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 .
Detailed Action
The following non-final action is in response to application 18/579,318 filed on 01/12/2024. The communication is the first action on the merits.
Status of Claims
Claims 1-23 are currently pending and have been rejected as follows.
Drawings
The drawings filed on 01/12/2024 are accepted.
Domestic Benefit/National Stage
Applicant’s claim to Domestic Benefit/National Stage has been acknowledged and the corresponding documents have been received.
IDS
The IDS has been received, and the documents within it have been considered.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 13 and 23 are rejected on the grounds of nonstatutory double patenting as being unpatentable over claims 1, 11 and 20 of co-pending application 18/579,319 (hereinafter “’319”). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 13 and 23 are obvious over claims 1, 11 and 20 of the ‘319 co-pending application in view of Zoll (US 20180083833 A1).
Instant Application ‘318
Co-pending application ‘319
Claim 1
Claim 1
A method of detecting or predicting an aspiration fault in an automated diagnostic analysis system, the method comprising: performing aspiration pressure measurements via a pressure sensor as a liquid is being aspirated in the automated diagnostic analysis system; analyzing an aspiration pressure measurement signal waveform via a processor executing an artificial intelligence (AI) algorithm configured to perform: cluster analysis of the aspiration pressure measurement signal waveform, or probabilistic graphical modeling based on the aspiration pressure measurement signal waveform; and identifying and responding to an aspiration fault via the processor in response to the analyzing.
A method of detecting a short-sample aspiration fault in an automated diagnostic analysis system, the method comprising: performing aspiration pressure measurements via a pressure sensor as a liquid is being aspirated in the automated diagnostic analysis system; analyzing an aspiration pressure measurement signal waveform via a processor executing an algorithm configured to derive a slope waveform from the aspiration pressure measurement signal waveform and to compute: a moving average of the slope waveform, or a wavelet transform of the slope waveform; and identifying and responding to a short-sample aspiration fault via the processor in response to the analyzing.
Claim 13
Claim 11
An automated aspirating and dispensing apparatus, comprising: a robotic arm; a probe coupled to the robotic arm; a pump coupled to the probe; a pressure sensor configured to perform aspiration pressure measurements as a liquid is being aspirated via the probe; and a processor configured to execute an artificial intelligence (AI) algorithm to detect or predict and respond to an aspiration fault during an aspiration process, the AI algorithm configured to analyze an aspiration pressure measurement signal waveform derived from the pressure sensor using cluster analysis or probabilistic graphical modeling.
An automated aspirating and dispensing apparatus, comprising: a robotic arm; a probe coupled to the robotic arm; a pump coupled to the probe; a pressure sensor configured to perform aspiration pressure measurements as a liquid is being aspirated via the probe; and a processor configured to execute an algorithm to detect and respond to a short-sample aspiration fault during an aspiration process, the algorithm configured to analyze an aspiration pressure measurement signal waveform received from the pressure sensor by deriving a slope waveform from the aspiration pressure measurement signal waveform and performing a spectral analysis of the slope waveform by computing a moving average or a wavelet transform of the slope waveform.
Claim 23
Claim 20
A non-transitory computer-readable storage medium, comprising an artificial intelligence (AI) algorithm configured to detect or predict an aspiration fault based on analysis of an aspiration pressure measurement signal waveform using cluster analysis of the aspiration pressure measurement signal waveform or using probabilistic graphical modeling based on the aspiration pressure measurement signal waveform.
A non-transitory computer-readable storage medium,
comprising a processor-executable algorithm configured to
detect a short-sample aspiration fault based on spectral analysis of a pressure slope waveform derived from an aspiration pressure measurement signal waveform, the algorithm
configured to perform the spectral analysis of the pressure
slope waveform by computing a moving average or a wavelet
transform of the pressure slope waveform.
Regarding claims 1, 13 and 23, co-pending application ‘319 does not explicitly teach executing an artificial intelligence (AI) algorithm and using cluster analysis or probabilistic graphical modeling.
