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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/10/2026 has been entered.
Status of Claims
Claim 15-26 are pending and under examination.
Claims 1-14 have been canceled.
Response to Amendment
Applicant’s amendments to the claims received on 04/10/2026 have overcome the 112(b) rejection(s) set forth in the Final Rejection mailed on 02/03/2026. Therefore, the 112(b) rejection(s) have been withdrawn.
Based on the amended claims and remarks, the 101 rejection(s) have been modified to address the claim amendments.
Based on the amended claims and remarks, the previous prior art rejection over Satomura has been withdrawn and new prior art rejection(s) have been set forth (see below).
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 15-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 15 is directed toward a system. Claim 22 is directed towards an analyzer. Claim 24 is directed towards a method.
Step 2A, Prong One: Identify the law of nature/natural phenomenon/abstract ideas.
Claims 15 recites the abstract ideas, “recommends an action to be performed on the first automatic analyzer”, “a learning model generated according to a predetermined action”, “recommends performing the predetermined action on the first automatic analyzer when a first probability value output from the learning model is greater than or equal to a predetermined threshold”, “recommends performing the predetermined action”, “updates the learning model”, “a detection of an abnormality occurrence from a pertinent automatic analyzers”, “generates the learning dataset”, and “effectiveness evaluation of the predetermined action”, “the learning dataset is generated only when the predetermined action is performed in response to the detection of the abnormality occurrence”.
These abstract ideas are mental processes and/or mathematical concepts that could be performed by a human person by pen and paper or by a black box computer. The learning processor configured to perform the recited steps/processes is simply a general-purpose computer for which to apply the abstract ideas, and/or a model using a mathematical relationship between variables or numbers, but does not preclude the steps from being considered an abstract idea. See MPEP 2106.04(a)(2) subsections (I) and (III). In other words, MPEP 2106.04(a)(2)III is clear that using a computer/controller to perform the abstract idea does not preclude the steps from being considered an abstract idea.
Step 2A Prong Two: Has the abstract idea been integrated into a particular practical application?
No. These judicial exceptions are not integrated into a particular application because after the learning dataset is generated and the effectiveness evaluation then there is no action and therefore there is no particular practical application.
The additional elements in claim 15 include: (a) “a plurality of automatic analyzers including a first automatic analyzer”, (b) “a learning processor networked to the automatic analyzers”, (c) “a processing portion that receives one of sample analysis result data and maintenance result data from the first automatic analyzer, supplies a learning model … with related device data settled for the predetermined action including one of the sample analysis result data and maintenance result data”, (d) “the first probability value output from the learning model”, (e) “an update portion that updates the learning model based on learning datasets from the automatic analyzer”, (f) “a dataset generation portion of the pertinent automatic analyzer displays an action evaluation input screen on a display portion of the pertinent automatic analyzer”, (g) “collects the related data during a predetermined period based on a date and time of performing the predetermined action, and … includes the collected related device data … input from the action evaluation screen, and an abnormality cause input from the action evaluation input screen”, (h) “the learning dataset … is not generated for a regular maintenance action performed without the detection of the abnormality occurrence”.
Claim element (a) “a plurality of automatic analyzers including a first automatic analyzer” is interpreted as insignificant pre-solution activity and generally linking the judicial exception to a particular technological environment or field of use in which to apply the judicial exception, but does not amount to significantly more than the exception itself and cannot integrate the judicial exception into a practical application (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment)
Claim element (b) “a learning processor networked to the automatic analyzers” is just a general purpose computer. However, a general purpose computer is not a particular machine, and does not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more (see MPEP § 2106.05(b)(I), Particular Machine and MPEP § 2106.05(c), Particular Transformation).
Claim element (c) “a processing portion that receives one of sample analysis result data and maintenance result data from the first automatic analyzer, supplies a learning model … with related device data settled for the predetermined action including one of the sample analysis result data and maintenance result data” is just a general purpose computer. However, a general purpose computer is not a particular machine, and does not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more (see MPEP § 2106.05(b)(I), Particular Machine and MPEP § 2106.05(c), Particular Transformation). Furthermore, receiving and supplying data is interpreted as generally linking the abstract idea to the field of endeavor, and also as extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment).
Claim element (d) “the first probability value output from the learning model” is interpreted as generally linking the abstract idea to the field of endeavor, and also as extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment).
Claim element (e) “an update portion that updates the learning model based on learning datasets from the automatic analyzer” is interpreted as mere instructions to implement the abstract idea to the field of use and insignificant extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, MPEP § 2106.05(h), Field of Use and Technological Environment and § 2106.05(f), Mere Instructions To Apply an Exception).
Claim element (f) “a dataset generation portion of the pertinent automatic analyzer displays an action evaluation input screen on a display portion of the pertinent automatic analyzer” is just a general purpose computer. However, a general purpose computer is not a particular machine, and does not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more (see MPEP § 2106.05(b)(I), Particular Machine and MPEP § 2106.05(c), Particular Transformation). Displaying is not considered a practical application, such as improving the functioning of a computer, effecting a transformation, effecting a particular treatment, or applying the judicial exception in some other meaningful way. Indeed, the Court did not find that displaying information on a computer display without any limitations specifying how to achieve the desired result (information display) was not sufficient to show patent eligibility. Nor did the court find that arranging information on a graphical user interface in a manner that assists in processing information more quickly was sufficient to show patent eligibility. MPEP 2106.05(a)(I).
Claim element (g) “collects the related data during a predetermined period based on a date and time of performing the predetermined action, and … includes the collected related device data … input from the action evaluation screen, and an abnormality cause input from the action evaluation input screen” is interpreted as generally linking the abstract idea to the field of endeavor, and also as extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment).
Claim element (h) “the learning dataset … is not generated for a regular maintenance action performed without the detection of the abnormality occurrence” is interpreted as generally linking the abstract idea to the field of endeavor, and also as extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment).
Step 2B: Does the claim recite any elements which are significantly more than the abstract idea?
The claim recites the additional elements of (a) “a plurality of automatic analyzers including a first automatic analyzer”, (b) “a learning processor networked to the automatic analyzers”, (c) “a processing portion that receives one of sample analysis result data and maintenance result data from the first automatic analyzer, supplies a learning model … with related device data settled for the predetermined action including one of the sample analysis result data and maintenance result data”, (d) “the first probability value output from the learning model”, (e) “an update portion that updates the learning model based on learning datasets from the automatic analyzer”, (f) “a dataset generation portion of the pertinent automatic analyzer displays an action evaluation input screen on a display portion of the pertinent automatic analyzer”, (g) “collects the related data during a predetermined period based on a date and time of performing the predetermined action, and … includes the collected related device data … input from the action evaluation screen, and an abnormality cause input from the action evaluation input screen”, (h) “the learning dataset … is not generated for a regular maintenance action performed without the detection of the abnormality occurrence”.
These additional elements do not amount to significantly more as they are well-understood, routine, and conventional (WURC) in the art as evidenced by Satomura et al. (US 2007/0255756 – hereinafter “Satomura”), Horrell et al. (US 2019/0087256 – hereinafter “Horrell”), and Heinemann et al. (US 2019/0271713 – hereinafter “Heinemann”). Satomura, Horrell, and Heinemann disclose:
(a) “a plurality of automatic analyzers including a first automatic analyzer”, Satomura; fig. 1, [0046], Horrell; [0057], and Heinemann; [0049].
(b) “a learning processor networked to the automatic analyzers”, Satomura; [0046-0047], Horrell; [0011-0013, 0054, 0086, 0114-0118, 0186-0191, 0200-0208, 0256-0257], Heinemann; [0017, 0049].
(c) “a processing portion that receives one of sample analysis result data and maintenance result data from the first automatic analyzer, supplies a learning model … with related device data settled for the predetermined action including one of the sample analysis result data and maintenance result data”; Satomura; [0053, 0090-0091, 0100, 0107-0111, 0086, 0100, 0110], Heinemann; [0017, 0049].
