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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 29, 2023 has been considered by the Examiner.
Claim Objections
Claims 1-18 are objected to because of the following informalities. Appropriate correction is required.
Regarding independent claim 1, there is no antecedent basis for “the first neural network model” recited therein. Claim 1 previously recites a “first neural network model-based feature prediction unit,” but not a “first neural network model” per se. Similarly, there is no antecedent basis for “the second neural network model” recited in claim 1. Claim 1 previously recites a “second neural network model-based standard operation level prediction unit” but not a “second neural network model” per se. Also in claim 1, there is no antecedent basis for “the standard operation level assessment” recited therein.
Claims 2-11 depend from claim 1 and thereby include all of the limitations of claim 1. Accordingly, claims 2-11 are objected to for the same reasons as claim 1 noted above. Furthermore, there is no antecedent basis for “the categorical data” further recited in claim 3. There is no antecedent basis for “the operation time” recited in claim 4. There is no antecedent basis for “the load range” recited in claim 8. There is no antecedent basis for “the appropriate load range” recited in claim 9. In particular, claim 9 previously recites an “appropriate load range adjustment unit” but not an “appropriate load range” per se. Also, there is no antecedent basis for “the target output level” recited in claim 10, and for “the outcome” recited in claim 11.
Regarding independent claim 12, there is no antecedent basis for “the first neural network model” recited therein. Claim 12 previously recites a “first neural network model-based feature prediction process,” but not a “first neural network model” per se. Similarly, there is no antecedent basis for “the second neural network model” recited in claim 12. Claim 12 previously recites a “second neural network model-based standard operation level prediction process” but not a “second neural network model” per se. Also in claim 12, there is no antecedent basis for “the target equipment,” “the non-time-series data” and “the standard operation level assessment” recited therein.
Claims 13-18 depend from claim 12 and thereby include all of the limitations of claim 12. Accordingly, claims 13-18 are objected to for the same reasons as claim 12 noted above. Furthermore, in claim 13, there is no antecedent basis for “the feature vector” recited therein. There is no antecedent basis for “the load range” recited in claim 16. There is no antecedent basis for “the appropriate load range” and “the target output level” recited in claim 17. Moreover, there is no antecedent basis for “the outcome” recited in claim 18.
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.
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 limitations use a generic placeholder (i.e. “unit”) that is coupled with functional language (e.g. “that receives time-series data of a target equipment using the first neural network model and derives data with non-time series feature”) without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: the “first neural network model-based feature prediction unit” and the “second neural network model-based standard operation level prediction unit” initially recited in claim 1; the “equipment operation control unit” initially recited in claim 7; the “standard operation level guide unit” recited in claim 8; the “appropriate load range adjustment unit” recited in claim 9; the “target output configuration unit” recited in claim 10; and the “predictor variable analysis unit” recited in claim 11.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure (i.e. a programmed processor) described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid 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 limitations recite sufficient structure to perform the claimed function so as to avoid being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a mental process) without significantly more.
As described in MPEP § 2106, the analysis as to whether a claim qualifies as eligible subject matter under 35 U.S.C. § 101 includes the following determinations:
(1) Whether the claim is to a statutory category, i.e. to a process, machine, manufacture or composition of matter (“Step 1”) – see MPEP §§ 2106, subsection III, and 2106.03
(2) If the claim is to a statutory category, whether the claim recites any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes) (“Step 2A, Prong One”) – see MPEP §§ 2106, subsection III, and 2106.04
(3) If the claim recites a judicial exception, whether the claim recites additional elements that integrate the judicial exception into a practical application (“Step 2A, Prong Two”) – see MPEP §§ 2106, subsection III, and 2106.04
(4) If the claim does not recite additional elements that integrate the judicial exception into a practical application, whether the claim recites additional elements that amount to significantly more than the judicial exception (“Step 2B”) – see MPEP §§ 2106, subsection III, and 2106.05
Claim 1
Regarding “Step 1,” independent claim 1 is to a statutory category, as claim 1 is directed to a device, which is considered a machine or manufacture.
Accordingly, the analysis proceeds to “Step 2A, Prong One” to determine if the claim recites a judicial exception. In this case, the claim recites mental processes. “’[T]he mental processes’ abstract idea grouping in particular is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions.” MPEP § 2106.04(a)(2), subsection III. “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claims recites an abstract idea. MPEP § 2106.04(a)(2), subsection III,B (citations omitted). As noted in MPEP § 2106.04IIB, a claim may recite multiple judicial exceptions, and the same eligibility analysis is to be applied regardless of the number of exceptions recited therein. In this case, the recitations of “derives data with non-time-series feature” and “predicts the standard operation level assessment” are considered indicative of a mental process. Such tasks can practically be performed in the human mind when given their broadest, reasonable interpretations.
