DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is non-final and is in response to the claims filed July 16, 2024. Claims 1-12 are currently pending, of which claims 1-12 are currently rejected.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Objections
Claim 8 is objected to because of the following informalities:
Claim 8 recites “at least one of a accuracy score, precision score…” and there appears to be typographical/grammatical issues. The claim should read “at least one of an accuracy score, a precision score…”
Appropriate correction is required.
Claim Interpretation – 35 USC 112(f)
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: “collection unit”, “detection unit”, and “execution unit” in claims 1-10.
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 Interpretation – Contingent Language
Claim 11 is directed to a method that recites “in a case where the detection result that is output from the trained learning model is different…”. The conditional nature of this claim language allows for an interpretation where any prior art meets the broadest reasonable interpretation of the claim without having the conditional language even occurring. Therefore, the prior art only needs to read on the “collecting” and “inputting” limitations of the claim. If there is never a determination that the detection result is different from a determination result, then this claim language never has to occur. Applicant should explicitly recite that determination occurring. See MPEP 2111.04(II); see also Ex parte Schulhauser. Examiner notes that even though the broadest reasonable interpretation of the method of claim(s) 12 requires none of these conditions, the prior art cited below reads on the structure for performing all of the functionality in the non-method claims, and thus, in an effort to advance compact prosecution, reads on all of the method claim(s) as well.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 6, and 10 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.
Claim 4 recites “that is the output from the training learning model and the determination result” and it is unclear, based on the structure of the claim language, whether the detection result is part of just the output from the trained learning model or both part of (1) the output from the trained learning model and (2) the determination result.
Claim 6 recites “a detection result” and it is unclear if this is a new detection result or the same detection result introduced in claim 1.
Claim 10 recites “display the screen generated, in a form enabling a switchover from display or non-display through manipulation by the user” and the metes and bounds of this claim cannot be determined. Specifically, it is unclear how a display of a terminal generates a screen in both a “display” and “non-display” mode. The screen is generated either way and having the display be a non-display is contradictory and confusing. Moreover, the claim could be referring to the display and non-display of a type of data on the graph, which is an entirely different interpretation. For purposes of this Action, the claim will be interpreted as “enabling a switchover from display or non-display of the normal state through manipulation by the user”.
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 an abstract idea(s) without significantly more.
As per claim 11, at Step 1, the claim is directed to a statutory category of invention (method/process). At Step 2A, Prong 1, the claim language has been reproduced below and the abstract ideas addressed therein:
A detection method that causes a computer to execute a process comprising:
collecting measurement data measured by a measurement device (mental process – observation and evaluation);
inputting the measurement data collected, into a trained learning model and obtaining a detection result that is output from the trained learning model, the trained learning model being a learning model that predicts a predetermined event in response to input of measurement data (mental process – evaluation and judgment); and
executing, in a case where the detection result that is the output from the trained learning model is different from a determination result by a user who has checked the measurement device for which the predetermined event was predicted, retraining of the trained learning model by using a label value input by the user and the measurement data collected, and thereby generating a retrained model.
Examiner first notes that due to the dependent claim language, the “executing” step does not explicitly have to occur and therefore is different from eligible independent claims 1 and 12. Herein, a user could merely measure data and use that data to predict events based on historical data and trends. At Step 2A, Prong 2, the additional elements have been bolded above. Specifically, the computer and measurement device are general computing components that are essentially adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Moreover, at this level of generality, he learning model is merely an evaluation tool. However, if this too were an additional element, it would be another “apply it” scenario, and/or generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP 2106.05(f) and (h). Therefore, these additional elements are not indicative of integration into a practical application.
At Step 2B, there are no additional elements that amount to significantly more than the judicial exception(s), for at least the same reasons as discussed above in Step 2A, Prong 2.
Claim 12 is/are further rejected under 35 U.S.C. 101, based upon consideration of all the relevant factors, because the claimed invention is directed to non-statutory subject matter. Applicant does not include any language in the specification providing any further guidance or definition to the term “computer-readable recording medium.” Therefore, the broadest reasonable interpretation of the claim covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer-readable recording medium. See Ex parte Mewherter, BPAI Appeal No. 2012-7692 (May 8, 2013). Since signals per se do not fall under any of the four statutory categories (i.e., process, machine, manufacture, or composition of matter), they are ineligible for patenting. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007).
