DETAILED ACTION
Claims 1-16 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to Japanese Patent Application No. 2022-029736, filed on 2/28/2022.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Instances in the claims such as ‘abnormality diagnosis unit’, ‘description presentation processing unit’, ‘diagnosis model storage unit’, ‘definition unit’, ‘training unit’ etc. are interpreted under 35 U.S.C. 112(f) as incorporating a processor in accordance with [0034] of the specification/PGPub.
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.
Claim(s) 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to the abstract idea (mental process) of diagnosing an abnormality by analyzing data and determining a corresponding description.
Claim 1 recites an abnormality diagnosis device, i.e. a machine, which is a statutory category of invention. The claim recites:
an abnormality diagnosis unit which diagnoses an abnormality of a diagnosis target by using data related to the diagnosis target as an input;
a description presentation processing unit… a description corresponding to the abnormality of the diagnosis target based on a result of diagnosis by the abnormality diagnosis unit that may be performed in the human mind, or by a human using a pen and paper. Thus the claim recites an abstract idea (mental processes), see MPEP 2106.04(a).
This judicial exception is not integrated into a practical application because the additional elements, i.e. an abnormality diagnosis device with an abnormality diagnosis unit and a description presentation processing unit (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C — see 112(f) interpretation above) and presenting description data on a display unit which displays a description corresponding to the abnormality of the diagnosis target presented by the description presentation processing unit on a screen (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)) do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, an abnormality diagnosis device with an abnormality diagnosis unit and a description presentation processing unit (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C — see 112(f) interpretation above) and presenting description data on a display unit which displays a description corresponding to the abnormality of the diagnosis target presented by the description presentation processing unit on a screen (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)) are not considered significantly more. Considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus the claim is not patent eligible.
Claim 2 recites a diagnosis model storage unit (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C) which stores a machine learning model in which a relationship between a feature of data related to the diagnosis target and a diagnosis result is trained as a diagnosis model, wherein the abnormality diagnosis unit diagnoses the abnormality of the diagnosis target using a feature extracted from related to the diagnosis target at a time of abnormality diagnosis and the diagnosis model stored in the diagnosis model storage unit (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C), and outputs a diagnosis result of the diagnosis to the description presentation processing unit (insignificant extra-solution elements – merely using generic computer technology, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d) e.g. receiving or transmitting data over a network). Thus this claim recites an abstract idea.
Note that machine learning and neural networks are well-understood, routine and conventional, see for example Agarwal et al. U.S. Patent Publication No. 20200137585 [0028], Redmond et al. U.S. Patent Publication No. 20210342730 [0131], or Gross et al. U.S. Patent Publication No. 20210256389 [0019-0028].
Claim 3 recites the diagnosis model stored in the diagnosis model storage unit is a neural network (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C). Thus this claim recites an abstract idea.
Claim 4 recites the diagnosis result is a factor of the abnormality (abstract data). Thus this claim recites an abstract idea.
Claim 5 recites the diagnosis result is a countermeasure to be taken against the abnormality (abstract task description). Thus this claim recites an abstract idea.
Claim 6 recites the description presentation processing unit presents a diagnosis knowledge item related to the abnormality of the diagnosis target in association with the result of diagnosis of the abnormality, and the display unit displays the result of diagnosis presented from the description presentation processing unit and a diagnosis knowledge item related to the abnormality associated with the result of diagnosis on the screen (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 7 recites a definition unit which holds a diagnosis knowledge item; a description presentation model storage unit (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C) which holds a machine learning model based on a past abnormality case as a description presentation model (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C); and a description presentation unit (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C) which presents a diagnosis knowledge item related to the abnormality of the diagnosis target in association with the result of diagnosis of the abnormality using processing information obtained by processing data related to the diagnosis target by the abnormality diagnosis unit and the description presentation model held in the description presentation model storage unit (insignificant extra-solution elements – merely using generic computer technology, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d) e.g. receiving or transmitting data over a network). Thus this claim recites an abstract idea.
Claim 8 recites the diagnosis knowledge item held in the definition unit is relationship information between a confirmation item (abstract correspondence data) and the result of diagnosis in a case where an operator manually performs diagnosis of the diagnosis target (mental process). Thus this claim recites an abstract idea.