Zoll (US 20180083833) teaches probabilistic graphical modeling (Any suitable approach may be taken to perform fault diagnosis. One possible approach in some embodiments is to employ a Bayesian network 303 to perform the fault diagnosis. Bayesian network analysis is an approach to use a probabilistic graphical model to identify probabilistic relationships between different variables and their probable dependencies [0043]).
Zoll also teaches executing an artificial intelligence (AI) algorithm (One or more predictive models 122 are employed which have been generated based upon training data corresponding to the component-level signal data. The model training process, such as a supervised learning algorithm, creates the predictive models that function to identify predicted values for certain signals collected at the component level. Any suitable approach can be taken in the various embodiments to perform model training for any suitable model type, including for example, decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and/or ensemble classification models [0029]).
It would have been obvious before the effective filing date of the claimed invention to modify the algorithm used in co-pending application ‘319 to use probabilistic graphical modeling performed by an artificial intelligence (AI) algorithm in aspiration fault detection in view of Zoll to achieve more accurate classification and fault detection and better identify uncertainty by modeling complex cause-effect relationships between variables, which can provide a better diagnostic reasoning (see Zoll in [0003-4]).
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Claim 1 recites:
A method of detecting or predicting an aspiration fault in an automated diagnostic analysis system, the method comprising:
performing aspiration pressure measurements via a pressure sensor as a liquid is being aspirated in the automated diagnostic analysis system;
analyzing an aspiration pressure measurement signal waveform via a processor executing an artificial intelligence (AI) algorithm configured to perform:
cluster analysis of the aspiration pressure measurement signal waveform, or probabilistic graphical modeling based on the aspiration pressure measurement signal waveform;
and identifying and responding to an aspiration fault via the processor in response to the analyzing.
The bolded language in the claim limitations indicate abstract ideas, and the remaining limitations are considered to be additional elements.
Step 1 – Statutory Categories of Invention:Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a process claim. Claims 13 and 23 are machine claims.
Step 2A – Judicial Exception Analysis, Prong 1:
Under Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Under Step 2A, Prong One, the broadest reasonable interpretation consistent with the specification of the limitations recited in Claim 1 recite at least one judicial exception, that being a mental process (observations/evaluation/judgement/ or opinion). and a mathematical concept (mathematical calculations/relationships/formulas/ or equations).
The claim limitation of “analyzing an aspiration pressure measurement signal waveform configured to perform: cluster analysis of the aspiration pressure measurement signal waveform, or probabilistic graphical modeling based on the aspiration pressure measurement signal waveform” recites mathematical calculations and relationships and thus falls into the category of mathematical concept.
The claim limitations of “a method of detecting or predicting an aspiration fault in an automated diagnostic analysis system” and “identifying and responding to an aspiration fault in response to the analyzing” involve determining for each sampled time instant of the aspiration pressure measurement signal waveform, [0074-0076]
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An aspiration fault may also be identified by executing concurrently two HMMs (one trained for normal aspiration and the other trained for abnormal aspiration)…sequence likelihood first and second thresholds may be applied to each sampled time instant of the aspiration pressure measurement signal waveform to identify normal aspirations as follows:
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Wherein…the first threshold may be 0.90 and the second threshold may be 0.50…the sequence likelihood is computed based on a composite probability of an observation sequence (length = 15) conditioned on the corresponding known state sequence [0083-0088].
The claim limitations recite mathematical calculations and relationships and thus falls into the category of mathematical concept.
Claims 13 and 23 recite similar abstract ideas.
Step 2A – Judicial Exception Analysis, Prong 2:
Step 2A, Prong Two of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55.
Claim 1 recites the following additional elements:“performing aspiration pressure measurements via a pressure sensor as a liquid is being aspirated in the automated diagnostic analysis system”Claim 13 recites similar additional elements.