(d) “the first probability value output from the learning model”, Satomura; [0073, 0085-0086, 0091, 0102, 0104-0105, 0106-0111] and Horrell; [0256].
(e) “an update portion that updates the learning model based on learning datasets from the automatic analyzer”, Satomura; [0106-0107] and Horrell; [0027, 0118, 0186-0191].
(f) “a dataset generation portion of the pertinent automatic analyzer displays an action evaluation input screen on a display portion of the pertinent automatic analyzer”, Satomura; [0067, 0073, 0085-0086, 0106-0111] and Horrell; [0027, 0186-0191, 0200-0211, 0229-0230].
(g) “collects the related data during a predetermined period based on a date and time of performing the predetermined action, and … includes the collected related device data … input from the action evaluation screen, and an abnormality cause input from the action evaluation input screen”, Satomura; [0067, 0073, 0085-0086, 0106-0111] and Horrell; [0027, 0186-0191, 0200-0211, 0229-0230].
(h) “the learning dataset … is not generated for a regular maintenance action performed without the detection of the abnormality occurrence” Satomura; [0067, 0073, 0085-0086, 0106-0111] and Horrell; [0027, 0186-0191, 0200-0211, 0229-0230].
A similar rejection is also made over claims 22 and 24. The examiner notes that claim 22 additionally recites “a storage portion to store a learning model including an input layer and an output layer”. However, performing the abstract idea on a general-purpose computer is not enough to integrate the exception into a practical application (MPEP 2105.05(b)I), and storing the learning model is interpreted as insignificant extra solution activity and/or generally linking the abstract idea to the field of use. Further, under step 2B, this feature is WURC as Satomura and Horrell discloses a storage portion (Satomura; [0053] and Horrell; [0091]).
Claim 16 recites elements directed towards limiting the cited elements of claim 15 and the abstract idea of “wherein the learning model updated by the update portion”. The claims further recites “the learning model updated … is delivered to the first automatic analyzer”, but this does not integrate the exception under 2A prong 2 because applying the abstract idea on a computer and is not considered sufficient to integrate a judicial exception into a practical application. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment).
Claim 17 recites the additional elements of “an input layer”, “an output layer”, and “an inference result”. However, these elements further define the learning model and result/probability value/recommendation which are the abstract ideas themselves under step 2A prong one. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment).
Claim 18 further limits the inferences result as including “an average remaining time”. However, these elements further define the learning model and result/probability value/recommendation which are the abstract ideas themselves under step 2A prong one. The claim also recites the element of “the dataset generation portion … allows the learning dataset to further include a time elapsed”. However, calculating and/or determining an elapsed time does not integrate the exception under 2A prong 2 because performing the abstract idea on a general-purpose computer is not enough to integrate the exception into a practical application (MPEP 2105.05(b)I). These elements are interpreted as extra-solution activity which are incidental to the primary process and are mere data gathering which is not considered significantly more than the abstract idea (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity).
Claim 19 recites the abstract ideas “their respective probability value recommends performing their respective predetermined action”, “a plurality of learning models that output a probability value greater than or equal to a predetermined threshold”, and “recommends their respective predetermined actions corresponding to the two or more learning model whose probability value are greater than or equal to their respective predetermined thresholds” performed by the processing portion. However, performing the abstract idea on a general-purpose computer is not enough to integrate the exception into a practical application (MPEP 2105.05(b)I). These elements are interpreted as extra-solution activity which are incidental to the primary process and are mere data gathering which is not considered significantly more than the abstract idea (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity). Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity and the use of a learning model on a generic analyzer is just generally linking the abstract idea to the field of analyzers, and is not a particular practical application (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment).
Claim 20 recites the additional elements of “a recommended action display screen on a display portion of the first automatic analyzer”. Displaying is not considered a practical application, such as improving the functioning of a computer, effecting a transformation, effecting a particular treatment, or applying the judicial exception in some other meaningful way. Indeed, the Court did not find that displaying information on a computer display without any limitations specifying how to achieve the desired result (information display) was not sufficient to show patent eligibility. Nor did the court find that arranging information on a graphical user interface in a manner that assists in processing information more quickly was sufficient to show patent eligibility. MPEP 2106.05(a)(I). Further, these additional elements do not amount to significantly more as they are well-understood, routine, and conventional (WURC) in the art. See Satomura; figs. 3, 4-2, 5-7, 11-1 & 11-2, “TIME OF USE”, “RECEIVE AND DISPLAY WRITE INSTRUCTION OF “UNUSABLE” AND REASON DATA”, step S27, step S45, step S75, [0057, 0065, 0071, 0079, 0086-0088, 0094-0095, 0097, 0100, 0106-0111] and Horrell; figs. 6-9, [0011-0013, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0208, 0229, 0233, 0240-0242, 0256-0257].
Claim 21 recites “the update portion groups the automatic analyzer based on a similarity of operational situations including operations and inspection contents and updates the learning model based on the learning dataset from the automatic analyzers grouped based on the similarity of operational situations” but does not integrate the abstract idea into a practical application as these limitations amount to merely indicating a field of use or technological environment in which to apply a judicial exception and do not amount to significantly more than the exception itself (see MPEP § 2106.05(h), Field of Use and Technological Environment). Additionally, these elements do not amount to significantly more in view of Satomura and Horrell. See Satomura; fig. 3, #62, [0055] and Horrell; [0014-0015, 0027, 0078, 0118, 0150, 0153, 0186-0191, 0216, 0219, 0225].
Claim 23 recites the additional elements of “a learning processor … is used to update the learning model” but this does not integrate the exception under 2A prong 2 because applying the abstract idea on a computer is not considered sufficient to integrate a judicial exception into a practical application. Updating a model with new data is a mathematical relationship between variables or numbers. Receiving or transmitting data over a network has been recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP §2106.04(d), § 2106.05(f)). Additionally, the learning device does not amount to significantly more in view of Satomura and Horrell. See Satomura fig. 1, #5, [0046-0047] and Horrell [0027, 0118, 0186-0191].
Claim 25 recites “the learning processor groups the automatic analyzer based on the similarity of operational situations including operations and inspection contents and updates the learning model based on the learning dataset from the automatic analyzers grouped based on the similarity of operational situations” but does not integrate the abstract idea into a practical application as these limitations amount to merely indicating a field of use or technological environment in which to apply a judicial exception and do not amount to significantly more than the exception itself (see MPEP § 2106.05(h), Field of Use and Technological Environment). Updating a model with new data is a mathematical relationship between variables or numbers. Additionally, these elements do not amount to significantly more in view of Satomura and Horrell. See Satomura; fig. 3, #62, [0055] and Horrell [0014-0015, 0027, 0078, 0118, 0150, 0153, 0186-0191, 0216, 0219, 0225].
Claim 26 recites the abstract idea “the learning model is configured to recommend multiple actions, wherein degree of recommendation is determined based on their respective probability values”. However, these additional elements are interpreted as extra-solution activity which are incidental to the primary process and are mere data gathering which is not considered significantly more than the abstract idea (see MPEP 2106.05(g), Insignificant Extra-Solution Activity). The claim recites the additional elements of “an information display portion”. However, displaying is not considered a practical application, such as improving the functioning of a computer, effecting a transformation, effecting a particular treatment, or applying the judicial exception in some other meaningful way. Indeed, the Court did not find that displaying information on a computer display without any limitations specifying how to achieve the desired result (information display) was not sufficient to show patent eligibility. Nor did the court find that arranging information on a graphical user interface in a manner that assists in processing information more quickly was sufficient to show patent eligibility. MPEP 2106.05(a)(I). Further, these additional elements do not amount to significantly more as they are well-understood, routine, and conventional (WURC) in the art. See Satomura; figs. 3, 4-2, 5-7, 11-1 & 11-2, “TIME OF USE”, “RECEIVE AND DISPLAY WRITE INSTRUCTION OF “UNUSABLE” AND REASON DATA”, step S27, step S45, step S75, [0057, 0065, 0071, 0079, 0086-0088, 0094-0095, 0097, 0100, 0106-0111] and Horrell figs. 6-9, [0011-0013, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0208, 0229, 0233, 0240-0242, 0256-0257].
