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 .
Claim Status
Claim 15-26 are pending and under examination.
Claims 1-14 have been canceled.
Response to Amendment
The amended claims, received 12/23/2025, have overcome the previous claim objections. Therefore, the claim objections have been withdrawn.
The amendments have overcome the 112(f) claim interpretation(s) previously set forth. Accordingly, the 112(f) claim interpretations have been withdrawn.
The claim amendments have overcome most of the 112(b) rejection(s) previously set forth in the Non-Final Office Action mailed on 10/01/2025. However, based on the claim amendments, new 112(b) rejections have been set forth.
The 101 rejection has been modified to address the claim amendments.
Based on the amended claims and remarks received on 12/23/2025, the prior art rejection over Satomura has been modified to address the amended claims (see below).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 18, 20, 24 and 26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 18 recites “the interference result includes an average remaining time until the predetermined abnormality occurs and an average remaining time until the predetermined action is performed”. Claim 15 lines 16-17 recite “when the predetermined action is performed in response to a detection of an abnormality occurrence”. It is unclear how an average remaining time until the predetermined abnormality occurs is possible if the abnormality occurrence has already been detected. Further, it is unclear how the system determines an “average remaining time until the predetermined action is performed”. A system may schedule a task for an operation, but that does not necessarily guarantee the operation will be performed on schedule. Claim 20 is also rejected by its dependency from claim 18.
Claim 20 refers to “a display portion”. Claim 15 line 17 also refers to “a display portion”. It is unclear if the two are distinct or referring to the same feature of the claimed invention.
Claim 24 is directed to “A recommended action notification method …”, but fails to set forth a transitional phrase in the claim. Additionally, there does not appear to be positive method steps recited to perform the method, where a potential infringer would find it ambiguous as to what steps may or may not actually define the claimed method. Accordingly, it is unclear what applicant is defining as the scope of the claim since it cannot be determined where the preamble ends and where the method begins. See MPEP 2111.03. Claim 25 is also rejected by its dependency from claim 24.
Claim 26 recites “a large difference between its predetermined threshold value and its probability value”. The term “a large difference” in the claim is a relative term which renders the claim indefinite. The term “large” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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”, “detection of an abnormality occurrence from a pertinent automatic analyzers”, “generates the learning dataset”, and “effectiveness evaluation of the predetermined action”, 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. After the learning dataset is generated and the effectiveness evaluation then there is no action and therefore there is no particular practical application.
The recommending, generating, updating, detection, and evaluation are performed by a processing portion, update portion, and a dataset generation 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).
The claims also recite a plurality of automatic analyzers, receiving result data, supplying a learning model, and a display portion that displays an action evaluation input screen to collect device data. However, 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 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)). Furthermore, 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). Lastly, 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 (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 plurality of automatic analyzers, receiving result data, supplying a learning model, and a display portion that displays an action evaluation input screen to collect device data. 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”). Satomura disclose a recommended action notification system (Satomura disclose an analysis support method and system; fig. 1, [0007, 0046]) that includes a plurality of automatic analyzers including a first automatic analyzer (Satomura; fig. 1, #3a, #3b, [0046]) and a learning processor networked to the automatic analyzers (Satomura disclose learning device 5 connected to network 11; fig. 1, [0046-0047]), the recommended action notification system comprising: a processing portion (Satomura; fig. 3, #61, #62, #63, #64, [0053]); and an update portion (Satomura disclose the back end updates the learning model using data update unit 61 to update the first and second threshold data; fig. 3, [0106-0107]), wherein, when the predetermined action is performed in response to a detection of an abnormality occurrence from a pertinent automatic analyzers, a dataset generation portion of the pertinent automatic analyzer displays an action evaluation input screen on a display portion of the pertinent automatic analyzer and collects device data (Satomura; figs. 5 & 8, step S27, step S73, [0073, 0085]).
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 generally storing the learning model would be 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 discloses a storage portion (Satomura; fig. 3, #57, #58, #59, #65, [0053]).