These claim limitations generically recite collecting/outputting by sensors/devices measurement data (all independent claims), which represents the insignificant extra-solution activity of mere data gathering/outputting results. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Claim 1 also recites the additional element:
“a processor”
Claim 13 also recites the additional elements:
“a processor”
Claim 23 also recites the additional elements:
“A non-transitory computer-readable storage medium”
The recitation of a processor and corresponding hardware amount to a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 13 also recites the additional elements:
“An automated aspirating and dispensing apparatus”
“a robotic arm; a probe coupled to the robotic arm; a pump coupled to the probe; a pressure sensor…”
Dependent claim 22 recites the additional elements: “the automated aspirating and dispensing apparatus of claim 13; one or more analyzer stations for analyzing a biological sample; and an automated track for transporting sample containers and reaction vessels to and from the automated aspirating and dispensing apparatus and the one or more analyzer stations”.
The specification does not provide any additional detail regarding the aspirating and dispensing apparatus outside generic descriptions of a “robotic arm and other automated mechanisms” to perform the aspiration and dispensing (see at least [004-5, 0042-44]. The use of the automated aspirating and dispensing apparatus therefore amounts to a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself (MPEP § 2106.05(h) similar to example vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) wherein the additional elements do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use).
Claim 1 also recites the additional element: “a processor executing an artificial intelligence (AI) algorithm configured to perform” Claims 13 and 23 recite similar additional elements.
The limitation reciting “a processor executing an artificial intelligence (AI) algorithm configured to perform” provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The judicial exception of “analyzing an aspiration pressure measurement signal waveform” and “performing cluster analysis of the aspiration pressure measurement signal waveform, or probabilistic graphical modeling based on the aspiration pressure measurement signal waveform” is performed using the AI algorithm. The trained AI algorithm is used to generally apply the abstract idea without placing any limits on how the trained AI algorithm functions. Rather, these limitations only recite the outcome of “identifying and responding to an aspiration fault via the processor in response to the analyzing” and do not include any details about how the “identifying and responding” are accomplished. See MPEP 2106.05(f).
The recitation of using an “AI algorithm” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “a processor executing an artificial intelligence (AI) algorithm configured to perform” limits the identified judicial exceptions “identifying and responding to an aspiration fault via the processor in response to the analyzing”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (cluster analysis or probabilistic graphical modeling) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
Step 2B – Additional Elements that Amount to Significantly More:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 1 also recites: “a processor”. Claim 13 also recites: “a processor”. Claim 23 recites: “A non-transitory computer-readable storage medium”. Claim 1 also recites: “a processor executing an artificial intelligence (AI) algorithm configured to perform” Claims 13 and 23 recite similar additional elements.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claim 13 also recites: “An automated aspirating and dispensing apparatus”; “a robotic arm; a probe coupled to the robotic arm; a pump coupled to the probe; a pressure sensor…”. Dependent claim 22 recites the automated aspirating and dispensing apparatus of claim 13; one or more analyzer stations for analyzing a biological sample; and an automated track for transporting sample containers and reaction vessels to and from the automated aspirating and dispensing apparatus and the one or more analyzer stations. These limitations generally link the use of the judicial exception to a particular technological environment or field of use similar to vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) and thus do not amount to more than the judicial exception applied to a technological environment (see MPEP § 2106.05(g) stating “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use.”).
Claim 1 recites the following additional elements: “performing aspiration pressure measurements via a pressure sensor as a liquid is being aspirated in the automated diagnostic analysis system”. Claim 13 recites similar additional elements. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claims 1, 13 and 23 amount to significantly more than the abstract idea.
With regards to dependent claims 2-12 and 14-21, they provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claims 1-3, 9-10, 12-13, 16-20 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Dunfee (US 20190234787 A1) in view of Salunke (US 20200351283 A1).