16-21, 23, and 25 do not recite any additional features that are significantly more as they are well-understood, routine and conventional (WURC).
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 15-26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Horrell et al. (US 2019/0087256 – hereinafter “Horrell”).
Regarding claim 15, Horrell disclose a recommended action notification system (Horrell disclose a system for monitoring assets. The system configured to determine a heath metric via sensors and sensor values of one or more assets that reflect whether a failure will occur within a certain period of time into the future, and cause an output device to display one or more recommended actions that may affect the health metric, facilitate generating a work order to repair the asset, facilitate ordering a part for the asset, and/or transmit to the asset one or more commands that cause the asset to modify its operation; fig. 1, [0004, 0010, 0018, 0054]) that includes a plurality of automatic analyzers including a first automatic analyzer (Horrell disclose the assets may be medical machines such as medical imaging equipment, surgical equipment, medical monitoring systems, or medical laboratory equipment; [0057]) and a learning processor networked to the automatic analyzers, and recommends an action to be performed on the first automatic analyzer (Horrell disclose the assets 102 are networked to a remote computing system/analytics system 106/400 with machine-learning models for predicting the likelihood of failure based on the health metric, and uses feedback data from personnel to refine the health metric model; figs. 5, 8, 14, 15 [0011-0013, 0054, 0086, 0114-0118, 0186-0191, 0200-0208, 0256-0257]), the recommended action notification system comprising:
a processing portion (Horrell; figs. 1 & 4, #106/404, “remote computing system that takes the form of an analytics system”, [0054-0055, 0086-0087]) that receives one of sample analysis result data and maintenance result data from the first automatic analyzer (Horrell; [0027, 0114-0118, 0186-0191]), supplies a learning model generated according to a predetermined action with related device data settled for the predetermined action including one of the sample analysis result data and the maintenance result data (Horrell; [0027, 0114-0118, 0186-0191, 0208]), and recommends performing the predetermined action on the first automatic analyzer when a first probability value output from the learning model is greater than or equal to a predetermined threshold (Horrell disclose the system configured to determine a probability that one or more particular failures may occur at a given asset within a preselected period of time in the future. The system is configured to monitor the health metric of the asset and determine whether the health metric reaches a threshold value, and generate a list of recommended actions that may increase the health metric; [0010, 0013-0015, 0017-0018, 0132, 0134, 0138-0140, 0145, 0147, 0159-0160, 0165-0171, 0190, 0240-0241, 0252, 0256]), wherein the first probability value output from the learning model recommends performing the predetermined action (Horrell; fig. 14, [0256]); and
an update portion that updates the learning model based on learning datasets from the automatic analyzers (Horrell disclose the remote computing system/analytics system configured to receive feedback data from one or more output systems and then intelligently perform one or more operations based on the feedback data. The model is updated based on feedback data indicating whether an action was performed and successfully corrected the health metric; [0027, 0118, 0186-0191]),
wherein, to generate a learning dataset, when the predetermined action is performed in response to a detection of an abnormality occurrence from a pertinent automatic analyzer, a dataset generation portion of the pertinent automatic analyzer displays an action evaluation input screen on a display portion of the pertinent automatic analyzer, collects the related device data during a predetermined period based on a date and time of performing the predetermined action, and generates the learning dataset, which includes the collected related device data, effectiveness evaluation of the predetermined action input from the action evaluation input screen, and an abnormality cause input from the action evaluation input screen (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]), wherein
the learning dataset is generated only when the predetermined action is performed in response to the detection of the abnormality occurrence, and is not generated for a regular maintenance action performed without the detection of the abnormality occurrence (Hornell disclose the system relies on sensors and sensor signals for determining the health metric. The sensors are configured to monitor operating conditions of the asset or a particular subsystem of the asset such as temperature, pressure, fluid levels, power usage, voltages and currents, etc., where the learning dataset is generated is generated when the predetermined action is performed in response to the detection of the abnormality occurrence; fig. 9, [0004, 0009, 0027, 0069, 0072-0075, 0186-0191, 0200-0211, 0229-0230]. Accordingly, the learning dataset would not be generated for any subsystem or maintenance performed that does not rely on sensor data since no detection data would exist to detect an abnormality. An example would be wiping the unit with a cloth or swab to remove dust or debris from the system).
Regarding claim 16, Horrell teach the recommended action notification system according to claim 15 above, wherein the first automatic analyzer includes the processing portion (Horrell; fig. 2, #206, [0069, 0073, 0076-0078]) and the learning processor includes the update portion (Horrell; fig. 4, #410, [0086, 0094]); and wherein the learning model updated by the update portion is delivered to the first automatic analyzer (Horrell; [0094]).
Regarding claim 17, Horrell teach the recommended action notification system according to claim 15 above, wherein the learning model includes an input layer supplied with the related device data (Horrell; figs. 1 & 4, #110, #402, [0054, 0059-0060, 0064, 0086-0089, 0091-0092, 0094, 0110, 0154]) and an output layer to output an inference result related to the predetermined action in response to input of the related device data to the input layer (Horrell; figs. 1 & 4, #108, #408, [0054-0055, 0061-000063, 0091-0092, 0094, 0120, 0162-0163, 0168-0169, 0189]); and wherein the inference result includes the first probability value and a second probability value that notifies occurrence of a predetermined abnormality for which the predetermined action is effective (Horrell disclose assigning weight to individual probabilities output by individual failure models; [0010, 0013-0015, 0017-0018, 0132, 0134, 0138-0140, 0145, 0147-0148, 0159-0160, 0165-0171, 0190, 0240-0241, 0252, 0256]).
Regarding claim 18, Horrell teach the recommended action notification system according to claim 17, wherein the inference result includes an average remaining time until the predetermined abnormality notified by the second probability value occurs and an average remaining time until the predetermined action is estimated to be performed (Horrell disclose determining a heath metric via sensors and sensor values of one or more assets that reflect whether a failure from a group of failures will occur within a certain period of time into the future, and assigning weight to individual probabilities output by individual failure models; [0004, 0010, 0013-0015, 0017-0019, 0032-0033, 0054, 0112, 0132, 0134, 0138-0140, 0145, 0147-0148, 0159-0160, 0165-0171, 0190, 0240-0241, 0256]); and wherein the dataset generation portion of the pertinent automatic analyzer allows the learning dataset to further include a time elapsed from a first predetermined reference time until the predetermined abnormality occurs and a time elapsed from a second predetermined reference time until the predetermined action is performed (Horrell; figs. 6 & 9, [0130, 0229, 0240-0242]).
Regarding claim 19, Horrell teach the recommended action notification system according to claim 17 above, wherein the processing portion extracts a plurality of learning models whose input layer is supplied with one of the sample analysis result data and the maintenance result data defined as their respective related device data; and wherein the processing portion supplies the extracted plurality of learning models with their respective related device data including one of the sample analysis result data and the maintenance result data and, when their respective probability values recommend performing their respective predetermined actions and there are two or more learning models of the plurality of learning models that output their respective probability values greater than or equal to their respective predetermined thresholds, recommends their respective predetermined actions corresponding to the two or more learning models whose probability values are greater than or equal to their respective predetermined thresholds (Horrell teach one or more failure models and assigning weighted averages when determining the health-metric model, and recommending action when the probability values are greater than or equal to their respective predefined thresholds; [0013, 0027, 0114, 0134, 0147, 0186-0191, 0256]).
Regarding claim 20, Horrell teach the recommended action notification system according to claim 18 above, wherein the processing portion displays a recommended action display screen on a display portion of the first automatic analyzer; and wherein the recommended action display screen displays the first probability value that recommends performing the predetermined action as well as a name of the predetermined action recommended, date and time when the predetermined abnormality is estimated to occur, and date and time when the predetermined action is estimated to be performed, based on the inference result concerning the average remaining time until the predetermined abnormality occurs and the average remaining time until the predetermined action is performed (Horrell; figs. 6-9, [0011-0013, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0208, 0229, 0233, 0240-0242, 0256-0257]).