Because the claim recites a judicial exception (i.e. a mental processes), the analysis proceeds to “Step2A, Prong Two” to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. Other than the above-noted mental processes, claim 1 recites “receives time-series data of a target equipment” and “receives non-time-series data of the target equipment and data and vectors with non-time-series feature.” These tasks however are indicative of insignificant extra-solution activity, i.e. mere data gathering, and are therefore insufficient to integrate the abstract idea into a practical application. See MPEP § 2106.5(g). Claim 1 also additionally recites that an industrial equipment operation control device comprises “a first neural network model-based feature prediction unit” that receives the above-noted time-series data and uses “the first neural network model” to perform the above-noted mental process (i.e. derives the data with non-time-series features). Claim 1 additionally recites that the device comprises “a second neural network model-based standard operation level prediction unit” that receives the above-noted non-time-series data, etc. and further performs the above-noted mental process (i.e. predicts the standard operation level assessment) using “the second neural network model.” However, this recitation of a device and different units that receive the appropriate data and use neural networks to perform the above-noted mental processes represents no more than mere instructions to apply the mental process on a computer, and thus also does not integrate the judicial exception into a practical application. See MPEP § 2106.05(f). Particularly, the claim omits any details as to how the neural networks perform the recited tasks; the neural networks appear to be invoked merely as a tool for performing the judicial exception.
Accordingly, as claim 1 does not recite additional elements that integrate the judicial exception into a practical application, the analysis proceeds to “Step 2B” to determine whether the claim recites additional elements that amount to significantly more than the judicial exception. However, in this case, claim 1 does not. As noted above, claim 1 comprises additional elements reciting, “receives time-series data of a target equipment” and “receives non-time-series data of the target equipment and data and vectors with non-time-series feature.” As further noted above, these tasks are indicative of insignificant extra-solution activity, i.e. mere data gathering. See MPEP § 2106.5(g). Such data gathering is also well-understood, routine and conventional. See, e.g., Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Accordingly, the “receiving” tasks do not amount to significantly more than the judicial exception. As also noted above, claim 1 additionally recites that the mental processes are realized with a “first neural network model-based feature prediction unit” and with a “second neural network model-based standard operation level prediction unit,” wherein both units are comprised in an “industrial equipment operation control device.” These features represent mere instructions to apply the abstract idea on a generic computer, as is described above, and thus also do not amount to significantly more than the judicial exception. See MPEP § 2106.05(f).
Consequently, claim 1 recites an abstract idea but does not include additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea. As a result, and for the reasons described above, claim 1 is rejected as being patent ineligible under 35 U.S.C. § 101.
Claim 2
Claim 2 further characterizes the data gathering recited in claim 1 (i.e. “receives non-time-series data…”) by reciting characteristics of the data (i.e. “the non-time-series data includes preprocessed data that combines category data and quantitative analysis data, each corresponding to the target equipment, after preprocessing.”). As such, the limitations of claim 2 are further indicative of the data gathering (i.e. the type of data that is gathered). As noted above, data gathering is insignificant extra-solution activity that is well-understood, routine and conventional, and is therefore insufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 2 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 2 is also patent ineligible under 35 U.S.C. § 101.
Claim 3
Claim 3 further characterizes the data gathering recited in claims 1 and 2 (i.e. “receives non-time-series data…”) by reciting characteristics of the data (i.e. “wherein the categorical data includes vector data that has been preprocessed with one-hot encoding of classification information pre-configured corresponding to the target equipment.”). As such, the limitations of claim 3 are further indicative of this data gathering (i.e. the type of data that is gathered). As noted above, data gathering is insignificant extra-solution activity that is well-understood, routine and conventional, and is therefore insufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 3 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 3 is also patent ineligible under 35 U.S.C. § 101.
Claim 4
Claim 4 further characterizes the data gathering recited in claims 1 and 2 (i.e. “receives non-time-series data…”) by reciting characteristics of the data (i.e. “wherein the quantitative analysis data includes at least one of the non-time-series data that has been normalized and preprocessed, corresponding to the operation time, age, weight, size, average daily power consumption, and rated output acquired in relation to the target equipment”). As such, the limitations of claim 4 are further indicative of this data gathering (i.e. the type of data that is gathered). As noted above, data gathering is insignificant extra-solution activity that is well-understood, routine and conventional, and is therefore insufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 4 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 4 is also patent ineligible under 35 U.S.C. § 101.
Claim 5
Claim 5 further characterizes the mental process recited in claim 1 (i.e. “derives data with non-time-series features”) by reciting that “the data with non-time-series features are characterized as feature vectors derived from the time-series data of the target equipment….” As such, deriving such data is considered a mental process; deriving feature vectors from time-series data of target equipment can practically be performed in the human mind.
Claim 5 additionally recites that the time-series data of the target equipment is “being input into the first neural network model.” However, this additional recitation represents no more than mere instructions to apply the mental process on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 5 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 5 is also patent ineligible under 35 U.S.C. § 101.
