DETAILED ACTION
This communication is a Non-Final Office Action on the Merits. Claims 15-31 as per 9 October 2024 Preliminary Amendment 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 .
Drawings
The drawings are objected as failing to comply with 37 CFR 1.84(l) and 37 CFR 1.84(p)(1) because: the lettering in Fig. 2 is pixelated and blurry rather than clean and well-defined; and the lettering in Fig. 2 is pixelated and blurry rather than legible. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure:
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 contains, according to MS Word, 154 words exceeding the maximum of 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 19, 22-23, and 27 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per Claim 19, “the second algorithm” in line 2 and 3 lacks proper antecedent basis. Clarification is required.
As per Claim 19, “the process labels” in line 3 lacks proper antecedent basis. Clarification is required.
As per Claim 22, “the intermediate result” in line 2 lacks proper antecedent basis. Clarification is required. Claim 23 depending from Claim 22 is therefore rejected.
As per Claim 27, “the anomaly result” in line 2 lacks proper antecedent basis. Clarification is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 15-16, 21, and 28-31 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Amemiya (US Pub. No. 2020/0089209).
As per Claim 15, Amemiya discloses a method (Fig. 6) for evaluating or monitoring a robotic process (as per “The manipulator 26 carries out a task” in ¶22) (Figs. 1, 4, 6; ¶21-32, 55-72), comprising:
detecting (via sensor data acquisition unit 30) with a robot controller (10, 14) at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) (Figs. 1, 4, 5, 7A-C, 8A-B; ¶21-23, 27-32, 35-42), wherein:
the at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) describes at least one parameter (as per “A sensor 24 … detects the status of tasks carried out by the robot 22” in ¶23; as per “The sensor data acquisition unit 30 acquires the sensor data” in ¶28) of the process (as per “The manipulator 26 carries out a task” in ¶22) (Figs. 1, 4; ¶21-23, 27-32),
the data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) is created by the process (as per “The manipulator 26 carries out a task” in ¶22), which process (as per “The manipulator 26 carries out a task” in ¶22) executes a process program (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks. The teaching data stream may be obtained from an external device through an input-output interface 97 illustrated in FIG. 3, or may be stored in an HDD 96 or the like in advance” in ¶22) with process commands (as per “Examples of the task carried out by the manipulator 26 include picking a component …, delivering the component …, and mounting the component …” in ¶22) (Figs. 1, 4, 5, 7A-C, 8A-B; ¶21-23, 27-32, 35-42), and
the at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) is assigned (as per “The anomaly determination unit 32 determines whether an anomaly occurs by using the sensor data received from the sensor data acquisition unit 30” in ¶29, “The cause analyzing unit 34 analyzes the cause of an anomaly based on the sensor data” in ¶30, and “When the result of the analysis by the cause analyzing unit 34 reveals that the cause of the anomaly is a cause that can be eliminated by correction, the motion correction unit 36 corrects the motion of the robot 22” in ¶31) to a part (as per “corrects the motion” in ¶31) of the process program (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks. The teaching data stream may be obtained from an external device through an input-output interface 97 illustrated in FIG. 3, or may be stored in an HDD 96 or the like in advance” in ¶22) (Figs. 1, 4, 5, 7A-C, 8A-B; ¶21-23, 27-32, 35-42); and
determining with the robot controller (10, 14) a result (as per S72 via S58 in Fig. 6) using a first algorithm (as per “Various algorithms can be sued as an anomaly detection algorithm” in ¶38; as per “The algorithm of the anomaly cause classifier may be any algorithm used for classification” in ¶53), or {at least a part of the algorithm}, based on the at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72), wherein:
the result (as per S72 via S58 in Fig. 6) describes a state (as per “an anomaly occurs by using the sensor data” in ¶29) of the process (as per “The manipulator 26 carries out a task” in ¶22) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72), and
the result (as per S72 via S58 in Fig. 6) is assignable (as per “The anomaly determination unit 32 determines whether an anomaly occurs by using the sensor data received from the sensor data acquisition unit 30” in ¶29, “The cause analyzing unit 34 analyzes the cause of an anomaly based on the sensor data” in ¶30, and “When the result of the analysis by the cause analyzing unit 34 reveals that the cause of the anomaly is a cause that can be eliminated by correction, the motion correction unit 36 corrects the motion of the robot 22” in ¶31) to the part (as per “corrects the motion” in ¶31) of the process program (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks. The teaching data stream may be obtained from an external device through an input-output interface 97 illustrated in FIG. 3, or may be stored in an HDD 96 or the like in advance” in ¶22) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72).