Claim 12 is therefore rejected under 35 U.S.C. § 101 for covering non- statutory embodiments. See MPEP § 2106.03 (“A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. Such claims … should be rejected under 35 U.S.C. 101, for at least this reason.”).
Examiner recommends amending claim 12 by adding the words “non-transitory” in front of “computer-readable recording medium” in order to overcome this ground of rejection.
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-6, 8, 11, and 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gilberton et al. (U.S. Publication No. 2022/0277225, retrieved from IDS filed March 24, 2025; hereinafter, “Gilberton”).
As per claim 1, Gilberton teaches a detection apparatus comprising:
a collection unit that collects measurement data measured by a measurement device (See Gilberton paras. [0110-115]: data capture model that collects sensor data);
a detection unit that inputs the measurement data collected, into a trained learning model and obtains a detection result that is output from the trained learning model, the trained learning model being a learning model that predicts a predetermined event in response to input of measurement data (See Gilberton Fig. 1 and paras. [0110-117]: trained learning model(s) to predict anomalous events associated with current sensor values and measurements); and
an execution unit that executes, in a case where the detection result from the trained learning model is different from a determination result by a user who has checked the measurement device for which the predetermined event was predicted, retraining of the trained learning model by using a label value input by the user and the measurement data collected, and thereby generates a retrained model (See Gilberton paras. [0126-133] and [0142-143]: “generating a supplemental set of training data based on the user feedback and the sensor data from the plurality of N sensors, and re-training at least one of the N mono-modal models with the supplemental set of training data”; para. [0072-73]: data processor to execute instructions for anomaly detection).
As per claim 2, Gilberton further teaches the detection apparatus according to claim 1, wherein
the trained learning model is a learning model trained by unsupervised training using training data including each piece of measurement data on a normal value from plural pieces of measurement data collected in a predetermined time period (See Gilberton para. [0023]: model adapted from an unsupervised learning type), and
the execution unit executes retraining of the trained learning model by supervised training with teacher data assigned to the measurement data collected, the teacher data being the label value based on the determination result (See Gilberton paras. [0024-27] and [0126-138]: user feedback provided that is used to re-train the model if necessary).
As per claim 3, Gilberton further teaches the detection apparatus according to claim 1, wherein in a case where the detection result that is the output from the trained learning model corresponds to a predictor of an anomalous state and the anomalous state or an anomaly predicted state has not been affirmed by the determination result, the execution unit executes retraining of the trained learning model by using teacher data having the label value assigned to the measurement data collected, the label value being based on the determination result and indicating presence or absence of the anomalous state or the anomaly predicted state (See Gilberton paras. [0024-27] and [0143]: anomalous state determinations and user feedback associated therewith. This feedback can be used to re-train the models).
As per claim 4, Gilberton further teaches the detection apparatus according to claim 1, wherein the execution unit calculates an evaluation index of the trained learning model on the basis of the detection result that is the output from the trained learning model and the determination result (See Gilberton paras. [0126-133] and [0154]: determining the false detection rate).
As per claim 5, Gilberton further teaches the detection apparatus according to claim 4, wherein the execution unit executes retraining of the trained learning model on the basis of the evaluation index (See Gilberton paras. [0126-133] and [0154-155]: determining the false detection rate and can re-train the models if necessary).
As per claim 6, Gilberton further teaches the detection apparatus according to claim 1, wherein
the detection unit obtains a detection result that is output of each of plural trained learning models that predict the predetermined event (See Gilberton paras. [0123-125]: model output used in anomaly prediction), and
the execution unit
calculates an evaluation index of each of the plural trained learning models, on the basis of the detection result that is the output of each of the plural trained learning models and a determination result (See Gilberton paras. [0126-133] and [0154-155]: determining the false detection rate based on decision making at anomaly detection and the feedback provided), and
determines adoption of a trained learning model having the evaluation index equal to or larger than a predetermined value, from the plural trained learning models (See Gilberton paras. [01119-122] and [0126-134]: adapting one of the models based on the feedback and anomaly event as it relates to threshold S, which can be compared to the prediction “deciding that the final anomaly prediction p is an anomaly detection if it is greater than the threshold S”).