Claim 6
The recitation in claim 6 of “receiving data combining the data with the non-time-series feature and the non-time-series data of the target equipment” is indicative of insignificant extra-solution activity, i.e. mere data gathering, and is therefore insufficient to integrate the abstract idea into a practical application. See MPEP § 2106.5(g). Such data gathering is also well-understood, routine and conventional. See, e.g., Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Accordingly, the “receiving” in claim 6, does not amount to significantly more than the abstract idea.
Claim 6 additionally recites that the “second neural network model is characterized” by the above-noted “receiving.” However, this additional recitation represents no more than mere instructions to apply the mental process on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 6 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 6 is also patent ineligible under 35 U.S.C. § 101.
Claims 7-11
The recitation of the “equipment operation control unit” in claims 7-11 represents no more than mere instructions to apply the above-noted mental process on a computer, and thus does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP § 2106.05(f). It is further noted that the recitation in claim 11 of “analyzes how much a variable has influenced the outcome based on the standard operation level assessment” can additionally or alternatively be considered a mental process.
Claims 7-11 thus fail to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claims 7-11 are also patent ineligible under 35 U.S.C. § 101.
Claim 12
Regarding “Step 1,” independent claim 12 is to a statutory category, as claim 12 is directed to a method, i.e. a process.
Per “Step 2A, Prong One,” claim 12 recites a mental process. The recitations of “deriving data with a non-time-series feature” and “predicting…the standard operation level assessment” are considered indicative of a mental process. Such tasks can practically be performed in the human mind when given their broadest, reasonable interpretations.
Per “Step2A, Prong Two,” in addition to the above-noted mental process, claim 12 recites “receiving…a time-series data from the target equipment” and “receiving the non-time-series data and vectors with non-time-series features.” These tasks are indicative of insignificant extra-solution activity, i.e. mere data gathering, and are therefore insufficient to integrate the abstract idea into a practical application. See MPEP § 2106.5(g). Claim 12 also additionally recites that a “first neural network model-based feature prediction process that uses the first neural network model” receives the above-noted time series data from the target equipment and performs the above-noted mental process (i.e. “deriving data with a non-time-series feature”). Also, claim 12 recites that a “second neural network model-based standard operation level prediction process” performs the above-noted mental process (i.e. “predicting…the standard operation level assessment”) by “using the second neural network model.” However, these recitations of neural network model-based processes that receive data and use neural networks to perform the above-noted mental processes represent no more than mere instructions to apply the mental process on a computer, and thus also does not integrate the judicial exception into a practical application. See MPEP § 2106.05(f). Particularly, the claim omits any details as to how the neural networks perform the recited tasks; the neural networks appear to be invoked merely as a tool for performing the judicial exception.
Per “Step 2B,” claim 12 fails to recite any additional elements that amount to significantly more than the judicial exception. As noted above, claim 12 comprises additional elements reciting, “receiving…a time-series data from the target equipment” and “receiving the non-time-series data and vectors with non-time-series features.” As further noted above, these tasks are indicative of insignificant extra-solution activity, i.e. mere data gathering. See MPEP § 2106.5(g). Such data gathering is also well-understood, routine and conventional. See, e.g., Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Accordingly, the “receiving” tasks do not amount to significantly more than the judicial exception. As also noted above, claim 12 additionally recites that a “first neural network model-based feature prediction process” and a “second neural network model-based standard operation level prediction process” perform the above-noted mental process. However, these features represent mere instructions to apply the abstract idea on a generic computer, as is described above, and thus also do not amount to significantly more than the judicial exception. See MPEP § 2106.05(f).
Consequently, claim 12 recites an abstract idea but does not include additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea. As a result, and for the reasons described above, claim 12 is rejected as being patent ineligible under 35 U.S.C. § 101.
Claim 13
Claim 13 further characterizes the mental process recited in claim 12 (i.e. “deriving data with non-time-series features”) by reciting that “the data with non-time-series feature is characterized as the feature vector derived from the time-series data of the target equipment….” As such, deriving such data is considered a mental process; deriving a feature vector from time-series data of target equipment can practically be performed in the human mind.
Claim 13 additionally recites that the time-series data of the target equipment is “being input into the first neural network model.” However, this additional recitation represents no more than mere instructions to apply the mental process on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 13 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 13 is also patent ineligible under 35 U.S.C. § 101.
Claim 14
The recitation in claim 14 of “receiving data combining the data with the non-time-series feature and the non-time-series data of the target equipment” is indicative of insignificant extra-solution activity, i.e. mere data gathering, and is therefore insufficient to integrate the abstract idea into a practical application. See MPEP § 2106.5(g). Such data gathering is also well-understood, routine and conventional. See, e.g., Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Accordingly, the “receiving” in claim 14, does not amount to significantly more than the abstract idea.
Claim 14 additionally recites that the “second neural network model is characterized” by the above-noted “receiving.” However, this additional recitation represents no more than mere instructions to apply the mental process on a computer, and thus does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 14 thus fails to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claim 14 is also patent ineligible under 35 U.S.C. § 101.