As per Claim 16, Amemiya further discloses wherein the part (as per “corrects the motion” in ¶31) of the process program (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks. The teaching data stream may be obtained from an external device through an input-output interface 97 illustrated in FIG. 3, or may be stored in an HDD 96 or the like in advance” in ¶22) to which the at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) or the result (as per S72 via S58 in Fig. 6) is assigned (as per “The anomaly determination unit 32 determines whether an anomaly occurs by using the sensor data received from the sensor data acquisition unit 30” in ¶29, “The cause analyzing unit 34 analyzes the cause of an anomaly based on the sensor data” in ¶30, and “When the result of the analysis by the cause analyzing unit 34 reveals that the cause of the anomaly is a cause that can be eliminated by correction, the motion correction unit 36 corrects the motion of the robot 22” in ¶31) is a process command (as per “Examples of the task carried out by the manipulator 26 include picking a component …, delivering the component …, and mounting the component …” in ¶22) or a part (as per “correction method corresponding to the cause of the anomaly” in ¶66) of the process commands (as per “Examples of the task carried out by the manipulator 26 include picking a component …, delivering the component …, and mounting the component …” in ¶22) of the process program (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks. The teaching data stream may be obtained from an external device through an input-output interface 97 illustrated in FIG. 3, or may be stored in an HDD 96 or the like in advance” in ¶22) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72).
As per Claim 21, Amemiya further discloses wherein the at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) is selected from an overall data time series (as per “the cause analyzing unit 34 sparsifies the test data” in ¶40; as per “obtained by sparsifying sensor data” in ¶52) of the process (as per “The manipulator 26 carries out a task” in ¶22) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72).
As per Claim 28, Amemiya further discloses
detecting an overall data time series (as per “When the determination in step S58 IS No, … the process returns to S52” in ¶60; as per “the cause analyzing unit 34 sparsifies the test data” in ¶40; as per “obtained by sparsifying sensor data” in ¶52) in response to a failure (as per “No” at S58) to detect the at least one data time series with an assignment (as per S58) to a process command or process commands (as per “Examples of the task carried out by the manipulator 26 include picking a component …, delivering the component …, and mounting the component …” in ¶22) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72); and
decomposing (as per “When the determination in step S58 IS No, … the process returns to S52” in ¶60; as per “the cause analyzing unit 34 sparsifies the test data” in ¶40; as per “obtained by sparsifying sensor data” in ¶52) the detected overall data time series (as per “When the determination in step S58 IS No, … the process returns to S52” in ¶60) into data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) using a decomposition algorithm (as per “When the determination in step S58 IS No, … the process returns to S52” in ¶60; as per “the cause analyzing unit 34 sparsifies the test data” in ¶40; as per “obtained by sparsifying sensor data” in ¶52), wherein the overall data time series (as per “When the determination in step S58 IS No, … the process returns to S52” in ¶60) describes at least one parameter (as per “A sensor 24 … detects the status of tasks carried out by the robot 22” in ¶23; as per “The sensor data acquisition unit 30 acquires the sensor data” in ¶28) of the process (as per “The manipulator 26 carries out a task” in ¶22) and is created (via sensors 24) by the process (as per “The manipulator 26 carries out a task” in ¶22), which process (as per “The manipulator 26 carries out a task” in ¶22) executes a process program (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks. The teaching data stream may be obtained from an external device through an input-output interface 97 illustrated in FIG. 3, or may be stored in an HDD 96 or the like in advance” in ¶22) with process commands (as per “Examples of the task carried out by the manipulator 26 include picking a component …, delivering the component …, and mounting the component …” in ¶22) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-72).
