DETAILED ACTION
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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “acquiring unit” in claims 1-7 and 9, “generating unit” in claims 1, 7 and 9, “training unit” in claims 1, 7, and 9, “collecting unit” in claim 8, and “calculation unit” in claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 9 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 9 depends from claim 1; claim 9 recites “a plant device” while claim 1 recited “a device”. Given the broadest reasonable interpretation in light of the specification, the “a device” and “plant device” are the same element (e.g. [0013]-[0014]; [0021]; [0029]). As such, the scope of claim 9 is the same as the scope of claim 1 and, therefore, it fails to further limit the claim from which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 and 9 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 7 and 9 recite the limitation “generating unit [that] combines the pieces of normal data”, however, the specification fails to set forth or describe any method, equation or technique regarding how the claimed combining is achieved. At best, the specification indicates that the information processing apparatus concatenates the data (e.g. [0019], [0036], [0095], Fig. 3). The specification does not describe or set forth how the generating unit combines that data nor says that the generating unit is responsible for the concatenation. The specification fails to set forth whether the method or technique involves, for example, mathematics, a special program or software, a function or any other technique. Thus, the specification merely specifies a desired result but does not sufficiently describe how the function is performed. As presented, the scope attempts to encompass any and all methods or techniques to perform the combines pieces of normal data that are known and unknown.
Claims 2-6 and 8 depend from claim 1 and fail to remedy the deficiencies of claim 1.
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 1-11 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.
Claims 1 and 9-11 recite the limitation “a machine learning model that outputs a score indicating a normal condition of a system in which the device is installed, in response to an input of the measurement data”, however, this limitation is unclear. As presented, this appears to be a circular reference to the measurement data, which must already correspond to the measurement data that is acquired before the training phase. The intended scope of the recitation “in response to an input of the measurement data” is unclear. That is, it is unclear if the Applicant intended for this to be in response to something other than or in addition to the measurement data, whether the additional language is a grammatical error, or something else entirely. One of ordinary skill in the art would not be able to readily ascertain the meets and bounds of the claim, therefore the limitations render the claim indefinite.
Claims 2-8 depend from claim 1 and fail to remedy the deficiencies of claim 1.
Claim 3 recites “the acquiring unit displays a plurality of candidate intervals on the data display screen, and acquires each piece of normal data belonging to an interval”, however, this limitation is unclear. It is unclear how and from where the acquiring unit acquires each piece of normal data belonging to a selected interval, specifically the scope of the limitation is unclear because the limitation can be interpreted in two ways: first since the acquiring unit acquires measurement data from the sensors, could it be that the user selects in advance the intervals from which data should be acquired when these intervals happen? Or could it be that the acquiring unit acquires from a memory the pieces of data corresponding to the selected intervals which were previously acquired and stored? Further still, it unclear if this is a step that is to be performed by the generating unit since the generating unit needs to obtain the pieces of data in order to combine them. One of ordinary skill in the art would not be able to readily ascertain the meets and bounds of the claim, therefore the limitations render the claim indefinite.
Claim 4 depend from claim 3 and fail to remedy the deficiencies of claim 3.
Claim 8 recites “a collecting unit that collects the measurement data transmitted by the device” and claim 8 depends from claim 1, which recites “an acquiring unit that acquires…measurement data measured by a device”. As presented, both the acquiring unit and the collecting unit obtain the measurement data, thus, the difference between the acquiring unit and collecting unit is unclear. One of ordinary skill in the art would not be able to readily ascertain the meets and bounds of the claim, therefore the limitations render the claim indefinite.
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 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the preamble of the claim recites “A computer-readable recording medium”, and under the broadest reasonable interpretation it covers both statutory and non-statutory embodiments. A transitory signal, while physical and real, does not possess concrete structure that would qualify as a device or part under the definition of a machine, is not a tangible article or commodity under the definition of a manufacture (even though it is man-made and physical in that it exists in the real world and has tangible causes and effects), and is not composed of matter such that it would qualify as a composition of matter. As such, a transitory, propagating signal does not fall within any statutory category. A claim to a computer readable medium that can be a carrier wave covers a non-statutory embodiment and therefore is not patent eligible. See MPEP §2106.03.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to computer program per se.