Claims 15-18
The recitation of the “control process” in claims 15-18, which performs operation control of the target equipment based on the standard operation level assessment, represents no more than mere instructions to apply the above-noted mental process on a computer, and thus does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP § 2106.05(f). It is further noted that the recitation in claim 18 of “analyzing how much a variable has influenced the outcome based on the standard operation level assessment” can additionally or alternatively be considered a mental process.
Claims 15-18 thus fail to recite any additional elements that integrate the abstract idea into a practical application or that amount to significantly more than the abstract idea, and as a result, claims 15-18 are also patent ineligible under 35 U.S.C. § 101.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-7 and 12-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2020/0097921 to Ghosh et al. (“Ghosh”).
Regarding claims 1 and 12, Ghosh describes arrangements and techniques for training and using a machine learning model to determine repair actions (see e.g. paragraphs 0001 and 0024). Like claimed, Ghosh particularly teaches:
receiving, by a first neural network model-based feature prediction process that uses the first neural network model, time-series data from target equipment, and deriving data with a non-time-series feature (see e.g. paragraphs 0002 and 0026: Ghosh describes a system that receives data for first equipment, and uses extracted features from the data as training data to train a machine learning model; after being trained, and in response to receiving a repair request associated with second equipment, the machine learning model is able to determine at least one repair action for the second equipment. In particular, Ghosh discloses that the data used to train the machine learning model comprises a variety of data associated with the equipment, including sensor data from the equipment – see e.g. paragraphs 0026 and 0030. The repair request similarly includes sensor data from the equipment to be repaired – see e.g. paragraphs 0087-0088. The sensor data comprises time-series data from the equipment – see e.g. paragraph 0033. Prior to being input to the machine learning model, i.e. to either train the machine learning model or to determine a repair action, Ghosh discloses that features are extracted from the data – see e.g. paragraphs 0072-0082 and 0091-0092. With particular regard to sensor data, Ghosh discloses that features are extracted therefrom by inputting the sensor data into a neural network model, e.g. an LSTM-based model or a set of 1-D convolutional filters – see e.g. paragraphs 0132-0145, and FIGS. 8 and 9. The LSTM-based model or set of convolutional filters is considered a “first neural network model” like claimed. Accordingly, Ghosh teaches a first neural network model-based feature prediction process that uses a first neural network model, i.e. the LSTM-based model or set of 1-D convolutional filters, which receives time-series data, i.e. sensor data, from target equipment and derives data with a non-time-series feature, i.e. the extracted sensor features.); and
receiving non-time-series data and vectors with non-time-series features derived from the first neural network model-based feature prediction process, and predicting, by a second neural network model-based standard operation level prediction process, a standard operation level assessment using the second neural network model (see e.g. paragraphs 0002 and 0026: like noted above, Ghosh describes a system that receives a repair request associated with equipment, and uses at least one machine learning model to determine a repair action based on the received repair request. As further noted above, Ghosh discloses that the repair request includes sensor data from the equipment to be repaired, whereby non-time-series features are extracted from the sensor data using a first neural network model, e.g. an LSTM-based model or set of convolutional filters – see e.g. paragraphs 0087-0088, 0091-0092 and 0132-0145. The non-time-series features are particularly output in the form of a vector – see e.g. paragraphs 0136-0138 and 0143-0145. Moreover, Ghosh further discloses that the repair request can also include non-time-series data such as equipment attributes, usage data and event data – see e.g. paragraphs 0030-0032, 0034-0035 and 0088. Similar to the sensor data, this non-time-series data is processed to extract feature vectors from the non-time-series data – see e.g. paragraphs 0072-0082, 0091-0092, 0115-0120 and 0127-0131. To determine the repair action in response to the repair request, the feature vectors extracted from the sensor data, equipment attributes, usage data, etc. are concatenated and input to the machine learning model, which is in the form of one or more fully connected neural networks – see e.g. paragraphs 0092-0093, 0191, 0207-0208 and 0226-0227. The machine learning model outputs a repair action based on this input, wherein the repair action is represented within a hierarchy of systems and corresponding repair actions – see e.g. paragraphs 0208-0209 and 0227-0230. The repair action can indicate, for example, a component of the equipment in need of repair and the particular repair action to be performed – see e.g. paragraph 0209. Such a repair action is considered a “standard operation level assessment” like claimed, particularly when given its broadest reasonable interpretation, as the repair action indicates the operation level – or lack thereof – of components of the equipment. The machine learning model is a neural network model – see e.g. paragraphs 0045, 0083 and 0191 – and is thus considered a “second neural network model” like claimed. Accordingly, Ghosh teaches a second neural network model-based standard operation level prediction unit, which receives non-time-series data of the target equipment, e.g. the equipment attributes and usage data, and data and vectors with non-time-series feature derived from the first neural network model-based feature prediction unit, i.e. the extracted sensor feature vectors, and predicts a standard operation level assessment, i.e. a repair action, using a second neural network model.).