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 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)).
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.
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). Further, 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)). 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 (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].
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. See Satomura; fig. 3, #62, [0055].
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. See Satomura fig. 1, #5, [0046-0047].
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. See Satomura; fig. 3, #62, [0055].
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].
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
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 15-25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Satomura et al. (US 2007/0255756; already of record – hereinafter “Satomura”).
Regarding claim 15, Satomura disclose a recommended action notification system (Satomura disclose an analysis support method and system; fig. 1, [0007, 0010, 0046]) that includes a plurality of automatic analyzers including a first automatic analyzer (Satomura; fig. 1, #3a, #3b, [0046]) and a learning processor networked to the automatic analyzers (Satomura disclose learning processor 5 connected to network 11; fig. 1, [0046-0047]), and recommends an action to be performed on the first automatic analyzer (Satomura disclose the learning device 5 recommends exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153); figs. 5, 7-10, [0073, 0085-0086, 0091, 0102, 0104-0105]), the recommended action notification system comprising:
a processing portion (Satomura; fig. 3, #61, #62, #63, #64, [0053]) that receives one of sample analysis result data (Satomura disclose analysis result data and judgement results are sent and stored in the result file storage 57; figs 3, 8 & 9, step S105, step S111, step S123, [0090-0091, 0100]. Processing portion 61/62/63/64 receive data stored in the result file storage; fig. 3, [0107-0111]) and maintenance result data from the first automatic analyzer (Satomura disclose apparatus database 59 stores an analysis apparatus ID, a state flag representing “normal” or “abnormal”, operation state data, date and reason data when an occurrence of the abnormal state is detected, and the like; figs. 7 & 9, step S75, step S127, [0086, 0100]. Processing portion 64 receives data stored in the apparatus database 59; fig. 3, [0110]), 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 (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 recommends performing the predetermined action on the first automatic analyzer when a probability value output from the learning model is greater than or equal to a predetermined threshold, wherein the first probability value output from the learning model recommends performing the predetermined action (Satomura disclose statistical processing is performed on the results data and compared to a reference table to determine the probability of whether a normal or abnormal condition exists based on the comparison result. The reagent trouble predictor 63 and apparatus trouble predictor 64 are used to judge the probability of an abnormal condition, and an alert is transmitted to an operator to perform an action when the probability exceeds the threshold value; figs. 11-1 & 11-2, [0106-0111]. The action may include exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153); figs. 5, 7-10, [0073, 0085-0086, 0091, 0102, 0104-0105]); and
an update portion that updates the learning model based on learning datasets from the automatic analyzers (Satomura disclose the back end updates the learning model using data update unit 61 to update the first and second threshold data; fig. 3, [0106-0107]),
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 analyzers, 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; figs. 5 & 8, step S27, step S73, [0073, 0085]), collects the related device data during a predetermined period based on a date and time of performing the predetermined action (Satomura; figs. 5 & 8, step S11, step S75 [0068, 0086]), 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 (Satomura disclose the learning model collects data through operator responses to alerts after detecting an abnormality including a message that recommends the exchange of the reagent displays on a display device of the analysis apparatus 3 (S27). The message screen includes a button for the order of the reagent to be exchanged is provided, and the push of the button is recommended to the user. In response to an order instruction of the reagent to be exchanged or automatically, the order processor 39 stores an order log relating to the order of the reagent to be exchanged in the order log storage 40 and causes the network interface unit 38 to transmit order data of the reagent to be exchanged to the learning processor 5 (S29); [0073]. When the analysis device requires maintenance the apparatus check unit 522 generates an alert message about the apparatus trouble and causes the communication controller 51 to transmit the message to the analysis apparatus 3 (S71). An alert message about the apparatus trouble is displayed on the analysis apparatus (S73) for a user to carry out appropriate measures in order to solve the apparatus trouble to start the analysis, or the operator may push a maintenance order button also provided on the display screen of the alert to input a maintenance order instruction. Pushing the button for the maintenance order instruction generates maintenance order data specifying the abnormal portion, and stores the maintenance order data into the order log storage 40 and causes the network interface unit 38 to transmit the maintenance order data to the host computer 5 (S79). After (S71) the apparatus check unit 522 registers data representing the apparatus trouble into the apparatus DB 59 (S75). The apparatus DB 59 stores an analysis apparatus ID, a state flag representing “normal” or “abnormal”, operation state data, date and reason data when an occurrence of the abnormal state is detected, and the like. The reason why it is judged to be abnormal is registered with the date data. [0085-0086]. The process flow is performed in a control loop that uses inter-apparatus difference data to make predictions and alert a user when the probability of an abnormal state increases or exceeds a threshold value; figs. 5-10, [0067, 0106-0111]).