Regarding claim 21, Horrell teach the recommended action notification system according to claim 15 above, wherein the update portion groups the automatic analyzers based on a similarity of operational situations including operations and inspection contents and updates the learning model based on the learning datasets from the automatic analyzers grouped based on the similarity of operational situations (Horrell; [0014-0015, 0027, 0078, 0118, 0150, 0153, 0186-0191, 0216, 0219, 0225]).
Regarding claim 22, Horrell disclose an automatic analyzer to perform sample analysis and maintenance (Horrell disclose a system for monitoring assets which may be medical machines such as medical imaging equipment, surgical equipment, medical monitoring systems, or medical laboratory equipment; [0004, 0057]), comprising:
a storage portion (Horrell; fig. 4, #412, [0093-0094]) to store a learning model (Horrell; [0011-0013, 0027, 0114-0118, 0186-0191, 0208]) including an input layer (Horrell; figs. 1 & 4, #110, #402, [0054, 0059-0060, 0064, 0086-0089, 0091-0092, 0094, 0110, 0154]) and an output layer (Horrell; figs. 1 & 4, #108, #408, [0054-0055, 0061-000063, 0091-0092, 0094, 0120, 0162-0163, 0168-0169, 0189]), wherein the input layer is supplied with related device data configured according to a predetermined action performed on the automatic analyzer (Horrell; figs. 1 & 4, #110, #402, [0054, 0059-0060, 0064, 0086-0089, 0091-0092, 0094, 0110, 0154]) and the output layer outputs an inference result related to the predetermined action in response to input of the related device data to the input layer (Horrell; figs. 1 & 4, #108, #408, [0054-0055, 0061-000063, 0091-0092, 0094, 0120, 0162-0163, 0168-0169, 0189]);
a processing portion (Horrell; figs. 1 & 4, #106/404, “remote computing system that takes the form of an analytics system”, [0054-0055, 0086-0087]) that calls the learning model from the storage portion (Horrell; [0093-0094]), supplies the learning model with the related device data including result data concerning one of the sample analysis and the maintenance (Horrell; [0027, 0114-0118, 0186-0191, 0208]), and recommends performing the predetermined action when a probability value output from the learning model is greater than or equal to a predetermined threshold, wherein the probability value recommends performing the predetermined action (Horrell disclose the system configured to determine a probability that one or more particular failures may occur at a given asset within a preselected period of time in the future. The system is configured to monitor the health metric of the asset and determine whether the health metric reaches a threshold value, and generate a list of recommended actions that may increase the health metric; [0010, 0013-0015, 0017-0018, 0132, 0134, 0138-0140, 0145, 0147, 0159-0160, 0165-0171, 0190, 0240-0241, 0252, 0256]);
a display portion that displays a name of the predetermined action recommended by the processing portion (Horrell; figs. 6-9, [0011-0013, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0208, 0229, 0233, 0240-0242, 0256-0257]);
an abnormality detection portion that detects abnormalities (Horrell; figs. 2-3, #204, [0069, 0072-0075, 0077, 0080-0082, 0099-0110]); and
a dataset generation portion that displays an action evaluation input screen on the display portion when the predetermined action is performed in response to an abnormality occurrence detection from the abnormality detection portion, collects the related device data during a predetermined period based on a date and time of performing the predetermined action, and generates a learning dataset including the collected related device data, an effectiveness evaluation on the predetermined action input from the action evaluation input screen (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]), and
an abnormality cause input from the action evaluation input screen (Horrell; figs. 6-9, [0011-0013, 0027, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0211, 0229-0230, 0233, 0240-0242, 0256-0257]), wherein
the learning dataset is generated only when the predetermined action is performed in response to the abnormality occurrence detection from the abnormality detection portion, and is not generated for a regular maintenance action performed without the abnormality occurrence detection from the abnormality detection portion (Hornell disclose the system relies on sensors and sensor signals for determining the health metric. The sensors are configured to monitor operating conditions of the asset or a particular subsystem of the asset such as temperature, pressure, fluid levels, power usage, voltages and currents, etc., where the learning dataset is generated is generated when the predetermined action is performed in response to the detection of the abnormality occurrence; fig. 9, [0004, 0009, 0027, 0069, 0072-0075, 0186-0191, 0200-0211, 0229-0230]. Accordingly, the learning dataset would not be generated for any subsystem or maintenance performed that does not rely on sensor data since no detection data would exist to detect an abnormality. An example would be wiping the unit with a cloth or swab to remove dust or debris from the system).
Regarding claim 23, modified Horrell teach the automatic analyzer according to claim 22 above, wherein the learning dataset is sent to a learning processor and is used to update the learning model (Horrell disclose the remote computing system/analytics system configured to receive feedback data from one or more output systems and then intelligently perform one or more operations based on the feedback data. The model is updated based on feedback data indicating whether an action was performed and successfully corrected the health metric; [0027, 0118, 0186-0191]).
Regarding claim 24, Horrell disclose a recommended action notification method for a recommended action notification system (Horrell disclose a system for monitoring assets. The system configured to determine a heath metric via sensors and sensor values of one or more assets that reflect whether a failure will occur within a certain period of time into the future, and cause an output device to display one or more recommended actions that may affect the health metric, facilitate generating a work order to repair the asset, facilitate ordering a part for the asset, and/or transmit to the asset one or more commands that cause the asset to modify its operation; figs. 1 & 14, [0004, 0010, 0018, 0054, 0256]) including a plurality of automatic analyzers including a first automatic analyzer (Horrell disclose the assets may be medical machines such as medical imaging equipment, surgical equipment, medical monitoring systems, or medical laboratory equipment; [0057]) and a learning processor networked to the automatic analyzers (Horrell disclose the assets 102 are networked to a remote computing system/analytics system 106/400 with machine-learning models for predicting the likelihood of failure based on the health metric, and uses feedback data from personnel to refine the health metric model; figs. 5, 8, 14, 15 [0011-0013, 0054, 0086, 0114-0118, 0186-0191, 0200-0208, 0256-0257]), the method comprising:
allowing the recommended action notification system to recommend performing an action on the first automatic analyzer, wherein, when one of the automatic analyzers detects an abnormality occurrence, the automatic analyzer detecting the abnormality occurrence notifies an operator of the detection of the abnormality (Horrell; [0018, 0026-0028, 0069, 0072-0075, 0077, 0080-0082, 0099-0110, 0114-0118, 0186-0191, 0208, 0256]);
wherein, when a predetermined action is performed in response to a notification of the abnormality, the automatic analyzer detecting the abnormality occurrence displays an action evaluation input screen on a display portion of the automatic analyzer (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]);
wherein the automatic analyzer detecting the abnormality occurrence collects related device data corresponding to the predetermined action during a predetermined period based on a date and time of performing the predetermined action and generates a learning dataset including the collected related device data, an effectiveness evaluation on the predetermined action input from the action evaluation input screen, and an abnormality cause input from the action evaluation input screen (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]);
wherein the automatic analyzers transmit the learning dataset to the learning processor (Horrell disclose the assets 102 are networked to a remote computing system/analytics system 106/400 with machine-learning models for predicting the likelihood of failure based on the health metric, and uses feedback data from personnel to refine the health metric model; figs. 5, 8, 14, 15 [0011-0013, 0054, 0086, 0114-0118, 0186-0191, 0200-0208, 0256-0257]);
wherein the learning processor updates a learning model based on the learning dataset from the automatic analyzers (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]);
wherein the learning processor delivers the updated learning model to the first automatic analyzer (Horrell; [0094]);
wherein the first automatic analyzer performs one of sample analysis and maintenance (Horrell; [0027, 0057, 0114-0118, 0186-0191]); and
wherein the first automatic analyzer supplies the learning model with the related device data including result data concerning one of the sample analysis and the maintenance (Horrell; [0027, 0114-0118, 0186-0191, 0208]) and recommends performing the predetermined action when a probability value output from the learning model is greater than or equal to a predetermined threshold, while the learning model outputs the probability value recommends performing the predetermined action in response to input of the related device data (Horrell disclose the system configured to determine a probability that one or more particular failures may occur at a given asset within a preselected period of time in the future. The system is configured to monitor the health metric of the asset and determine whether the health metric reaches a threshold value, and generate a list of recommended actions that may increase the health metric; [0010, 0013-0015, 0017-0018, 0132, 0134, 0138-0140, 0145, 0147, 0159-0160, 0165-0171, 0190, 0240-0241, 0252, 0256]),
wherein the learning dataset is generated only when the predetermined action is performed in response to the detection of the abnormality occurrence, and is not generated for a regular maintenance action performed without the detection of the abnormality occurrence (Hornell disclose the system relies on sensors and sensor signals for determining the health metric. The sensors are configured to monitor operating conditions of the asset or a particular subsystem of the asset such as temperature, pressure, fluid levels, power usage, voltages and currents, etc., where the learning dataset is generated is generated when the predetermined action is performed in response to the detection of the abnormality occurrence; fig. 9, [0004, 0009, 0027, 0069, 0072-0075, 0186-0191, 0200-0211, 0229-0230]. Accordingly, the learning dataset would not be generated for any subsystem or maintenance performed that does not rely on sensor data since no detection data would exist to detect an abnormality. An example would be wiping the unit with a cloth or swab to remove dust or debris from the system).
