural network as per Haddadin.
As per Claim 7, the combination of Ishikawa, Nishimura, and Haddadin teaches or suggests all limitations of Claim 6. Ishikawa further discloses wherein the output data (as per 311, S28/S29) further indicate an extent of ratcheting (as per S29) that has occurred by indicating at least one of {a number of teeth that were skipped in a gear mechanism during ratcheting} and an angle skipped (as per Δθ ≥ θa) by the ratcheting (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa (US Pub. No. 2014/0379128) in view of Nishimura (US Pub. No. 2021/0053219), further in view of Haddadin (US Pub. No. 2018/0169864), further in view of Johnson (US Pub. No. 2010/0101346).
As per Claim 8, the combination of Ishikawa, Nishimura, and Haddadin teaches or suggests all limitations of Claim 7. Ishikawa further discloses wherein the number of teeth informs detection ratcheting (¶77, 80-81). Ishikawa does not expressly disclose wherein the collaborative robot is further configured to calculate at least one of a corrected trajectory and a corrected control instruction based on at least one of a number of teeth skipped during ratcheting and the angle skipped by the ratcheting indicated via the output data.
See rejection of Claim 5 for discussion of teachings of Nishimura.
See rejection of Claim 1 for discussion of teachings of Haddadin.
See rejection of Claim 4 for discussion of teachings of Johnson.
Therefore, from these teachings of Ishikawa, Nishimura, Haddadin, and Johnson, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Nishimura, Haddadin, and Johnson to the system of Ishikawa since doing would enhance the system by: adapting the system to support a variety of human related works; adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault; and compensating for detected joint slip. Applying the teachings of Nishimura, Haddadin, and Johnson to the system of Ishikawa would result in a system that operates “wherein the collaborative robot is further configured to calculate at least one of a corrected trajectory and a corrected control instruction based on at least one of a number of teeth skipped during ratcheting and the angle skipped by the ratcheting indicated via the output data” in that in that the robot (100) of Ishikawa would be configured to cooperate with a person as per Nishimura and in that the ratcheting detecting process system (201, 204, 421) of Ishikawa that is informed by a number of teeth would be adapted to compensate for joint slip as per Johnson.
As per Claim 9, the combination of Ishikawa, Nishimura, and Haddadin teaches or suggests all limitations of Claim 5. Ishikawa further discloses a central control unit (201) in communication with the motor control unit (201, 313) via bus (216) (Fig. 3; ¶40-44), wherein the motor control unit (201, 313) is configured to determine whether ratcheting has occurred (as per S27) in relation to the joint (111 to 116) and a number of teeth (¶77, 80-81) which were skipped in a gear mechanism (11) during ratcheting (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83).
Ishikawa does not expressly disclose wherein the central control unit is configured to calculate at least one of a corrected trajectory and a corrected control instruction.
See rejection of Claim 5 for discussion of teachings of Nishimura.
See rejection of Claim 1 for discussion of teachings of Haddadin.
See rejection of Claim 4 for discussion of teachings of Johnson.
Therefore, from these teachings of Ishikawa, Nishimura, Haddadin, and Johnson, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Nishimura, Haddadin, and Johnson to the system of Ishikawa since doing would enhance the system by: adapting the system to support a variety of human related works; adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault; and compensating for detected joint slip. Applying the teachings of Nishimura, Haddadin, and Johnson to the system of Ishikawa would result in a system that operates “wherein the central control unit is configured to calculate at least one of a corrected trajectory and a corrected control instruction” in that the ratcheting detecting process system (201, 204, 421) of Ishikawa would be adapted to compensate for joint slip as per Johnson.
Claims 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa (US Pub. No. 2014/0379128) in view of Inagaki (US Pub. No. 2017/0031329), further in view of Haddadin (US Pub. No. 2018/0169864), further in view of Nishimura (US Pub. No. 2021/0053219).
As per Claim 15, Ishikawa discloses a method (Fig. 11) for operating a ratcheting detecting process system (201, 204, 421) to determine at least one of (1) the presence (as per S29 via S27) or absence (as per S28 via S27) of ratcheting of a gear mechanism (11) of a robot (100) having a motor (1) (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83) and {(2) a number of teeth that were skipped in the gear mechanism during ratcheting}, comprising the steps of:
receiving, at the ratcheting detecting process system (201, 204, 421), input data (as per 403, 405) comprising data based on operating data (as per 10, 16) relating to a time behavior (as per Figs. 5-6) of the motor (1) of the robot (100) (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83); and
utilizing the received input data (as per 403, 405) to facilitate the ratcheting detecting process determining (as per 201, 204, 421) at least one of (1) the presence (as per S29 via S27) or absence (as per S28 via S27) of ratcheting of the gear mechanism (11) of the robot (101) having the motor (1) (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83) and {(2) the number of teeth that were skipped in the gear mechanism during ratcheting}.
Ishikawa does not expressly disclose:
wherein the operating involves training;
wherein ratcheting detecting process involves an artificial intelligence system;
wherein the robot is collaborative;
wherein the artificial intelligence system includes one or more neurons; and
wherein the ratcheting detecting process involves machine learning.
See rejection of Claim 2 for discussion of teachings of Inagaki. Inagaki further discloses wherein the learning unit (53) learns fault conditions in accordance with a neural network model featuring neurons (Fig. 3; ¶52).
See rejection of Claim 1 for discussion of teachings of Haddadin.