Regarding claim 16, Satomura disclose the recommended action notification system according to claim 15 above, wherein the first automatic analyzer includes the processing portion and the learning processor includes the update portion; and wherein the learning model updated by the update portion is delivered to the first automatic analyzer (Satomura; figs. 1-3, [0046, 0049, 0053]).
Regarding claim 17, Satomura disclose the recommended action notification system according to claim 15 above, wherein the learning model includes an input layer supplied with the related device data 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 (Satomura; figs. 5-10. See for example the flow of information corresponding to input layers and output layers between “TAG READER AND WRITE”, “ANALYSIS APPARATUS”, and “HOST COMPUTER” at the top of each figure; [0067-0105]); 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 (Satomura disclose statistical processing is performed on the results data and compared to a reference table to determine the probability of whether a normal or abnormal condition exists based on the comparison result. The reagent trouble predictor 63 and apparatus trouble predictor 64 are used to judge the probability of an abnormal condition, and an alert is transmitted to an operator to perform an action when the probability exceeds the threshold value; figs. 11-1 & 11-2, [0106-0111]. The action may include exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153); figs. 5, 7-10, 11-1 & 11-2, [0073, 0085-0086, 0091, 0097, 0102, 0104-0111]).
Regarding claim 18, Satomura disclose the recommended action notification system according to claim 17 above, wherein the inference result includes an average remaining time until the predetermined abnormality occurs and an average remaining time until the predetermined action is performed (Satomura disclose a threshold time is predefined from the beginning of the reaction for a specific measurement item, when the time exceeds the threshold time an abnormal condition is determined and the reagent replacement is performed; [0087-0088, 0094-0095]); 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 on the automatic analyzer (Satomura; figs. 3, 4-2, 5, 7, “TIME OF USE” and “EXPIRATION DATE” which establish a time elapsed and when the predetermined action is to be performed, step S23, step S75, [0057, 0065, 0071, 0086]).
Regarding claim 19, Satomura disclose the recommended action notification system according to claim 17, 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 (Satomura; figs. 5-10. See for example the flow of information corresponding to input layers and output layers between “TAG READER AND WRITE”, “ANALYSIS APPARATUS”, and “HOST COMPUTER” at the top of each figure; [0067-0105]. A plurality of threshold values for a plurality of probability determinations used in a plurality of learning models; [0078, 0085-0086, 0088, 0094, 0097, 0111]); 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 (Satomura; figs. 5-10. See for example the flow of information corresponding to input layers and output layers between “TAG READER AND WRITE”, “ANALYSIS APPARATUS”, and “HOST COMPUTER” at the top of each figure; [0067-0105]. A plurality of threshold values for a plurality of probability determinations used in a plurality of learning models; [0078, 0085-0086, 0088, 0094, 0097, 0111]) and,
when their respective probability values recommends performing their respective predetermined actions and there are two or more learning models of 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 is greater than or equal to their respective predetermined threshold (Satomura; figs. 11-1 & 11-2, [0097, 0106-0111] A plurality of threshold values for a plurality of probability determinations used in a plurality of learning models to determine a plurality of actions; [0078, 0085-0086, 0088, 0094, 0097, 0111]. Satomura disclose the learning device 5 recommends exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153); figs. 5, 7-10, [0073, 0085-0086, 0091, 0102, 0104-0105, 0109-0111]).