As per Claim 29, Amemiya further discloses wherein the decomposition algorithm (as per “When the determination in step S58 IS No, … the process returns to S52” in ¶60; as per “the cause analyzing unit 34 sparsifies the test data” in ¶40; as per “obtained by sparsifying sensor data” in ¶52) is a machine learning algorithm (as per Figs. 7A-C) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-72).
As per Claim 30, Amemiya discloses a system (100) for identifying error causes (as per Fig. 6) in a robotic process (as per “The manipulator 26 carries out a task” in ¶22) (Figs. 1, 4, 6; ¶21-25, 27-32, 55-72), the system (100) comprising:
means (12, 30) for detecting at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) (Figs. 1, 4, 5, 7A-C, 8A-B; ¶21-23, 27-32, 35-42), wherein:
the at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) describes at least one parameter (as per “A sensor 24 … detects the status of tasks carried out by the robot 22” in ¶23; as per “The sensor data acquisition unit 30 acquires the sensor data” in ¶28) of the process (as per “The manipulator 26 carries out a task” in ¶22) (Figs. 1, 4; ¶21-23, 27-32),
the data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) is created by the process (as per “The manipulator 26 carries out a task” in ¶22), which process (as per “The manipulator 26 carries out a task” in ¶22) executes a process program (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks. The teaching data stream may be obtained from an external device through an input-output interface 97 illustrated in FIG. 3, or may be stored in an HDD 96 or the like in advance” in ¶22) with process commands (as per “Examples of the task carried out by the manipulator 26 include picking a component …, delivering the component …, and mounting the component …” in ¶22) (Figs. 1, 4, 5, 7A-C, 8A-B; ¶21-23, 27-32, 35-42), and
the at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) is assigned (as per “The anomaly determination unit 32 determines whether an anomaly occurs by using the sensor data received from the sensor data acquisition unit 30” in ¶29, “The cause analyzing unit 34 analyzes the cause of an anomaly based on the sensor data” in ¶30, and “When the result of the analysis by the cause analyzing unit 34 reveals that the cause of the anomaly is a cause that can be eliminated by correction, the motion correction unit 36 corrects the motion of the robot 22” in ¶31) to a part (as per “corrects the motion” in ¶31) of the process program (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks. The teaching data stream may be obtained from an external device through an input-output interface 97 illustrated in FIG. 3, or may be stored in an HDD 96 or the like in advance” in ¶22) (Figs. 1, 4, 5, 7A-C, 8A-B; ¶21-23, 27-32, 35-42); and
means (10, 14) for determining a result (as per S72 via S58 in Fig. 6) using a first algorithm (as per “Various algorithms can be sued as an anomaly detection algorithm” in ¶38; as per “The algorithm of the anomaly cause classifier may be any algorithm used for classification” in ¶53), or {at least a part of the algorithm}, based on the at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72), wherein:
the result (as per S72 via S58 in Fig. 6) describes a state (as per “an anomaly occurs by using the sensor data” in ¶29) of the process (as per “The manipulator 26 carries out a task” in ¶22) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72), and
the result (as per S72 via S58 in Fig. 6) is assignable (as per “The anomaly determination unit 32 determines whether an anomaly occurs by using the sensor data received from the sensor data acquisition unit 30” in ¶29, “The cause analyzing unit 34 analyzes the cause of an anomaly based on the sensor data” in ¶30, and “When the result of the analysis by the cause analyzing unit 34 reveals that the cause of the anomaly is a cause that can be eliminated by correction, the motion correction unit 36 corrects the motion of the robot 22” in ¶31) to the part (as per “corrects the motion” in ¶31) of the process program (per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks. The teaching data stream may be obtained from an external device through an input-output interface 97 illustrated in FIG. 3, or may be stored in an HDD 96 or the like in advance” in ¶22) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72).