Regarding claim 1, Dunfee teaches a method of detecting or predicting an aspiration fault in an automated diagnostic analysis system, the method comprising (a method for detecting aspiration in a clinical analyzer…and determining, based on the comparison, if an aspiration was properly performed [Abstract] and a monitoring system for detecting aspiration abnormalities where an embodiment's transformative automation system allows for precise monitoring and abnormality identification even during such an even [0040]);
performing aspiration pressure measurements via a pressure sensor as a liquid is being aspirated in the automated diagnostic analysis system; (Fig. 1 shows an example of a typical metering system with pressure monitoring where element 103 acts as the pressure sensor and, in Fig. 2, an example of large volume aspiration pressure curves detected when liquid and air are aspirated is shown [0043]);
analyzing an aspiration pressure measurement signal waveform (See Figs. 4-8 and 10-24 where aspiration pressure measurement signal waveforms are created and analyzed) via a processor (by a processor [0009]);
and identifying and responding to an aspiration fault via the processor (if none of the above conditions are satisfied, then the aspiration is deemed to be a short. FIGS. 25-27 provide a visual representation of applying the error logic to real data from aspirating 7.8 μL fluid at very slow speed. The figures 25-27 contain a series of coded dots and x's that represent test statistic results from numerous aspirations. The dots correspond to results from full aspirations of fluids with viscosities 0, 1, 10, and 20 cp, as labeled. The x's correspond to partial (short) aspirations using the same color code for viscosity. For example, an x represents results from a partial aspiration of a fluid with viscosity as labeled [0107] where, using the test statistics, an embodiment first checks if the aspiration was successful. It first checks if the model fits the pressure data well by ensuring the fit error, φ.sub.FitError, is below a pre-determined value. FIG. 25 displays the viscosity test statistic vs. the fit error [0108] and once an embodiment determines if a full or partial aspiration has occurred, it then checks to see if the viscosity and density test statistics fall within the expected range. FIG. 26 displays the viscosity vs. the density test statistics. The shaded regions represent the domain of successful aspirations 2601, clogs 2602, or air/shorts 2603 [0110] and the system calibration process disclosed in para. [0112-0116]).
Dunfee does not explicitly teach executing an artificial intelligence (AI) algorithm configured to perform:
cluster analysis of the aspiration pressure measurement signal waveform, or probabilistic graphical modeling based on the aspiration pressure measurement signal waveform;
and identifying and responding to an aspiration fault via the processor in response to the analyzing, which was the analyzing process performed by the AI algorithm.
Salunke teaches cluster analysis in a method and system for anomaly detection (the anomaly detection system identifies similar data points through cluster analysis [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke to use an AI algorithm to perform cluster analysis to identify and respond to aspiration faults in response to an analysis such as the one disclosed in Salunke to allow for improved interpretability, resulting in more accurate, automated, and continuous monitoring.
Regarding claim 2, Dunfee in view of Salunke teach the method of claim 1, but Dunfee does not explicitly teach wherein the cluster analysis is based on unsupervised training data.
Salunke teaches wherein the cluster analysis is based on unsupervised training data (Techniques are described herein for training and evaluating anomaly detection models using machine learning. The techniques allow for transitions between unsupervised, machine-assisted supervised (also referred to herein as semi-supervised), and/or completely supervised training [0036] where the anomaly detection system identifies similar data points through cluster analysis [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke to use cluster analysis based on unsupervised training data to allow for improved interpretability, resulting in more accurate, automated, and continuous monitoring.
Regarding claim 3, Dunfee in view of Salunke teach the method of claim 1, but Dunfee does not explicitly teach wherein the cluster analysis is based on labeled training data using supervised learning algorithms to establish thresholds with classification ranges.
In a system and method for anomaly detection, Salunke teaches wherein the cluster analysis (cluster analysis [0040]) is based on labeled training data using supervised learning algorithms (When operating in a machine-assisted supervised mode, the anomaly detection system may upsample the user-set labels by propagating the user-set label to other similar data points [0040]) to establish thresholds with classification ranges (the training process determines whether the model is overfit (operation 214). Where an SVM classifier is trained, this operation may comprise validating whether the percentage of positive-class support vectors is less than a threshold value [0084] and the anomaly boundary region may be defined as a point representing the corner of a hyper-rectangle. With a threshold quantile of 95.sup.th percentile, the corner is for a hype-rectangle which contains most of the training data. The quantile point in turn generates a partition of the space into quadrants, which are regions that may be used as an additional classification during the evaluation stage [0087]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke to use cluster analysis based on labeled training data using supervised learning algorithms to establish thresholds with classification ranges when detecting fault aspirations to better define distinct actionable operating ranges for normal and abnormal aspirations, which improves the precision and reliability of fault detection.