As indicated above, the limitations of acquiring unit, generating unit and training unit invoke 35 U.S.C. 112(f). The broadest reasonable interpretation in light of the specification is that the claimed units are software, see e.g. [0051], [0115], and Fig. 15.
Claim(s) 1-9 sets forth an apparatus that stores software units without more, therefore it is directed to an apparatus containing a computer program per se. Software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. MPEP § 2106.03 I. Consequently, the claims covers material not found in any of the four statutory categories, and are not patent eligible.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is an apparatus which recite(s) combines the acquired pieces of normal data to generate combined data; and performs machine learning using the combined data to train a machine learning model that outputs a score indicating a normal condition of a system in which the device is installed, in response to an input of the measurement data.
In the context of the claim, the limitation of combines the acquire pieces encompasses concatenating datasets (see e.g. [0036]). Under its broadest reasonable interpretation when read in light of the specification, the “combines” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP § 2106.04(a)(2) III. In the context of the claim, the limitation of performs machine learning using the combined data to train a machine learning model encompasses using random forest classification, and a binary classification method, such as a boost decision tree that is a recursive partitioning, as a classification method (see e.g. [0050]). Under its broadest reasonable interpretation when read in light of the specification, the “performs machine learning” encompasses using the mathematical concepts of mathematical calculations, relationships, calculations or formulas. See MPEP § 2106.04(a)(2) I. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim limitations of an acquiring unit that acquires pieces of normal data belonging to a plurality of intervals of measurement data measured by a device, for each of the intervals; a generating unit and a training unit. The claim limitations of acquires pieces of normal data belonging to a plurality of intervals of measurement data measured by a device is recited at a high level of generality such that it provides nothing more than necessary data gathering. As such it amounts to no more than adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). The claims limitations of an acquiring unit, a generating unit and a training unit are recited at a high level of generality such that it amounts to no more than merely using generic computer components as a tool to perform an abstract idea. See MPEP § 2106.05(f). The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional claim limitations amount to no more than adding insignificant extra-solution activity to the judicial exception or merely using generic computer components as a tool to perform an abstract idea. Further, acquires pieces of normal data belonging to a plurality of intervals, is taught by e.g. combination JP 2022-0940374A ([0016]-[0074]) and JPH09-245013A (1-2, 4), and amounts to well-understood, routine, conventional activity. See MPEP § 2106.05(d). Therefore, this limitation remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. The claim is not patent eligible.
Claims 2-9 depend from claim 1 and recite the same abstract idea as claim 1. The additional claim limitations recited in claims 2-9 serve to add additional steps to the abstract idea (claims 7-9) and add additional extra-solution activity (claims 2-9). Regarding claims 7-9, as discussed above, combine[s] and perform machine learning encompass abstract ideas. Additionally, the broadest reasonable interpretation in light of the specification the limitation of calculates a score encompasses using the mathematical concepts of mathematical calculations, relationships, calculations or formulas. See MPEP § 2106.04(a)(2) I. Regarding claims 7-9, as discussed above, the claimed units are generically recited computer components such that it amounts to no more than merely using generic computer components as a tool to perform an abstract idea. See MPEP § 2106.05(f). The remaining additional claim limitations are recited at a high level of generality such that they amount to no more than necessary data gathering and outputting. As such, they are insignificant extra-solution activity. Further, the additional limitations limitation are known in e.g. JP 2022-0940374A, JPH09-245013A and WO2021132281 and amounts to well-understood, routine, conventional activity. See MPEP § 2106.05(d). Therefore, these limitation remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. The claim is not patent eligible.
Claim 10 is process which recite(s) combining the acquired pieces of normal data to generate combined data; and performing machine learning using the combined data to train a machine learning model that outputs a score indicating a normal condition of a system in which the device is installed, in response to an input of the measurement data.