See rejection of Claim 5 for discussion of teachings of Nishimura.
Therefore, from these teachings of Ishikawa, Inagaki, Haddadin, and Nishimura, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Inagaki, Haddadin, and Nishimura to the system of Ishikawa since doing so would enhance the system by: improving accuracy of fault prediction; adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault; and adapting the system to support a variety of human related works. Applying the teachings of Inagaki, Haddadin, and Nishimura to the system of Ishikawa would result in a system that operates:
“wherein the operating involves training” in that the ratcheting detecting process would be adapted to detect faults as per Inagaki;
“wherein ratcheting detecting process involves an artificial intelligence system” in that the ratcheting detecting process system (201, 204, 421) of Ishikawa would be adapted to detect faults as per Inagaki and operate using an artificial neural network as per Haddadin;
“wherein the robot is collaborative”; in that the robot (100) of Ishikawa would be configured to cooperate with a person as per Nishimura;
“wherein the artificial intelligence system includes one or more neurons” in that the ratcheting detecting process system (201, 204, 421) of Ishikawa would be adapted to detect faults using neurons as per Inagaki and operate using an artificial neural network as per Haddadin; and
“wherein the ratcheting detecting process involves machine learning” in that the ratcheting detecting process would be adapted to detect faults as per Inagaki.
As per Claim 16, the combination of Ishikawa, Inagaki, Haddadin, and Nishimura teaches or suggests all limitations of Claim 15. Ishikawa further discloses wherein the operating data (as per 10, 16) is current flow data (as per “input pulse signal” in ¶36, as per “output pulse signal” in ¶38) (Figs. 1-4, 10; ¶31-38, 40-43, 45-58, 75-78).
As per Claim 17, the combination of Ishikawa, Inagaki, Haddadin, and Nishimura teaches or suggests all limitations of Claim 16. Ishikawa further discloses wherein the input data (as per 403, 405) includes a plurality of data sets (as per “pulse signals” in ¶43, 47) comprising the data based on the operating data (as per 10, 16) relating to the time behavior (as per Figs. 5-6) of the motor (1) of the robot (100) (Figs. 1-4, 10-11; ¶30-38, 40-43, 45-53, 75-83).
Ishikawa does not expressly disclose:
wherein the robot is collaborative; and
wherein the data is provided with classifying labels as to whether the data corresponds to ratcheting that has or has not occurred.
See rejection of Claim 16 for discussion of teachings of Inagaki. Inagaki further discloses wherein the neural network learns fault conditions using labels (¶53).
See rejection of Claim 1 for discussion of teachings of Haddadin.
See rejection of Claim 5 for discussion of teachings of Nishimura.
Therefore, from these teachings of Ishikawa, Inagaki, Haddadin, and Nishimura, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Inagaki, Haddadin, and Nishimura to the system of Ishikawa since doing so would enhance the system by: improving accuracy of fault prediction; adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault; and adapting the system to support a variety of human related works. Applying the teachings of Inagaki, Haddadin, and Nishimura to the system of Ishikawa would result in a system that operates:
“wherein the robot is collaborative” in that the robot (100) of Ishikawa would be configured to cooperate with a person as per Nishimura; and
“wherein the data is provided with classifying labels as to whether the data corresponds to ratcheting that has or has not occurred” that the ratcheting detecting process system (201, 204, 421) of Ishikawa would be adapted to detect faults using labels as per Inagaki and operate using an artificial neural network as per Haddadin.
As per Claim 18, the combination of Ishikawa, Inagaki, Haddadin, and Nishimura teaches or suggests all limitations of Claim 17. Ishikawa further discloses wherein the number of teeth informs detection ratcheting (¶77, 80-81).
Ishikawa does not expressly disclose wherein the classifying labels further indicate a number of teeth skipped in a ratcheting corresponding to the data.
See rejection of Claim 16 for discussion of teachings of Inagaki. Inagaki further discloses wherein the neural network learns fault conditions using labels (¶53).
See rejection of Claim 1 for discussion of teachings of Haddadin. Haddadin further discloses ratcheting is defined in accordance with displacement of teeth (¶229).
See rejection of Claim 5 for discussion of teachings of Nishimura.
Therefore, from these teachings of Ishikawa, Inagaki, Haddadin, and Nishimura, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Inagaki, Haddadin, and Nishimura to the system of Ishikawa since doing so would enhance the system by: improving accuracy of fault prediction; adapting the ratcheting detecting process system (201, 204, 421) to operate using an artificial neural network as per Haddadin in circumstances in which less is known in advance about the relationship between symptom and fault; and adapting the system to support a variety of human related works. Applying the teachings of Inagaki, Haddadin, and Nishimura to the system of Ishikawa would result in a system that operates: “wherein the classifying labels further indicate a number of teeth skipped in a ratcheting corresponding to the data” in that the ratcheting detecting process system (201, 204, 421) of Ishikawa that is informed by a number of teeth would be adapted to detect faults using labels adapted to detect counting of teeth and operate using an artificial neural network as per Haddadin.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Osaka (US Pub. No. 2014/0084840), Urata (US Pub. No. 2015/0316428), Hatanaka (US Pub. No. 2020/0198128), and Hiraide (US Pub. No. 2021/0299871) disclose robot control systems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHEN HOLWERDA whose telephone number is (571)270-5747. The examiner can normally be reached M-F 8am - 4:30pm.
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/STEPHEN HOLWERDA/Primary Examiner, Art Unit 3656
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