Ghosh teaches that such tasks can be realized with a service computing device comprising the above-described first and second neural network models (see e.g. paragraphs 0029 and 0037-0042). Ghosh further discloses that the equipment can be industrial (e.g. manufacturing) equipment (see e.g. paragraph 0052). Accordingly, when applied to industrial equipment, the service computing device described by Ghosh is considered an industrial equipment operation control device like in claim 1, and implements a method like in claim 12 for operating an industrial equipment operation control device.
As per claim 2, Ghosh further teaches that the non-time-series data includes preprocessed data that combines category data and quantitative analysis data, each corresponding to the target equipment, after preprocessing (see e.g. paragraphs 0030-0032, 0034-0035 and 0088: like noted above, Ghosh discloses that the repair request can include a combination of non-time-series data such as equipment attributes, usage data and event data. Ghosh discloses that some of the non-time-series data, e.g. the equipment attributes, includes category data corresponding to the target equipment – see e.g. paragraphs 0031 and 0066. Others of the non-time-series data, e.g. the usage data, includes quantitative analysis data corresponding to the target equipment – see e.g. paragraph 0032. Ghosh further discloses that, before being input to the machine learning model, the non-time-series data is preprocessed, e.g. to normalize the data and extract features therefrom – see e.g. paragraphs 0065-0082, 0088-0092, 0115-0120 and 0127-0131. Accordingly, the non-time-series data includes preprocessed data that combined category data, e.g. equipment attributes, and quantitative analysis data, e.g. usage data, each corresponding to the target equipment, after preprocessing.). Accordingly, Ghosh further teaches a device like that of claim 2.
As per claim 3, Ghosh further teaches that the categorical data includes vector data that has been preprocessed with one-hot encoding of classification information pre-configured to the target equipment (see e.g. paragraphs 0031 and 0066: like noted above, Ghosh discloses that some of the non-time-series data, e.g. the equipment attributes, includes categorical data corresponding to the target equipment. Ghosh further discloses that, before being input to the machine learning model, such categorical data is preprocessed into vector data in part by using one-hot encoding of classification information pre-configured corresponding to the target equipment – see e.g. paragraphs 0098 and 0115-0120.). Accordingly, Ghosh further teaches a device like that of claim 3.
As per claim 4, Ghosh further teaches that the quantitative analysis data includes at least one of the non-time-series data that has been normalized and preprocessed, corresponding to the operation time, age, weight, size, average daily power consumption, and rated output acquired in relation to the target equipment (see e.g. paragraphs 0032 and 0066: like noted above, Ghosh discloses that some of the non-time-series data, e.g. the usage data, includes quantitative analysis data corresponding to the target equipment. Ghosh particularly discloses that the usage data includes non-time-series data corresponding to one or more of an operation time, age, mileage, payloads, etc. associated with the target equipment – see e.g. paragraph 0032. Moreover, Ghosh discloses that the usage data is normalized and preprocessed before being input to the machine learning model – see e.g. paragraphs 0127-0131. Ghosh is thus further considered to teach quantitative analysis data, i.e. usage data, that includes at least one of the non-time-series data that has been normalized and preprocessed, corresponding to the operation time, age, weight, size, average daily power consumption, and rated output in related to the target equipment.). Accordingly, Ghosh further teaches a device like that of claim 4.
As per claims 5 and 13, Ghosh further discloses that the data with non-time-series features are characterized as feature vectors derived from the time-series data of the target equipment being input into the first neural network model (see e.g. paragraphs 0132-0145, and FIGS. 8 and 9: Ghosh discloses that the data with non-time-series features are characterized as feature vectors derived from the time-series data, i.e. the sensor data, of the target equipment being input into the first neural network model, i.e. into the LSTM-based model or set of 1-D convolutional filters.). Accordingly, Ghosh further teaches a device and method like that of claims 5 and 13, respectively.
As per claims 6 and 14, Ghosh further teaches that the second neural network model is characterized by receiving data combining the data with the non-time-series feature and the non-time-series data of the target equipment (see e.g. paragraphs 0092-0093, 0191, 0207-0208 and 0226-0227: like noted above, Ghosh discloses that the feature vectors extracted from the sensor data, equipment attributes, usage data, etc. are concatenated and input to the machine learning model, which outputs one or more repair actions based on the input data. Accordingly, the machine learning model, i.e. the second neural network model, is characterized by receiving data combining the data with the non-time-series features, i.e. the extracted sensor data feature vector, and the non-time-series data, e.g. the extracted equipment attributes and usage data feature vectors, of the target equipment.). Accordingly, Ghosh further teaches a device and method like that of claims 6 and 14, respectively.