As per Claim 31, Amemiya further discloses a computer program product (as per “When the program is distributed, it may be sold in the form of a portable storage medium … storing the program” in ¶82) for evaluating or monitoring a robotic process (as per “The manipulator 26 carries out a task” in ¶22) (Figs. 1, 4, 6; ¶21-25, 27-32, 55-72, 81-83), the computer program product (as per “When the program is distributed, it may be sold in the form of a portable storage medium … storing the program” in ¶82) comprising program code (as per “processing details of the functions that a processing device (CPU is to have are written” in ¶81) stored in a non-transitory, computer-readable medium (as per portable storage medium … storing the program” in ¶82), the program code (as per “processing details of the functions that a processing device (CPU is to have are written” in ¶81), when executed a computer (as per “computer” in ¶81), causing the computer (as per “computer” in ¶81) to carry out the method of claim 15 (see rejection of Claim 15).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 17-20 and 24-27 are rejected under 35 U.S.C. 103 as being unpatentable over Amemiya (US Pub. No. 2020/0089209) in view of Guo (US Pub. No. 2012/0185728).
As per Claim 17, Amemiya discloses all limitations of Claim 15. Amemiya does not expressly disclose:
determining an intermediate result with the at least one part of the algorithm, or by at least one other part of the algorithm, or by a second algorithm, based on the at least one data time series;
wherein the at least one part of the algorithm or the at least one other part of the algorithm or the second algorithm receives the at least one data time series;
wherein the intermediate result is an interpretable intermediate result or an uninterpretable intermediate result; and
wherein the intermediate result describes an assignment to a predetermined process label.
Guo discloses a multi-variate system (100) that is governed by a control system (124) (Fig. 1-2; ¶82, 89-90). The control system (124) performs a fault detection/diagnosis process in which data from the multi-variate system (100) is received (302), additional data is received (304), multiple specific fault detection models (306a to 306i) are employed to detect a fault based on the received (as per 302, 304) data, and the detected faults are linked to an associated confidence level (as per 308a-i) (Fig. 3A; ¶127-144). The faults as per the multiple specific fault detection models (306a to 306i) are further processed (as per 310, 350) to evaluate the reliability of the detected faults (Figs. 3A, 9; ¶144-145, 264-305). As such, Guo discloses determining and evaluating an intermediate fault detection result (as per faults according to models in 306a to 306i). In this way, reliability of fault detection is enhanced (¶264). Like Amemiya, Guo is concerned with data analysis systems for machinery.
Therefore, from these teachings of Amemiya and Guo, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Guo to the system of Amemiya since doing so would enhance reliability of fault detection. Applying the teachings of Guo to the system of Amemiya would result in a system that operates by:
“determining an intermediate result with the at least {one part of the algorithm}, or {by at least one other part of the algorithm}, or by a second algorithm, based on the at least one data time series” in that faults detected as per Amemiya would be further processed according to models as per Guo;
“wherein the at least one part of the algorithm or {the at least one other part of the algorithm or {the second algorithm} receives the at least one data time series” in that Amemiya discloses detecting faults based on time-series data;
“wherein the intermediate result is an interpretable intermediate result or {an uninterpretable intermediate result}” in that faults detected as per Amemiya would be further processed according to models as per Guo; and
“wherein the intermediate result describes an assignment to a predetermined process label” in that faults detected as per Amemiya would be further processed in view of confidence level as per Guo.
As per Claim 18, the combination of Amemiya and Guo teaches or suggests all limitations of Claim 17. Amemiya does not expressly disclose wherein:
determining the result by the first algorithm or by the at least one part of an algorithm is further or alternatively based on the intermediate result; and
the first algorithm or the at least one part of an algorithm further or alternatively receives the intermediate result.