Claim 9 recites the description presentation model stored in the description presentation model storage unit (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C) is a machine learning model obtained by training a correspondence between processing information obtained by processing a past abnormal case by the abnormality diagnosis unit (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C) and the diagnosis knowledge item held in the definition unit (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C). Thus this claim recites an abstract idea.
Claim 10 recites the description presentation processing unit further comprises a training unit (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C)), and the training unit trains a machine learning model for estimating the diagnosis knowledge item from the processing information by using processing information obtained by processing data related to the diagnosis target by the abnormality diagnosis unit and the diagnosis knowledge item held in the definition unit (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C)), and stores the trained machine learning model (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C)). Thus this claim recites an abstract idea.
Claim 11 recites the display unit displays the description corresponding to the abnormality of the diagnosis target presented by the description presentation processing unit on a screen in association with a flow of processing (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)) in a case where an operator manually performs diagnosis of the diagnosis target (mental process). Thus this claim recites an abstract idea.
Claim 12 recites an abnormality diagnosis method for diagnosing an abnormality of a diagnosis target using an abnormality diagnosis device, i.e. a process, which is a statutory category of invention. However, the process is similar to that recited in claim 1 and is rejected under the same rationale.
Claim 13 recites presenting, by the description presentation processing unit, a diagnosis knowledge item related to the abnormality of the diagnosis target in association with a diagnosis result of the abnormality (insignificant extra-solution elements – merely using generic computer technology, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d) e.g. receiving or transmitting data over a network); and displaying a diagnosis result of the abnormality presented from the description presentation processing unit and a diagnosis knowledge item related to the abnormality associated with the diagnosis result on the screen of the display unit (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 14 recites a description presentation model storage unit (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C), the abnormality diagnosis method comprising presenting, by the description presentation processing unit, a diagnosis knowledge item related to the abnormality of the diagnosis target as a description corresponding to the abnormality of the diagnosis target in association with a diagnosis result of the abnormality by using processing information obtained by processing data related to the diagnosis target by the abnormality diagnosis unit and the description presentation model held in the description presentation model storage unit (insignificant extra-solution elements – merely using generic computer technology, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d) e.g. receiving or transmitting data over a network) which holds a machine learning model based on a past abnormality case as a description presentation model (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C). Thus this claim recites an abstract idea.
Claim 15 recites displaying a description corresponding to the abnormality of the diagnosis target presented by the description presentation processing unit on the screen of the display unit in association with a flow of processing (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)) in a case where an operator manually performs diagnosis of the diagnosis target (mental process). Thus this claim recites an abstract idea.
Claim 16 recites definition unit, the abnormality diagnosis method comprising training, by the description presentation processing unit, a machine learning model for estimating the diagnosis knowledge item from the processing information by using the processing information obtained by processing data related to the diagnosis target by the abnormality diagnosis unit (applying the exception with generic computer technology using a known algorithm, see MPEP 2106.04(a)(2) III C) and the diagnosis knowledge item held in the definition unit, and storing the trained machine learning model (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C). Thus this claim recites an abstract idea.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim(s) 1-2 and 4-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tanaka U.S. Patent Publication No. 20210178615 (hereinafter Tanaka) in view of Honda et al. U.S. Patent Publication No. 20230132587 (hereinafter Honda).
Regarding claim 1, Tanaka teaches an abnormality diagnosis device [0021, Figs. 1-2 — the abnormality diagnosis device 102] comprising:
an abnormality diagnosis unit which diagnoses an abnormality of a diagnosis target by using data related to the diagnosis target as an input [0031 — the abnormality diagnosis device 102 is described below. The abnormality diagnosis device 102 includes a control unit 51 and various kinds of databases (DBs). The control unit 51 includes a communication unit 21, an abnormality level determination unit 22, an abnormality prediction unit 23, a notification content setting unit 24, and a correlation analysis unit 25; 0039, Fig. 1 — abnormality level determination unit 22 acquires the past disturbance torque applied to the motor mounted on the respective speed reducers 14 from the sensor DB 31, and calculates an abnormality level indicating a degree of abnormality of the disturbance torque acquired; 0043 — abnormality prediction unit 23 determines or predicts an abnormality caused in the respective speed reducers 14 in accordance with the abnormality level calculated for each speed reducer 14.];
a description presentation processing unit which presents a description corresponding to the abnormality of the diagnosis target based on a result of diagnosis by the abnormality diagnosis unit [0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71 ]; and
a display unit which displays a description corresponding to the abnormality of the diagnosis target presented by the description presentation processing unit [0021, Fig. 1 — display the diagnosis results on the display 62; 0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71].