Regarding claim 9, Dunfee in view of Salunke teach the method of claim 1. Claim 9 recites a "wherein" clause further descriptive of an alternative not required by the scope of the claims. Therefore, claim 9 is rejected for the same reasons as claim 1.
Claim 10 is rejected in the alternative on the same grounds as claim 9.
Regarding claim 12, Dunfee in view of Salunke teach the method of claim 1, and Dunfee further teaches wherein the aspiration fault is a gel or undesirable material pickup or a short-volume aspiration (This metric has been found to be very useful in identifying both short aspirations and partial or full probe obstructions or clogs [0115]); (In one embodiment, an algorithm, which is used to determine if a fluid was successfully aspirated or if a short or clog has occurred [0073]).
Regarding claim 13, Dunfee teaches an automated aspirating and dispensing apparatus (Fig. 1), comprising: a robotic arm (transfer arm [0112]); a probe coupled to the robotic arm (probe 104 in Fig. 1); a pump coupled to the probe (pump 101 in Fig. 1); a pressure sensor configured to perform aspiration pressure measurements as a liquid is being aspirated via the probe (pressure transducer 103); and a processor (a processor [0008-0009]).
Dunfee in view of Salunke further teach a processor configured to execute an artificial intelligence (AI) algorithm to detect or predict and respond to an aspiration fault during an aspiration process, the AI algorithm configured to analyze an aspiration pressure measurement signal waveform derived from the pressure sensor using cluster analysis based on the claim 1 analysis.
Regarding claim 16, Dunfee in view of Salunke teach the automated aspirating and dispensing apparatus of claim 14, and Dunfee further teaches a first of the only two metrics captures a time-rate of change of pressure of the aspiration pressure measurement signal waveform; and a second of the only two metrics captures an inflection characteristic of the aspiration pressure measurement signal waveform based on the analysis of claim 6.
Regarding claim 17, Dunfee in view of Salunke teach the automated aspirating and dispensing apparatus of claim 13, and Dunfee further teaches collecting aspiration data for detecting an aspiration fault, but does not explicitly teach wherein the cluster analysis employs a plurality of cluster classifications wherein one of the plurality of cluster classifications represents normal aspiration data and others of the plurality of cluster classifications each represent a different type of abnormal aspiration data.
Salunke teaches wherein the cluster analysis employs a plurality of cluster classifications (the anomaly detection system stores mappings between different respective anomaly regions and respective anomaly classifiers. During the evaluation phase, the anomaly detection system may assign anomaly classifiers to detected anomalies based on the mappings. The anomaly classifiers may be used to identify different types of anomalies and/or root causes for the anomalies [0044]) wherein one of the plurality of cluster classifications represents normal data and others of the plurality of cluster classifications each represent a different type of abnormal data (A user may label examples of anomalous and unanomalous behavior. A supervised machine learning process may then train the model by inferring a function from the labeled training data. The trained model may be used to classify new examples as anomalous or unanomalous [0035] and a model classifier in this context is a model component that is able to classify data as positive (unanomalous) and negative (anomalous) [0082] and the support vectors define boundaries for classifying new examples as anomalous or unanomalous. For example, a support vector may be a line dividing a plane in two parts where examples that fall on one side of the line are classified as anomalous and on the other side as unanomalous [0083] and FIG. 8 illustrates an example set of learned boundaries used to classify anomalous behavior in accordance with some embodiments. Plot 800 depicts an example result of the evaluation stage. In some embodiments, plot 800 may display data points in different colors (not shown) depending on whether the data point is anomalous, part of a support vector, or unanomalous. For example, blue points may represent support vectors from the training data, black points may be unanomalous data points from the test data, yellow data points may represent anomalous data points classified as FINDING, orange data points may represent anomalous data points classified as WARNING, and red data points may represent anomalies classified as CRITICAL [0157, Fig. 8] where the evaluation process may use the trained classifiers to map incoming data points to a positive class or a negative class [0133] and where mappings may be stored to link attributes associated with a detected anomaly to corresponding classifiers, labels or summaries for the anomalies [0136]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke to use a cluster analysis that employs a plurality of cluster classifications wherein one of the plurality of cluster classifications represents normal aspiration data and others of the plurality of cluster classifications each represent a different type of abnormal aspiration data to enable more accurate classification and diagnosis based on a more clear distinction between abnormalities and normal aspirations.