Regarding claim 25, Horrell teach the recommended action notification method according to claim 24 above wherein the learning processor groups the automatic analyzers based on a similarity of operational situations including operations and inspection contents and updates the learning model based on the learning dataset from the automatic analyzers grouped based on the similarity of operational situations (Horrell; [0014-0015, 0027, 0078, 0118, 0150, 0153, 0186-0191, 0216, 0219, 0225]).
Regarding claim 26, Horrell teach the recommended action notification system according to claim 15 above, wherein the learning model is configured to recommend multiple actions, wherein degree of recommendation is determined based on their respective probability values, wherein a highly prioritized action of the multiple actions has a difference larger than a given value between its predetermined threshold value and its probability value (Horrell disclose determining a heath metric via sensors and sensor values of one or more assets that reflect whether a failure from a group of failures will occur within a certain period of time into the future, and assigning weight to individual probabilities output by individual failure models; [0004, 0010, 0013-0015, 0017-0019, 0032-0033, 0054, 0112, 0132, 0134, 0138-0140, 0145, 0147-0148, 0159-0160, 0165-0171, 0190, 0240-0241, 0256]), and wherein the system further comprises an information display portion configured to display device date that is important to determine whether to require a predetermined recommended action (Horrell; figs. 6-9, [0011-0013, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0208, 0229, 0233, 0240-0242, 0256-0257]).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 15-26 are alternatively rejected under 35 U.S.C. 103 as being unpatentable over Horrell in view of Heinemann et al. (US 2019/0271713 – hereinafter “Heinemann”).
Regarding claim 15, Horrell disclose a recommended action notification system (Horrell disclose a system for monitoring assets. The system configured to determine a heath metric via sensors and sensor values of one or more assets that reflect whether a failure will occur within a certain period of time into the future, and cause an output device to display one or more recommended actions that may affect the health metric, facilitate generating a work order to repair the asset, facilitate ordering a part for the asset, and/or transmit to the asset one or more commands that cause the asset to modify its operation; fig. 1, [0004, 0010, 0018, 0054]) that includes a plurality of automatic analyzers including a first automatic analyzer (Horrell disclose the assets may be medical machines such as medical imaging equipment, surgical equipment, medical monitoring systems, or medical laboratory equipment; [0057]) and a learning processor networked to the automatic analyzers, and recommends an action to be performed on the first automatic analyzer (Horrell disclose the assets 102 are networked to a remote computing system/analytics system 106/400 with machine-learning models for predicting the likelihood of failure based on the health metric, and uses feedback data from personnel to refine the health metric model; figs. 5, 8, 14, 15 [0011-0013, 0054, 0086, 0114-0118, 0186-0191, 0200-0208, 0256-0257]), the recommended action notification system comprising:
a processing portion (Horrell; figs. 1 & 4, #106/404, “remote computing system that takes the form of an analytics system”, [0054-0055, 0086-0087]) that receives one of sample analysis result data and maintenance result data from the first automatic analyzer (Horrell; [0027, 0114-0118, 0186-0191]), supplies a learning model generated according to a predetermined action with related device data settled for the predetermined action including one of the sample analysis result data and the maintenance result data (Horrell; [0027, 0114-0118, 0186-0191, 0208]), and recommends performing the predetermined action on the first automatic analyzer when a first probability value output from the learning model is greater than or equal to a predetermined threshold (Horrell disclose the system configured to determine a probability that one or more particular failures may occur at a given asset within a preselected period of time in the future. The system is configured to monitor the health metric of the asset and determine whether the health metric reaches a threshold value, and generate a list of recommended actions that may increase the health metric; [0010, 0013-0015, 0017-0018, 0132, 0134, 0138-0140, 0145, 0147, 0159-0160, 0165-0171, 0190, 0240-0241, 0252, 0256]), wherein the first probability value output from the learning model recommends performing the predetermined action (Horrell; fig. 14, [0256]); and
an update portion that updates the learning model based on learning datasets from the automatic analyzers (Horrell disclose the remote computing system/analytics system configured to receive feedback data from one or more output systems and then intelligently perform one or more operations based on the feedback data. The model is updated based on feedback data indicating whether an action was performed and successfully corrected the health metric; [0027, 0118, 0186-0191]),
wherein, to generate a learning dataset, when the predetermined action is performed in response to a detection of an abnormality occurrence from a pertinent automatic analyzer, a dataset generation portion of the pertinent automatic analyzer displays an action evaluation input screen on a display portion of the pertinent automatic analyzer, collects the related device data during a predetermined period based on a date and time of performing the predetermined action, and generates the learning dataset, which includes the collected related device data, effectiveness evaluation of the predetermined action input from the action evaluation input screen, and an abnormality cause input from the action evaluation input screen (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]), wherein
the learning dataset is generated only when the predetermined action is performed in response to the detection of the abnormality occurrence, and is not generated for a regular maintenance action performed without the detection of the abnormality occurrence (Hornell disclose the system relies on sensors and sensor signals for determining the health metric. The sensors are configured to monitor operating conditions of the asset or a particular subsystem of the asset such as temperature, pressure, fluid levels, power usage, voltages and currents, etc., where the learning dataset is generated is generated when the predetermined action is performed in response to the detection of the abnormality occurrence; fig. 9, [0004, 0009, 0027, 0069, 0072-0075, 0186-0191, 0200-0211, 0229-0230]. Accordingly, the learning dataset would not be generated for any subsystem or maintenance performed that does not rely on sensor data since no detection data would exist to detect an abnormality. An example would be wiping the unit with a cloth or swab to remove dust or debris from the system).
If it is deemed that Horrell does not teach the processing portion receives one of sample analysis result data and supplies the learning model the sample analysis result data, then Heinemann teach the analogous art of a learning model and automatic analyzer (Heinemann; fig. 1, #1, #2, [0049]) wherein a processing portion is configured to receive sample analysis result data and supply the learning model the sample analysis result data (Heinemann; fig. 1, [0017, 0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the processing portion and learning model of Horrell to be configured to process sample analysis result data and supply the learning model with the sample analysis result data, as taught by Heinemann, because Heinemann teach the processor and learning model supplied with sample analysis result data is used to predict a failure state of the automated analyzer; [0049]. One of ordinary skill in the art would have expected this modification could have been performed with a reasonable expectation of success since Horrell and Heinemann both teach forecasting and predicting a failure for medical laboratory instruments.
Regarding claim 16, modified Horrell teach the recommended action notification system according to claim 15 above, wherein the first automatic analyzer includes the processing portion (Horrell; fig. 2, #206, [0069, 0073, 0076-0078]) and the learning processor includes the update portion (Horrell; fig. 4, #410, [0086, 0094]); and wherein the learning model updated by the update portion is delivered to the first automatic analyzer (Horrell; [0094]).