As per claims 7 and 15, Ghosh further teaches that an equipment operation control unit of the industrial equipment operation control device executes a control process that performs operation control of the target equipment based on the standard operation level assessment (see e.g. paragraphs 0038, 0040, 0052-0054, 0087-0093 and 0225-0227: Ghosh discloses that the service computing device executes a repair management program, which, like described above: (i) receives a repair request associated with target equipment; (ii) in response, prepares and extracts feature vectors from sensor data, equipment attributes, usage data, etc. associated with the target equipment; and (iii) applies the extracted feature vectors to the machine learning model, which outputs a corresponding repair action. Ghosh further discloses that the repair management program subsequently executes a repair plan execution program, which determines and executes a repair plan based on the repair action output by the machine learning model – see e.g. paragraphs 0054-0055, 0094-0095 and 0227-0233. The repair plan execution program can particularly cause the equipment to execute instructions to perform or initiate the repair itself, such as by changing operating conditions of the equipment – see e.g. paragraphs 0055, 0095 and 0232. Accordingly, the processor of the service computing device, i.e. the industrial equipment control device, executing the repair management program or repair plan execution program is considered “an equipment operation control unit” like claimed, which executes a control process that performs operation control of the target equipment based on the standard operation level assessment, i.e. causes the equipment to execute instructions to perform or initiate the repair itself, based on the repair action output by the machine learning model.). Accordingly, Ghosh further teaches a device and method like that of claims 7 and 15, respectively.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 8, 9, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the U.S. Patent Application Publication to Ghosh described above, and also over WIPO Publication No. WO 2019/229081 A1 to Numajiri (“Numajiri”).
Regarding claim 8, Ghosh teaches a device like that of claim 7, as is described above, whereby a neural network model is used to predict a standard operation level assessment (i.e. a repair action), and an equipment operation control unit performs operation control of target equipment based on the standard operation level assessment. Ghosh, however, does not explicitly disclose that the equipment operation control unit includes a standard operation level guide unit that outputs standard operation level guide information based on the standard operation level assessment, which includes at least one of the load range, operation voltage or optimal output of the target equipment, as is required by claim 8.
Numajiri generally describes a device (i.e. a power transmission device) having a plurality of driving machines, a gear member, and a controller that controls the plurality of driving machines based on damage to the gear member (see e.g. page 1, line 25 – page 2, line 20). In particular, Numajiri teaches identifying damaged or deteriorated components (e.g. a gear member) of the device based on sensor data, inter alia (see e.g. page 14, lines 1-15; and page 24, lines 9-21). In response to identifying the damaged or deteriorated components, the controller changes a load (i.e. torque) range associated with the damaged or deteriorated components, and thereby reduces the risk of damage expansion and extends the service life of the components (see e.g. page 2, lines 1-20; page 18, line 19 – page 19, line 7; page 20, line 24 – page 21, line 22; and page 24, line 15 – page 25, line 18).
It would have been obvious to one of ordinary skill in the art, having the teachings of Ghosh and Numajiri before the effective filing date of the claimed invention, to modify the industrial equipment operation control device taught by Ghosh such that the standard operation level assessment (i.e. repair action) additionally or alternatively indicates to change a load range associated with the target equipment, as is taught by Numajiri. It would have been advantageous to one of ordinary skill to utilize such a combination because it can reduce the risk of damage expansion and extend the service life of the equipment’s components, as is taught by Numajiri (see e.g. page 2, lines 14-20; page 18, line 19 – page 19, line 7; and page 24, line 22 – page 25, line 3). The processor executing the programming necessary to determine and output the repair actions (including the changed load range) for controlling the equipment as such is considered an “equipment operation control unit” that includes a “standard operation level guide unit” that outputs standard operation level guide information (i.e. instructions to control the equipment) based on the standard operation level assessment (i.e. the repair action, including the changed load range), which includes at least one of the load range, operation voltage, or optimal output of the target equipment, as is claimed. Accordingly, Ghosh and Numajiri are considered to teach, to one of ordinary skill in the art, a device like that of claim 8.
Regarding claim 9, Ghosh teaches a device like that of claim 7, as is described above, whereby a neural network model is used to predict a standard operation level assessment (i.e. a repair action), and an equipment operation control unit performs operation control of target equipment based on the standard operation level assessment. Ghosh, however, does not explicitly disclose that the equipment operation control unit includes an appropriate load range adjustment unit that varies the appropriate load range of the target equipment based on the standard operation level assessment, as is required by claim 9.
Nevertheless, like noted above, Numajiri generally teaches changing a load (i.e. torque) range associated with damaged or deteriorated components, and thereby reducing the risk of damage expansion and extending the service life of the components (see e.g. page 2, lines 1-20; page 18, line 19 – page 19, line 7; page 20, line 24 – page 21, line 22; and page 24, line 15 – page 25, line 18).
Like further noted above, it would have been obvious to one of ordinary skill in the art, having the teachings of Ghosh and Numajiri before the effective filing date of the claimed invention, to modify the industrial equipment operation control device taught by Ghosh such that the standard operation level assessment (i.e. repair action) additionally or alternatively indicates to change a load range associated with the target equipment, as is taught by Numajiri. It would have been advantageous to one of ordinary skill to utilize such a combination because it can reduce the risk of damage expansion and extend the service life of the equipment’s components, as is taught by Numajiri (see e.g. page 2, lines 14-20; page 18, line 19 – page 19, line 7; and page 24, line 22 – page 25, line 3). The processor executing the programming necessary to determine and output the repair actions (including the changed load range) for controlling the equipment as such is considered an “equipment operation control unit” that includes an “appropriate load range adjustment unit that varies the appropriate load range of the target equipment based on the standard operation level assessment, as is claimed. Accordingly, Ghosh and Numajiri are further considered to teach, to one of ordinary skill in the art, a device like that of claim 9.