But Tanaka fails to clearly specify displaying on a screen.
However, Honda teaches displaying on a screen [0037 — a display unit 24 including a display screen 241; 0042-0046 — display step S103 includes displaying, on the display screen 241, the at least one response procedure RP for each abnormal event AE in the abnormal device 2A… the plurality of response procedures RP are displayed on the display screen 241].
Tanaka and Honda are analogous art. They relate to abnormality detection and diagnosis systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to simply substitute the known displaying on a screen, as taught by Honda, for the known displaying on a generic display, as taught by Tanaka, for the predictable result of a device that displays abnormality information on a screen.
Regarding claim 2, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches a diagnosis model storage unit which stores a machine learning model in which a relationship between a feature of data related to the diagnosis target and a diagnosis result is trained as a diagnosis model [0104-0110 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34],
wherein the abnormality diagnosis unit diagnoses the abnormality of the diagnosis target using a feature extracted from related to the diagnosis target at a time of abnormality diagnosis and the diagnosis model stored in the diagnosis model storage unit, and outputs a diagnosis result of the diagnosis to the description presentation processing unit [0104-0110 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34; 0052 — notification content setting unit 24 sets the contents to be provided to the user through the indication on the display 62 of the user interface 103. The notification content setting unit 24 generates a tree image 73 indicating, with a tree structure, the abnormality diagnosis results of the speed reducers 14 determined to have an abnormality by the abnormality prediction unit 23].
Regarding claim 4, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches that the diagnosis result is a factor of the abnormality [0053, Figs. 8-10 — in the block q2, an image indicating “Abnormality level 2.1>Reference value 1.0” is created in which the numerical value of the abnormality level “2.1” calculated this time is greater than the reference value “1.0”. In addition, an image of sign K1 is created for easy recognition of the correspondence with the waveform of the abnormality level, as described below. The term “sign” as used herein encompasses a concept including characters, a predetermined mark, and an icon].
Regarding claim 5, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches that the diagnosis result is a countermeasure to be taken against the abnormality [0055, Figs. 8-10 — an image indicating the contents of the maintenance to be executed for this abnormality. The abnormality in this case is presumed to be caused in the speed reducer 14a or the speed reducer 14b, and an image indicating the execution of the maintenance, “Need to measure the density of iron powder in the grease for the speed reducers 14a and 14b”, is creased as shown in the block q5. The tree image 73 includes maintenance commands for the speed reducer 14a and for the speed reducer 14b predicted to have an abnormality, and is associated with the relation between the occurrence of the abnormality and the maintenance commands].
Regarding claim 6, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches that the description presentation processing unit presents a diagnosis knowledge item related to the abnormality of the diagnosis target in association with the result of diagnosis of the abnormality, and the display unit displays the result of diagnosis presented from the description presentation processing unit and a diagnosis knowledge item related to the abnormality associated with the result of diagnosis [0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71; 0081 — maintenance history image 72 displayed includes the image of sign K2 and the image indicating “Speed reducer 14b: requiring attention”. The same images as the sign K2 and the letters are indicated in the block q4 of the tree image 73].
Further, Honda teaches displaying on a screen [0037 — a display unit 24 including a display screen 241; 0042-0046 — display step S103 includes displaying, on the display screen 241, the at least one response procedure RP for each abnormal event AE in the abnormal device 2A… the plurality of response procedures RP are displayed on the display screen 241].
Regarding claim 7, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches that the description presentation processing unit further comprises: a definition unit which holds a diagnosis knowledge item; a description presentation model storage unit which holds a machine learning model based on a past abnormality case as a description presentation model; and a description presentation unit which presents a diagnosis knowledge item related to the abnormality of the diagnosis target in association with the result of diagnosis of the abnormality using processing information obtained by processing data related to the diagnosis target by the abnormality diagnosis unit and the description presentation model held in the description presentation model storage unit [0031-0035, 0043-0050 — control unit 51 includes a communication unit 21, an abnormality level determination unit 22, an abnormality prediction unit 23, a notification content setting unit 24, and a correlation analysis unit 25. The databases include a sensor DB 31, the maintenance history DB 32 (the maintenance history storage unit), an abnormality prediction DB 33, a correlation storage DB 34 (a correlation storage unit), and a work history DB 35.; 0061-0064, Fig. 3 — The flowchart shown in FIG. 3 illustrates a process of the correlation analysis processing executed by the correlation analysis unit 25; 0104-0110 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34; 0052 — notification content setting unit 24 sets the contents to be provided to the user through the indication on the display 62 of the user interface 103. The notification content setting unit 24 generates a tree image 73 indicating, with a tree structure, the abnormality diagnosis results of the speed reducers 14 determined to have an abnormality by the abnormality prediction unit 23].