As per claim 8, Gilberton further teaches the detection apparatus according to claim 1, wherein the execution unit
calculates, as an evaluation index or evaluation indices of the trained learning model, at least one of a accuracy score, precision score, a recall score, specificity, and an F value, on the basis of the detection result that is the output of the trained learning model and the determination result by the user who has checked the measurement device for which the predetermined event was predicted (See Gilberton paras. [0118] and [0123-133]: feedback interface for adjusting anomaly calculations and weight factors), and
causes a terminal used by the user to display the evaluation index (See Gilberton paras. [0095] and [0015-109]: settings as they relate to the user feedback and profiles displayed in a user interface).
As per claim 11, the claim is directed to a method that implements the same features as the apparatus of claim 1, and is therefore rejected for at least the same reasons therein.
As per claim 12, the claim is directed to a computer-readable recording medium that implements the same features as the apparatus of claim 1, and is therefore rejected for at least the same reasons therein.
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) 7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gilberton as applied above, and further in view of Martin et al. (U.S. Publication No. 2022/0092112; hereinafter, “Martin”).
As per claim 7, further teaches the detection apparatus according to claim 1. However, while Gilberton teaches training and fine-tuning models, as well as “N mono-modal anomaly models” (See Gilberton paras. [0126-132]), Gilberton does not teach selecting one model from a plurality of models.
Martin teaches wherein the execution unit determines adoption of a trained learning model selected by the user from a plurality of the trained learning models (See Martin Fig. 2 and paras. [0066-70]: model selection from a plurality of models).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the data detection and model training of Gilbertson with the model selection of Martin. One would have been motivated to combine these references because both references disclose model training datasets, including with forecasting models. Martin further enhances the features of Gilbertson by providing improvements to how the models are selected, and the ease with which they are selected. This further includes “efficient interactions between the user interfaces and underlying systems and components” as well as “more efficient interaction with, and presentation of, various types of electronic data” (See Martin paras. [0013-14]).
As per claim 9, Gilberton further teaches the detection apparatus according to claim 1. However, while Gilberton displays a UI for the user, Gilbert does not graph time series data.
Martin generates a time series graph representing a history of the measurement data collected, causes a terminal used by the user to display the time series graph, and determines adoption of a learning model on the basis of the time series graph displayed (See Martin Figs. 2-4 and paras. [0054-55] and [0066-70]: selecting model from plurality of model. Model is associated with the graphed time series data; para. [0048]: time series data is a series of information referenced to time, including past values and measurements).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Gilberton with the teachings of Martin for at least the same reasons as discussed above in claim 7.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gilberton as applied above, and further in view of Balasubramanian et al. (U.S. Publication No. 2022/0172100 retrieved from IDS filed March 24, 2025; hereinafter, “Balasubramanian”).
As per claim 10, Gilberton teaches the detection apparatus according to claim 1. However, while Gilberton displays a UI for the user, Gilbert does not graph state data.
Balasubramanian teaches wherein the execution unit generates a screen indicating a time period of a normal state that is the detection result, and causes a terminal used by the user to display the screen generated, in a form enabling a switchover from display or non-display through manipulation by the user (See Balasubramanian Figs. 7-9, 19, and paras. [0080-85] and [0108-111]: filtering data at different points in time, showing information like normal data and the ability to change that display to a different type of data, such as abnormal data, where the normal data will no longer be displayed).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the data detection and UI features of Gilbertson with the data display filtering of Balasubramanian. One would have been motivated to combine these references because both references disclose model training datasets and user interfaces to interact therewith. Balasubramanian further enhances the features of Gilbertson by allowing for increased visualization as the models are trained and re-trained. This allows users to easily identify different features that may be incorrectly marked in Gilbertson, thereby further “learning from human expertise and getting better over time” (See Balasubramanian paras. [0035-36]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas Klicos whose telephone number is (571)270-5889. The examiner can normally be reached Mon-Fri 9:00 AM-5:00 PM.
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/NICHOLAS KLICOS/Primary Examiner, Art Unit 2118
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