In the context of the claim, the limitation of combining the acquired pieces encompasses concatenating datasets (see e.g. [0036]). Under its broadest reasonable interpretation when read in light of the specification, the “combining” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP § 2106.04(a)(2) III. In the context of the claim, the limitation of performing machine learning using the combined data to train a machine learning model encompasses using random forest classification, and a binary classification method, such as a boost decision tree that is a recursive partitioning, as a classification method (see e.g. [0050]). Under its broadest reasonable interpretation when read in light of the specification, the “performing machine learning” encompasses using the mathematical concepts of mathematical calculations, relationships, calculations or formulas. See MPEP § 2106.04(a)(2) I. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim limitations of acquiring pieces of normal data belonging to a plurality of intervals of measurement data measured by a device, for each of the intervals. The claim limitations of acquiring pieces of normal data belonging to a plurality of intervals of measurement data measured by a device is recited at a high level of generality such that it provides nothing more than necessary data gathering. As such it amounts to no more than adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional claim limitations amount to no more than adding insignificant extra-solution activity to the judicial exception. Further, acquiring pieces of normal data belonging to a plurality of intervals, is taught by e.g. combination JP 2022-0940374A ([0016]-[0074]) and JPH09-245013A (1-2, 4), and amounts to well-understood, routine, conventional activity. See MPEP § 2106.05(d). Therefore, this limitation remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. The claim is not patent eligible.
Claim 11 is process which recite(s) combining the acquired pieces of normal data to generate combined data; and performing machine learning using the combined data to train a machine learning model that outputs a score indicating a normal condition of a system in which the device is installed, in response to an input of the measurement data.
In the context of the claim, the limitation of combining the acquired pieces encompasses concatenating datasets (see e.g. [0036]). Under its broadest reasonable interpretation when read in light of the specification, the “combining” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP § 2106.04(a)(2) III. In the context of the claim, the limitation of performing machine learning using the combined data to train a machine learning model encompasses using random forest classification, and a binary classification method, such as a boost decision tree that is a recursive partitioning, as a classification method (see e.g. [0050]). Under its broadest reasonable interpretation when read in light of the specification, the “performing machine learning” encompasses using the mathematical concepts of mathematical calculations, relationships, calculations or formulas. See MPEP § 2106.04(a)(2) I. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim limitations of an information processing program causing a computer to execute a process, the process comprising: acquiring pieces of normal data belonging to a plurality of intervals of measurement data measured by a device, for each of the intervals. The claim limitations of acquiring pieces of normal data belonging to a plurality of intervals of measurement data measured by a device is recited at a high level of generality such that it provides nothing more than necessary data gathering. As such it amounts to no more than adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). The claims limitations of an information processing program causing a computer to execute a process is recited at a high level of generality such that it amounts to no more than merely using generic computer components as a tool to perform an abstract idea. See MPEP § 2106.05(f). The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional claim limitations amount to no more than adding insignificant extra-solution activity to the judicial exception. Further, acquiring pieces of normal data belonging to a plurality of intervals, is taught by e.g. combination JP 2022-0940374A ([0016]-[0074]) and JPH09-245013A (1-2, 4), and amounts to well-understood, routine, conventional activity. See MPEP § 2106.05(d). Therefore, this limitation remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5 and 8-11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yamamoto et al. in WO 2021132281 (see IDS filed 2024 Feb 09 see translation). (as they are best understood).