Regarding claim 8, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches the diagnosis knowledge item held in the definition unit is relationship information between a confirmation item and the result of diagnosis in a case where an operator manually performs diagnosis of the diagnosis target [0104-0110 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34… The machine learning extracts the rules from the past abnormality data and maintenance data included in the maintenance history so as to create the learning model. A method of the machine learning may be known “supervised learning” (supervised/manually)].
Regarding claim 9, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches the description presentation model stored in the description presentation model storage unit is a machine learning model obtained by training a correspondence between processing information obtained by processing a past abnormal case by the abnormality diagnosis unit and the diagnosis knowledge item held in the definition unit [0034 — The maintenance history DB 32 stores the maintenance data on maintenance made for the robot 101 acquired when an abnormality is caused or the occurrence of an abnormality is predicted in the respective speed reducers 14; 0044 — abnormality prediction unit 23 detects an abnormality caused in each of the speed reducers 14a and 14b according to the maintenance data (such as the maintenance time and the contents of the maintenance) of the speed reducers 14a and 14b stored in the maintenance history DB 32 and the correlation stored in the correlation storage DB 34; 0104-0110 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34… The machine learning extracts the rules from the past abnormality data and maintenance data included in the maintenance history so as to create the learning model].
Regarding claim 10, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches the description presentation processing unit further comprises a training unit, and the training unit trains a machine learning model for estimating the diagnosis knowledge item from the processing information by using processing information obtained by processing data related to the diagnosis target by the abnormality diagnosis unit and the diagnosis knowledge item held in the definition unit, and stores the trained machine learning model [0034 — The maintenance history DB 32 stores the maintenance data on maintenance made for the robot 101 acquired when an abnormality is caused or the occurrence of an abnormality is predicted in the respective speed reducers 14; 0044 — abnormality prediction unit 23 detects an abnormality caused in each of the speed reducers 14a and 14b according to the maintenance data (such as the maintenance time and the contents of the maintenance) of the speed reducers 14a and 14b stored in the maintenance history DB 32 and the correlation stored in the correlation storage DB 34; 0104-0110, Figs. 1-2 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34… The machine learning extracts the rules from the past abnormality data and maintenance data included in the maintenance history so as to create the learning model].
Regarding claim 11, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches the display unit displays the description corresponding to the abnormality of the diagnosis target presented by the description presentation processing unit in association with a flow of processing in a case where an operator manually performs diagnosis of the diagnosis target [0104-0110 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34… The machine learning extracts the rules from the past abnormality data and maintenance data included in the maintenance history so as to create the learning model. A method of the machine learning may be known “supervised learning” (supervised/manually); 0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71; 0081 — maintenance history image 72 displayed includes the image of sign K2 and the image indicating “Speed reducer 14b: requiring attention”. The same images as the sign K2 and the letters are indicated in the block q4 of the tree image 73].
Further, Honda teaches displaying on a screen [0037 — a display unit 24 including a display screen 241; 0042-0046 — display step S103 includes displaying, on the display screen 241, the at least one response procedure RP for each abnormal event AE in the abnormal device 2A… the plurality of response procedures RP are displayed on the display screen 241].