Regarding claim 20, Satomura disclose 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 (Satomura disclose the learning model collects data through operator responses to alerts after detecting an abnormality including a message that recommends the exchange of the reagent displays on a display device of the analysis apparatus 3 (S27). The message screen includes a button for the order of the reagent to be exchanged is provided, and the push of the button is recommended to the user. In response to an order instruction of the reagent to be exchanged or automatically, the order processor 39 stores an order log relating to the order of the reagent to be exchanged in the order log storage 40 and causes the network interface unit 38 to transmit order data of the reagent to be exchanged to the learning processor 5 (S29); [0073]. When the analysis device requires maintenance the apparatus check unit 522 generates an alert message about the apparatus trouble and causes the communication controller 51 to transmit the message to the analysis apparatus 3 (S71). An alert message about the apparatus trouble is displayed on the analysis apparatus (S73) for a user to carry out appropriate measures in order to solve the apparatus trouble to start the analysis, or the operator may push a maintenance order button also provided on the display screen of the alert to input a maintenance order instruction. Pushing the button for the maintenance order instruction generates maintenance order data specifying the abnormal portion, and stores the maintenance order data into the order log storage 40 and causes the network interface unit 38 to transmit the maintenance order data to the host computer 5 (S79). After (S71) the apparatus check unit 522 registers data representing the apparatus trouble into the apparatus DB 59 (S75). The apparatus DB 59 stores an analysis apparatus ID, a state flag representing “normal” or “abnormal”, operation state data, date and reason data when an occurrence of the abnormal state is detected, and the like. The reason why it is judged to be abnormal is registered with the date data. [0085-0086]. The process flow is performed in a control loop that uses inter-apparatus difference data to make predictions and alert a user when the probability of an abnormal state increases or exceeds a threshold value; figs. 5-10, [0067, 0106-0111]); 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 (Satomura disclose the analysis apparatus manager displays a message representing the reagent cannot be used and the reason data on the display device. The calculated probability values by the learning model include remarks for associated values; 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]).
Regarding claim 21, Satomura disclose 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 dataset from the automatic analyzers grouped based on the similarity of operational situations (Satomura; fig. 3, #62, [0055]).
Regarding claim 22, Satomura disclose an automatic analyzer to perform sample analysis and maintenance (Satomura disclose an analysis support method and system; fig. 1, #3a, #3b, [0007, 0046]), comprising:
a storage portion to store a learning model (Satomura disclose learning device 5 comprising storage databases connected to network 11; figs. 1 & 3, [0046-0047, 0053]) including an input layer and an output layer (Satomura; figs. 5-10. See for example the flow of information corresponding to input layers and output layers between “TAG READER AND WRITE”, “ANALYSIS APPARATUS”, and “HOST COMPUTER” at the top of each figure; [0067-0105]), wherein the input layer is supplied with related device data configured according to a predetermined action performed on the automatic analyzer (Satomura; figs. 4-5, steps S1-S11, [0067-0068) 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 (Satomura; figs. 11-1 & 11-2, [0097, 0106-0111]);