Regarding claim 17, modified Horrell teach the recommended action notification system according to claim 15 above, wherein the learning model includes an input layer supplied with the related device data (Horrell; figs. 1 & 4, #110, #402, [0054, 0059-0060, 0064, 0086-0089, 0091-0092, 0094, 0110, 0154]) and an output layer to output an inference result related to the predetermined action in response to input of the related device data to the input layer (Horrell; figs. 1 & 4, #108, #408, [0054-0055, 0061-000063, 0091-0092, 0094, 0120, 0162-0163, 0168-0169, 0189]); and wherein the inference result includes the first probability value and a second probability value that notifies occurrence of a predetermined abnormality for which the predetermined action is effective (Horrell disclose assigning weight to individual probabilities output by individual failure models; [0010, 0013-0015, 0017-0018, 0132, 0134, 0138-0140, 0145, 0147-0148, 0159-0160, 0165-0171, 0190, 0240-0241, 0252, 0256]).
Regarding claim 18, modified Horrell teach the recommended action notification system according to claim 17, wherein the inference result includes an average remaining time until the predetermined abnormality notified by the second probability value occurs and an average remaining time until the predetermined action is estimated to be performed (Horrell disclose determining a heath metric via sensors and sensor values of one or more assets that reflect whether a failure from a group of failures will occur within a certain period of time into the future, and assigning weight to individual probabilities output by individual failure models; [0004, 0010, 0013-0015, 0017-0019, 0032-0033, 0054, 0112, 0132, 0134, 0138-0140, 0145, 0147-0148, 0159-0160, 0165-0171, 0190, 0240-0241, 0256]); and wherein the dataset generation portion of the pertinent automatic analyzer allows the learning dataset to further include a time elapsed from a first predetermined reference time until the predetermined abnormality occurs and a time elapsed from a second predetermined reference time until the predetermined action is performed (Horrell; figs. 6 & 9, [0130, 0229, 0240-0242]).
Regarding claim 19, modified Horrell teach the recommended action notification system according to claim 17 above, wherein the processing portion extracts a plurality of learning models whose input layer is supplied with one of the sample analysis result data and the maintenance result data defined as their respective related device data; and wherein the processing portion supplies the extracted plurality of learning models with their respective related device data including one of the sample analysis result data and the maintenance result data and, when their respective probability values recommend performing their respective predetermined actions and there are two or more learning models of the plurality of learning models that output their respective probability values greater than or equal to their respective predetermined thresholds, recommends their respective predetermined actions corresponding to the two or more learning models whose probability values are greater than or equal to their respective predetermined thresholds (The modification of the processing portion and learning model of Horrell to be configured to process sample analysis result data and supply the learning model with the sample analysis result data, as taught by Heinemann, has previously been discussed in claim 15 above. Horrell additionally teach one or more failure models and assigning weighted averages when determining the health-metric model, and recommending action when the probability values are greater than or equal to their respective predefined thresholds; [0013, 0027, 0114, 0134, 0147, 0186-0191, 0256]).
Regarding claim 20, modified Horrell teach the recommended action notification system according to claim 18 above, wherein the processing portion displays a recommended action display screen on a display portion of the first automatic analyzer; and wherein the recommended action display screen displays the first probability value that recommends performing the predetermined action as well as a name of the predetermined action recommended, date and time when the predetermined abnormality is estimated to occur, and date and time when the predetermined action is estimated to be performed, based on the inference result concerning the average remaining time until the predetermined abnormality occurs and the average remaining time until the predetermined action is performed (Horrell; figs. 6-9, [0011-0013, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0208, 0229, 0233, 0240-0242, 0256-0257]).
Regarding claim 21, modified Horrell teach the recommended action notification system according to claim 15 above, wherein the update portion groups the automatic analyzers based on a similarity of operational situations including operations and inspection contents and updates the learning model based on the learning datasets from the automatic analyzers grouped based on the similarity of operational situations (Horrell; [0014-0015, 0027, 0078, 0118, 0150, 0153, 0186-0191, 0216, 0219, 0225]).
Regarding claim 22, Horrell disclose an automatic analyzer to perform sample analysis and maintenance (Horrell disclose a system for monitoring assets which may be medical machines such as medical imaging equipment, surgical equipment, medical monitoring systems, or medical laboratory equipment; [0004, 0057]), comprising:
a storage portion (Horrell; fig. 4, #412, [0093-0094]) to store a learning model (Horrell; [0011-0013, 0027, 0114-0118, 0186-0191, 0208]) including an input layer (Horrell; figs. 1 & 4, #110, #402, [0054, 0059-0060, 0064, 0086-0089, 0091-0092, 0094, 0110, 0154]) and an output layer (Horrell; figs. 1 & 4, #108, #408, [0054-0055, 0061-000063, 0091-0092, 0094, 0120, 0162-0163, 0168-0169, 0189]), wherein the input layer is supplied with related device data configured according to a predetermined action performed on the automatic analyzer (Horrell; figs. 1 & 4, #110, #402, [0054, 0059-0060, 0064, 0086-0089, 0091-0092, 0094, 0110, 0154]) and the output layer outputs an inference result related to the predetermined action in response to input of the related device data to the input layer (Horrell; figs. 1 & 4, #108, #408, [0054-0055, 0061-000063, 0091-0092, 0094, 0120, 0162-0163, 0168-0169, 0189]);
a processing portion (Horrell; figs. 1 & 4, #106/404, “remote computing system that takes the form of an analytics system”, [0054-0055, 0086-0087]) that calls the learning model from the storage portion (Horrell; [0093-0094]), supplies the learning model with the related device data including result data concerning one of the sample analysis and the maintenance (Horrell; [0027, 0114-0118, 0186-0191, 0208]), and recommends performing the predetermined action when a probability value output from the learning model is greater than or equal to a predetermined threshold, wherein the probability value recommends performing the predetermined action (Horrell disclose the system configured to determine a probability that one or more particular failures may occur at a given asset within a preselected period of time in the future. The system is configured to monitor the health metric of the asset and determine whether the health metric reaches a threshold value, and generate a list of recommended actions that may increase the health metric; [0010, 0013-0015, 0017-0018, 0132, 0134, 0138-0140, 0145, 0147, 0159-0160, 0165-0171, 0190, 0240-0241, 0252, 0256]);
a display portion that displays a name of the predetermined action recommended by the processing portion (Horrell; figs. 6-9, [0011-0013, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0208, 0229, 0233, 0240-0242, 0256-0257]);
an abnormality detection portion that detects abnormalities (Horrell; figs. 2-3, #204, [0069, 0072-0075, 0077, 0080-0082, 0099-0110]); and
a dataset generation portion that displays an action evaluation input screen on the display portion when the predetermined action is performed in response to an abnormality occurrence detection from the abnormality detection portion, collects the related device data during a predetermined period based on a date and time of performing the predetermined action, and generates a learning dataset including the collected related device data, an effectiveness evaluation on the predetermined action input from the action evaluation input screen (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]), and
an abnormality cause input from the action evaluation input screen (Horrell; figs. 6-9, [0011-0013, 0027, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0211, 0229-0230, 0233, 0240-0242, 0256-0257]), wherein
the learning dataset is generated only when the predetermined action is performed in response to the abnormality occurrence detection from the abnormality detection portion, and is not generated for a regular maintenance action performed without the abnormality occurrence detection from the abnormality detection portion (Hornell disclose the system relies on sensors and sensor signals for determining the health metric. The sensors are configured to monitor operating conditions of the asset or a particular subsystem of the asset such as temperature, pressure, fluid levels, power usage, voltages and currents, etc., where the learning dataset is generated is generated when the predetermined action is performed in response to the detection of the abnormality occurrence; fig. 9, [0004, 0009, 0027, 0069, 0072-0075, 0186-0191, 0200-0211, 0229-0230]. Accordingly, the learning dataset would not be generated for any subsystem or maintenance performed that does not rely on sensor data since no detection data would exist to detect an abnormality. An example would be wiping the unit with a cloth or swab to remove dust or debris from the system).