Regarding claim 18, Dunfee in view of Salunke teach the method of claim 13. Claim 18 recites a "wherein" clause further descriptive of an alternative not required by the scope of independent claim 13 on which claim 18 relies.. Therefore, claim 18 is rejected for the same reasons as claim 13.
Claim 19 is rejected in the alternative on the same grounds as claim 18.
Claim 20 is rejected in the alternative on the same grounds as claim 18.
Regarding claim 23, Dunfee teaches a non-transitory computer-readable storage medium (a memory device that stores instructions executable by the processor [0008]), but does not explicitly teach a non-transitory computer-readable storage medium.
Salunke teaches a non-transitory computer-readable storage medium (stored in non-transitory storage media accessible to processor 1004, render computer system 1000 into a special-purpose machine that is customized to perform the operations specified in the instructions [0188]).
Dunfee in view of Salunke further teach an artificial intelligence (AI) algorithm configured to detect or predict an aspiration fault based on analysis of an aspiration pressure measurement signal waveform using cluster analysis of the aspiration pressure measurement signal waveform based on the claim 1 analysis.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke to use a non-transitory computer-readable storage medium when using an artificial intelligence (AI) algorithm configured to detect or predict an aspiration fault based on analysis of an aspiration pressure measurement signal waveform using cluster analysis of the aspiration pressure measurement signal waveform to improve reliability and effectively store data and instructions via tangible storage.
Claims 4-8 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Dunfee (US 20190234787 A1) in view of Salunke (US 20200351283 A1) further in view of Manzi (Manzi, D., Brentan, B., Meirelles, G., Izquierdo, J., & Luvizotto, E., Jr. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water, 11(11), 2279).
Regarding claim 4, Dunfee in view of Salunke teach the method of claim 1, and Dunfee further teaches the aspiration pressure measurement signal waveform (as disclosed in the claim 1 analysis), but does not explicitly teach
wherein the cluster analysis comprises based on the aspiration pressure measurement signal waveform.
Salunke teaches the cluster analysis (the anomaly detection system identifies similar data points through cluster analysis [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke to use a cluster analysis based on the aspiration pressure measurement signal waveform to improve the accuracy of fault detection and allow for a system that pinpoints anomalies without the need for pre-labeled training data.
Salunke does not explicitly teach the cluster analysis using only two metrics.
Manzi teaches the cluster analysis using only two metrics (The transient pressure signals caused by bursts contain important information about their location and magnitude where a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy [p.1]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke and Manzi to use a cluster analysis using only two metrics based on the aspiration pressure measurement signal waveform to improve visual simplicity and clarity while reducing processing overhead and latency by focusing purely on the two most critical parameters.
Regarding claim 5, Dunfee in view of Salunke further in view of Manzi teach the method of claim 4, but Dunfee does not explicitly teach wherein the cluster analysis comprises K-means clustering employing a four-cluster classification based on the only two metrics.
Salunke teaches wherein the cluster analysis comprises K-means clustering (other clustering techniques may be used to group data points based on similarity between the feature vectors. Examples include, K-means clustering [0109]).