a processing portion (Satomura; fig. 3, #61, #62, #63, #64, [0053]) that calls the learning model from the storage portion (Satomura disclose analysis result data and judgement results are sent and stored in the result file storage 57; figs 3, 8 & 9, step S105, step S111, step S123, [0090-0091, 0100]. Processing portion 61/62/63/64 receive data stored in the result file storage; fig. 3, [0107-0111]. Satomura also disclose apparatus database 59 stores an analysis apparatus ID, a state flag representing “normal” or “abnormal”, operation state data, date and reason data when an occurrence of the abnormal state is detected, and the like; figs. 7 & 9, step S75, step S127, [0086, 0100]. Processing portion 64 receives data stored in the apparatus database 59; fig. 3, [0110]), supplies the learning model with the related device data including result data concerning one of the sample analysis and the maintenance (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 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 (Satomura disclose statistical processing is performed on the results data and compared to a reference table to determine the probability of whether a normal or abnormal condition exists based on the comparison result. The reagent trouble predictor 63 and apparatus trouble predictor 64 are used to judge the probability of an abnormal condition, and an alert is transmitted to an operator to perform an action when the probability exceeds the threshold value; figs. 11-1 & 11-2, [0106-0111]. The action may include exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153); figs. 5, 7-10, [0073, 0085-0086, 0091, 0102, 0104-0105]);
a display portion that displays a name of the predetermined action recommended by the processing portion (Satomura; figs. 1-2, 5 & 8, step S27, step S73, [0049, 0073, 0085]);
an abnormality detection portion that detects abnormalities (Satomura; fig. 3, #62, #63, #64, [0055]); 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 (Satomura; figs. 5 & 8, step S27, step S73, [0073, 0085]), collects the related device data during a predetermined period based on a date and time of performing the predetermined action (Satomura; figs. 5 & 8, step S11, step S75 [0068, 0086]), 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 (Satomura disclose the learning model collects data through operator responses to alerts after detecting an abnormality including a message that recommends the exchange of the reagent displays on a display device of the analysis apparatus 3 (S27). The message screen includes a button for the order of the reagent to be exchanged is provided, and the push of the button is recommended to the user. In response to an order instruction of the reagent to be exchanged or automatically, the order processor 39 stores an order log relating to the order of the reagent to be exchanged in the order log storage 40 and causes the network interface unit 38 to transmit order data of the reagent to be exchanged to the learning processor 5 (S29); [0073]. When the analysis device requires maintenance the apparatus check unit 522 generates an alert message about the apparatus trouble and causes the communication controller 51 to transmit the message to the analysis apparatus 3 (S71). An alert message about the apparatus trouble is displayed on the analysis apparatus (S73) for a user to carry out appropriate measures in order to solve the apparatus trouble to start the analysis, or the operator may push a maintenance order button also provided on the display screen of the alert to input a maintenance order instruction. Pushing the button for the maintenance order instruction generates maintenance order data specifying the abnormal portion, and stores the maintenance order data into the order log storage 40 and causes the network interface unit 38 to transmit the maintenance order data to the host computer 5 (S79). After (S71) the apparatus check unit 522 registers data representing the apparatus trouble into the apparatus DB 59 (S75). The apparatus DB 59 stores an analysis apparatus ID, a state flag representing “normal” or “abnormal”, operation state data, date and reason data when an occurrence of the abnormal state is detected, and the like. The reason why it is judged to be abnormal is registered with the date data. [0085-0086]. The process flow is performed in a control loop that uses inter-apparatus difference data to make predictions and alert a user when the probability of an abnormal state increases or exceeds a threshold value; figs. 5-10, [0067, 0106-0111]).
Regarding claim 23, Satomura disclose 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 (Satomura disclose the back end updates the learning model using data update unit 61 to update the first and second threshold data; fig. 3, [0106-0107]).