Regarding claim 1, Yamamoto et al. teaches:
An information processing apparatus (Fig. 1) comprising:
an acquiring unit that acquires pieces of normal data belonging to a plurality of intervals of measurement data measured by a device, for each of the intervals (see “That is, the data collecting device 13 collects each of the state information and each of the operation information in relation to the time. The state information and the operation information include measurement values based on the detection values obtained by the camera 11c, the operation force detection sensor 12a, and the like”, p 4 par 2);
a generating unit that combines the acquired pieces of normal data to generate combined data (see “The training data selection unit 25 selects training data for constructing a learning model from the evaluated data stored by the storage unit 22 according to the instruction of the worker presented with the evaluation result of the data evaluation unit 21. Thereby, by selecting the training data from the collected data using the data evaluation model 20, it is possible to easily prepare the training data consisting of preferable data for machine learning. As a result, the time required to build the learning model can be shortened. Further, the training data sorting device 2 of the present embodiment includes a presentation device 23 and an input device 24. The presenting device 23 presents the evaluation result of the data evaluation unit 21 to the operator”, p. 11 par 1-3); and
a training unit that performs machine learning using the combined data to train a machine learning model (see “performing machine learning on at least a part of collected data”, Abstract; p. 2 last par.) that outputs a score indicating a normal condition of a system in which the device is installed, in response to an input of the measurement data (see “…output as an evaluation value… the operation information in the predetermined time range is similar to the reference operation corresponding to the work state B…assigns the label…to the operation information…”, p. 7 the evaluation value is an indication of whether the robot executes the operation set by the operator, e.g. normal operation, or not).
Regarding claim 2, Yamamoto et al. teaches the limitations as indicated above. Further, Yamamoto et al. teaches wherein the acquiring unit outputs a data display screen (see “presentation device 23 shown in FIG. 1 is a dot matrix type display such as a liquid crystal or an organic EL”, p. 8 par 1, Fig. 1) displaying the measurement data using a predetermined graph (see “visually displays…the collected data in the form of a graph…”, p. 8 par 2), and acquires, for each of the plurality of intervals designated by a user on the output data display screen, each piece of normal data belonging to the interval (see “…also displays the time series information. Is continuous and the data part to which the label of the same numerical value is assigned is displayed as one block.” , p. 8 par 2).
Regarding claim 3, Yamamoto et al. teaches the limitations as indicated above. Further, Yamamoto et al. teaches wherein the acquiring unit displays a plurality of candidate intervals on the data display screen, and acquires each piece of normal data belonging to an interval selected from the candidate intervals (see “visually displays…the collected data in the form of a graph, and also displays the time series information. Is continuous and the data part to which the label of the same numerical value is assigned is displayed as one block”, p. 8 par 2).
Regarding claim 4, Yamamoto et al. teaches the limitations as indicated above. Further, Yamamoto et al. teaches wherein the acquiring unit displays intervals including measurement data exceeding a predetermined threshold as the candidate intervals, and acquires pieces of normal data belonging to an interval selected from the candidate intervals (see “the evaluation value output by the data evaluation model 20 is equal to or higher than the predetermined threshold value with respect to the operation information”, p 7 par 3; p. 12 par 1-2; see “The data evaluation unit is the most when the best evaluation value among the evaluation values output by the data evaluation model for each of the plurality of reference operations when the partial time series information is input is better than the threshold value”, p. 14 par 11-12).
Regarding claim 5, Yamamoto et al. teaches the limitations as indicated above. Further, Yamamoto et al. teaches wherein the acquiring unit outputs a data designation screen displaying intervals from which pieces of normal data are to be acquired from the measurement data (see “visually displays the operation information (for example, the operation force) included in the collected data in the form of a graph, and also displays the time series information…”, p. 8 par 2), in a manner enabling an input or a selection to be made (see “As a result, the operator can check the operation information more intuitively.”, p. 8 par 2), and acquires, for each of the plurality of intervals input or selected by the user on the output data designation screen, each piece of normal data belonging to the interval (see “…collected data may be displayed…receives an instruction from the operator regarding whether or not to adopt the evaluated operation information presented by the presentation device…”, p. 8 par. 3-4).
Regarding claim 8, Yamamoto et al. teaches the limitations as indicated above. Further, Yamamoto et al. teaches a collecting unit (Fig. 1) that collects the measurement data transmitted by the device (see “That is, the data collecting device 13 collects each of the state information and each of the operation information in relation to the time. The state information and the operation information include measurement values based on the detection values obtained by the camera 11c, the operation force detection sensor 12a, and the like”, p 4 par 2); and
a calculating unit (Fig. 1) that calculates a score indicating a normal condition of a system in which the device is installed, based on a result obtained by inputting the collected measurement data to the trained machine learning model (see “This state information may include information calculated based on the sensor information (for example, a value indicating a change over time of the sensor information from the past to the present).”, p. 6 par 4).