Manzi teaches the cluster analysis using only two metrics (The transient pressure signals caused by bursts contain important information about their location and magnitude where a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy [p.1]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke and Manzi to use a cluster analysis using only two metrics based on the aspiration pressure measurement signal waveform to improve visual simplicity and clarity while reducing processing overhead and latency by focusing purely on the two most critical parameters.
Manzi also teaches on considering any number of k-clusters to determine the optimal number of clusters [last paragraph on p. 6-7 and equation 7].The selection of 4 clusters for the k value amounts to routine optimization found in any k-nearest neighbor clustering algorithms (see MPEP § 2144.05(II)(A)) with the expected results of assigning a value to k based on a tradeoff between the quantization error and the training time (see Manzi in the first paragraph on p. 7).
Regarding claim 6, Dunfee in view of Salunke further in view of Manzi teach the method of claim 4, and Dunfee further teaches wherein a first of the only two metrics captures a time-rate of change of pressure of the aspiration pressure measurement signal waveform that includes a moving average of aspiration pressure slope, (See Fig. 8 showing a time-rate of change of pressure of the aspiration pressure measurement signal waveform and a moving average of aspiration pressure slope as an average is represented for a data subset over a period of time) and a second of the only two metrics captures an inflection characteristic of the aspiration pressure measurement signal waveform (See the peaks and valleys (inflection characteristics) of the aspiration pressure measurement signal waveform in Fig. 8).
Regarding claim 7, Dunfee in view of Salunke further in view of Manzi teach the method of claim 4. Claim 7 recites a "wherein" clause further descriptive of an alternative not required by the scope of the claims. Therefore, claim 7 is rejected for the same reasons as claim 4. See at least MPEP 2143.03: “In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art”, and MPEP 2111.04(I): “Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure.”
Claim 8 is rejected in the alternative on the same grounds as claim 7.
Regarding claim 14, Dunfee in view of Salunke teach the automated aspirating and dispensing apparatus of claim 13, and Dunfee in view of Salunke further in view of Manzi further teach wherein the cluster analysis comprises using only two metrics based on the aspiration pressure measurement signal waveform based on the claim 4 analysis.
Regarding claim 15, Dunfee in view of Salunke teach the automated aspirating and dispensing apparatus of claim 14, and Dunfee in view of Salunke further in view of Manzi further teach wherein the cluster analysis comprises K-means clustering employing a four-cluster classification based on the only two metrics based on the claim 5 analysis.
Claims 11 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Dunfee (US 20190234787 A1) in view of Salunke (US 20200351283 A1) further in view of Dunfee ‘386 (US 20090075386 A1).
Regarding claim 11, Dunfee in view of Salunke teach the method of claim 1, and Dunfee further teaches identifying and responding to an aspiration fault via the processor (as disclosed in the claim 1 analysis), but does not explicitly teach identifying and responding within 100 msec of commencement of the liquid being aspirated.
Dunfee ‘386 teaches identifying and responding to an aspiration fault within 100 msec of commencement of the liquid being aspirated (The starting time for the first predetermined period of time Pe may be calculated and empirically confirmed based on the amount of liquid to be aspirated into pipette 12 and the characteristic vacuum profile of the combined aspiration pressure control 30, tubing 26 and pipette 12 where an exemplary amount of time during the first predetermined period of time Pe may be in the range of about 50 milliseconds and during Pe, an Average Aspiration Pressure AAPn of n vacuum pressure readings by pressure transducer 28 may be calculated by computer 24 [0033] and in a system typically designed to aspirate sample volumes in the range of 2 micro-liters, first predetermined period of time indicated as Pe would be in the range of about 50 milliseconds and about ten n vacuum pressure readings would be recorded by transducer 28 at a pressure reading rate of about one reading every 5 milliseconds. During aspiration process P, if Pn is greater than min-CLOG, then it is determined that no clog occurred during aspiration process P. However, if Pn is less than min-CLOG, then it is determined that a clog has occurred during aspiration process P like illustrated in FIG. 4 [0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke and Dunfee ‘386 to identify and respond to an aspiration fault via a processor within 100 msec of commencement of the liquid being aspirated in order to achieve timely detection of aspiration faults.