Regarding claim 16, Ghosh teaches a method like that of claim 15, as is described above, whereby a neural network model is used to predict a standard operation level assessment (i.e. a repair action), and a control process performs operation control of target equipment based on the standard operation level assessment. Ghosh, however, does not explicitly disclose that the control process is characterized by outputting standard operation level guide information based on the standard operation level assessment, which includes at least one of the load range, operation voltage, or optimal output of the target equipment, as is required by claim 16.
Nevertheless, like noted above, Numajiri generally teaches changing a load (i.e. torque) range associated with damaged or deteriorated components, and thereby reducing the risk of damage expansion and extending the service life of the components (see e.g. page 2, lines 1-20; page 18, line 19 – page 19, line 7; page 20, line 24 – page 21, line 22; and page 24, line 15 – page 25, line 18).
It would have been obvious to one of ordinary skill in the art, having the teachings of Ghosh and Numajiri before the effective filing date of the claimed invention, to modify the method taught by Ghosh such that the standard operation level assessment (i.e. repair action) additionally or alternatively indicates to change a load range associated with the target equipment, as is taught by Numajiri. The control process that performs operation control of the target equipment based on such a standard operation level assessment would thus be characterized by the output of standard operation level guide information (e.g. instructions to change the load) based on the standard operation level assessment, which includes at least one of the load range, operation voltage or optimal output of the target equipment. It would have been advantageous to one of ordinary skill to utilize such a combination because it can reduce the risk of damage expansion and extend the service life of the equipment’s components, as is taught by Numajiri (see e.g. page 2, lines 14-20; page 18, line 19 – page 19, line 7; and page 24, line 22 – page 25, line 3). Accordingly, Ghosh and Numajiri are further considered to teach, to one of ordinary skill in the art, a method like that of claim 16.
Regarding claim 17, Ghosh teaches a method like that of claim 15, as is described above, whereby a neural network model is used to predict a standard operation level assessment (i.e. a repair action), and a control process performs operation control of target equipment based on the standard operation level assessment. Ghosh, however, does not explicitly disclose that the control process is characterized by: (i) varying the appropriate load range of the target equipment based on the standard operation level assessment; or (ii) varying the target output level of the target equipment based on the standard operation level assessment, as is required by claim 17.
Nevertheless, like noted above, Numajiri generally teaches changing a load (i.e. torque) range associated with damaged or deteriorated components, and thereby reducing the risk of damage expansion and extending the service life of the components (see e.g. page 2, lines 1-20; page 18, line 19 – page 19, line 7; page 20, line 24 – page 21, line 22; and page 24, line 15 – page 25, line 18).
Like further noted above, it would have been obvious to one of ordinary skill in the art, having the teachings of Ghosh and Numajiri before the effective filing date of the claimed invention, to modify the method taught by Ghosh such that the standard operation level assessment (i.e. repair action) additionally or alternatively indicates to change a load range associated with the target equipment, as is taught by Numajiri. The control process that performs operation control of the target equipment based on such a standard operation level assessment would thus be characterized by varying the appropriate load range of the target equipment based on the standard operation level assessment. It would have been advantageous to one of ordinary skill to utilize such a combination because it can reduce the risk of damage expansion and can extend the service life of the equipment’s components, as is taught by Numajiri (see e.g. page 2, lines 14-20; page 18, line 19 – page 19, line 7; and page 24, line 22 – page 25, line 3). Accordingly, Ghosh and Numajiri are further considered to teach, to one of ordinary skill in the art, a method like that of claim 17.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over the U.S. Patent Application Publication to Ghosh described above, and also over U.S. Patent Application Publication No. 2012/0156034 to Sabannavar et al. (“Sabannavar”).
Regarding claim 10, Ghosh teaches a device like that of claim 7, as is described above, whereby a neural network model is used to predict a standard operation level assessment (i.e. a repair action), and an equipment operation control unit performs operation control of target equipment based on the standard operation level assessment. Ghosh, however, does not explicitly disclose that the equipment operation control unit includes a target output configuration unit that varies the target output level of the target equipment based on the standard operation level assessment, as is required by claim 10.
Sabannavar generally teaches remotely monitoring gearbox component health via sensors coupled to the component and/or neighboring components (see e.g. paragraph 0025). In particular, Sabannavar teaches identifying damaged or deteriorated components (e.g. a gearbox component) based on the sensor data (see e.g. paragraphs 0004-0006, 0052 and 0058). Sabannavar further teaches, in response to identifying the damaged or deteriorated components, varying the target output level of the target equipment so as to prevent or delay failure (see e.g. paragraph 0034).