Regarding claim 24, Satomura disclose a recommended action notification method for a recommended action notification system (Satomura disclose an analysis support method and system; figs. 1, 5-10, [0007, 0046, 0067-0105]) including a plurality of automatic analyzers including a first automatic analyzer (Satomura; fig. 1, #3a, #3b, [0046]) and a learning processor networked to the automatic analyzers (Satomura disclose learning device 5 connected to network 11; fig. 1, [0046-0047]), the method allowing the recommended action notification system to recommend performing an action on the first automatic analyzer (Satomura disclose the learning device 5 recommends exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153); figs. 5, 7-10, [0073, 0085-0086, 0091, 0102, 0104-0105]),
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 (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]);
wherein, when a predetermined action is performed in response to a notification of the abnormality (Satomura disclose the learning device 5 recommends exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153) in response to detecting an abnormal condition; figs. 5, 7-10, [0073, 0085-0086, 0091, 0102, 0104-0105]), the automatic analyzer detecting the abnormality occurrence displays an action evaluation input screen on a display portion of the automatic analyzer (Satomura; figs. 5 & 8, step S27, step S73, [0073, 0085]);
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 (Satomura; figs. 5 & 8, step S11, step S75 [0068, 0086]) 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 (Satomura disclose statistical processing is performed on the results data and compared to a reference table to determine the probability of whether a normal or abnormal condition exists based on the comparison result. The reagent trouble predictor 63 and apparatus trouble predictor 64 are used to judge the probability of an abnormal condition, and an alert is transmitted to an operator to perform an action when the probability exceeds the threshold value; figs. 11-1 & 11-2, [0106-0111]. The action may include exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153); figs. 5, 7-10, [0073, 0085-0086, 0091, 0102, 0104-0105]). Satomura further disclose the learning model collects data through operator responses to alerts after detecting an abnormality including a message that recommends the exchange of the reagent displays on a display device of the analysis apparatus 3 (S27). The message screen includes a button for the order of the reagent to be exchanged is provided, and the push of the button is recommended to the user. In response to an order instruction of the reagent to be exchanged or automatically, the order processor 39 stores an order log relating to the order of the reagent to be exchanged in the order log storage 40 and causes the network interface unit 38 to transmit order data of the reagent to be exchanged to the learning processor 5 (S29); [0073]. When the analysis device requires maintenance the apparatus check unit 522 generates an alert message about the apparatus trouble and causes the communication controller 51 to transmit the message to the analysis apparatus 3 (S71). An alert message about the apparatus trouble is displayed on the analysis apparatus (S73) for a user to carry out appropriate measures in order to solve the apparatus trouble to start the analysis, or the operator may push a maintenance order button also provided on the display screen of the alert to input a maintenance order instruction. Pushing the button for the maintenance order instruction generates maintenance order data specifying the abnormal portion, and stores the maintenance order data into the order log storage 40 and causes the network interface unit 38 to transmit the maintenance order data to the host computer 5 (S79). After (S71) the apparatus check unit 522 registers data representing the apparatus trouble into the apparatus DB 59 (S75). The apparatus DB 59 stores an analysis apparatus ID, a state flag representing “normal” or “abnormal”, operation state data, date and reason data when an occurrence of the abnormal state is detected, and the like. The reason why it is judged to be abnormal is registered with the date data. [0085-0086]. The process flow is performed in a control loop that uses inter-apparatus difference data to make predictions and alert a user when the probability of an abnormal state increases or exceeds a threshold value; figs. 5-10, [0067, 0106-0111]);
wherein the automatic analyzers transmit the learning dataset to the learning processor (Satomura disclose analysis result data and judgement results are sent and stored in the result file storage 57; figs 3, 8 & 9, step S105, step S111, step S123, [0090-0091, 0100]. Processing portion 61/62/63/64 receive data stored in the result file storage; fig. 3, [0107-0111]);
wherein the learning processor updates a learning model based on the learning dataset from the automatic analyzers (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]);
wherein the learning processor delivers the updated learning model to the first automatic analyzer (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]);
wherein the first automatic analyzer performs one of sample analysis and maintenance (Satomura; figs. 5 & 7-8, step S27, step S77, step S101, step S113, [0073, 0086, 0090-0091]); 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 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 (Satomura disclose statistical processing is performed on the results data and compared to a reference table to determine the probability of whether a normal or abnormal condition exists based on the comparison result. The reagent trouble predictor 63 and apparatus trouble predictor 64 are used to judge the probability of an abnormal condition, and an alert is transmitted to an operator to perform an action when the probability exceeds the threshold value; figs. 11-1 & 11-2, [0106-0111]. The action may include exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153); figs. 5, 7-10, [0073, 0085-0086, 0091, 0102, 0104-0105]).
Regarding claim 25, Satomura disclose 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 automatic analyzers grouped based on the similarity of operational situations (Satomura; fig. 3, #62, [0055]).