If it is deemed that Horrell does not teach the processing portion supplies the learning model the sample analysis result data, then Heinemann teach the analogous art of a learning model and automatic analyzer (Heinemann; fig. 1, #1, #2, [0049]) wherein a processing portion is configured to supply the learning model the sample analysis result data (Heinemann; fig. 1, [0017, 0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the processing portion and learning model of Horrell to be configured to process sample analysis result data and supply the learning model with the sample analysis result data, as taught by Heinemann, because Heinemann teach the processor and learning model supplied with sample analysis result data is used to predict a failure state of the automated analyzer; [0049]. One of ordinary skill in the art would have expected this modification could have been performed with a reasonable expectation of success since Horrell and Heinemann both teach forecasting and predicting a failure for medical laboratory instruments.
Regarding claim 23, modified Horrell teach the automatic analyzer according to claim 22 above, wherein the learning dataset is sent to a learning processor and is used to update the learning model (Horrell disclose the remote computing system/analytics system configured to receive feedback data from one or more output systems and then intelligently perform one or more operations based on the feedback data. The model is updated based on feedback data indicating whether an action was performed and successfully corrected the health metric; [0027, 0118, 0186-0191]).
Regarding claim 24, Horrell disclose a recommended action notification method for a recommended action notification system (Horrell disclose a system for monitoring assets. The system configured to determine a heath metric via sensors and sensor values of one or more assets that reflect whether a failure will occur within a certain period of time into the future, and cause an output device to display one or more recommended actions that may affect the health metric, facilitate generating a work order to repair the asset, facilitate ordering a part for the asset, and/or transmit to the asset one or more commands that cause the asset to modify its operation; figs. 1 & 14, [0004, 0010, 0018, 0054, 0256]) including a plurality of automatic analyzers including a first automatic analyzer (Horrell disclose the assets may be medical machines such as medical imaging equipment, surgical equipment, medical monitoring systems, or medical laboratory equipment; [0057]) and a learning processor networked to the automatic analyzers (Horrell disclose the assets 102 are networked to a remote computing system/analytics system 106/400 with machine-learning models for predicting the likelihood of failure based on the health metric, and uses feedback data from personnel to refine the health metric model; figs. 5, 8, 14, 15 [0011-0013, 0054, 0086, 0114-0118, 0186-0191, 0200-0208, 0256-0257]), the method comprising:
allowing the recommended action notification system to recommend performing an action on the first automatic analyzer, wherein, when one of the automatic analyzers detects an abnormality occurrence, the automatic analyzer detecting the abnormality occurrence notifies an operator of the detection of the abnormality (Horrell; [0018, 0026-0028, 0069, 0072-0075, 0077, 0080-0082, 0099-0110, 0114-0118, 0186-0191, 0208, 0256]);
wherein, when a predetermined action is performed in response to a notification of the abnormality, the automatic analyzer detecting the abnormality occurrence displays an action evaluation input screen on a display portion of the automatic analyzer (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]);
wherein the automatic analyzer detecting the abnormality occurrence collects related device data corresponding to the predetermined action during a predetermined period based on a date and time of performing the predetermined action and generates a learning dataset including the collected related device data, an effectiveness evaluation on the predetermined action input from the action evaluation input screen, and an abnormality cause input from the action evaluation input screen (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]);
wherein the automatic analyzers transmit the learning dataset to the learning processor (Horrell disclose the assets 102 are networked to a remote computing system/analytics system 106/400 with machine-learning models for predicting the likelihood of failure based on the health metric, and uses feedback data from personnel to refine the health metric model; figs. 5, 8, 14, 15 [0011-0013, 0054, 0086, 0114-0118, 0186-0191, 0200-0208, 0256-0257]);
wherein the learning processor updates a learning model based on the learning dataset from the automatic analyzers (Hornell disclose updating the learning model based on feedback data provided by personnel indicating whether the recommended action successfully corrected an issue; figs. 8 & 9, [0027, 0186-0191, 0200-0211, 0229-0230]);
wherein the learning processor delivers the updated learning model to the first automatic analyzer (Horrell; [0094]);
wherein the first automatic analyzer performs one of sample analysis and maintenance (Horrell; [0027, 0057, 0114-0118, 0186-0191]); and
wherein the first automatic analyzer supplies the learning model with the related device data including result data concerning one of the sample analysis and the maintenance (Horrell; [0027, 0114-0118, 0186-0191, 0208]) and recommends performing the predetermined action when a probability value output from the learning model is greater than or equal to a predetermined threshold, while the learning model outputs the probability value recommends performing the predetermined action in response to input of the related device data (Horrell disclose the system configured to determine a probability that one or more particular failures may occur at a given asset within a preselected period of time in the future. The system is configured to monitor the health metric of the asset and determine whether the health metric reaches a threshold value, and generate a list of recommended actions that may increase the health metric; [0010, 0013-0015, 0017-0018, 0132, 0134, 0138-0140, 0145, 0147, 0159-0160, 0165-0171, 0190, 0240-0241, 0252, 0256]),
wherein the learning dataset is generated only when the predetermined action is performed in response to the detection of the abnormality occurrence, and is not generated for a regular maintenance action performed without the detection of the abnormality occurrence (Hornell disclose the system relies on sensors and sensor signals for determining the health metric. The sensors are configured to monitor operating conditions of the asset or a particular subsystem of the asset such as temperature, pressure, fluid levels, power usage, voltages and currents, etc., where the learning dataset is generated is generated when the predetermined action is performed in response to the detection of the abnormality occurrence; fig. 9, [0004, 0009, 0027, 0069, 0072-0075, 0186-0191, 0200-0211, 0229-0230]. Accordingly, the learning dataset would not be generated for any subsystem or maintenance performed that does not rely on sensor data since no detection data would exist to detect an abnormality. An example would be wiping the unit with a cloth or swab to remove dust or debris from the system).
If it is deemed that Horrell does not teach the first automatic analyzer performs one of sample analysis result data and supplies the learning model the sample analysis result data, then Heinemann teach the analogous art of a learning model and automatic analyzer (Heinemann; fig. 1, #1, #2, [0049]) wherein the first automatic analyzer is configured to receive sample analysis result data and supply the learning model the sample analysis result data (Heinemann; fig. 1, [0017, 0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the processing portion and learning model of Horrell to be configured to process sample analysis result data and supply the learning model with the sample analysis result data, as taught by Heinemann, because Heinemann teach the processor and learning model supplied with sample analysis result data is used to predict a failure state of the automated analyzer; [0049]. One of ordinary skill in the art would have expected this modification could have been performed with a reasonable expectation of success since Horrell and Heinemann both teach forecasting and predicting a failure for medical laboratory instruments.
Regarding claim 25, modified Horrell teach the recommended action notification method according to claim 24 above wherein the learning processor groups the automatic analyzers based on a similarity of operational situations including operations and inspection contents and updates the learning model based on the learning dataset from the automatic analyzers grouped based on the similarity of operational situations (Horrell; [0014-0015, 0027, 0078, 0118, 0150, 0153, 0186-0191, 0216, 0219, 0225]).
Regarding claim 26, Horrell teach the recommended action notification system according to claim 15 above, wherein the learning model is configured to recommend multiple actions, wherein degree of recommendation is determined based on their respective probability values, wherein a highly prioritized action of the multiple actions has a difference larger than a given value between its predetermined threshold value and its probability value (Horrell disclose determining a heath metric via sensors and sensor values of one or more assets that reflect whether a failure from a group of failures will occur within a certain period of time into the future, and assigning weight to individual probabilities output by individual failure models; [0004, 0010, 0013-0015, 0017-0019, 0032-0033, 0054, 0112, 0132, 0134, 0138-0140, 0145, 0147-0148, 0159-0160, 0165-0171, 0190, 0240-0241, 0256]), and wherein the system further comprises an information display portion configured to display device date that is important to determine whether to require a predetermined recommended action (Horrell; figs. 6-9, [0011-0013, 0090, 0096, 0114-0118, 0130, 0162-0164, 0178, 0186-0191, 0200-0208, 0229, 0233, 0240-0242, 0256-0257]).