Regarding claim 9, Yamamoto et al. teaches the limitations as indicated above. Further, Yamamoto et al. teaches wherein the acquiring unit acquires pieces of normal data belonging to a plurality of intervals of measurement data collected by a plant device installed in a plant (see “That is, the data collecting device 13 collects each of the state information and each of the operation information in relation to the time. The state information and the operation information include measurement values based on the detection values obtained by the camera 11c, the operation force detection sensor 12a, and the like”, p 4 par 2), the generating unit combines the pieces of normal data collected by the plant device and belonging to the intervals to generate combined data (see “The training data selection unit 25 selects training data for constructing a learning model from the evaluated data stored by the storage unit 22 according to the instruction of the worker presented with the evaluation result of the data evaluation unit 21. Thereby, by selecting the training data from the collected data using the data evaluation model 20, it is possible to easily prepare the training data consisting of preferable data for machine learning. As a result, the time required to build the learning model can be shortened. Further, the training data sorting device 2 of the present embodiment includes a presentation device 23 and an input device 24. The presenting device 23 presents the evaluation result of the data evaluation unit 21 to the operator”, p. 11 par 1-3), and the training unit performs machine learning using the combined data to train a machine learning model that outputs a score indicating a normal condition of the plant in which the plant device is installed, in response to an input of measurement data (see “…output as an evaluation value… the operation information in the predetermined time range is similar to the reference operation corresponding to the work state B…assigns the label…to the operation information…”, p. 7 the evaluation value is an indication of whether the robot executes the operation set by the operator, e.g. normal operation, or not).
Regarding claim 10, Yamamoto et al. teaches:
An information processing method (Figs. 1, 3-4) comprising:
acquiring pieces of normal data belonging to a plurality of intervals of measurement data measured by a device, for each of the intervals (see “That is, the data collecting device 13 collects each of the state information and each of the operation information in relation to the time. The state information and the operation information include measurement values based on the detection values obtained by the camera 11c, the operation force detection sensor 12a, and the like”, p 4 par 2);
combining the acquired pieces of normal data to generate combined data (see “The training data selection unit 25 selects training data for constructing a learning model from the evaluated data stored by the storage unit 22 according to the instruction of the worker presented with the evaluation result of the data evaluation unit 21. Thereby, by selecting the training data from the collected data using the data evaluation model 20, it is possible to easily prepare the training data consisting of preferable data for machine learning. As a result, the time required to build the learning model can be shortened. Further, the training data sorting device 2 of the present embodiment includes a presentation device 23 and an input device 24. The presenting device 23 presents the evaluation result of the data evaluation unit 21 to the operator”, p. 11 par 1-3); and
performing machine learning using the combined data to train a machine learning model (see “performing machine learning on at least a part of collected data”, Abstract; p. 2 last par.) that outputs a score indicating a normal condition of a system in which the device is installed, in response to an input of the measurement data (see “…output as an evaluation value… the operation information in the predetermined time range is similar to the reference operation corresponding to the work state B…assigns the label…to the operation information…”, p. 7 the evaluation value is an indication of whether the robot executes the operation set by the operator, e.g. normal operation, or not).