Regarding claim 26, Satomura disclose 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 larger difference between its predetermined threshold value and its probability value, 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 (Satomura disclose the learning model recommends multiple actions S27 & S73; figs. 5 & 8 [0073, 0085], and a degree of recommendation is determined based on their respective probability values; figs. 11-1 & 11-2, [0108-0109]. Values are assigned based on differences in the measured dataset and model dataset to determine priority, and remarks are assigned to each priority category and the analysis device displays an alert indicating the cause of the alarm; figs. 11-1 & 11-2, [0077, 0079, 0080, 0086, 0100, 0108-0109]).
Response to Arguments
Applicant’s arguments, filed 12/23/2025, have been fully considered.
Applicant argues, see pages 10-12 of their remarks, that claim 15 does not recite mental steps but instead are directed to a specific technological system for improving the operation of automatic analyzers, and the recited limitations define a coordinated, multi-component analyzer control architecture that includes hardware which no human could performed using pen and paper (Step 2A prong One).
The examiner respectfully disagrees. Step 2A prong One is directed toward identifying the law of nature/natural phenomenon/abstract idea in the claims. Independent claim 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”, “detection of an abnormality occurrence from a pertinent automatic analyzers”, “generates the learning dataset”, and “effectiveness evaluation of the predetermined action”. The abstract idea(s) is/are of the type that is in the grouping of “mathematical concepts” and/or “mental process” (See MPEP 2106.04(a)(2) subsections (I) and (III)) because a user could, in their mind or with pen and paper or using a generic computer, calculate a probability value from a dataset, and compare the probability value to a threshold value from a model to determine whether an abnormality has occurred, recommend an action based on a comparison between the calculated value the threshold value, then update the model with the new data. 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.
Applicant argues on pages 12-13 of their remarks that claim 15 requires specific hardware including multiple automatic analyzers and their display portions, device-generated datasets, a learning model updated based on analyzer datasets, and a dataset generation portion that operates in response to detected abnormalities, operator actions, operator inputted effectiveness evaluation, and operator inputted abnormality cause, which are technological functions that provide an improvement that integrates the judicial exception in a practical application (Step 2A prong Two). The examiner respectfully disagrees. The additional elements recited in the claims are interpreted as mere data gathering and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception, but do not integrate the judicial exception into a particular practical application because data gathering is merely insignificant extra-solution activity. See MPEP § 2106.05(g), Insignificant Extra-Solution Activity and § 2106.05(f), Mere Instructions To Apply an Exception. 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 to apply an exception. See MPEP Well-Understood, Routine, Conventional Activity. Further, 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). In summary, outside of the abstract idea, the claim is generally any generic automatic analyzer with a computer that receives data and compares/links the data with a mathematical relationship, where any conventional analyzer and computer would perform these processes since they prompt a user to input sample data, analyze sample, and compare measured values with threshold values to determine an abnormality. Additionally, detecting an abnormality using datasets alone does not lead to an alleged improvement because all analyzer would aim to detect sensor and/or sample data abnormalities, and the abstract idea (recommending/detecting/generating) itself cannot be the alleged improvement in a particular technology. See MPEP 2106.05(a) paragraphs 4-7.
Applicant argues on pages 13-14 of their remarks that claim 15 recites significantly more than any alleged abstract idea and the rejection cites no evidence showing that analyzer systems used learning models tied to operator-performed actions, update learning models using datasets after corrective actions, or use data tied to specific actions to retrain predictive models (Step 2B).