Regarding claim 12, Tanaka teaches an abnormality diagnosis method for diagnosing an abnormality of a diagnosis target using an abnormality diagnosis device [0005 — the present invention provides an abnormality diagnosis device and an abnormality diagnosis method; 0021, Figs. 1-2 — the abnormality diagnosis device 102] comprising an abnormality diagnosis unit [0021, Figs. 1-2 — FIG. 1 is a block diagram illustrating a configuration of an abnormality diagnosis device according to one embodiment of the present invention, and peripheral apparatuses. As illustrated in FIG. 1, the abnormality diagnosis device 102 according to the present embodiment is connected to a robot 101 (an apparatus) and a user interface 103 (indicated by reference sign “UI” in FIG. 1). The abnormality diagnosis device 102 diagnoses an abnormality of the robot 101, and outputs data regarding the diagnosis results to a display 62 (a display unit) provided in the user interface 103 so as to display the diagnosis results on the display 62. ], a description presentation processing unit [0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71], and a display unit [0021, Fig. 1 — display the diagnosis results on the display 62; 0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71], the abnormality diagnosis method comprising:
diagnosing an abnormality of the diagnosis target by the abnormality diagnosis unit using data related to the diagnosis target as an input [0031 — the abnormality diagnosis device 102 is described below. The abnormality diagnosis device 102 includes a control unit 51 and various kinds of databases (DBs). The control unit 51 includes a communication unit 21, an abnormality level determination unit 22, an abnormality prediction unit 23, a notification content setting unit 24, and a correlation analysis unit 25; 0039, Fig. 1 — abnormality level determination unit 22 acquires the past disturbance torque applied to the motor mounted on the respective speed reducers 14 from the sensor DB 31, and calculates an abnormality level indicating a degree of abnormality of the disturbance torque acquired; 0043 — abnormality prediction unit 23 determines or predicts an abnormality caused in the respective speed reducers 14 in accordance with the abnormality level calculated for each speed reducer 14.];
presenting a description corresponding to the abnormality of the diagnosis target by the description presentation processing unit based on a result of diagnosis by the abnormality diagnosis unit [0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71]; and
displaying a description corresponding to the abnormality of the diagnosis target presented by the description presentation processing unit on the display unit [0021, Fig. 1 — display the diagnosis results on the display 62; 0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71].
But Tanaka fails to clearly specify displaying on a screen.
However, Honda teaches displaying on a screen [0037 — a display unit 24 including a display screen 241; 0042-0046 — display step S103 includes displaying, on the display screen 241, the at least one response procedure RP for each abnormal event AE in the abnormal device 2A… the plurality of response procedures RP are displayed on the display screen 241].
Tanaka and Honda are analogous art. They relate to abnormality detection and diagnosis systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to simply substitute the known displaying on a screen, as taught by Honda, for the known displaying on a generic display, as taught by Tanaka, for the predictable result of a method that displays abnormality information on a screen.
Regarding claim 13, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches presenting, by the description presentation processing unit, a diagnosis knowledge item related to the abnormality of the diagnosis target in association with a diagnosis result of the abnormality [0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71; 0081 — maintenance history image 72 displayed includes the image of sign K2 and the image indicating “Speed reducer 14b: requiring attention”. The same images as the sign K2 and the letters are indicated in the block q4 of the tree image 73]; and
displaying a diagnosis result of the abnormality presented from the description presentation processing unit and a diagnosis knowledge item related to the abnormality associated with the diagnosis result on the display unit [0021, Fig. 1 — display the diagnosis results on the display 62; 0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71].
Further, Honda teaches displaying on a screen [0037 — a display unit 24 including a display screen 241; 0042-0046 — display step S103 includes displaying, on the display screen 241, the at least one response procedure RP for each abnormal event AE in the abnormal device 2A… the plurality of response procedures RP are displayed on the display screen 241]
Regarding claim 14, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches the abnormality diagnosis device further comprises a description presentation model storage unit, the abnormality diagnosis method comprising presenting, by the description presentation processing unit, a diagnosis knowledge item related to the abnormality of the diagnosis target as a description corresponding to the abnormality of the diagnosis target in association with a diagnosis result of the abnormality by using processing information obtained by processing data related to the diagnosis target by the abnormality diagnosis unit and the description presentation model held in the description presentation model storage unit which holds a machine learning model based on a past abnormality case as a description presentation model [0031-0035, 0043-0050 — control unit 51 includes a communication unit 21, an abnormality level determination unit 22, an abnormality prediction unit 23, a notification content setting unit 24, and a correlation analysis unit 25. The databases include a sensor DB 31, the maintenance history DB 32 (the maintenance history storage unit), an abnormality prediction DB 33, a correlation storage DB 34 (a correlation storage unit), and a work history DB 35.; 0061-0064, Fig. 3 — The flowchart shown in FIG. 3 illustrates a process of the correlation analysis processing executed by the correlation analysis unit 25; 0104-0110 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34; 0052 — notification content setting unit 24 sets the contents to be provided to the user through the indication on the display 62 of the user interface 103. The notification content setting unit 24 generates a tree image 73 indicating, with a tree structure, the abnormality diagnosis results of the speed reducers 14 determined to have an abnormality by the abnormality prediction unit 23].