Response to Arguments
Applicant’s arguments, filed 04/10/2026, have been fully considered.
Applicant’s argue on pages 10-13 of their remarks towards step 2A prong 1 and step 2A prong 2 of the 101 rejection that claims 15, 22, and 24 recite a concrete practical application in the context of automatic analyzer troubleshooting and operation by generating a learning dataset from action effect information entered by a device operator and device data stored in a database, that the learning dataset is transmitted to the learning device, that the update portion updates the learning model based on the learning dataset, and that the updated learning model is then delivered back to the automatic analyzer, which inputs related device data to the learning model and outputs a recommended action for display to the operator. Applicant states the action to generate the learning dataset is triggered by an abnormality detection portion that detects an abnormality from sample measurement results or maintenance results and notifies the operator of the abnormality.
The examiner respectfully disagrees. Each of the recited operations is an abstract concept that is in the grouping of “mental process” (See MPEP 2106.04(a)(2) subsection (III)) because generating a dataset, updating a model, detecting an abnormality, and recommending an action could be performed in the human mind as an observation, evaluation, or judgement, and the use of a computer does not preclude these concepts from being an abstract concept. Specifically, a user could, in their mind, detect an abnormality in a result, make a recommendation to correct the detected abnormality, create a log of the actions taken, and update a model based on the actions taken. 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 claim recites an abstract idea. MPEP 2106.04(a)(2)(III) is clear that using a computer/processor to perform the abstract idea does not preclude the steps from being considered an abstract idea. Further, the processor to perform the recited steps/processes is simply a general-purpose computer for which to apply the abstract ideas, but does not preclude the steps from being considered an abstract idea. See MPEP 2106.04(a)(2)(III).
Regarding applicant’s argument on pages 10-13 that the invention solves the problem of identifying the cause of an abnormality by recommending countermeasures considered appropriate as early as the stage where an abnormality is predicted, the examiner respectfully disagrees. The alleged improvement seems to be the abstract idea itself and the alleged improvement cannot be the abstract idea, but must be in a particular technology. See MPEP 2106.05(a), paragraphs 4 - 7. Further, the alleged improvement must be in a particular technology, and in this case the claims essentially recite a processor, which is a general purpose computer. The processor (i.e. computer) are not used outside their normal capacity and the computer does not appear to be improved in any way. A general purpose computer is not a particular machine, and performing the abstract idea on a general purpose computer is not enough to integrate the exception into a practical application. MPEP 2106.05(b)I discusses why the antenna was considered particular (included details such as shape of the antenna, length, conductors, etc.) and why a Fourdrinier machine was particular. Although the claims recite an automatic analyzer, the automatic analyzer is still used in its conventional manner and is not transformed into anything different and therefore there is no improvement in the analyzer itself.
Regarding applicant’s arguments on pages 13-16 towards step 2A prong 2 of the 101 rejection that the claimed invention is more than mere data gather or insignificant extra-solution activity because claim 15 recites additional elements including a mechanism to acquire a qualified learning dataset incorporated in each analyzer, the examiner respectfully disagrees. Claim element “a plurality of automatic analyzers including a first automatic analyzer” is interpreted as insignificant pre-solution activity and generally linking the judicial exception to a particular technological environment or field of use in which to apply the judicial exception, but does not amount to significantly more than the exception itself and cannot integrate the judicial exception into a practical application (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment). Claim element “a learning processor networked to the automatic analyzers” is just a general purpose computer. However, a general purpose computer is not a particular machine, and does not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more (see MPEP § 2106.05(b)(I), Particular Machine and MPEP § 2106.05(c), Particular Transformation). Claim element “a processing portion that receives one of sample analysis result data and maintenance result data from the first automatic analyzer, supplies a learning model … with related device data settled for the predetermined action including one of the sample analysis result data and maintenance result data” is just a general purpose computer. However, a general purpose computer is not a particular machine, and does not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more (see MPEP § 2106.05(b)(I), Particular Machine and MPEP § 2106.05(c), Particular Transformation). Furthermore, receiving and supplying data is interpreted as generally linking the abstract idea to the field of endeavor, and also as extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment). Claim element “the first probability value output from the learning model” is interpreted as generally linking the abstract idea to the field of endeavor, and also as extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment). Claim element “an update portion that updates the learning model based on learning datasets from the automatic analyzer” is interpreted as mere instructions to implement the abstract idea to the field of use and insignificant extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, MPEP § 2106.05(h), Field of Use and Technological Environment and § 2106.05(f), Mere Instructions To Apply an Exception). Claim element “a dataset generation portion of the pertinent automatic analyzer displays an action evaluation input screen on a display portion of the pertinent automatic analyzer” is just a general purpose computer. However, a general purpose computer is not a particular machine, and does not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more (see MPEP § 2106.05(b)(I), Particular Machine and MPEP § 2106.05(c), Particular Transformation). Displaying is not considered a practical application, such as improving the functioning of a computer, effecting a transformation, effecting a particular treatment, or applying the judicial exception in some other meaningful way. Indeed, the Court did not find that displaying information on a computer display without any limitations specifying how to achieve the desired result (information display) was not sufficient to show patent eligibility. Nor did the court find that arranging information on a graphical user interface in a manner that assists in processing information more quickly was sufficient to show patent eligibility. MPEP 2106.05(a)(I). Claim element “collects the related data during a predetermined period based on a date and time of performing the predetermined action, and … includes the collected related device data … input from the action evaluation screen, and an abnormality cause input from the action evaluation input screen” is interpreted as generally linking the abstract idea to the field of endeavor, and also as extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment). Lastly, claim element “the learning dataset … is not generated for a regular maintenance action performed without the detection of the abnormality occurrence” is interpreted as generally linking the abstract idea to the field of endeavor, and also as extra-solution activity incidental to the primary process as mere data gathering which is not considered significantly more than the abstract idea. Receiving and transmitting data over a network has been recognized as a generic computer function and is interpreted as insignificant extra-solution activity (see MPEP § 2106.05(g), Insignificant Extra-Solution Activity, and MPEP § 2106.05(h), Field of Use and Technological Environment). Accordingly, the additional elements do not integrate the abstract idea into a particular practical application.
Regarding applicant’s arguments on pages 16-17 of their remarks towards Step 2B of the 101 rejection that Satomura does not teach the acquisition of a learning dataset has been fully considered. The examiner respectfully disagrees with applicant’s arguments because, in the examiner view, Satomura disclose learning device 5 comprises a front end where processing portions 61/62/63/64 use threshold values from historical sample analysis result data and maintenance result data to determine a normal or abnormal condition, and a back end that updates the threshold values after statistical processing with the current sample analysis data and maintenance result data, which are then used for each subsequent processing/judgement process to determine the normal or abnormal condition; figs. 3-11, [0053, 0094-0102, 0106-0110], and thus teaches “acquiring” an updated learning dataset. However, in an effort to advance prosecution Step 2B of the 101 rejection has been modified to incorporate additional references for teaching the argued elements of the claimed invention.
Regarding applicant’s arguments on pages 17-26 towards the 102 rejection over Satomura that the prior art does not teach each and every element of the claimed invention have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Citations to art
In the above citations to documents in the art, an effort has been made to specifically cite representative passages, however rejections are in reference to the entirety of each document relied upon. Other passages, not specifically cited, may apply as well.
Other References Cited
The prior art of made of record and not relied upon is considered pertinent to Applicant’s disclosure include:
Noda et al. (US 2015/0160098) disclose a health management system and fault diagnosis system that calculates a time of maintenance work from a history of the recovery effect of each maintenance work.
Li et al. (US 2019/0207388) disclose a first learning model and a second learning model, where the first learning model is trained using outputs from the second learning model.
Jain et al. (US 2019/0207814) disclose a system that uses machine learning techniques to identify the combinations of interventions that best improve outcomes for different devices or groups of devices.
Conclusion
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/C.A.T./Examiner, Art Unit 1798
/JOHN MCGUIRK/Examiner, Art Unit 1798
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