See rejection of Claim 17 for discussion of teachings of Guo.
Therefore, from these teachings of Amemiya and Guo, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Guo to the system of Amemiya since doing so would enhance reliability of fault detection. Applying the teachings of Guo to the system of Amemiya would result in a system that operates by:
“determining the result by the first algorithm or {by the at least one part of an algorithm} is further or {alternatively} based on the intermediate result” in that faults detected repeatedly (see ¶67) as per Amemiya would be processed as per Guo; and
“the first algorithm or {the at least one part of an algorithm} further or {alternatively} receives the intermediate result” in that faults detected repeatedly (see ¶67) as per Amemiya would be processed as per Guo.
As per Claim 19, Amemiya discloses all limitations of Claim 15. Amemiya does not expressly disclose wherein:
the second algorithm is a machine learning algorithm; and
machine learning comprises training the second algorithm on the process labels based on the at least one data time series to determine an intermediate result.
See rejection of Claim 17 for discussion of teachings of Guo. In one embodiment, the multiple specific fault detection models (306a to 306i) include machine learning processes (¶131-140).
Therefore, from these teachings of Amemiya and Guo, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Guo to the system of Amemiya since doing so would enhance reliability of fault detection. Applying the teachings of Guo to the system of Amemiya would result in a system that operates wherein:
“the second algorithm is a machine learning algorithm” in that the system of Amemiya would employ multiple specific fault detection models include machine learning processes as per Guo;
“machine learning comprises training the second algorithm on the process labels based on the at least one data time series to determine an intermediate result” in that the system of Amemiya would employ multiple specific fault detection models include machine learning processes and associated confidence levels as per Guo.
As per Claim 20, the combination of Amemiya and Guo teaches or suggests all limitations of Claim 17. Amemiya further discloses wherein:
the first algorithm (as per “Various algorithms can be sued as an anomaly detection algorithm” in ¶38; as per “The algorithm of the anomaly cause classifier may be any algorithm used for classification” in ¶53) is based on machine learning (as per Fig. 5; ¶33-57).
Amemiya does not expressly disclose wherein: machine learning comprises training the first algorithm on the process labels with the intermediate result of the at least one other part of the algorithm or of the second algorithm.
See rejection of Claim 17 for discussion of teachings of Guo. In one embodiment, the multiple specific fault detection models (306a to 306i) include machine learning processes (¶131-140).
Therefore, from these teachings of Amemiya and Guo, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Guo to the system of Amemiya since doing so would enhance reliability of fault detection. Applying the teachings of Guo to the system of Amemiya would result in a system that operates wherein “machine learning comprises training the first algorithm on the process labels with the intermediate result of the at least one other part of the algorithm or of the second algorithm” in that the system of Amemiya would detect faults repeatedly (see ¶67) and employ multiple specific fault detection models include machine learning processes and associated confidence levels as per Guo.
As per Claim 24, the combination of Amemiya and Guo teaches or suggests all limitations of Claim 17. Amemiya further discloses at least one of:
{outputting at least one of the result or the intermediate result via a user interface};
{evaluating the process based on at least one of the intermediate result or the result}; or
controlling (as per “the process returns to step S52” in ¶67) the robotic process (as per “The manipulator 26 carries out a task” in ¶22) based on at least one of {the intermediate result} or the result (as per S72 via S58 in Fig. 6) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72).
As per Claim 25, the combination of Amemiya and Guo teaches or suggests all limitations of Claim 24. Amemiya further discloses {wherein outputting comprises representing at least one of the result or the intermediate result in a flow chart of the process} (limitation further describes alternative embodiment in Claim 24 directed to “outputting”).