Regarding claim 15, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches displaying a description corresponding to the abnormality of the diagnosis target presented by the description presentation processing unit on the display unit in association with a flow of processing in a case where an operator manually performs diagnosis of the diagnosis target [0104-0110 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34… The machine learning extracts the rules from the past abnormality data and maintenance data included in the maintenance history so as to create the learning model. A method of the machine learning may be known “supervised learning” (supervised/manually); 0069, Fig. 4 — In step S34, the notification content setting unit 24 generates the display data on the disturbance torque and the abnormality level to be displayed on the display 62 of the user interface 103. The abnormality level display image 71 as the display data is thus generated as illustrated in FIG. 9; 0079-0083, Figs. 8-10 — In step S44, the notification content setting unit 24 generates the display data of the tree image 73 as illustrated in FIG. 8. The tree image 73 is configured to allow the operator to easily recognize the details resulting in the contents of operation for instructing the operator in response to the detection of the abnormality in the speed reducer 14a… the abnormality level display image 71. The abnormality diagnosis image 70 is thus displayed with the configuration easy to recognize the correspondence relation between the tree image 73 and the abnormality level display image 71; 0081 — maintenance history image 72 displayed includes the image of sign K2 and the image indicating “Speed reducer 14b: requiring attention”. The same images as the sign K2 and the letters are indicated in the block q4 of the tree image 73].
Further, Honda teaches displaying on a screen [0037 — a display unit 24 including a display screen 241; 0042-0046 — display step S103 includes displaying, on the display screen 241, the at least one response procedure RP for each abnormal event AE in the abnormal device 2A… the plurality of response procedures RP are displayed on the display screen 241]
Regarding claim 16, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches the abnormality diagnosis device further comprises a definition unit, the abnormality diagnosis method comprising training, by the description presentation processing unit, a machine learning model for estimating the diagnosis knowledge item from the processing information by using the processing information obtained by processing data related to the diagnosis target by the abnormality diagnosis unit and the diagnosis knowledge item held in the definition unit, and storing the trained machine learning model [[0034 — The maintenance history DB 32 stores the maintenance data on maintenance made for the robot 101 acquired when an abnormality is caused or the occurrence of an abnormality is predicted in the respective speed reducers 14; 0044 — abnormality prediction unit 23 detects an abnormality caused in each of the speed reducers 14a and 14b according to the maintenance data (such as the maintenance time and the contents of the maintenance) of the speed reducers 14a and 14b stored in the maintenance history DB 32 and the correlation stored in the correlation storage DB 34; 0104-0110, Figs. 1-2 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34… The machine learning extracts the rules from the past abnormality data and maintenance data included in the maintenance history so as to create the learning model].
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Tanaka and Honda in view of Ochiiwa et al. U.S. Patent Publication No. 20220402184 (hereinafter Ochiiwa).
Regarding claim 3, the combination of Tanaka and Honda teaches all the limitations of the base claims as outlined above.
Further, Tanaka teaches the diagnosis model stored in the diagnosis model storage unit is a machine learning model [0104-0110 — the correlation analysis unit 25 executes the machine learning for learning patterns of the maintenance data having a high probability of the occurrence of abnormality, in accordance with the maintenance data on the respective speed reducers 14 at least within a part of periods stored in the maintenance history DB 32. The correlation analysis unit 25 also detects an abnormality of the respective speed reducers 14 in accordance with the results of the machine learning… The leaning model created is stored in the correlation storage DB 34].
But the combination of Tanaka and Honda fails to clearly specify that a model is a neural network.
However, Ochiiwa teaches that a model is a neural network [0146-0152 — the first learner 91 is a neural network… the first learner 91 is a trained model].
Tanaka, Honda and Ochiiwa are analogous art. They relate to abnormality detection and/or diagnosis systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to simply substitute the known neural network model, as taught by Ochiiwa, for the known machine learning model, as taught by Tanaka, for the predictable result of an abnormality diagnosis device that utilizes a neural network.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matsumoto et al. U.S. Patent No. 5333240 discloses a neural network state diagnostic system.
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERNARD G. LINDSAY whose telephone number is (571)270-0665. The examiner can normally be reached Monday through Friday from 8:30 AM to 5:30 PM EST.
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119
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