It would have been obvious to one of ordinary skill in the art, having the teachings of Ghosh and Sabannavar before the effective filing date of the claimed invention, to modify the industrial equipment operation control device taught by Ghosh such that the standard operation level assessment (i.e. repair action) additionally or alternatively indicates to vary the target output level of the target equipment like taught by Sabannavar. It would have been advantageous to one of ordinary skill to utilize such a combination because it can prevent or delay failure of the target equipment, as is taught by Sabannavar (see e.g. paragraph 0034). The processor executing the programming necessary to determine and output the repair actions (including the varied output) for controlling the equipment as such is considered an “equipment operation control unit” that further includes a “target output configuration unit” that varies the target output level of the target equipment based on the standard operation level assessment (i.e. the repair action, including the varied output), as is claimed. Accordingly, Ghosh and Sabannavar are considered to teach, to one of ordinary skill in the art, a device like that of claim 10.
Claims 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the U.S. Patent Application Publication to Ghosh described above, and also over the article entitled, “A Unified Approach to Interpreting Model Predictions” by Lundberg et al. (“Lundberg”).
Regarding claim 11, Ghosh teaches a device like that of claim 7, as is described above, whereby a neural network model is used to predict a standard operation level assessment (i.e. a repair action), and an equipment operation control unit performs operation control of target equipment based on the standard operation level assessment. Ghosh, however, does not explicitly disclose that the equipment operation control unit includes a predictor variable analysis unit that analyzes how much a variable has influenced the outcome based on the standard operation level assessment, as is required by claim 11.
Lundberg nevertheless generally teaches analyzing how much a variable (i.e. a feature) influences a particular prediction output by a machine learning model based on the prediction (see e.g. the Abstract, which recites: “Understanding why a model makes a certain prediction can be as crucial as the prediction’s accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction.”).
It would have been obvious to one of ordinary skill in the art, having the teachings of Ghosh and Lundberg before the effective filing date of the claimed invention, to modify the industrial equipment operation control device taught by Ghosh so as to further analyze how much a variable has influenced the outcome (i.e. the predicted standard operation assessment) of the machine learning model based on the prediction (i.e. the predicted standard operation assessment), as is taught by Lundberg. It would have been advantageous to one of ordinary skill to utilize such an analysis because it can help users interpret the predictions, as is taught by Lundberg (see e.g. the Abstract). The processor executing the programming necessary to analyze how much a variable has influenced the outcome like taught by Lundberg is considered “a predictor variable analysis unit’ like claimed. Accordingly, Ghosh and Lundberg are considered to teach, to one of ordinary skill in the art, a device like that of claim 11.
Regarding claim 18, Ghosh teaches a method like that of claim 15, as is described above, whereby a neural network model is used to predict a standard operation level assessment (i.e. a repair action), and a control process performs operation control of target equipment based on the standard operation level assessment. Ghosh, however, does not explicitly teach that the control process is characterized by analyzing how much a variable has influenced the outcome based on the standard operation level assessment, as is required by claim 18.
Nevertheless, like noted above, Lundberg generally teaches analyzing how much a variable (i.e. a feature) influences a particular prediction output by a machine learning model based on the prediction (see e.g. the Abstract).
It would have been obvious to one of ordinary skill in the art, having the teachings of Ghosh and Lundberg before the effective filing date of the claimed invention, to modify the method taught by Ghosh so as to further analyze how much a variable has influenced the outcome (i.e. the predicted standard operation assessment) of the machine learning model based on the prediction (i.e. the predicted standard operation assessment), as is taught by Lundberg. It would have been advantageous to one of ordinary skill to utilize such an analysis because it can help users interpret the predictions, as is taught by Lundberg (see e.g. the Abstract). Accordingly, Ghosh and Lundberg are further considered to teach, to one of ordinary skill in the art, a method like that of claim 18.
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant’s disclosure. The applicant is required under 37 C.F.R. §1.111(C) to consider these references fully when responding to this action. In particular, the article by Chen et al. cited therein (“Time Series Data for Equipment Reliability Analysis with Deep Learning”) describes a deep learning-based approach that uses time series data for equipment reliability analysis. The article by Kim et al. cited therein (“Wafer Edge Yield Prediction Using a Combined Long Short-Term Memory and Feed Forward Neural Network Model for Semiconductor Manufacturing”) describes a method for wafer edge yield prediction using a combined long short-term memory (LSTM) and feed-forward neural network (FFNN) model, whereby time-series data are input to the LSTM, and non-time-series data are input to the FFNN. The U.S. Patent Application Publication to Oh et al. cited therein describes an equipment diagnosis system that includes a data acquisition unit to acquire time series data of equipment, a preprocessing unit to convert the time series data into frequency data, a deep learning unit to perform deep learning by using the frequency data, and a diagnosis unit to determine a state of the equipment based on the deep learning.
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/BTB/
6/12/2026
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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