As per Claim 26, the combination of Amemiya, Guo, and Sugaya teaches or suggests all limitations of Claim 25. Amemiya further discloses {wherein representing at least one of the result or the intermediate result comprises graphically representing at least one of the result or the intermediate result} (limitation further describes alternative embodiment in Claim 24 directed to “outputting”).
As per Claim 27, the combination of Amemiya and Guo teaches or suggests all limitations of Claim 24. Amemiya further discloses wherein at least one of {the detection} or the determination of at least one of the result (as per S72 via S58 in Fig. 6), {the intermediate result}, or {the anomaly result} of the at least one data time series (as per “The controller 14 operates the robot 22 based on time-series teaching data stream of a series of tasks” in ¶22; as per “The sparsified sensor data is time-series data as well as the original data” in ¶42) is carried out at least one of centrally (as per “implemented by a computer” in ¶81) (Figs. 1, 4, 5, 6, 7A-C, 8A-B; ¶21-23, 27-32, 35-42, 55-72, 81-83) or {decentrally}.
Claims 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Amemiya (US Pub. No. 2020/0089209) in view of Guo (US Pub. No. 2012/0185728), further in view of Bolich (US Pub. No. 2015/0149392).
As per Claim 22, Amemiya discloses all limitations of Claim 15. Amemiya does not expressly disclose:
analyzing the intermediate result using an anomaly detection algorithm; and
determining an anomaly result;
wherein the anomaly result describes an error in the execution of the part of the process program.
See rejection of Claim 17 for discussion of teachings of Guo.
Bolich discloses system for monitoring robots (Fig. 1; ¶18-19). In operation, hardware diagnostic information and software diagnostic information is received (as per “(A)”) from each of the robots (Fig. 2A; ¶20). The software diagnostic information is evaluated to identify an error within the robotic operating system (Fig. 5A-B, 6A-B; ¶26-28) and/or a software vulnerability (Fig. 7; ¶29). An appropriate remedy is implemented in response to the identified error(s) (Fig. 5A-B, 6A-B, 7; ¶26-29). In this way, the system assists a technician in a repair (¶2-3). Like Amemiya, Bolich is concerned with data analysis systems for machinery.
Therefore, from these teachings of Amemiya, Guo, and Bolich, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Guo and Bolich to the system of Amemiya since doing so would enhance the system by: enhancing reliability of fault detection; and assisting a technician in repair. Applying the teachings of Guo and Bolich to the system of Amemiya would result in a system that operates by:
“analyzing the intermediate result using an anomaly detection algorithm” in that faults detected as per Amemiya would be further processed as per Guo;
“determining an anomaly result” in that Amemiya discloses detecting faults; and
“wherein the anomaly result describes an error in the execution of the part of the process program” in that the system of Amemiya would detect software faults as per Bolich.
As per Claim 23, the combination of Amemiya and Guo teaches or suggests all limitations of Claim 17. Amemiya does not expressly disclose wherein the error is an error in the execution of the process command or the part of the process commands of the process program.
See rejection of Claim 17 for discussion of teachings of Guo.
See rejection of Claim 22 for discussion of teachings of Bolich.
Therefore, from these teachings of Amemiya, Guo, and Bolich, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Guo and Bolich to the system of Amemiya since doing so would enhance the system by: enhancing reliability of fault detection; and assisting a technician in repair. Applying the teachings of Guo and Bolich to the system of Amemiya would result in a system that operates “wherein the error is an error in the execution of the process command or {the part of the process commands of the process program}” that the system of Amemiya would detect software faults as per Bolich.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Negishi (US Pub. No. 2015/0148956) discloses a robot control method, robot control apparatus, robot control program, and storage medium. Sugaya (US Pub. No. 2019/0221037) discloses an information processing apparatus and control method of display apparatus. Graabæk (US Pub. No. 2024/0351209) discloses a robot system for anomaly detection.
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.
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, KHOI TRAN can be reached at (571) 272-6919. 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.
/STEPHEN HOLWERDA/Primary Examiner, Art Unit 3656