The examiner respectfully disagrees. Step 2B examines the additional elements (aside from the abstract idea) be considered as to whether they are well-understood routine and conventional in the art. In this case, the additional elements include a plurality of generic automatic analyzers, receiving result data, supplying a learning model, and a display portion that displays an action evaluation input screen to collect device data. 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”). Satomura disclose a recommended action notification system (Satomura disclose an analysis support method and system; fig. 1, [0007, 0046]) that includes a plurality of automatic analyzers including a first automatic analyzer (Satomura; fig. 1, #3a, #3b, [0046]) and a learning processor networked to the automatic analyzers (Satomura disclose learning device 5 connected to network 11; fig. 1, [0046-0047]), the recommended action notification system comprising: a processing portion (Satomura; fig. 3, #61, #62, #63, #64, [0053]); and an update portion (Satomura disclose the back end updates the learning model using data update unit 61 to update the first and second threshold data; fig. 3, [0106-0107]), wherein, when the predetermined action is performed in response to a detection of an abnormality occurrence from a pertinent automatic analyzers, a dataset generation portion of the pertinent automatic analyzer displays an action evaluation input screen on a display portion of the pertinent automatic analyzer and collects device data (Satomura; figs. 5 & 8, step S27, step S73, [0073, 0085]). Therefore, the claims do not integrate the abstract ideas into a particular practical application nor do the physical actions that cannot be performed mentally amount to more than well-understood, routine, and conventional activities in the art.
Applicant argues on pages 14-15 of their remarks towards the 102 rejection over Satomura that the prior art fails to disclose “a learning model … wherein the first probability value output from the learning model recommends performing the predetermined action” or “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 … displays an action evaluation input screen … 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”.
The examiner respectfully disagrees. Satomura disclose a learning device 5 comprising 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].
The learning device 5 recommends actions including exchanging a reagent (step S27, S147), maintenance (steps S71, S113, S151), remeasurement (S135), or fault correction (S153); figs. 5, 7-10, [0073, 0085-0086, 0091, 0102, 0104-0105], based on the calculated probability values.
The learning model collects data through operator responses to alerts after detecting an abnormality including a message that recommends the exchange of the reagent displays on a display device of the analysis apparatus 3 (S27). The message screen includes a button for the order of the reagent to be exchanged is provided, and the push of the button is recommended to the user. In response to an order instruction of the reagent to be exchanged or automatically, the order processor 39 stores an order log relating to the order of the reagent to be exchanged in the order log storage 40 and causes the network interface unit 38 to transmit order data of the reagent to be exchanged to the learning processor 5 (S29); [0073]. When the analysis device requires maintenance the apparatus check unit 522 generates an alert message about the apparatus trouble and causes the communication controller 51 to transmit the message to the analysis apparatus 3 (S71). An alert message about the apparatus trouble is displayed on the analysis apparatus (S73) for a user to carry out appropriate measures in order to solve the apparatus trouble to start the analysis, or the operator may push a maintenance order button also provided on the display screen of the alert to input a maintenance order instruction. Pushing the button for the maintenance order instruction generates maintenance order data specifying the abnormal portion, and stores the maintenance order data into the order log storage 40 and causes the network interface unit 38 to transmit the maintenance order data to the host computer 5 (S79). After (S71) the apparatus check unit 522 registers data representing the apparatus trouble into the apparatus DB 59 (S75). The apparatus DB 59 stores an analysis apparatus ID, a state flag representing “normal” or “abnormal”, operation state data, date and reason data when an occurrence of the abnormal state is detected, and the like. The reason why it is judged to be abnormal is registered with the date data. [0085-0086]. The process flow is performed in a control loop that uses inter-apparatus difference data to make predictions and alert a user when the probability of an abnormal state increases or exceeds a threshold value; figs. 5-10, [0067, 0106-0111].
Accordingly, the message screen in Satomura is equivalent to an action evaluation input screen that collects and stores operator input. The dataset of Satomura is stored in database and a processor is configured to function as the claimed learning model to compare new sample/maintenance data with model data by calculating probability values, and then updating the model after the comparison with the new data.
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.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CURTIS A THOMPSON whose telephone number is (571)272-0648. The examiner can normally be reached on M-F: 7:00 a.m. - 5:00 p.m..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Capozzi can be reached at 571-270-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/C.A.T./Examiner, Art Unit 1798
/BENJAMIN R WHATLEY/Primary Examiner, Art Unit 1798