Regarding claim 21, Dunfee in view of Salunke teach the automated aspirating and dispensing apparatus of claim 13, and Dunfee in view of Salunke further in view of Dunfee ‘386 further teach wherein the processor is configured via execution of the AI algorithm to identify and respond to an aspiration fault within 100 msec of commencement of a liquid being aspirated during the aspiration process based on the claim 11 analysis.
Regarding claim 22, Dunfee in view of Salunke teach the automated aspirating and dispensing apparatus of claim 13, but Dunfee does not explicitly teach an automated diagnostic analysis system comprising: one or more analyzer stations for analyzing a biological sample and an automated track for transporting sample containers and reaction vessels to and from the automated aspirating and dispensing apparatus and the one or more analyzer stations.
Dunfee ‘386 teaches an automated diagnostic analysis system (the liquid aspiration system 10 may be used in an automated clinical analyzer [0023]) comprising: one or more analyzer stations for analyzing a biological sample (See the station shown in Fig. 1) and an automated track for transporting sample containers and reaction vessels to and from the automated aspirating and dispensing apparatus and the one or more analyzer stations (Liquid aspiration system 10 typically includes a transport device 22 where the transport device 22 is capable of moving the pipette 12 laterally (the X-direction), vertically (the Z-direction) and from front to back (the Y-direction) in an analyzer to enable the pipette 12 to pick up a pipette tip 20 (when disposable tips are used), aspirate liquid 14 into the pipette tip 20 from a sample liquid reservoir 16 or tube 16 and to dispense a desired amount of sample liquid into a test assay element or other container (not shown). Generally, stepper-motors, electronic drivers, interface circuits and limit-switches are used within transport device 22 to control transporting the pipette 12 and these are interfaced to system computer 24. Alternately, pipette 12 may be translated along the vertical z-axis by a rack-and-pinion drive. Conventional electronics are used to interface the transport device to the computer 24 [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dunfee with the teachings of Salunke and Dunfee ‘386 to use an automated diagnostic analysis system comprising an automated aspirating and dispensing apparatus and one or more analyzer stations for analyzing a biological sample and an automated track for transporting sample containers and reaction vessels to and from the automated aspirating and dispensing apparatus and the one or more analyzer stations to increase throughput, reproducibility and safety in aspiration fault analysis.
Pertinent Prior Art
US 6370942 B1: Method for Verifying the Integrity of a Fluid Transfer
A liquid aspiration method which includes a method for determining the quality of the aspirated sample through mathematical analysis of the pressure profile generated before, during, and after the aspiration process and comparison of the results with predetermined known values.
US 20130132001 A1: Tool and Method for Fault Detection of devices by Condition Based Maintenance
The present tool and method relate to device fault detection, diagnosis and prognosis. More particularly, the present tool and method store in a database a plurality of measured indicators representative of at least one dynamic condition of the device. The present tool and method uses a machine learning data tool for extracting at least one pattern from the binarized measured indicators by adding at least one different constraint to each iteration. The at least one extracted pattern is indicative of whether the device has a fault or not.
US 20160300126 A1: METHOD OF CONSTRUCTION OF ANOMALY MODELS FROM ABNORMAL DATA
A method of constructing a probabilistic graphical model of a system from data that includes both normal and anomalous data includes the step of learning parameters of a structure for the probabilistic graphical model. The structure includes at least one latent variable on which other variables are conditional, and has a plurality of components. The method further includes the steps of: iteratively associating one or more of the plurality of components of the latent variable with normal data; constructing a matrix of the associations; detecting abnormal components of the latent variable (26) based on one of a low association with the normal data or the matrix of associations; and deleting the abnormal components of the latent variable from the probabilistic graphical model.
Conclusion
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/LOGAN D COONS/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
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