Regarding claim 11, Yamamoto et al. teaches:
A computer-readable recording medium having stored therein an information processing program causing a computer to execute (Fig. 1, e.g. #22) a process, the process comprising:
acquiring pieces of normal data belonging to a plurality of intervals of measurement data measured by a device, for each of the intervals (see “That is, the data collecting device 13 collects each of the state information and each of the operation information in relation to the time. The state information and the operation information include measurement values based on the detection values obtained by the camera 11c, the operation force detection sensor 12a, and the like”, p 4 par 2);
combining the acquired pieces of normal data to generate combined data (see “The training data selection unit 25 selects training data for constructing a learning model from the evaluated data stored by the storage unit 22 according to the instruction of the worker presented with the evaluation result of the data evaluation unit 21. Thereby, by selecting the training data from the collected data using the data evaluation model 20, it is possible to easily prepare the training data consisting of preferable data for machine learning. As a result, the time required to build the learning model can be shortened. Further, the training data sorting device 2 of the present embodiment includes a presentation device 23 and an input device 24. The presenting device 23 presents the evaluation result of the data evaluation unit 21 to the operator”, p. 11 par 1-3); and
performing machine learning using the combined data to train a machine learning model (see “performing machine learning on at least a part of collected data”, Abstract; p. 2 last par.) that outputs a score indicating a normal condition of a system in which the device is installed, in response to an input of the measurement data (see “…output as an evaluation value… the operation information in the predetermined time range is similar to the reference operation corresponding to the work state B…assigns the label…to the operation information…”, p. 7 the evaluation value is an indication of whether the robot executes the operation set by the operator, e.g. normal operation, or not).
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.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yamamoto et al. in WO 2021132281 (see IDS filed 2024 Feb 09 see translation), in view of Trinh et al. in U.S. Patent Publication 20200379454. (as they are best understood)
Regarding claim 6, Yamamoto et al. teaches the limitations as indicated above. Further, Yamamoto et al. discloses “a plurality of collected data corresponding to the operation of the worker” (p. 9 par 2) and “the training data to be trained by the learning model from a plurality of collected data for a plurality of types of work” (p. 10 par 7). Yamamoto et al. differs from the claimed invention in that it does not expressly teach wherein the acquiring unit acquires, among a plurality of different types of correlated measurement data measured by the device, the respective types of measurement data belonging to an interval for which all of such types of measurement data have been determined to be normal data.
Trinh et al. teaches “A predictive maintenance server receives data from sensors of equipment. The server uses one or more machine learning models to assign an anomaly score” (Abstract) wherein the acquiring unit acquires, among a plurality of different types of correlated measurement data measured by the device (see “measurements of different sensors of a piece of normal equipment 150 may often have a correlation with each other.”, [0068]; see “neural network 900 that analyzes data from various sensor channels of equipment 150, the sensor measurements may have a certain correlation with each other.”, [0093]), the respective types of measurement data belonging to an interval for which all of such types of measurement data have been determined to be normal data (see “measurements of different sensors of a piece of normal equipment 150 may often have a correlation with each other.”, [0068]). Trinh et al. teaches “using machine learning based techniques to detect anomaly of equipment based on sensor measurements of the equipment” ([0002]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have utilized the teachings of Trinh et al. in the invention of Yamamoto et al. to improve Yamamoto et al. with a reasonable expectation that it would facilitate improving the models’ ability to detect anomalies and normal operation, thereby improving the motion prediction of the equipment of Yamamoto et al.
Allowable Subject Matter
Claim 7 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(a), 35 U.S.C. 112(b) and 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Regarding claim 7, the prior art fails to anticipate or render obvious wherein the acquiring unit further acquires predicted normal data that is normal data among pieces of predicted data including predicted values of the measurement data, the generating unit combines the pieces of normal data and the predicted normal data to generate combined predicted data, and the training unit performs machine learning using the combined predicted data to train the machine learning model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Inagaki et al. in Foreign Patent Document JP2017033526A teaches “A fault prediction system 1 includes a machine learning device 5 that learns a condition associated with a fault in an industrial machine 2. The machine learning device 5 is provided with: a state observation unit 52 for observing a state variable constituted with the output data of a sensor 11, the internal data of control software, or calculation data obtained on the basis of the foregoing while the industrial machine 2 is operating or stationary; a determination data acquisition unit 51 for acquiring determination data in which the presence or the degree of a fault in the industrial machine 2 is determined; and a learning unit 53 for learning a condition associated with a fault in the industrial machine 2 in accordance with a training data set created on the basis of a combination of the state variable and the determination data” (Abstract).
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/MI'SCHITA' HENSON/Primary Examiner, Art Unit 2857